# Patent

## Title
Method and apparatus for estimating a likelihood of shoulder dystocia

## Abstract

A method and an apparatus for estimating a level of risk of shoulder dystocia with neonatal injury associated to an obstetrics patient are provided. A set of information data elements associated to an obstetrics patient is received including information derived from a maternal weight component, a maternal height component and a fetal weight component. The set of information data elements is processing to derive a ranking data element associated to the obstetrics patient. The ranking data element conveys a level of risk of shoulder dystocia associated to the obstetrics patient. The ranking data element is then released.

## Background
FIELD OF THE INVENTION
The present invention relates generally to the field of obstetrics, and more specifically to a method and apparatus for estimating a likelihood of shoulder dystocia for an obstetrics patient.
BACKGROUND OF THE INVENTION
Shoulder dystocia (SD) refers to excessive difficulty in delivering the baby vaginally after the head is born because of the position or diameter of the shoulders. The reported incidence of shoulder dystocia varies from 0.6% to 1.4% among vaginal deliveries. When the baby's head is born and the shoulders are trapped within the birth canal, the baby's chest is compressed within the birth canal preventing it from breathing and the umbilical cord is compressed reducing the amount of oxygen supplied to the baby. Special delivery procedures are recommended to ease the passage of the shoulders and allow the baby to be completely born without undue delay or force. During such a birth, the nerves in the baby's neck, leading to the arm (the brachial plexus), can be stretched and injured. Neonatal injuries associated with shoulder dystocia include brachial plexus injury in 4 to 40%, clavicular fracture in 5.1 to 7.5% and very rarely brain damage or death. Although most brachial plexus injuries will recover, those that do not may result in permanent weakness or paralysis in the arm. Reported rates of persistent palsy range from 3 to 50% (ACOG Technical Bulletin Number 40, 2002, Baskett T F, 2002, Gherman R B. 2002). Injury from shoulder dystocia is a common cause of litigation and failure to adequately predict and or communicate the risk of shoulder dystocia and injury is commonly alleged in legal actions.
If the likelihood of shoulder dystocia with neonatal injury for a given obstetrics patient could be accurately estimated, it would desirable to intervene when the likelihood of shoulder dystocia is high such as to avoid (or at least reduce the rate of) neonatal injuries caused by this situation. Intervention may be in the form of elective cesarean delivery for example.
For the reader's information, the following are a few studies related to shoulder dystocia:

    
    
        1. ACOG practice bulletin: Shoulder dystocia. Number 40, November 2002. (Replaces practice pattern number 7, October 1997).
        2. Baskett T F. Shoulder Dystocia. Best Practice & Research Clinical Obstetrics and Gynecology 2002;16:57-68.
        3. Rouise D J, Owen J, Goldenberg R L, Cliver S P. The Effectiveness and Costs of Elective Cesarean Delivery for Fetal Macrosomia diagnosed by Ultrasound. JAMA. 11996;276(18): 480-6.
        4. Gherman R B. Shoulder Dystocia: An Evidence-Based Evaluation of the Obstetric Nightmare. Clinical Obstetrics and Gynecology 2002;45: 345-362.

The contents of the above documents are herein incorporated by reference.

    
    


Generally, professional societies recommend intervention based on a hard threshold of estimated fetal weight, which may be modified by the presence or absence of diabetes, or recommend making a qualitative judgment about risk factors in general. Large fetal weight is generally considered as being the most consistent and important factor in predicting an increase in the risk of shoulder dystocia (SD). For that reason, some professional societies recommend elective cesarean if the fetal weight is estimated to be over 5000 g in women without diabetes and 4500 g in women with diabetes. Other studies could not justify intervention at the 4500 gram estimated weight threshold even in the presence of gestational diabetes.
A deficiency associated with existing methods, such as the ones described in the above noted publications, is that in order to detect a high percentage of babies with the relatively rare condition of shoulder dystocia, an unacceptable rate of false positive predictions occurs in the more common uncomplicated pregnancies. This could lead to excessive numbers of unnecessary interventions in women rendering the prediction of little clinical value. For example, it has been estimated that by having a policy of intervention at 4500 gram, an extra 443 cesareans would be required to avoid a single case of permanent brachial palsy injury. The financial cost of such a policy would be about $930,000US for each avoided case of permanent brachial palsy injury without counting the emotional and physical trauma of the 443 mothers who underwent essentially unnecessary cesareans. (Rouse D J et al 1996—reference 3 above) Moreover, several studies have shown that a substantial number of cases of shoulder dystocia occurs in women with infants weighing less than 4000 g. Therefore, even though fetal weight is an important factor in predicting shoulder dystocia, it is nevertheless not sufficiently reliable and does not provide a suitable prediction of the level of risk shoulder dystocia for an individual mother. In fact, the ACOG Practice bulletin (reference 1 above) concludes, “Shoulder dystocia is most often unpredictable and unpreventable”.
Therefore, in the context of the above, there is a need to provide a method and apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient that alleviates at least in part problems associated with the existing methods and devices.
SUMMARY OF THE INVENTION
In accordance with another broad aspect, the present invention provides a method for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The method comprises receiving a set of information data elements associated to an obstetrics patient. The set of information data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. The method also comprises processing the set of information data elements to derive a ranking data element associated to the obstetrics patient, the ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient. The method also includes releasing the ranking data element.
In specific non-limiting examples of implementation, the ranking data element is indicative of a likelihood score or of a risk level selected from a set of risk levels.
In a specific example of implementation, the information derived from the material weight component, the maternal height component and the fetal weight component includes a maternal BMI (Body Mass Index) and an estimated fetal weight component.
In a specific non-limiting example of implementation, the method comprises generating a false detection data element conveying a false positive detection rate associated to the ranking data element. The method also comprises generating a successful true positive detection data element conveying a true positive detection rate associated to the ranking data element. Optionally, the method comprises generating at least one recommendation data element associated to the obstetrics patient at least in part on the basis of the ranking data element.
In a specific non-limiting example of implementation, the method comprises processing the ranking data element on the basis of data conveying a policy associated to shoulder dystocia to derive the recommendation data element. The data conveying the policy data associated to shoulder dystocia may be intrinsic to the method or may be received from an external entity. The data conveying the policy data associated to shoulder dystocia may for example include a set of risk levels and its associated set of recommendation data elements.
In a specific non-limiting implementation, the information data elements include a previous vaginal birth indicator data element indicating whether the obstetrics patient is nulliparous or multiparous. The ranking data element associated to the obstetrics patient is derived at least in part on the basis of the previous vaginal birth indicator data element.
In accordance with another aspect the invention provides signal conveying a level of risk of shoulder dystocia associated to an obstetrics patient, the signal being generated by the above-described method.
In accordance with another broad aspect, the present invention provides an apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with the above-described method.
In accordance with yet another broad aspect, the present invention provides a computer readable storage medium including a program element suitable for execution by a computing apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The computing apparatus has a memory unit and a processor operatively connected to the memory unit for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with the above-described method.
In accordance with yet another broad aspect, the present invention provides a system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The system comprises an interface for receiving a set of information data elements associated to an obstetrics patient. The system further comprises an apparatus having an input for receiving the set of information data elements, a processing unit coupled to the input, and an output coupled to the processing unit. The set of patient data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. The processing unit is operative for processing the set of information data elements to derive a ranking data element associated to the obstetrics patient, the ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient. The output is suitable for releasing an output signal for causing an output unit to display information conveying the ranking data element.
In accordance with yet another broad aspect, the present invention provides a client-server system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The client-server system includes a client system and a server system operative to exchange messages over a data network. The server system stores a program element for execution by a CPU. The program element comprises a first program element component executed on the server system for receiving a set of information data elements associated to an obstetrics patient. The set of patient data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. The program element comprises a second program element component executed on the server system for processing the set of information data elements to derive a ranking data element associated to the obstetrics patient, the ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient. The program element also comprises a third program element component executed on the server system for sending messages to the client system for causing the client system to display information on the basis of the ranking data element. The program element also comprises a fourth program element component executed on the client system for receiving a message from the server system for displaying the information to a user.
In accordance with another broad aspect, the invention provides a method for generating a model suitable for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The method comprises receiving a plurality of records, each record being associated to an obstetrics patient. Each record includes a set of patient data elements and a shoulder dystocia outcome data element. The set of patient data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. A predictive model is generated at least in part on the basis of the plurality of records, the predictive model establishing a relationship between the set of patient data elements and the shoulder dystocia outcome data element. Data conveying the predictive model is then released.
In a specific example of implementation, the method comprises generating a receiver operating curve associated to the predictive model. The receiver operating curve establishes a relationship between a false positive rate and a true positive prediction rate associated to an estimate of the level of risk of shoulder dystocia generated on the basis of the predictive model.
In accordance with another broad aspect, the present invention provides an apparatus for generating a model for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with the above-described method.
These and other aspects and features of the present invention will now become apparent to those of ordinary skill in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying drawings.



BRIEF DESCRIPTION OF THE DRAWINGS
In the accompanying drawings:
FIG. 1 shows a high-level functional block diagram of a system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with a specific example of implementation of the present invention;
FIG. 2 shows a more detailed functional block diagram of the apparatus for estimating a level of risk of shoulder dystocia shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 3 shows a set of information data elements associated to an obstetrics patient in accordance with a non-limiting example of implementation of the present invention for use in the apparatus shown in FIG. 1;
FIG. 4 a flow diagram of a method for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with a specific example of implementation of the present invention;
FIG. 5 shows a first non-limiting example of a visual representation of the data released by the processing unit shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 6 shows a second non-limiting example of a visual representation of the data released by the processing unit shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 7 shows a receiver operating curve associated to a predictive model suitable for use by the processing unit shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 8 shows a flow diagram of a process for generating a predictive model for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with a specific example of implementation of the present invention;
FIG. 9 shows a set of information data elements associated to an obstetrics patient in accordance with a non-limiting example of implementation of the present invention suitable for use in the process shown in FIG. 8:
FIG. 10 shows a high-level functional block diagram of an apparatus for establishing an intervention policy for shoulder dystocia in accordance with a non-limiting example of implementation of the present invention;
FIG. 11 shows a functional block diagram of an apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with another specific example of implementation of the present invention;
FIG. 12 shows a functional block diagram of a client-server system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient in accordance with an alternative specific example of implementation of the present invention;
FIG. 13 is a conceptual representation of a computer readable storage medium storing a program element suitable for use in connection with the client-server system shown in FIG. 12 for estimating a level of risk of shoulder dystocia associated to in obstetrics patient in accordance with another specific example of implementation of the present invention;
FIG. 14 is a frequency distribution of the scores for multiparous women generated by a predictive model applied to a reference population, the predictive model being suitable for use in the apparatus shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 15 is a frequency distribution of the scores for nulliparous women generated by a predictive model applied to a reference population, the predictive model being suitable for use in the apparatus shown in FIG. 1 in accordance with a specific example of implementation of the present invention;
FIG. 16 shows two simple receiver operating curves, one for a predictive model using the fetal weight alone and the second for a predictive model using a combination of the maternal weight, maternal height and fetal weight.



Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.
DETAILED DESCRIPTION
The present detailed description describes a method and apparatus and system directed to estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The expression shoulder dystocia is used to refer to either one or both the incidence of shoulder dystocia with neonatal injury and shoulder dystocia without neonatal injury. It will however be appreciated that shoulder dystocia with neonatal injury is considered more desirable to predict. Consequently, specific embodiments of the invention may be implemented to estimate a level of risk of shoulder dystocia with neonatal injury.
In biomechanical terms shoulder dystocia is a misfit between the bisacromial diameter of the baby's shoulders and the anteroposterior diameter of the mother's pelvis. Factors associated with a large shoulder diameter include a high birth weight, maternal diabetes, and later gestational age. Factors associated with smaller pelvic diameters include small or short mothers and maternal obesity. These factors can be estimated prior to labour. Other factors that are known later, such as slow labour and the need for instrumental vaginal delivery, may be functional reflections of a potential misfit. The system described herein below makes use of a multifactorial mathematical predictive model using some of these factors in order to provide an estimate of risk of shoulder dystocia with neonatal injury.
With reference to FIG. 1, there is shown a configuration of a system 100 for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The system 100 may be used prior to labour onset (during late pregnancy) or during labour to estimate a level of risk of shoulder dystocia. Although, in theory, the system may be used at any time during labour prior to delivery, convenient times for using the system include at labour onset (very early in labour), at the end of the first stage of labour and prior to use of an instrumental vaginal delivery. Optionally, the system 100 can be user prior to labour onset or at the beginning of labour and then again at certain points throughout the labour as information about the labour progression becomes available. The level of risk of shoulder dystocia may be used, alone or in combination with a hospital policy, to assist a physician in determining whether an intervention is advisable.
As shown in FIG. 1, the system 100 comprises a user interface 102, an apparatus 101 including a processing unit 104, and an output unit 106.
The user interface 102 includes any one or a combination of a keyboard, a pointing device, a touch sensitive surface, a speech recognition unit or any other suitable device. Alternatively, the user interface 102 may be in the form of a data input device such as, but not limited to, a disk drive, CD-ROM and flash memory. The user interface 102 enables a user to provide a set of information data elements associated to a certain obstetrics patient.
FIG. 3 of the drawings depicts a non-limiting example of a set of information data elements associated to a certain obstetrics patient. In the specific example depicted, the set of information data elements 300 includes pre-labour components 302 and labour components 304. It will be apparent that where the system is used prior to labour onset, labour components 304 are omitted from the set of information data elements. In a specific example of implementation, the set of information data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. In a specific implementation, the set of information elements includes a maternal Body Mass Index (BMI). The maternal BMI is derived on the basis of a maternal weight component and a maternal height component in accordance with well-know methods. It will be appreciated that, prior to delivery, the actual fetal weight is not known. Consequently, the fetal weight component may be an estimated fetal weight or an estimated range of fetal weights. Any suitable method for obtaining an estimate of a fetal weight (or range of fetal weights) for a fetus in utero may be used. Such methods are well known in the art of obstetrics and as such will not be described further here. Optionally, the set of information data elements also includes, without being limited to, information derived from a maternal age component, a maternal diabetes indicator, gestational age, a previous vaginal birth indicator (nulliparous or multiparous patient) and shoulder dystocia in previous pregnancies. Optionally still, when the system is used subsequent to labour onset, the set of information data elements may include information derived from labour information elements. Examples of labour information elements include, without being limited to, induction of labour, epidural anesthesia, length of the first labour stage, the length of the labour second stage and intended operative vaginal delivery. Other suitable information data elements may also be provided through user interface 102 without detracting from the spirit of the invention.
In the specific embodiment shown in FIG. 1, apparatus 101 includes a processing unit 104, an input 110 and an output 114. Input 110 is operative for receiving signals from the user interface 102 indicative of a set of information data elements associated to the obstetrics patient.
As shown in FIG. 1, the processing unit 104 is in communication with input 110 for receiving the signal or signals indicative of a set of information data elements associated to the obstetrics patient. As will be described in more detail below, on the basis of the signal or signals received at input 110, the processing unit 104 is operative to generate a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient. The ranking data element is generated at least in part on the basis of a predictive model, which combines the set of information data elements associated to the obstetrics patient. Various predictive models may be used here. In a specific implementation, the predictive model includes a combination of a maternal height and weight and a fetal weight. In a non-limiting implementation the predictive model computes a score including a factor having the form BMI×fetal weight where BMI denotes a maternal body mass index. The manner in which suitable predictive models may be generated is described later on in the specification.
The ranking data element may be expressed in the form of a score, a likelihood, a risk level selected from a set of risk levels, as a percentile value or in any other format suitable for conveying a level of risk. The following table illustrates a limited number of formats for expressing a ranking data element. It will be appreciated that the ranking data element may be expressed in other suitable formats without detracting from the spirit of the invention.










Sample Formats for Ranking Data Elements












Likelihood
Classification
Score
Colour Scheme






.10
Low risk
100
Green



.25
Medium risk
150
Yellow



.5
High risk
190
Orange



.8
Very high risk
225
Red









In a specific implementation where the set of information data element associated to the obstetrics patients includes a fetal weight component in the form of a range of fetal weights, the processing unit 104 is operative for generating a range of ranking data elements associated to the range of fetal weights. More specifically, ranking data elements can be derived using the upper and lower expected fetal weights as parameters. In this case, the processing unit generates a range of ranking data conveying a range of levels of risk of shoulder dystocia associated to the obstetrics patient.
The following table illustrates a limited number of formats for expressing a ranking data element as a range of values. It will be appreciated that the ranking data element may be expressed in other suitable formats without detracting from the spirit of the invention.










Sample Formats for Range of Ranking Data Elements










Likelihood
Classification
Score
Colour Scheme





.10-.15
Low risk
100-120
Green


.25-.30
Medium risk to High
150-160
Yellow to Orange



risk




 .5-.7
High risk
190-200
Orange


 .8-.92
High risk to Very
225-240
Orange to Red



high risk









In a specific example of implementation, processing unit 104 is operative for generating a false detection data element conveying a false positive detection rate associated to the ranking data element and a successful detection data element conveying a true positive detection rate associated to the ranking data element. In a specific implementation, the false detection data element and the successful detection data element are derived on the basis of a receiver operating curve associated to the predictive model used to generate the ranking data element. The receiver operating curve characterizes the effectiveness of the predictive model in its ability to estimate the level of risk of shoulder dystocia in a reference population. In a non-limiting implementation, the receiver operating curve is provided in the form of a mapping between various ranking data elements (or scores corresponding to various ranking data element) to associated false detection and successful detection data elements. The table below shows, in simplified form, a receiver operating curve for an arbitrary predictive model. It will be appreciated that the table below is shown for the purpose of illustration only and does not necessarily represent an actual receiver operating curve or actual scores or rankings.













False detection data
Successful detection data


Ranking
element
element







5
 1%
20%


3
 3%
41%


2
15%
50%


1
40%
80%









In the table above, we see that for an obstetrics patient having ranking of “1”, with respect to a reference population, 80% of obstetrics patient where shoulder dystocia was present would be detected. However, 40% of patient who do not have shoulder dystocia would be falsely predicted to have shoulder dystocia. At the other end of the spectrum, we see that for an obstetrics patient having ranking of “5”, with respect to a reference population, 20% of obstetrics patient where shoulder dystocia was present would be detected and only 1% of patient who would not have shoulder dystocia would be falsely predicted to have shoulder dystocia.
In a specific example of implementation, processing unit 104 is operative for deriving at least one recommendation data element associated to the ranking data element. The recommendation is derived on the basis of an intervention policy associated to shoulder dystocia. The policy is generally determined by a health care institution or professional association, usually a hospital, which determines a certain level of care. In a non-limiting implementation, the recommendation is derived at least in part on the basis of the ranking data element. In a non-limiting implementation, the policy data is provided in the form of a mapping between various ranking data elements (or scores corresponding to various ranking data element) and various recommendations. Alternatively, the policy data is provided in the form of a mapping between either one or both of a false detection data element and a successful detection data element and various recommendations. The table below shows, in simplified form, policy data for an arbitrary predictive model. It will be appreciated that the table below is shown for the purpose of illustration only and does not necessarily represent an actual intervention policy. In the table below, ranking data elements conveying risk levels associated to the various scores are also provided.














Score
Ranking
Recommendation








225 or higher
High risk
Intervention highly





recommended



175-225
Medium risk
Intervention recommended



below 175
Low risk
Intervention not recommended









The policy data may be integral to processing unit 104 or may be received as an input via a user interface of the type described with reference to user interface 102.
Output 114 releases the data derived by processing unit 104 towards an output unit 106. The output unit 106 is coupled to the output 114 of apparatus 101 and is responsive to the released signal for displaying information conveyed by the ranking data element derived by processing unit 104 and optionally information conveyed by the recommendation data element, successful detection data element and the false detection data element. The output unit 106 may be in the form of any suitable device for conveying information to the physician or other health care professional. In a specific example of implementation, the output unit 106 can include a display screen, or in an alternative example of implementation, the output unit 106 can include a printing device for displaying the data in printed form.
The processing unit 104 in apparatus 101 is described in greater detail herein below with reference to FIG. 2.
As shown, the processing unit 104 includes a likelihood estimation module 210 and a display control module 240. Optionally, the processing unit 104 also includes a recommendation generation module 250. In use, the recommendation generation module 250 and display control module 240 make use of shoulder dystocia intervention policy data 220.
In the embodiment shown, the shoulder dystocia intervention policy data 220 is integrated as part of the processing unit 104. In an alternative embodiment (no shown in the figures), the policy data 220 may be provided to processing unit 104 through an input for receiving data signals conveying intervention policy data associated to shoulder dystocia. The input may be in communication with a data module or user interface device of a type similar to that described in connection with user interface 102 for receiving the intervention policy data associated to shoulder dystocia.
The likelihood estimation module 210 processes the set of information data elements received from the user interface 102 to generate a ranking data element 230 on the basis of a predictive model.
The display control module 240 receives the ranking data element 230 and generates control signals for causing output unit. 106 to display information conveying the ranking data element.
The recommendation generation module 250 processes the ranking data element 230 on the basis of intervention policy data 220 to generate information conveying a recommendation.
The method for generating ranking data element 230 as implemented by likelihood estimation module 210 is described in greater detail herein below with reference to the flow chart shown in FIG. 4.
At step 400, the set of information data elements associated to an obstetrics patient is received. At step 410 (optional), the set of information data elements associated to an obstetrics patient is processed to select a predictive model from a set of predictive models. In a specific implementation, different predictive models are used for nulliparous and multiparous mothers. In such an implementation, the set of information data elements includes a previous vaginal birth indicator which is used to select between the two models. Another example where multiple predictive models may be used is the diabetes status. It will be appreciated that different predictive models may be used for other factors without detracting from the spirit of the invention. Where a single predictive model is used by likelihood estimation module 210, step 410 may be omitted. In a specific example of implementation, the set of information data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component. The predictive model may include a factor having a ratio between the maternal weight component and the maternal height component. This factor may for example be a BMI (body mass index) factor. In a specific implementation, the predictive model includes a factor having the form [maternal BMI×fetal weight].
At step 420, the set of information data elements is processed by the predictive model to generate a ranking data element associated to the obstetrics patient. A false positive detection rate and a true positive detection rate associated to the ranking data element are also derived. In a non-limiting example of implementation, the false positive and true positive detection rates are derived on the basis of a receiver operating curve associated to the predictive model selected at step 410.
At step 430, the ranking data element is released by likelihood estimation module 210. A false detection data element conveying the false positive detection rate and a successful detection data element conveying the true positive detection rate are also released.
The recommendation generation module 250 receives the ranking data element, false detection data element and the successful detection data element and derives an intervention recommendation on the basis of the shoulder dystocia intervention policy data 220. The intervention recommendation is then released.
The display control module 240 is in communication with the likelihood estimation module 210 and the recommendation generation module 250. The display control module 240 generates a signal for causing a visual representation of the ranking data element and optionally the false detection data element, the successful detection data element and the intervention recommendation to be displayed to a user at output unit 106.
FIGS. 5 and 6 show non-limiting examples of specific visual representations caused to be displayed by output unit 106.
The visual representation shown in FIG. 5 is in the form of a graphical window 500 that could be shown on the display screen of a computer, PDA or other suitable device. Alternatively, the visual representation shown in FIG. 5 may be in the form of a printout. The graphical window 500 includes a set of information sections 502, 504, 506 and 508. Information sections 502 and 504 are in the form of text boxes displaying a set of information data elements associated to the obstetrics patient. These text boxes include the information provided through user interface 102 (FIG. 1). Optionally, the text boxes include fields that may be edited by the user of the system 100 (FIG. 1) such as to modify or add information associated to the obstetrics patient. Information section 502 includes information element associated to the obstetrics patient which were obtained prior to labour onset. These pre-labour factors include, without being limited to, a maternal weight, a maternal height and an expected fetal weight. In this case the expected fetal weight is expressed as a range of fetal weights. The labour factors in information section 504 are marked “N/A” since the system is being used prior to labour onset. Information section 506 is a text box displaying the ranking data element. In this specific implementation, the range of fetal weights has resulted in a range of ranking data elements namely 8/10 to 9/10. Alternatively, information section 506 may be in the form of a graph depicting the ranking data element and its associated false detection data element and successful detection data element. Such a graph may be in the form of a receiver-operating curve for example. Information section 508 is a text box displaying an intervention recommendation associated to the ranking data element or range of ranking data elements.
The visual representation 601 shown in FIG. 6 may be shown on the display screen of a computer, PDA or other suitable device. Alternatively, the visual representation shown in FIG. 6 may be in the form of a paper printout. The paper printout may be particularly useful for example where the system 100 (FIG. 1) estimating a level of risk of shoulder dystocia associated to an obstetrics patient is part of a centralised testing service. A physician tracking an expectant mother may gather information and request that a likelihood of shoulder dystocia test be performed on the basis of that information. The paper printout, of the type shown in FIG. 6, or an electronic version thereof, may be provided to the physician as the result of such a test and be inserted into the expectant mother's file. The visual representation 601 shown in FIG. 6 includes a set of information sections 600, 602 and 606. Information section 600 displays data elements associated to the obstetrics patient including the information provided through user interface 102 (FIG. 1). Optionally, where the visual representation 601 is shown on a display screen, the information section 600 includes editable fields that may be modified by the user of the system 100 such as to modify or add information associated to the obstetrics patient. Information section 602 displays the ranking data element 604 and its associated false detection data element and successful detection data element. In the example depicted, the information is in the form of a graph showing a receiver-operating curve and the position of the ranking data element 604 on that curve. Information section 606 includes information explaining the results shown in information section 602 and optionally an intervention recommendation (not shown) associated to the ranking data element.
It should be understood that window 500, as shown in FIG. 5, and sample output 601 shown in FIG. 6, are only examples of two specific visual representations of how the data derived by processing unit 104 (FIG. 1) can be displayed. It is within the scope of the invention for a visual representation to contain more or less information. For example, the output unit 106 could display the intervention recommendation associated to the ranking data element in a graphical format, or by using a coded colour scheme, or other display convention.
Predictive Model Generation
The system 100 depicted in FIG. 1 makes use of a predictive model that combines a set of information data elements associated to an obstetrics patient to derive a score conveying a level of risk of shoulder dystocia. This predictive model is implemented by the likelihood estimation module 210 depicted in FIG. 2 of the drawings. The following portion of the present specification will describe the generation of a specific predictive model as an example. It will be readily apparent to the person skilled in the art of statistical analysis in light of the present specification that different predictive models may be used to derive a score conveying a level of risk of shoulder dystocia without detracting from the spirit of the invention.
In a specific implementation, the predictive model combines a BMI (Body Mass Index) associated to the mother and the fetal weight to derive a score.
FIG. 8 of the drawings shows a flow diagram of a specific example of implementation of a process for generating a predictive model for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The process depicted in FIG. 8 releases a predictive model that combines a set of information data elements associated to an obstetrics patient. In a specific implementation, the process also releases a receiver operating curve associated to the predictive model. The receiver operating curve includes data elements mapping scores conveying levels of risk of shoulder dystocia to corresponding false positive detection rates and successful true positive detection rates.
As depicted, at step 800 a database of patient records with shoulder dystocia is received, each record being associated to an obstetrics patient for which shoulder dystocia was observed. Preferably, the patient records received at step 800 include records where shoulder dystocia with neonatal injury was observed.
For the purpose of illustration only, a sample record in the database of patient records received at step 800 is depicted in FIG. 9. Each record includes a set of patient information data elements 903 and a shoulder dystocia outcome data element 906. As shown, the set of patient data elements 903 includes pre-labour components including a maternal weight component, a maternal height component and a fetal weight component. Alternatively (not shown), the set of patient data elements includes a maternal Body Mass Index component and a fetal weight component. Optionally, the set of patient data elements 903 includes a previous vaginal birth indicator data element indicating whether the mother was nulliparous or multiparous. Optionally still, the set of patient data elements 903 includes labour components 904. It will be appreciated that the set of patient data elements 903 may include additional components without detracting from the spirit of the invention. The shoulder dystocia outcome data element 906 indicates whether shoulder dystocia was observed for the patient or whether shoulder dystocia was observed for that patient. In the case of the records in the database of patient records with shoulder dystocia, the shoulder dystocia outcome data element 906 indicates that shoulder dystocia was observed. In a non-limiting implementation, when shoulder dystocia was observed for a given patient, the shoulder dystocia outcome data element 906 may further indicate whether shoulder dystocia was present with neonatal injury or without neonatal injury. It will be appreciated that in order to generate a suitable model, a sufficient number of patient records with shoulder dystocia should be provided. It has been observed that improved performance has been obtained where the database of patient records with shoulder dystocia included records where the shoulder dystocia outcome data element 906 indicated that shoulder dystocia was present with neonatal injury.
At step 802, a control database including a plurality of records is received, each record being associated to an obstetrics patient where shoulder dystocia was not observed. The records in the control database are similar in format to the records in the database of patient records described in connection with FIG. 9. In the case of the records in the control database, the shoulder dystocia outcome data element 906 indicates that shoulder dystocia was not present. Preferably, in order to generate a model having a higher level of performance, the records received at step 800 and 802 should reflect the population in which the end system (of the type shown in FIG. 1) will be used. For example, the maternal weights and heights of the patients in the records received at steps 800 and 802 should be similar to those of the patients on which the system will be used. If the model is developed on records where the mothers were consistently overweight or obese, the model may not perform well in a population where the population does not have this characteristic and vice versa—a model developed on records where the mothers are of average weight and height may not perform well in a population where the mothers are frequently overweight.
In a specific non-limiting example of implementation, the database of patient records received at step 800 included a plurality of patient records for which the shoulder dystocia outcome data element 906 indicated that shoulder dystocia was present with neonatal injury. Each record was then matched one to one (1:1) based on parity, race, fetal birthweight and induction to a record in the control database received at step 802.
At step 804, a predictive model is generated. The model establishes a relationship between the set of patient data elements, a score and the shoulder dystocia outcome data element. In a non-limiting implementation, the mathematical model combines the set of patient data elements such as to derive a score. The score is then used to assign a ranking to the obstetrics patient. The ranking data element may be the score itself, a number, a likelihood, a risk level selected from a set of risk levels or any other format suitable for conveying a likelihood.
Different combinations of patient data elements in the set of patient data elements can be used. The combinations may be derived using heuristic techniques alone or using a combination of heuristic techniques and statistical tools.
In a specific implementation, a crude predictive model combining a maternal weight component, a maternal height component and a fetal weight component was tested having the form:

SCORE=BMI×fetal weight  (Equation # 1)

where BMI denotes the body mass index of the mother. The Body mass index (BMI) of the mother is a well-known metric derived on the basis of the maternal weight component and the maternal height component. The Body mass index (BMI) is a measure of body fat based on height and weight that applies to both adult men and women. It is a number that shows body weight adjusted for height. BMI can be calculated with simple math using inches and pounds, or meters and kilograms.

Body Mass Index can be calculated using pounds and inches with this equation:




  BMI
  =
  
    
      (
      
        
          Weight
          ⁢
          
            
          
          ⁢
          in
          ⁢
          
            
          
          ⁢
          Pounds
        
        
          
            (
            
              Height
              ⁢
              
                
              
              ⁢
              in
              ⁢
              
                
              
              ⁢
              inches
            
            )
          
          ×
          
            (
            
              Height
              ⁢
              
                
              
              ⁢
              in
              ⁢
              
                
              
              ⁢
              inches
            
            )
          
        
      
      )
    
    ×
    703
  




Body Mass Index can also be calculated using kilograms and meters (or centimeters):



  
    BMI
    =
    
      
        Weight
        ⁢
        
          
        
        ⁢
        in
        ⁢
        
          
        
        ⁢
        Kilograms
      
      
        
          (
          
            Height
            ⁢
            
              
            
            ⁢
            in
            ⁢
            
              
            
            ⁢
            Meters
          
          )
        
        ×
        
          (
          
            Height
            ⁢
            
              
            
            ⁢
            in
            ⁢
            
              
            
            ⁢
            Meters
          
          )
        
      
    
  




  or




  
    BMI
    =
    
      
        (
        
          
            Weight
            ⁢
            
              
            
            ⁢
            in
            ⁢
            
              
            
            ⁢
            Kilograms
          
          
            
              (
              
                Height
                ⁢
                
                  
                
                ⁢
                in
                ⁢
                
                  
                
                ⁢
                centimeters
              
              )
            
            ×
            
              (
              
                Height
                ⁢
                
                  
                
                ⁢
                in
                ⁢
                
                  
                
                ⁢
                centimeters
              
              )
            
          
        
        )
      
      ×
      10
      ⁢
      
        ,
      
      ⁢
      000
    
  



It will be observed that terms 703 and 1000 in the above noted equations simply multiply the score by a constant value and therefore may be omitted entirely from the BMI.
Other more generalized forms for predictive models combining a maternal weight component, a maternal height component and a fetal weight component that could be used are the following:




  
    
      
        SCORE
        =
        
          
            
              (
              
                α
                ×
                BMI
              
              )
            
            m
          
          ×
          
            
              (
              
                β
                ×
                fetal
                ⁢
                
                  
                
                ⁢
                weight
              
              )
            
            n
          
        
      
    
    
      
        (
        
          Equation
          ⁢
          
            
          
          ⁢
          #2
        
        )
      
    
  
  
    
      
        SCORE
        =
        
          
            
              [
              
                
                  
                    (
                    
                      α
                      ×
                      maternal
                      ⁢
                      
                        
                      
                      ⁢
                      weight
                    
                    )
                  
                  k
                
                
                  
                    (
                    
                      δ
                      ×
                      maternal
                      ⁢
                      
                        
                      
                      ⁢
                      height
                    
                    )
                  
                  w
                
              
              ]
            
            m
          
          ×
          
            
              (
              
                β
                ×
                fetal
                ⁢
                
                  
                
                ⁢
                weight
              
              )
            
            n
          
        
      
    
    
      
        (
        
          Equation
          ⁢
          
            
          
          ⁢
          #3
        
        )
      
    
  




Where α, β, δ, “k”, “w”, “m” and “n” are either whole numbers or ratios. Different predictive models may be generated for different combinations of α, β, δ, “k”, “w”, “m” and “n”. The relative performance of the different predictive models generated can then be compared using known statistical methods including for example receiver operating curves (ROC). The ROCs can be used to effect of a selection between several predictive models in order to choose a model yielding the best performance. The use of ROCs will be described later on in the specification.

It will be appreciated by the person skilled in the art of statistical analysis that more sophisticated predictive models could be used. Those more sophisticated model may include more complex mathematical functions and could be obtained using known statistical methods. In addition, although the above described predictive models includes a maternal weight component, a maternal height component and a fetal weight component, it is to be appreciated that additional suitable factors may be included in a predictive model without detracting from the spirit of the invention. Such factors include, without being limited to maternal age, maternal diabetes, multiparity, gestational age, previous vaginal birth, shoulder dystocia in previous pregnancies, induction of labour, epidural anesthesia, length of the first labour stage, the length of the labour second stage and intended operative vaginal delivery.
In a specific example of implementation, the predictive model generated includes a component associated to a previous vaginal birth indicator data element indicating whether the mother was nulliparous or multiparous. Alternatively, different predictive models are generated for nulliparous and multiparous mothers. Experimentation has shown that risk factors operate at different levels for nulliparous and multiparous women. By generating separate predictive models on the basis of whether the mothers had had previous births and generating separate predictive models for each group, predictive models having a higher level of performance can be obtained.
In a second specific example of implementation, different predictive models are generated for records with outcomes of shoulder dystocia with neonatal injury and for records with outcomes of shoulder dystocia without injury. The latter are medically and legally less significant and are not identified in a consistent fashion by clinicians. The models derived on records with outcomes of shoulder dystocia with neonatal injury have been found to be have a higher level of performance.
At step 806 the predictive model generated at step 804 is applied to a plurality of patient records. In a specific implementation, the predictive model is applied to the records in the database of patient records and the control group database received at steps 800 and 802.
The predictive model is applied to each record in the database of patient records with identified shoulder and to the control database records to derive a score for each record.
In a first experiment, the predictive model shown in equation 1 was applied to actual patient records from a control group, to a group of patient records having an identified shoulder dystocia with neonatal injury and to a group of patient records having an identified shoulder dystocia without neonatal injury. The patients in all three groups were multiparous women. FIG. 14 shows the frequency distribution of the scores derived on the basis of the predictive model shown in equation 1 plotted against the % of cases in the control group. This distribution is labeled 1400. FIG. 14 also shows the distribution of the scores plotted against the % of cases in the group of patient records for which shoulder dystocia without neonatal injury was observed. This distribution is labeled 1404. The figure also shows the distribution of the scores plotted against the % of cases in the group of patient records for which shoulder dystocia with neonatal injury was observed. This distribution is labeled 1402.
In a second experiment, the predictive model shown in equation 1 was applied to actual patient records from a control group, to a group of patient records having an identified shoulder dystocia with neonatal injury and to a group of patient records having an identified shoulder dystocia without neonatal injury. The patients in all three groups were nulliparous women. FIG. 15 shows the frequency distribution of the scores derived on the basis of the predictive model shown in equation 1 plotted against the % of cases in the control group. This distribution is labeled 1500. The figure also shows the distribution of the scores plotted against the % of cases in the group of patient records for which shoulder dystocia without neonatal injury was observed. This distribution is labeled 1504. The figure also shows the distribution of the scores plotted against the % of cases in the group of patient records for which shoulder dystocia with neonatal injury was observed. This distribution is labeled 1502.
As can be observed from FIGS. 14 and 15, the scores are generally higher for the group of patient records for which shoulder dystocia without neonatal injury was observed than for the control group. It can also be observed that scores are generally higher for the group of patient records for which shoulder dystocia with neonatal injury was observed than for the group of patient records for which shoulder dystocia without injury was observed. From the graphs, it can be said that the greater the score, the greater the likelihood of shoulder dystocia. By selecting one or more threshold scores, various levels of risk of shoulder dystocia can be defined.
In a non-limiting implementation, at step 806 a receiver operating curve is generated. More specifically, for each score or range of scores, the true positive rate for the records in the database of records with shoulder dystocia received at step 800 and the false positive rate for the records in the control database is derived. The sets of true positive rates and false positive rates describe a receiver operating curve associated to the predictive model generated at step 804.
FIG. 7 of the drawings shows a generalised receiver operating curve 700 for a predictive model adapted for estimating a level of risk of shoulder dystocia associated to an obstetrics patient. The receiver operating curve 700 illustrates the effectiveness of the predictive model in estimating the level of risk in a reference population for various scores. The Y-axis shows the true positive rate or the percentage of women with shoulder dystocia and neonatal injury who are detected. The X-axis shows the false positive rate or the percentage of women without shoulder dystocia who are incorrectly detected. Each point on the curve corresponds to a particular level of score with its associated true positive and false positive rate. In FIG. 7, a certain obstetrics patient has been given a score which corresponds to point 702 on the receiver operating curve 700. This point 702 is associated to a true positive detection rate of 41% and a false positive detection rate of 3%. This means that in the reference population at point 702 the predictive model detected 41% of cases of shoulder dystocia (Sensitivity rate). This also means that in the reference population, the predictive model at point 702 detected 3% of women who delivered without shoulder dystocia (False positive rate). It will be appreciated that the numbers shown in FIG. 7 are used for the purpose of illustration only and not necessarily numbers indicative of the actual performance of the predictive model described in equation 1 above.
FIG. 7 also shows two threshold scores associated to reference numeral 710 and 720. This illustrates a manner in which an intervention policy for shoulder dystocia may be established. For example, at point 710, the predictive model detected 58% of cases of shoulder dystocia (Sensitivity rate) and detected 11% of women who delivered without shoulder dystocia (False positive rate). Any score falling to the left of point 710 has been classified as having a high risk 704 of shoulder dystocia. A corresponding recommendation for a high risk classification may then be established. Similarly, at point 720, the predictive model detected 80% of cases of shoulder dystocia (Sensitivity rate) and detected 30% of women who delivered without shoulder dystocia (False positive rate). Any score falling between point 710 and 720 has been classified as having a medium or moderate risk 706 of shoulder dystocia. A corresponding recommendation for a moderate risk classification may then be established. Finally, scores falling to the right of point 720 have been classified as having a low risk 708 of shoulder dystocia. A corresponding recommendation for a low risk classification may then be established. The table below illustrates a non-limiting example of a simplified shoulder dystocia intervention policy.













False positive

Corresponding



detection rate
Classification
Threshold Score
Recommendation







 3% or less
High risk
175
Intervention highly





recommended


10% or less
Medium risk
125
Intervention





recommended


over 10%
Low risk
 80
Intervention not





recommended









In the above table, the classification is based on three classes namely: high risk, medium risk and low risk. It will be appreciated that more or fewer classes may be used to effect the classification, each class being associated to a respective level of risk. In another specific example, a classification based on a colour scheme is used where each level of risk is associated to a respective colour. The table below illustrates a non-limiting example of a shoulder dystocia intervention policy using a colour scheme:














False positive





detection rate
Classification
Recommendation








 3% or less
RED
Intervention highly





recommended



 8% or less
ORANGE
Intervention recommended



10% or less
YELLOW
Intervention not usually





recommended



over 10%
GREEN
Intervention not recommended









Advantageously, the receiver operating curve associated with the predictive model allows demonstrating the discriminating performance of the predictive model and provides a health care institution with information upon which to set policies for intervention.
FIG. 10 shows a block diagram of a device for selecting a threshold score on the basis of a desired false positive rate. More specifically, the device includes an input 812 for receiving the receiver operating curve for the predictive model, an input 1012 for receiving a desired false positive rate, or alternatively true positive rate (not shown), and a threshold selector unit 1010. The threshold selector unit processes the data received at inputs 812 and 1012 and releases the corresponding threshold score.
The discriminating ability of a given predictive model can be measured and compared to other predictive models using the Receiver Operating Curve associated to each predictive model. In the basis of a reference population (control database+database of patient records with observed shoulder dystocia), it has been observed that the use of a predictive model which combines components of maternal weight, maternal height and fetal weight provides an improved performance in its ability to discriminate between cases with and without shoulder dystocia compared with the use of fetal weight alone.
FIG. 16 shows two receiver operating curves 1600 and 1602. Receiving curve 1600 was generated on the basis of a predictive model using the fetal weight alone. Receiving curve 1602 was generated on the basis of a predictive model for nulliparous women using a combination of the maternal weight, maternal height and fetal weight in the form if BMI×fetal weight. Both predictive models were tested on the same set of records. As can be observed, for a same true positive value, the false positive is consistently lower for the predictive model using a combination of maternal weight, maternal height and fetal weight compared to the use of fetal weight alone for nulliparous women. Therefore, the use of a predictive model using a combination of maternal weight, maternal height and fetal weight in the form if BMI×fetal weight appears detect a higher percentage of babies with the condition of shoulder dystocia than the use of fetal weight alone for a same rate of false positive predictions for nulliparous mothers.
In implementations where multiple predictive models are used, as in the case for example of different models for multiparous mothers and nulliparous mothers, a receiver operating curve associated to each of the predictive models is generated at step 806.
The predictive model and the receiver operating curve associated to the predictive model are then released at step 808.
Non-Limiting Specific Practical Implementations
Those skilled in the art should appreciate that in some embodiments of the invention, all or part of the functionality for estimating a level of risk of shoulder dystocia associated to an obstetrics patient previously described herein with respect to the apparatus 101, may be implemented as pre-programmed hardware or firmware elements (e.g., application specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), etc.), or other related components.
In other embodiments of the invention, all or part of the functionality previously described herein with respect to the apparatus 101 for estimating a level of risk of shoulder dystocia associated to an obstetrics patient may be implemented as software consisting of a series of instructions for execution by a computing unit. The series of instructions could be stored on a medium which is fixed, tangible and readable directly by the computing unit, (e.g., removable diskette, CD-ROM, ROM, PROM, EPROM, flash memory or fixed disk), or the instructions could be stored remotely but transmittable to the computing unit via a modem or other interface device (e.g., a communications adapter) connected to a network over a transmission medium. The transmission medium may be either a tangible medium (e.g., optical or analog communications lines) or a medium implemented using wireless techniques (e.g., microwave, infrared or other transmission schemes).
The apparatus 101 for estimating a level of risk of shoulder dystocia associated to an obstetrics patient may be configured as a computing unit 1100 of the type depicted in FIG. 11, including a processing unit 1101 and a memory 1102 connected by a communication bus 1104. The memory 1102 includes data 1106 and program instructions 1108. The processing unit 1101 is adapted to process the data 1106 and the program instructions 1108 in order to implement the method described in the specification and depicted in the drawings. The computing unit 1100 may also comprise a number of interfaces 1110, and 1114 for receiving or sending data elements to external devices. For example, interface 1110 receives signals from user interface 102 as described with respect to FIG. 1, and as such is used for receiving data streams indicative of information data elements associated to an obstetrics patient. The processing unit 1101 is operative for processing the information data elements to derive a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient. Interface 1114 is for releasing a signal conveying the ranking data element. The released signal is transmitted to output unit 106 (FIG. 1), such that output unit 106 may convey the ranking data derived by processing unit 1101 to a health care professional.
In a specific example of implementation, the memory 1102 includes a program element within the program instructions 1108, for execution by the computing unit 1100. Once the processing unit 1101 has derived the ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, the program element is operative to process the data element so as to be able to convey information to a user on an output unit. As described above, in specific embodiment, the output unit 106 can include either one of a display screen or a paper printout.
It will be appreciated that the system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient may also be of a distributed nature where the set of information data elements associated to an obstetrics patient is collected at one location or more locations and transmitted over a network to a server unit implementing the method as described above. The server unit may then transmit a signal for causing an output unit to convey information to the user. The output unit may be located in the same location where the set of information data elements is being obtained or in the same location as the server unit or in yet another location. FIG. 12 illustrates a network-based client-server system 1200 for estimating a level of risk of shoulder dystocia associated to one or more obstetrics patients. The client-server system 1200 includes a plurality of client systems 1202, 1204, 1206 and 1208 connected to a server system 1210 through network 1212. The communication links 1214 between the client systems 1202, 1204, 1206 and 1208 and the server system 1210 can be metallic conductors, optical fibres or wireless, without departing from the spirit of the invention. The network 1212 may be any suitable network including but not limited to a global public network such as the Internet, a private network and a wireless network. The server 1210 may be adapted to process and issue signals concurrently using suitable methods known in the computer related arts.
The server system 1210 includes a program element 1216 for execution by a CPU. Program element. 1216 implements similar functionality as program instructions 1108 (shown in FIG. 11) and includes the necessary networking functionality to allow the server system 1210 to communicate with the client systems 1202, 1204, 1206 and 1208 over network 1212. In a non-limiting example of implementation, program element 1216 includes a number of program element components, each program element components implementing a respective portion of the functionality of apparatus 101.
FIG. 13 shows a non-limiting example of the architecture of an example of implementation of program element 1216 at the server system:

    
    
        1. The first program element component 1300 is executed on server system 1210 and is for receiving a set of information data elements associated to an obstetrics patient. In a specific implementation, the set of information data elements includes information derived from a maternal weight component, a maternal height component and a fetal weight component.
        2. The second program element component 1302 is executed on server system 1210 and is for processing the set of information data elements to derive a ranking data element associated to the obstetrics patient. The ranking data element conveys a level of risk of shoulder dystocia associated to the obstetrics patient.
        3. The third program element component. 1304 is executed on server system 1210 and is for sending messages to a client system (1202, 1204, 1206 or 1208) for causing the client system to display information on the basis of the data ranking data element.
        4. The fourth program element component 1306 is executed on the client system and is for receiving a message from the server system 1210 for displaying the information to a user.
    
    


Those skilled in the art should further appreciate that the program instructions may be written in a number of programming languages for use with many computer architectures or operating systems. For example, some embodiments may be implemented in a procedural programming language (e.g., “C”) or an object oriented programming language (e.g., “C++” or “JAVA”).
Although the present invention has been described in considerable detail with reference to certain preferred embodiments thereof, variations and refinements are possible without departing from the spirit of the invention. Therefore, the scope of the invention should be limited only by the appended claims and their equivalents.

## Claims

1. An apparatus suitable for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said apparatus comprising:
a) an input for receiving a set of information data elements associated to an obstetrics patient, the set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component;

b) a processing unit in communication with said input, said processing unit being operative for deriving a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
c) an output for releasing said ranking data element.

2. An apparatus as defined in claim 1, wherein said ranking data element is indicative of a likelihood score.

3. An apparatus as defined in claim 1, wherein said ranking data element is indicative of a risk level selected from a set of risk levels.

4. An apparatus as defined in claim 1, wherein said processing unit is further operative for generating a false detection data element conveying a false positive detection rate associated to the ranking data element.

5. An apparatus as defined in claim 4, wherein said processing unit is further operative for generating a successful detection data element conveying a successful true positive detection rate associated to the ranking data element.

6. An apparatus as defined in claim 5, wherein said output is adapted for releasing said successful detection data element and said false detection data element.

7. An apparatus as defined in claim 1, wherein said processing unit is further operative for:
generating at least one recommendation data element associated to the obstetrics patient.

8. An apparatus as defined in claim 7, wherein said processing unit is operative for processing said ranking data element to generate the at least one recommendation data element associated to the obstetrics patient.

9. An apparatus as defined in claim 7, wherein said processing unit is operative for generating the at least one recommendation data element in part based on data conveying a policy associated to shoulder dystocia.

10. An apparatus as defined in claim 9, said processing unit being operative for processing the ranking data element based on data conveying a policy associated to shoulder dystocia to derive said at least one recommendation data element.

11. An apparatus as defined in claim 10, wherein said processing unit is operative for receiving data conveying the policy associated to shoulder dystocia.

12. An apparatus as defined in claim 11, wherein said data conveying the policy data associated to shoulder dystocia includes a set of risk levels.

13. An apparatus as defined in claim 12, wherein said data conveying the policy data associated to shoulder dystocia includes a set of recommendation data elements associated to respective risk levels.

14. An apparatus as defined in claim 1, wherein said information data elements include information derived from at least one labour component.

15. An apparatus as defined in claim 14, wherein said labour component is selected from the set consisting of induction of labour, epidural anesthesia, length of the first labour stage, the length of the labour second stage and intended operative vaginal delivery.

16. An apparatus as defined in claim 1, wherein said information data elements include information derived from at least one component selected from the set consisting of a maternal age component, a maternal diabetes indicator, a multiparity indicator, gestational age, previous vaginal birth indicator and shoulder dystocia in previous pregnancies.

17. An apparatus as defined in claim 1, wherein said processing unit is operative for deriving the ranking data element associated to the obstetrics patient at least in part based on a previous vaginal birth indicator data element.

18. An apparatus as defined in claim 1, wherein the fetal weight component is an estimated fetal weight.

19. An apparatus as defined in claim 1, wherein the fetal weight component is a range of fetal weights.

20. An apparatus as defined in claim 1, wherein said processing unit is adapted for generating a false positive rate and a positive prediction rate associated to the ranking data element.

21. A method for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said method comprising:
a) receiving a set of information data elements associated to an obstetrics patient, the set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component

b) deriving with a computing apparatus a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
c) releasing said ranking data element.

22. A method as defined in claim 21, wherein said ranking data element is indicative of a likelihood score.

23. A method as defined in claim 21, wherein said ranking data element is indicative of a risk level selected from a set of risk levels.

24. A method as defined in claim 21, wherein said method comprises generating a false detection data element conveying a false positive detection rate associated to the ranking data element.

25. A method as defined in claim 24, wherein said method comprises generating a successful detection data element conveying a successful true positive detection rate associated to the ranking data element.

26. A method as defined in claim 25, wherein said method comprises releasing said successful detection data element and said false detection data element.

27. A method as defined in claim 21, said method comprising:
generating at least one recommendation data element associated to the obstetrics patient.

28. A method as defined in claim 27, wherein said method comprises processing said ranking data element to generate said at least one recommendation data element associated to the obstetrics patient.

29. A method as defined in claim 27, wherein said method comprises deriving said at least one recommendation data element at least in part based on data conveying a policy associated to shoulder dystocia.

30. A method as defined in claim 29, said method comprising processing the ranking data element based on data conveying a policy associated to shoulder dystocia to derive said at least one recommendation data element.

31. A method as defined in claim 30, wherein said method comprises receiving data conveying the policy data associated to shoulder dystocia.

32. A method as defined in claim 31, wherein said data conveying the policy data associated to shoulder dystocia includes a set of risk levels.

33. A method as defined in claim 32, wherein said data conveying the policy data associated to shoulder dystocia includes a set of recommendation data elements associated to respective risk levels.

34. A method as defined in claim 21, wherein said information data elements include information derived from at least one labour component.

35. A method as defined in claim 34, wherein said labour component is selected from the set consisting of induction of labour, epidural anesthesia, length of the first labour stage, the length of the labour second stage and intended operative vaginal delivery.

36. A method as defined in claim 21, wherein said information data elements include information derived from at least one component selected from the set consisting of a maternal age component, a maternal diabetes indicator, a multiparity indicator, gestational age, previous vaginal birth indicator and shoulder dystocia in previous pregnancies.

37. A method as defined in claim 21, wherein said method comprises deriving the ranking data element associated to the obstetrics patient at least in part based on a previous vaginal birth indicator data element.

38. A method as defined in claim 21, wherein the fetal weight component is an estimated fetal weight.

39. A method as defined in claim 21, wherein the fetal weight component is a range of fetal weights.

40. A method as defined in claim 21, wherein said method comprises generating a false positive rate and a positive prediction rate associated to the ranking data element.

41. A computer readable storage medium including a program element suitable for execution by a computing apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said computing apparatus comprising:
a) a memory unit;
b) a processor operatively connected to said memory unit, said program element when executing on said processor implementing a method comprising:
(i) receiving a set of information data elements associated to an obstetrics patient, said set of information data elements including information derived from:
1. a maternal weight component;
2. a maternal height component; and
3. a fetal weight component;

(ii) deriving a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
(iii) releasing said ranking data element.

42. A computer readable storage medium as defined in claim 41, wherein said ranking data element is indicative of a likelihood score.

43. A computer readable storage medium as defined in claim 41, wherein said ranking data element is indicative of a risk level selected from a set of risk levels.

44. A computer readable storage medium as defined in claim 41, wherein the method implemented by said program element when executing on said processor further comprises generating a false detection data element conveying a false positive detection rate associated to the ranking data element.

45. A computer readable storage medium as defined in claim 44, wherein the method implemented by said program element when executing on said processor further comprises generating a successful detection data element conveying a successful true positive detection rate associated to the ranking data element.

46. A computer readable storage medium as defined in claim 45, wherein the method implemented by said program element when executing on said processor further comprises releasing said successful detection data element and said false detection data element.

47. A computer readable storage medium as defined in claim 41, wherein the method implemented by said program element when executing on said processor comprises:
generating at least one recommendation data element associated to the obstetrics patient.

48. A computer readable storage medium as defined in claim 47, wherein the method implemented by said program element when executing on said processor further comprises processing said ranking data element to generate said at least one recommendation data element associated to the obstetrics patient.

49. A computer readable storage medium as defined in claim 47, wherein the method implemented by said program element when executing on said processor further comprises deriving said at least on recommendation data element at least in part based on data conveying a policy associated to shoulder dystocia.

50. A computer readable storage medium as defined in claim 49, wherein the method implemented by said program element when executing on said processor further comprises processing the ranking data element based on data conveying a policy associated to shoulder dystocia to derive said at least one recommendation data element.

51. A computer readable storage medium as defined in claim 50, wherein the method implemented by said program element when executing on said processor further comprises receiving data conveying the policy data associated to shoulder dystocia.

52. A computer readable storage medium as defined in claim 51, wherein said data conveying the policy data associated to shoulder dystocia includes a set of risk levels.

53. A computer readable storage medium as defined in claim 52, wherein said data conveying the policy data associated to shoulder dystocia includes a set of recommendation data elements associated to respective risk levels.

54. A computer readable storage medium as defined in claim 41, wherein said information data elements include information derived from at least one labour component.

55. A computer readable storage medium as defined in claim 54, wherein said labour component is selected from the set consisting of induction of labour, epidural anesthesia, length of the first labour stage, the length of the labour second stage and intended operative vaginal delivery.

56. A computer readable storage medium as defined in claim 41, wherein said information data elements include information derived from at least one component selected from the set consisting of a maternal age component, a maternal diabetes indicator, a multiparity indicator, gestational age, previous vaginal birth indicator and shoulder dystocia in previous pregnancies.

57. A computer readable storage medium as defined in claim 41, wherein the method implemented by said program element when executing on said processor further comprises deriving the ranking data element associated to the obstetrics patient at least in part based on a previous vaginal birth indicator data element.

58. A computer readable storage medium as defined in claim 41, wherein the fetal weight component is an estimated fetal weight.

59. A computer readable storage medium as defined in claim 41, wherein the fetal weight component is a range of fetal weights.

60. A computer readable storage medium as defined in claim 41, wherein the method implemented by said program element when executing on said processor further comprises generating a false positive rate and a positive prediction rate associated to the ranking data element.

61. A system for monitoring an obstetrics patient, comprising:
a) an interface for receiving a set of information data elements associated to an obstetrics patient;
b) an apparatus comprising:
(1) an input operatively coupled to said interface for receiving the set of information data elements, said set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component;

(2) a processing unit operatively coupled to said input, said processing unit being operative deriving a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
(3) an output for releasing an output signal for causing an output unit to display information conveying said ranking data element.

62. A system as defined in claim 61, wherein the interface is selected from a keyboard, a pointing device, a touch sensitive screen, and a voice recognition unit.

63. A system as defined in claim 61, wherein said output unit is either one of a display screen and a printing device.

64. A client-server system for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said client-server system comprising a client system and a server system, said client system and said server system being operative to exchange messages over a data network, said server system storing a program element for execution by a CPU, said program element comprising:
a) a first program element component executed on said server system for receiving a set of information data elements associated to an obstetrics patient, said set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component;

b) a second program element component executed on said server system for deriving a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
c) a third program element component executed on said server system for sending messages to said client system for causing said client system to display information related to the level of risk of shoulder dystocia based on said ranking data element;
d) a fourth program element component executed on said client server system for receiving a message from said server system for displaying the information related to the level of risk of shoulder dystocia to a user.

65. A client-server system as defined in claim 64, wherein said fourth program element component is adapted for causing the information related to the level of risk of shoulder dystocia to be displayed on a display screen.

66. A client-server system as defined in claim 64, wherein said fourth program element is adapted for causing the information related to the level of risk of shoulder dystocia to be displayed on a printed medium.

67. A client-server system as defined in claim 64, wherein the data network is the Internet.

68. An apparatus for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said apparatus comprising:
a) means for receiving a set of information data elements associated to an obstetrics patient, said set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component;

b) means for deriving a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
c) means for releasing said ranking data element.

69. A method for estimating a level of risk of shoulder dystocia associated to an obstetrics patient, said method comprising:
a) providing at a computer a set of information data elements associated to an obstetrics patient, the set of information data elements including information derived from:
(i) a maternal weight component;
(ii) a maternal height component; and
(iii)a fetal weight component;

b) causing the computer to transmit said set of information data elements to a remote processing unit, the remote processing unit being adapted to derive a ranking data element conveying a level of risk of shoulder dystocia associated to the obstetrics patient, said ranking data element depending at least in part on the fetal weight component and on a ratio between the maternal weight component and the maternal height component;
c) receiving at the computer a signal conveying said ranking data element.