NEONATAL ILLNESS SEVERITY/MORTALITY COMPUTERIZED DETERMINATION SYSTEM & METHOD

Abstract: A computerized method and system for measuring and determining severity of illness of a neonatal ICU patient uses a computer and a software program to process measured parameter values from preselected physical conditions. Measurement ranges for each measured physical condition are divided into contiguous zones; the contiguous zones are given predetermined weighting factors using the software program. The software program, using user inputs, optimally selects a single value of each measured physical condition from several measurements. The single selected value of each parameter is then modified using the software program. In one embodiment, for achieving the modification, the software program provides a predetermined weighting factor depending on the parameter value selection. For each selected measured value, an applicable zone and its predetermined weighting factor is determined to generate a modified partial score representing each measured physical condition. Values of modified partial scores for all the measured physical conditions are summed by the computer using the software program to provide an illness-severity measure which can be compared with data held in a database for similar patient population. As described in one embodiment, the physical conditions preselected are: lowest mean blood pressure, lowest pH, lowest temperature, lowest oxygenation ratio, urine output, and the presence of multiple seizures. Three additional measurements of birth weight, smallness for gestational age and low Apgar score, after optimal selection and modification as provided by the program, are used to provide a mortality rate assessment for a neonatal patient being monitored.

Description: Neonatal Illness Severitv/Mortalitv Computerized Determination System & Method Field Of the Invention This invention relates to an improved method and system for determination of illness seventy of patients particularly in newborn/neonatal intensive care units (ICUs) The invention also provides a method and system for determining the mortality nsk of neonatal ICU patients as an extension of a determined illness seventy Applicants are endeavoπng to publicize the system of the present invention to be known as SNAP -II, which stands for Score for Neonatal Acute Physiology II, a successor for a system earlier know as SNAP, which stands for Score for Neonatal Acute Physiology Background of the Invention Momtonng and treatment of premature infants or cntically ill newboms is complicated and expensive. The efficacy of the momtonng system, among other things, depends on which parameters are being momtored, and how many of the measurements can be made automatic without human intervention, without sacnficmg system reliability Several approaches to measurement of illness seventy have been known to be used hitherto, and those have varying degrees of cost and reliability Most known approaches have been devised, however, without specific emphasis on the patient to be momtored being a neonatal intensive care unit (NICU) patient Description of Prior Art Nearly all IC (intensive care) illness seventy scores based on physiologic derangements hav e been directly or indirectly denved from the Acute Physiolog and Chronic Health Evaluation (.APACHE) score by Knaus et al (see Knaus et al "APACHE-acute physiologically based classification s stem" Cnt Care Med, 1981.9(8) 5 1 -59") He reasoned that derangements from physiologic norm are a measure of illness, and the more se ere the derangements, the more severe the illness He selected and weighted 34 vital signs and laboratory results routinely available m the first 24 hours of admission to form the APACHE He showed that higher scores correlated ith death, morbidity, and resource use (see Knaus et al ' AP ACHE-acute physiologically based classification svstem" Cnt Care Med, 1981 ,9(8) 591-59 " ) The advantage generally of such physiology-based measures is that thev are objective, reliable and credible APACHE was simplified to APACHE-II (see Knaus et al "APACHE II A seventy of disease classification system" Cnt Care Med, 1985, 13(10) 818-829), and was copied and simplified into the Simplified Acute Physiology Score (SAPS) (see Le Gall et al "A simplified acute physiology score for ICU patients" Cnt Care Med, 1984,12 975-7) With increasing sophistication, each of these was revised into APACHE-III (see Knaus et al "The APACHE III prognostic system Risk prediction of hospital mortality for critically ill hospitalized adults" Chest 1991 , 100 1619-36) and SAPS-II respectively (see Le Gall et al "A new simplified Acute Physiology Score {SAPS II} based on a European/North Amencan multicenter study" JAMA 1993,270 2957-63) In pediatnc intensive care, APACHE was modified to create the Physiologic Stability Index (PSI)(see Yeh et al "Validation of a physiologic stability index for use m cntically ill infants and children-' Pediatr Res 1984, 18 445-451), which was then simplified to create the Pediatnc Risk of Mortality Score (PRISM) (see Pollack et al "Pediatnc Risk of Mortality {PRISM} score" Cnt Care Med. 1988. 16 1 1 10- 1 1 16), and later the PRISM-III (see Pollack et al "PRISM III an updated Pediatnc Risk of Mortality score" Cnt Care Med, 1996, 24 743-52) and PRISM-III APS (for "Acute Physiology Score") (see Pollack et al "The Pediatnc Risk of Mortality 111-- Acute Physiology Score {PRISM III- APS} a method of assessing physiologic instability for pediatnc intensive care unit patients" J Pediatr 19971 131 575-81) Pnor Art Illness Seventy. Scores for New borns The importance of measunng illness seventy became clear m neonatal intensive care which prompted similar score dev elopment In 1993 Richardson, et al used the APACHE concepts but all new vanables in creating and v alidating the Score for Neonatal Acute Phvsiologv (SNAP) (see Richardson et al "Score for Neonatal Acute Phvsiologv {SNAP ! ■ alidation of a new physiology -based seventy of illness index" Pediatncs 1993 91 617-623) William Tamow-Mordi. et al used the PRISM concept and all new vanables in creating and validating the Clinical Risk Index for Babies (CRIB) m 1993 (see International Neonatal Network "The CRIB {clinical nsk index for babies score}- a tool for assessing initial neonatal nsks and companng performance of neonatal intensive care units" Lancet 1993. 342 193- 198) SNAP is a 34-ιtem physiology-based score measunng seventy of illness, applicable to all newborn intensive care unit (NICU) admissions Based on the same concepts published for APACHE and PRISM, Dr William Tamow-Mordi denved and validated the CRJB (Lancet 1993 342,193-198) for very low birth weight infants (<1500 grams) treated m NICUs in Great Bπtain CRIB uses only three physiologic vanables, denved from routine vital signs and laboratory values, along with three standard markers for newborn nsk, l e , birth weight, gestational age and the presence of a hfe-threatemng congemtal anomaly CRIB is m widespread use for research m Europe There are, however, several important shortcomings of CRIB a. Vahdated onlv for verv premature infants (<1500 gm) While this is valuable for research purposes, it is unacceptably restnctive for hospital and ICU managers who need to assess performance of all admissions, not just a special subset The present invention, m contrast, is vahdated for all birth weights b Questionable performance for outborn babies The CRIB makes assumptions about incomplete records that are untenable for infants under emergency transport conditions The present invention begins sconng only after the infant enters the ICU. thereby avoiding measurement assumptions c Admission-onlv score no sequential sconng CRIB is designed to reflect seventy onlv m the first day of life Half of its components are fixed at birth (birth weight, gestational age, presence of anomalies) The present invention has a much broader dynamic range, designed to measure changing condition ov er time All of the adult and pediatnc sconng systems have this broader dynamic range charactenstic d. Single organ-svstem: CRIB's physiologic items sample only the respiratory system. This may be adequate in a homogeneous population of very premature infants all of whom will have degrees of respiratory failure as their illness on admission. For full term infants, a much wider variety of organ-system failures requires a broader sample of organ-system items. The present invention samples several organ systems. The "Berlin" score is a recently reported German score (Maier RP, Arch Dis Child 1997; 76:F146-F151) and is more of an epidemiologic adjustment tool than a true neonatal illness severity score. It too is an admission-only score and applies only to very premature babies (<1500 grams). There are several other important adult ICU scores that require mention, because they have used parallel techniques to construct the scoring systems. Mortality Prediction Model (MPM): A series of mathematically sophisticated adult ICU risk order was developed by Teres and Lemeshow (Care Med 1987; 15:208-213). A brief attempt at commercializing these appeared to be unsuccessful. S.APS I and SAPS II: The unwieldiness of the original APACHE led to an independent revision by LeGall et al. (Le Gall JR: Crit Care Med 1984; 12:975-7) into the Simplified Acute Physiology Score (SAPS), creating a direct competitor to the concurrently derived APACHE II. Later, with the ~ collaboration of the inventors of the MPM, the SAPS inventors revised and simplified their score to create SAPS II (LeGall JR: JAMA 1993; 270:2957- 63). The SAPS has been developed in France and used widely throughout Europe. A need still exists in healthcare to provide a system and method which obviates the shortcomings and disadvantages of known systems and methodology for determining severity of illness of patients, in particular, neonatal ICU patients from the moment of admission. Summary of the Invention It is an object of the invention to provide a computenzed method and system for determining seventy of illness of a neonatal patient, using a computer method and process, and other measurement hardware The invention, m its broad form, resides m a computenzed method of making an online determination of illness-seventy of a neonatal patient m a predetermined time span, by using a software program and optimal weighted measurement values of a predetermined 'n' number of on-lme parameters from the patient being momtored, said parameters relating to n measurable predetermined physical conditions, said method compnsmg the steps of (a) obtaining, in said predetermined time span from the neonatal patient being momtored. several carefully selected values of each of said n physical conditions and producing, using a program, a single optimal value from said plurality of measured values for each of n measured physical conditions, (b) using said software program, obtaining from said single optimal value a modified weighting partial score, thus generating n modified weighted partial scores for n on-line parameters from the patient being monitored, and (c) summing the n modified weighted partial scores to provide an indication of illness-sev enty of the neonatal patient, which sev enty indication can be displayed and compared with other known values from databases It is another object of the invention to provide a computenzed method and system for measunng a mortality nsk level of a neonatal ICU patient from the measure of illness seventy Applicants ha\e re\ιewed the measurement of illness severity in newborns and concluded that no comparable scale dexelopment has occurred in neonatal intensive care until now To a large degree, birth weight has successfully served as a proxy indicator for seventy of illness along with other nsk factors including gestational age. sex and race Nonetheless there is strong evidence as presented above that these pnor art models fail to accurately capture seventy of illness as evidenced by the large residual vanation m studies of mortality and newborn lung disease Used m applicants invention is a selection of scale items, and sconng of therapies and physiologic derangements Applicants followed the APACHE convention of abstracting the most severely deranged physiology m a 24-hour penod following admission The hypothesis to be tested was that the traditional nsk factors (including birth weight, Apgars, sex, race, etc ) were strong predictors of mortality across the birth weight spectrum, but inadequate for distinguishing mortality nsks withm birth weight strata A companson of vanations in outcomes and resources is one application/use of the present inv ention Another use is the refinement and improvement of the pnor study In as much as SNAP-II is a sconng system to classify illness seventy m newborns m intensive care units (N CUs), it measures the degree of physiologic derangement across multiple organ systems, using vital signs and laboratory values and other information routinely recorded in climcal records Five core components of the present system, which are significant, are 1 Score items Of the hundreds of potential markers of newborn illness seventy, Applicants have selected a v ery limited subset that are reliably available, easily captured, and robust as predictors This specific list of vanables, and their exact definitions, are unique to the present invention improvement ov er SNAP 2 Items score weights Each score item is weighted according to a carefully denved nsk value, so that less senous items have relatively low score points, and more senous items hav e relativ ely high score points The SNAP-II is the sum of points for each item These vanables and eights hav e been denved on an initial large cohort of patients, and then validated on a second large cohort The present invention improv ement takes into account two subscores { > a Risk factors These are standard, scientifically recognized nsks for neonatal mortality, including birth weight, gestational age, Apgar scores and gender 3 b Acute Phvsiologv Score These items reflect the seventy of physiologic derangement 3 Equations and coefficients These equations associate the SNAP-II (or its individual components) with an array of outcomes, resource use, costs 10 and process benchmarks They are denved from regression equations (linear, logistic, polynomial) through a model fitting process that involves modification of the input vanables and selective inclusion to optimize the discnmmation and calibration of the equations 15 4 Reference database This database consists initially of the combined NICU data sets from the 7 NICU research study in New England, funded by AHCPR, and the six NICU study in California, funded by Kaiser Permanente Division of Research, and the 18 NICU data set of the Canadian NICU network, founded by the Canadian Medical research council 20 5 Reporting systems These reports lay out nsk-adjusted compansons of pre-specified groups of patients in an individual NICU with an appropnate reference group, drawn from the reference database ? The present invention improvement is unique compared to other newborn seventy scores in sev eral w ays it is shorter and simpler to use than the onginal SNAP It applies to all newborns admitted to NICUs, m contrast to the CRIB score which applies only to babies weighing <1500 grams It is adaptable to sequential sconng. so that new scores can be generated daily In contrast, CRIB is an admission-only 30 score The present invention is distinct from the adult and pediatnc illness seventy scores of pnor art in that it applies specifically to newborns in NICUs The .APACHE I, II. and III, and SAPS I and II are not applicable to newborns The PRISM, PRISM II. and PRISM III .APS were denv ed using some full term newborns but their applicability ana calibration specifically for premature infants has never been evaluated Thus, even though in pnor art several attempts have been made to provide sv stems which are intended to provide an indication of the illness seventy of a patient, there have been the following significant disadvantages m applying them to neonatal care situations m some pnor art systems, as many as 34 different physical condition measurements were used, thus making the process very elaborate, expensive, and prone to miscalculation, efforts to reduce the number 34 were made, but an identification of those physical conditions/parameters which would be crucial to neonatal situations was not made based on available illness seventy scores from similar patient population. - A concerted effort was apparently not made m pnor art to make an automated selection and optimization from several available readings or measured values of a single physical condition for neonatal applications over a predetermined time span The choice of the single optimized selected measured v alue of a given physical conditiony parameter in the present invention is so made by a program that the choice is linked with the most appropnate weighting factor based on vast amounts of accumulated pnor data, and the choice additionally ensures the highest possible reliability of the seventy measurements generated Certain physical conditions selected for being monitored m pnor art were not ideally the best suitable physical conditions for neonatal measurements For instance, m the present inv ention, m a preferred embodiment, the physical conditions chosen for momtonng are blood pressure, temperature, oxygenation ratio serum pH. presence absence of seizures and unne output The program m one preferred embodiment makes the optimal choice of those parameters as the lowest mean blood pressure, the lowest temperature, lowest oxygenation ratio, the lowest serum pH. presence absence of seizures, and unne output Consequently, in at least three of the six phy sical conditions selected for neonatal momtonng. four of the physical conditions offer optimal selection by the program resulting in the choice of the lowest of measurements The fact that the lowest of the measurements for blood pressure temperature oxygenation ratio and serum pH were selected by the program is also associated with certain predetermined weighting factors which would result in the most reliable illness seventy determination when compared ith available data of similar patient population Altemativ ely, if instead of the lowest measured values of blood pressure, temperature, oxygenation ratio, and serum pH, other values were to be chosen by the program, the weighted factors to be used would correspondingly be different, in order to ensure reliability of the resulting illness seventy measurement Applicants have found the use of the lowest mean blood pressure, lowest serum pH, lowest temperature, lowest oxygen (pA0 /Fι0 ) ratio, as well as unne output and the presence of multiple seizures, along with the weighted factors disclosed herein would provide an extremely highly reliable measurement of the illness seventy for neonatal patients The task of selecting a single optimal measurement value from several generated measurement values of a physical condition, and the task of matching up the selected optimal measurement based on the nature of the optimization, for e g , the selection having to be the lowest value with an appropnate weighting factor, is done by a computer program m the present invention Brief Description of the Drawings The foregoing and other objects, features and advantages of the inv ention will be apparent from the following more particular descnption of preferred embodiments of the ιn\ ention, as illustrated in the accompanying drawings m which like reference characters refer to the same parts throughout the different views The drawings are not necessanlv to scale emphasis instead being placed upon illustrating the pnnciples of the inv ention Figure 1 is a schematic system diagram of a preferred embodiment of the invention illustrating neonatal patients being monitored and with a vital signs interface, programmed computer, database, and a laboratory interacting, Figure 2 illustrates the process flow of ho data is acquired on a neonatal patient admitted to the NICU is monitored using the pnnciples of the invention, Figure 3 illustrates how from several different changing values of a given physical condition/parameter, an optimal measured value can be selected using the program in the mv ention, Figure 4 illustrates an alternative embodiment for the inventive system illustrating a processor including the momtonng software and its interaction with inputs and outputs as shown, Figure 5 shows a scatterplot of SNAP-I versus birth weight, Figure 6 shows a scatterplot of SNAP-II versus birth weight for companson Figure 7 is a bar-graph of mortality by birth weight and illness seventy, Figures 8 shows mortality by SNAPPE-II score, Figure 9 shows a correlation between SNAP-I and SNAP-II scores. Detailed Description of Preferred Embodiments Figure 1 illustrates components of the system of an embodiment of the invention, wherein neonatal patients admitted to the NICU 101 are momtored to obtain measurements of standard vital signs and/or other predetermined parameters such as. for example, blood pressure, temperature measurements, unne output, and presence or absence of multiple seizures These individual values are collected 102 and registered on the computenzed (or paper) patient records Laboratory results on individual patients are similarly registered on patient records. to At fixed time intervals, all readings of the six key physiological parameters are obtained and transmitted to the computational soft are 105 as necessary A measured parameter may have several recorded values in the specified time interval The worst value, for example, is selected using carefully defined predetermined cntena The degree of derangement is ascertained and a partial score is assigned for each parameter In the illustrated embodiment, the weighting consists of multiplying each worst parameter w ith a predetermined score for the measured parameter The program 105 advantageously chooses one of different methods of optimization such as exemplified m Fig 3 Depending on which method of optimizing is chosen, for example, depending on whether the maximum measured parameter value in a given time span is chosen, or the minimum 303 lowest logical value or the weighted average 302 is chosen, a corresponding weighting factor for multiplication is lined up by the program 105 for multiplication Advantageously, the program 105 is made to ignore or trap a measured value which is illogical By user-intervention, command default and the like, program 105 is made to proceed with whatever kind of optimizing is desired The kind of optimization so chosen will have its corresponding predetermined multiplication weighting factors based on which zone the measured physical condition values he in In the preferred embodiment descnbed herein, as stated earlier, the parameters chosen are the lowest mean blood pressure, the lowest pH, the lowest temperature, the lowest oxygenation ratio, unne output, and presence of multiple seizures It is conceivable that for optimal values, average values of measured predetermined parameters (physical conditions) may be used in the system and method of the inv ention if so chosen bv the user In such an event, weighting factors stored in the database 106 corresponding to average values of the predetermined measured physical conditions will be used Advantageously, the weighting factors may automatically be v enfied as to their applicability before use m the illness sev enty measurement In an extension of the preferred embodiment referenced abov e, three additional physical conditions (factors) are measured and considered after weighting as (( nn „_, J0 . PCT/US98/24585 99/27482 controlled by the software in the program 105 of Figure 1, to assess the mortality nsk of an NICU patient being monitored The three-factor addition is termed as Pennatal Extension, or PE. which, when added to SNAP II. will provide SNAPPE II Descnbed in the following passages are some considerations which governed the choice of physical conditions and their optimal selection The oπginal SNAP was cumbersome and time-consuming, limiting its use predominately to research With the example of the successful simplification of APACHE, SAPS, and PRISM, applicants have revised and simplified SNAP into SNAP-II Applicants had to bear m mmd that the spectrum of illness and causes of death and other considerations are different m neonatal patients as compared with pediatnc adult situations Accordingly, for NICU patients, the preferred embodiment of SNAP-II descnbed herein is a six item physiology-based seventy of illness measurement that has predictive performance that equals SNAP Consistent with "parent" physiology scores, it uses readily available vital signs and laboratory measures obtained from routine medical records It provides an objective, reliable measure of the initial status of an NICU patient at the time of NICU admission SNAP-II advantageously consists of six related components 1 Item selection The 6 items contained in SNAP-II are listed m table 1 These were carefully selected to include definable, measurable items that maximize discnmmation between sick and well newborns Applicants, after research, have retained electronically accessible items and eliminated unreliable, weak, or infrequent items I TABLE 1 Items Included in the SNAP-II Scores ! Physiological items (SNAP-II) ♦ lowest mean blood pressure ♦ lowest pH j ♦ lowest temperature i ♦ lowest pAO?/FiO 2 ratio I ♦ unne output ♦ multiple seizures Perinatal extension SNAPPE-II ♦ birth weight ♦ small for gestational age (3 rd %) ♦ low Apgar score Mortality risk ♦ computed from above 2. Specific definitions: These definitions and abstracting rules are clear and simple, ensuring accurate data acquisition. Data are collected either prospectively or retrospectively from the medical charts corresponding to the first 12 hours of admission, starting at the time of the first set of vital signs on admission and extending for exactly 12 hours. The single worst value for each item is selected and recorded, ensuring consistent reliability among abstractors. If a test were not ordered, it is assumed the clinician estimated it near normal and not needed to direct care. These items and other measured items, which are near normal, receive zero points. 3. Scoring ranges of each item: A gradient in risk for each item is reflected by dividing the derangements into zones (see below). These zones are contiguous ranges and have been optimized in their power to discriminate between survivors and non-survivors. For convenience, applicants have designed a data form to simplify collection. 4. Point score for each item-range combination: Points are then awarded for each item according to degree of derangement. These points are optimized to predict mortality/survival. /3 TABLE 2 SNAP-II ™ Score (Preferred Embodiment) 5. SNAP-II: The sum of points awarded for each item quantifies the degree of physiologic derangement and creates a unique marker for overall illness severity. This measurement is the SNAP-II illness severity measurement. SNAP-II = sum of points awarded for the following: (low mean BP) + (low temp) + (low oxygenation ratio) (low pH) + (multiple seizures) + (low urine output) 6. SNAPPE-II: SNAP-II measures illness severity, a critical component of mortality risk. However, other factors are known to make the risk even higher. Applicants tested numerous risk factors following the same process as 1-6 above, and selected three additional perinatal items (see table below), developed specific definitions and score ranges, and assigned supplemental points. The sum of SNAP- II plus the Perinatal Extension points creates the SNAPPE-II, an estimate of mortality risk. if TABLE 3 SNAPPE-II T'M M Score SNAPPE-II = sum of points awarded for the following: SNAP-II + (BW points) + (SGA points) + (low Apgar points) 7. SNAPPE-II mortality risk: The points for each item of SNAPPE-II represent the coefficients from the logistic regression equation predicting mortality. This process can be inverted by scaling the coefficients down by a factor of 0.783, subtracting the constant, and exponentiating the mathematical term. The result is a point estimate of individual mortality risk, P(death): P(death) = 1:(1 + e R ) where R = (SNAPPE-II * 0.783) - 5.1656 SNAPPE-II is defined as: 9. if moderately abnormal low mean blood pressure OR 19, if severely abnormal low mean blood pressure + 8. if moderately abnormal low temperature OR 15, if severely abnormal low temperature ÷ 5, if moderately abnormal oxygenation ratio OR 16, if severely abnormal oxygenation ratio OR 28. if extremely abnormal oxygenation ratio ÷ " . if moderately abnormal low pH OR 16, if severely abnormal low pH ÷ 19. if presence of multiple seizures + l 5. if moderately abnormal unne output OR 18, if severely abnormal unne output - 10. if moderately low birth weight in grams - 5 OR 17. if severely low birth w eight m grams - 8, if moderate smallness for gestational age J - 18, if severe "Apgar" at 5 minutes 10 Figure 2 is a flow diagram of an exemplary method of illness seventy measurement according to the invention, and is self-explanatory The embodiment m the Figure 2 flow diagram is not limited to six or mne parameters At step 201. a neonatal patient to be monitored is admitted into the neonatal intensive care unit, the patient, at step 15 202 is connected to the patient vital sign monitors, these m turn and to the computer and the database At this time, arrangements are also made to obtain any manually entered physical condition readings which are entered into the patient medical records. It is within the scope of this invention to use any physical condition measurements, which can be made automatically, without any human intervention 20 At step 203, the n different physical items to be momtored are identified This can be done manually, or can be done automatically by default or other methods of making a choice, e g , by pushing a single button which is connected to make a selection of n', predetermined physical conditions to be measured In block 204, all measurements of values for each chosen physical condition are extracted and fed 2D into a buffer Values determined to be illogical are then excluded Then, for each parameter, the "worst" of the values is selected Step 205 pertains to identifying the physiologic range that contains the value selected in 204 Ev en though m the preferred embodiment, three four zones are indicated for practicality/ simplicity, alternatively, there may be two zones or five or more zones Step 206 pertains to 30 attributing eighing factors for each zone of a physical conditioa parameter The software program, after taking into account several cases for similar patient population as a patient m question, decides suitable weighting factors which are used as multipliers for an optimized v alue of the measured physical condition Generally, the w orse the ph y sical condition the higher is the weighting factor Typical Z : w eightmg factors are shown m connection with a preferred embodiment m Table 2 and Table 3 In steps 207 and 208, weighted partial score for each measured parameter is chosen with the help of the software program An example is illustrated m figure 3 where varying values of a measured parameter with respect to time are sήown Out of the av ailable values, as examples, a maximum or minimum or an average or a root mean square value can be chosen by the software program Other methods of optimizing are conceivable and with the purview of this invention In step 209, the optimizing value is modified to generate a modified partial score for a measured parameter In the preferred example, the modification consists in automatically multiplying the optimized value of a score with its associated weighting factor Other methods of modifying are withm the purview of this invention An optimized value of a parameter may, for example, be divided by a suitable weighting factor to obtain the modified partial score for the physical condition (parameter) m question In step 210, there is an automatic addition performed of all the modified partial computational values or scores to determine illness seventy Step 21 1 compares the determined illness seventy with other comparable information for similar patient population The data of illness seventy measurements can be used as in step 212 to review and update future illness seventy and mortality nsk measurements and for other suitable purposes. Some of the possible implementations of the SNAP-II system are presented m this text under the subheading "Specific applications of SNAP-II" Other uses and applications not specifically stated herein are conceivable and are intelligible to those skilled the art Figure 4 illustrates an alternative embodiment of the inventive system showing the interaction of a processor, momtonng software, and NICU patient measurement inputs. Having collected background data and generated orking modules (the coefficients for supporting the models), Applicants formed the sconng system apparatus for hospital neonatal ICUs and physicians As illustrated in Figure 4. this embodiment of the present inv ention includes three components--(ι) momtonng software, (n) optional input accessory and (in) system environment / / The monitoring software m the Figure 4 embodiment is at the heart of the invention (with its beginning m SNAP-II) The software generally is executable code or a circuit or combination thereof withm a digital processor/ computer processor 401 (represented bv dashed line box m Figure 4) The software may include a database configured through known database techniques (I e , programs such as Access, Paradox, and the like) A user interface enables user input of SNAP-II data items which are then stored m the database according to the database program employed A processing member of the software is responsive to the data held m the database and calculates the SNAP-II score according to the working models of the present invention (and formulated by Applicants m the study descnbed above) The software provides the SNAP-II score on output to the user through output means (e g , a monitor 406, pnnter 409. etc ) Optionally, vanous front-end, mput accessones 403,404 may be employed This includes hardware, software or combinations thereof that provide data to the database For example, an optical scanner may be used to convert data m hard copy or paper form to electronic/digital form and store the latter m the database If the database is coupled to a network or a general hospital system, a multiplicity of mput streams may be involved Thus, a caching subsystem and/or other cleanng house subprogram may be employed to extract from the input streams pertinent SNAP-II data and store the same m the database 406 The real utility will accrue to users 405 who harness the SNAP II (invention) measurements with the predictive equations (working models) to produce vanance reports on clinical performance, resource use and inter-institutional benchmarking There are two strategies for developing such NICU management information systems a Stand alone systems These are expansions on the sconng software descnbed above to include equation-based benchmarks that generate regular reports for NICL managers These svstems mav be quite comprehensiv e, and can be designed to accept data-feeds from hospital Admission/ Discharge Transfer (ADT) systems, or even hospital laboratory systems b Integrated systems. In the rapidly consolidating market for electronic medical records, an alternative strategy is to partner with a major vendor of hospital electronic records With their expertise and funding, one can work to embed the SNAP II score into the electronic chart, providing automated data acquisition and sconng, and use this to prompt actions for the clinicians m real-time. Many of the decision-support processes would depend on SNAP and related data, but also denve from the high quality, w ell organized care practices. More specifically, there are several major vendors of integrated medical records systems: i. Hewlett Packard. CareVue Svsteπv CareVue is an electronic charting system for intensive care units, including nursing flow sheets, physician notes and clinical pathways. Such a charting system is an ideal vehicle for an integrated system, because the electronic accessibility of all SNAP II components reduces data collection costs to near zero. In this setting, one can concentrate on developing the seventy-adjusted vanance reports and decision-support feedback in real time. n. Cemer Cerner is a major vendor of hospital laboratory data management and reporting software. It has expanded into a comprehensive data repository for clinical records. The system is state-of-the-art, flexible, based on networked workstations, and using web-based technology. The Cerner system has focused its development efforts on outpatient records, so it has no specific ICU charting software similar to CareVue. in. Pices/Phamus: This is another major vendor of hospital comprehensive software. Others are suitable. Figure 5 sho s a scatterplot of SNAP-I versus birth weight. Dark spots (diamonds) represent deaths, showing that regardless of the birth weight, higher SNAP scores are associated with the patient death. Also seen is a concentration of dark spots at low birth weights regardless of the SNAP scores. Figure 6 shows a scatterplot of SNAP-II versus birth weight. Darker circles represent death. It is seen that SNAP-II effectively discriminates beuveen survivors and deaths across all birth weights. Figure 7 is a bar- graph of mortality by birth weight and illness severity, confirming findings from Fig. 5. Mortality rates for three different ranges of SNAP scores are illustrated. The graph shows the highest mortality rate for birth weight group of under 750 grams, even for SNAP scores of 20+. Figures 8 shows mortality by SNAPPE-II score, showing gradient in mortality risk for increasing SNAPPE-II score values. This relates to data unpublished at the time of this application and is limited to the New England area of the United States. Figure 9 generally shows correlation between SNAP-I and SNAP-II measurements. It is generally seen that patients with higher SNAP-II scores (thin circles) died. An excellent correlation between the SNAP and the SNAP-II scores is seen from Fig. 9. Substantative differences between the SNAP-II. CRIB. SNAP. APACHE-IH. and PRISM-III: This section highlights the differences among the scores that may be relevant to the present invention. In summary. CRIB and prior art SNAP are the most closely related, but are public domain scores, and the latter was invented by the applicants. APACHE-III is proprietary, but apparently applies only to adult ICUs. PRISM-III relates to children, but serves a distinctly different population in pediatric ICUs rather than NICUs, and has been developed and validated for this different purpose. 1. CRIB: CRIB was developed and validated for premature infants weighing <1500 grams at birth and/ or delivered at <32 weeks gestation. It does not apply to larger or older babies and therefore cannot be used to characterize the entire population of a NICU. The score is computed from the time of birth (not admission) which makes it difficult or impossible to apply to outbom infants (infants transfened to the current hospital). Two of the physiologic items (highest and lowest "appropriate" concentration of oxygen administered) are extremely complicated to understand and to abstract and are therefore error-prone. Furthermore, these items 2° cannot be obtained from electronic sources (1 e , laboratory computers) CRIB was validated on a cohort of infants m 1990 and has not been re-cahbrated since, despite a well recognized 50 percent fall m the mortality rates (see Richardson et al 'Declining Seventy Adjusted Mortality Evidence of Improving NICU care ' Pediatncs [ 1998 102 893-899}) These six CRIB items blur the distinction between the mortality nsk factors (birth weight, gestational age. congenital anomalies) that do not change from day to day, and degree of illness (physiologic derangements including worst base deficit, and highest and lowest "appropnate" oxygen requirement) CRIB was openly published and is now in the public domain 2 SNAP from prior art SNAP is a 34-ιtem score that is cumbersome and time- consuming SNAP-II has eliminated 28 unnecessary or redundant items SNAP had a number of vanables that are difficult and unreliable SNAP used a 24-hour baseline data collection and sconng penod (The SNAP-II sconng penod has been reduced to 12 hours to minimize "contamination" of sconng with responsiveness to treatment) SNAP weighted items based on estimates of an expert panel (SNAP-II weights item empincally based on a logistic regression model) SNAP has supplemental points to recognize mortality nsk not captured by physiologic derangement - the Pennatal Extension points of the SNAP-PE (SNAP-II uses the same three items but these are now empincally weighted according to the logistic regression model) SNAP was openly published and is now m the public domain 3 APACHE-III APACHE and APACHE-II w ere both openly published public domain scores APACHE-III was developed as a pnv ate venture The scores and definitions have been released publicly The rest has been held as trade secrets This includes the score ranges and points, the equations relating APACHE-III to a vanety of outcomes, the computer programs developed to compute and display results, and the APACHE reference database APACHE sconng applies only to adult ICUs and is not applicable to children or particularly to newboms because of their very different phvsiologv and diseases 4 PRISM The PSI and PRISM were openly published scores and are m public domain PRISM-III is, as aforesaid, disclosed in published European application EP 11 0 764 914 A2 At least some of the distinction beuveen SNAP-II and PRISM-III which are cn ical to an understanding of the present invention follow a Patient population SNAP-II claims applicability to all patients m newbom intensiv e care units (NICUs) PRISM-III claims applicability to all patients pediatnc intensive care units (PICUs) which may include some newboms Patient assignment to NICU or PICU may vary slightly among hospitals, but is dominated by age PICUs serve predominantl y older children and virtually no premature infants, whereas NICUs have all ne boms, predominantly premature infants Another cntical difference in population beuveen NICUs and PICUs is the range of diagnoses. PICUs serve a wide vanety of conditions including infections, cancer, surgery, etc , whereas NICUs server predominantly premature infants, or term infants with disorders of fetal-neonatal transition, congenital anomalies, and birth accidents b. Score items: Each score has selected and optimized items to predict mortality in the respective ICU setting. The definitions and score ranges reflect the physiology of the respective age groups and technologies and practice patterns in the different types of ICUs PRISM-III adjusts for diagnoses, and the current SNAP-II does not c Scores The scores for the different methods of illness seventy measurement in pnor art are the sum of the individual selected parameters and are therefore not related nor compatible with illness seventy measurements from the present invention. d Equations While the forms of the equations are similar (both denved from the logistic models) the coefficients and constants are different and optimized to the populations and diseases of the respectiv e ICUs e Reference databases Applicants believe that there is nothing propnetary regarding databases for either product, both retain control through limited access and trade secret protections In both systems, the reference database is vital for providing benchmarking of performance on sev eral outcomes Advantages of the Present Invention 1 Pnonty in newborn sconng Applicants believe they were the first to develop and report physiology-based illness sev enty sconng for newborns with SNAP SNAP-II is denved from SNAP 2 Unique items selection Of the hundreds of potential predictors, applicants have selected the 6 that best capture illness sev eπty, and include items from each organ group 3 Unique score weights The score weights are based on the logistic regression model used to denve the score These weights represent optimized measures of mortality nsk. 4 Unique scores for SNAP-II and SNAPPE-II The summary scores are computer determinations from unique components to create a umque combination, reflecting multi-system physiologic arrangements and mortality nsk. 5 Umque mortality nsk equation. This equation was denved and validated on over 27.000 cases, ensunng a precise and robust estimate of mortality nsk. 6 Unique computer program The items, definitions, scores and computations are embodied m a first generation computer program that calculates scores and mortality nsks for populations of patients in newborn intensive care Implementation of the SN.AP-II svstem SNAP-II. SN.APPE-II and SNAP-II mortality nsk are intended as an integral part of a comprenensive NICU outcome evaluation and reporting system Applicants are committing each component to a software implementation, both as stand-alone programs and embedded m larger commercial ICU charting and decision support programs The components of applicants' system are 1 NICU admissions and outcomes database It is essential to record the key nsk factors treatment processes and outcomes of all NICU admissions An example of this is tne Neonatal Minimum Database Svstem (NMDS) which contains a stnctlv 4 * 3 limited set of the key nsk factors, t cnts and outcomes that permit full charactenzation of the performance of a given NICU 2 SN.AP-II The collection of the 6 items, plus the Pennatal Extension nsk factors permit measurement of sev enty of illness and mortality nsk on admission 3 Risk adjustment of outcomes The nsk-adjusted performance of a NICU can be calculated for each of several outcomes Each nsk adjustment equation is intended for inclusion in this patent application The process of nsk adjustment is earned out as follo s Individual nsk factors for each patient are combined with the SNAP-II on that patient to generate an individualized nsk (I e , a probability between 0 and 1) for that specific outcome The sum of these nsks for a designated population represents be "expected" incidence of that adverse outcome The actual or "observed" incidence for that same population is then talhed and compared to the expected rate The ratio of the observed to the expected rate is called a standardized rate, and is used for compansons of performance (see below) Applicants have developed nsk-adjustment equations for the following a Mortality Mortality was used as the standard for developing the SNAP-II score It is possible to calculate standardized mortality rates for any size population (see equations above) b Morbidity Currently, equations are available m pnor art to calculate nsk of mtraventncular hemorrhage (a dangerous complication of prematunty associated with brain damage), and neonatal chronic lung disease (an expensive, disabling consequence of extreme prematunty) c Lengthv of Stay Illness sev enty has a powerful influence on length of stav for newboms The present inv ention can assist to predict the length of stay and of populations of newboms which is extremely helpful m projecting workload and occupancy and companng efficiency of clinical practice styles tf 4 Report generation using simple charts and graphics is rendered possible by the system of the present invention, to compare current and past performance regarding illness seventy prediction 5 Benchmarking L smg the reference data gathered by our three research institutions, available m the database and new data which can be generated by the present inv ention. it is possible to compare performance of any gπ en NICU with all others and with NICUs of similar type of patient charactenstics This benchmarking is extremely valuable to clinicians, admimstrators and insurers 10 Specific applications of SN4P-II In addition to the "system" implementation descnbed above. SNAP-II can be used independently m research, quality improvement, financial projections and medico- legal nsk management 15 1 Research The vanety of research applications of SNAP-II has been wide and innovative (see Richardson et al "Neonatal Illness Seventy Score can they predict mortality and morbidity 9 " Clinics in Pennatology, 1998,25 591-611) Applicants are interested m making SNAP-II available to legitimate researchers to 20 sustain those innovative applications 2 Quality Improvement SNAP-II can serve as an important marker for quality improvement activities A pattern of unexpectedly ill admissions should prompt rev lew of obstetnc practices and pre-admission stabilization A pattern of ? =; death or morbidity in patients with limited nsk should prompt review of care practices and clinicians A pattern of admission of low nsk patients should prompt review of staffing and admissions policies Many of the review processes are mandated by the Joint Commission for the accreditation of hospitals and other organizations and bv manv state regulations 3 Medico-legal risk management Sev eral of the quality improvement screens noted abov e can also be sued by nsk management to select cases for review to identify and reduce nsks of litigation 2 4. Financial projections. Current case mix adjusters are crude and retrospective. SNAP-II offers the possibility of an objective, prospective, quantitative measure of expected costs and length of stay. This can be used by hospital systems for budgeting and staffing projections, and by insurers for gauging 5 financial exposure and for gauging financial exposure and for negotiating better reimbursement contracts. 5. Not intended for use in the ethical decision making. Applicants make no claims for the use of SNAP-II or any associated scores in estimating individual 10 mortality risks for the purposes of withdrawing life support. All of applicants' publications have emphasized that there is insufficient certainty in estimates on any individual patient and that such decisions must be made based on clinical judgement and not based on scores. 15 SCORE FOR NEONATAL ACUTE PHYSIOLOGY-II fSNAP-II™) Definitions Of Physiologic Variables LOWEST MEAN BLOOD PRESSURE: Lowest mean BP during the first 12 hours of admission, as recorded in the nursing flow sheet. If only systolic and diastolic are recorded, assume mean BP = diastolic 20 ÷ 1/3 (systolic-diastolic). LOWEST TEMPERATURE Lowest body temperature (axillary or rectal but NOT skin probe). This is usually recorded in °F. If recorded in °C, must convert to °F in order to score. -><. LOWEST pH: Lowest pH during the first 12 hours of admission. This may be obtained by ABG. CBG or VBG. and need not be related to the @LOW, @AWP or (αHIO blood gasses listed below. % LINKED RESPIRATORY VARIABLES: The goal is to identify the three worst arterial blood gasses. These are then used to compute the pA0 2 Fiθ 2 ratio assessing oxygenation status. These three blood gasses are labeled @LOW, @AWP and @.HIO and are recorded as RAW DATA. (SLOW: Low Blood gas is that with ABSOLUTE LOWEST pA0 2 during the TIME PERIOD. CBG, VBG. TcP0 2 are not acceptable alternatives. Record the following corresponding to the @LOW blood gas: ♦ Fi0 2 @LOW: Highest Fi0 2 expressed as percent (21 % - 100%). ♦ pA0 2 @LOW: pA0 2 in mm Hg. @.AWP: Lowest pA0 2 documented at AWP BLOOD GAS. AWT Blood gas is that with the HIGHEST MEAN AIRWAY PRESSURE. If there are several ABGs at that Mean Airway pressure, select the one with the worst pA0 2 unless that is already recorded as the pAO 2 @LOW. In that case, select the next lowest. If only one blood gas was obtained, skip this step and proceed to computations. CBG, VBG, TcP0 2 are not acceptable alternatives. Record each of the following conesponding to the @AWP blood gas: ♦ Fi0 2 -@AWP: Highest Fi0 2 expressed as percent (21% - 100%). Should conespond to the highest MEAN AIRWAY PRESSURE not already recorded in (SLOW. ♦ pAO 2 @.LOW: p.A0 2 in mm Hg. @HIO: Lowest pA0 2 documented at HIO BLOOD GAS. HIO Blood gas is that with the HIGHEST Fi0 2 . If there are several ABGs at that highest Fi0 2 , select the one with the worst pA0 2 unless that is already recorded as the (SLOW or the @AWP p.A0 2 . In that case, select the next lowest. If only one blood gas was obtained, skip this step and proceed to computations. CBG, VBG. TcP0 2 are not acceptable alternatives. Record each of the following corresponding to the (SHIO blood gas: ♦ Fi0 2 - (SHIO: Highest Fi0 2 expressed as percent (21 %- 100%). Should correspond to the highest Fi0 2 not already recorded in (SLOW or (S: AWP. ♦ pA0 2 (SHIO: pA0 2 in mm Hg. LOWEST P A0 2 /Ti0 2 RATIO: The lowest pA0 2 Fi0 2 ratio is computed using pA0 in torr, and Fi0 2 as percent (e.g.: 80torr/40% inspired oxygen = 20). Use the pairs from (SjLOW, (S.AWP or @HIO, and select whichever is lowest. URINE OUTPUT (CC KG HR): Total Cubic centimeters of urine output during the first 12 hours of admission divided by BIRTH WEIGHT IN KG, and then divided by 12 HOURS . If notes indicate that some output was lost/unmeasured, then score as 0. SEIZURE: Multiple seizures, confirmed or high degree of suspicion. A single seizure or suspected seizure does not qualify. ALTERNATIVE PARAMETER MEASUREMENT: Blood pressure, pH, temperature and oxygenation ratio as well as Apgar ratio may be measured, tracked, and transmitted in any alternative conventional manner, either in an analog or digital fashion, as feasible in the NICU setting. Structured details of such monitors, transducers, analog/digital converters, and measuring systems are not critical to the present invention and are known to those skilled in the art. Physical conditions such as presence of seizures and urine measurement may be done either automatically without human intervention, or manually, as conditions permit. In any event, measured values of these parameters/physical conditions also can be subjected to the step of optimizing, to choose a desirable type of measurement. In the particular embodiment described herein above, the presence of multiple seizures is a potentially significant predictor. SNAP-II score computation may be done in real time as data items are entered in the database. Data entry for a given patient may be at one or multiple sittings, such that a current SNAP score is computed at each sitting. Likewise, the present invention may generate real time model predictions based on computed SNAP score (as often as the score is computed). The present invention would thus be linked to reporting systems or other output means for presenting model predictions m real time to physicians, utilization review, hospital administrators, and the like. To that end. output may initially be m the form of prompts for decision (logic) making according to industry, local or health care insurance standards, or a combination thereof. A recommended data entry format for severity of illness evaluation by using the present invention is shown in the chart below. SNAP-II™ Severity of Illness Evaluation Instruction: 1. The most abnormal values of each physiologic parameter should not be noted on this sheet. 2. The scoring period from SNAP in the first 12 hours of each patient's admission. The time of the first vital signs will be used as the time of admission. 3. Please refer to the sconng manual for details of definitions. Abstractors must adhere stnctlv to these definitions. TABLE 4 Arterial Blood Fi0 2 | p0 2 p0 2 /Fi0 2 j SCORE Gases percent mmHG ratio (select worst) i i 1 i ! i i | i ' SCORE Multiple Seizures I Absent ' Present 1 | Total Unne Oumut in fπ -st 12 hours ' Cc i cc/kglir * SNAP-II™ Score > Birth Weight : ! j Gestational Age ! I - - ' -" _. " " .-"- ~ " -- " _• * " SGA «3 rα percentile) | Yes No ! Apgar <7 at 5 minutes Yes No i I SNAPPE-II™ Score Equivalents While this invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and the scope of the invention as defined by the appended claims. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described specifically herein. For instance, even though the monitoring of the absence/presence of seizures, and the measurement of urine output are shown in the exemplary embodiment as manual inputs, it is conceivable that urine output measurements and monitoring for seizures can be automated without any human intervention. The automated inputs from the urine output measurements and seizure monitoring can be directly fed into the user interface and/or the unit 105 comprising the software program and computer the schematic Figure 1. Also, instead of the preselected physical conditions of blood pressure, pH. temperature, oxygenation ratio, urine output, and presence/ absence of multiple seizures, other physical conditions may be chosen for monitoring, if such choice facilitates a specific study or comparison. Likewise, in lieu of the added measurements on parameters of birth weight, smallness for gestational age, and low Apgar score, other parameters can be chosen to arrive at a specific aspect of illness severity measurement or mortality rate. All such choices of measurable physical conditions, and different possible methods of measurement including automated, manual, and hybnd measurements are within the purview of this invention. Such equivalents are intended to be encompassed in the scope of the appended claims.

Claims: WHAT IS CLAIMED: 1 A computenzed method of making an on-line determination of illness- seventy of a neonatal patient a predetermined time span, by using a software program and optimal weighted measurement values of a predetermined 'n' number of on-line parameters from the patient being momtored, said parameters relating to n measurable predetermined physical conditions, said method compnsmg the steps of (a) obtaining, in said predetermined time span from the neonatal patient being monitored, a plurality of measured values of each of said n physical conditions and producing, using the software program, a single optimal value from said plurality of measured values for each of n measured physical conditions, (b) using said software program, obtaining from said single optimal value a modified weighted partial score, thus generating n modified weighted partial scores for n on-lme parameters from the patient being monitored, and (c) summing the n modified weighted partial scores to provide an indication of on-lme illness-seventy of the neonatal patient, which on-lme seventy indication can be displayed and compared with other known values 2 The method as in claim 1 , said method including the steps of (d) defining a range of possible measurement values for each of said n parameters, said range including and extending from known possible minimum to known possible maximum values of measurements of each of said n parameters. e) dividing, by using user-inputs, each said range into a plurality of contiguous zones, (f) attributing, by using user-inputs and using said program, numencal weighting factors to charactenze each zone for each of said n parameters, (g) determining using a computer, a zone in which an optimal value of a measured physical condition from step (a) lies, and identifying its corresponding weighting factor from step (d) above, wherein the step of obtaining a modified partial score compnses (h) automatically determining, using said software program, m which zone said single optimal value of each of said n parameters from step (a) lies, and using an associated weighting factor from step (g) to obtain a modified and weighted partial score for each measured optimal v alue of each of n measured parameters, thus generating n modified weighted partial scores 3 The method as m claim 1 where n is at least six, and where said at least six on-line parameters relating to said optimal values of said measurable predetermined physical conditions compnse (1) lowest mean blood pressure, (2) lowest serum pH, (3) lowest temperature, (4) lowest pA0 2 /Fι0 ratio, 1 e , the oxygenation ratio, (5) unne output, and (6) presence of multiple seizures 4 The method of claim 3, wherein in step (d), the defined complete range of possible measurement values for each said parameter compnses (1) more than 30 for said known maximum value and less than 20 for said known mimmum value for lowest mean blood pressure, (2) more than 96° F for said known maximum value and less than 95° F for said known minimum value for lowest temperature, (3) more than 2 5 for said known maximum and less than 0 3 for said known minimum value for oxygenation ratio, (4) more than or equal to 7 20 for said known maximum and less than 7 10 for said known mimmum value for serum pH, (5) zero for said known minimum and 19 for said known maximum value for number of seizures, and (6) more than 0 9 for said known maximum and less than 0 1 for said known minimum v alue of unne output measured in ccl g/hr 5 The method of claim 4 wherein said numencal weightin sg factors recited in step (f) above compnse v alues normal moderate severe extreme zone zone zone zone for lowest mean blood pressure 0 9 19 for lowest temperature 0 8 15 for oxygenation ratio 0 5 16 28 for low est serum pH 0 7 16 $L for seizures: 0 - 19 ; and for urine output: 0 5 18, and wherein said illness severity comprises the sum of: 9. if moderately abnormal low mean blood pressure 5 OR 19, if severely abnormal low mean blood pressure - 8, if moderately abnormal low temperature OR 15, if severely abnormal low temperature - 10 5 , if moderately abnormal oxygenation ratio OR 16, if severely abnormal oxygenation ratio OR 28, if extremely abnormal oxygenation ratio -r- 7, if moderately abnormal low pH 15 OR 16, if severely abnormal low pH ÷ 19, if presence of multiple seizures - 5, if moderately abnormal urine output 20 OR 18, if severely abnormal urine output + 6. The method of making an on-line determination of an illness-severity-score of a monitored patient, as in claim 1 including the step of using three additional measured optimized predetermined parameters. 7. The method as in claim 6, wherein the patient is a prematurely bom infant 25 and wherein said three additional optimized parameters comprise birth weight, smallness for gestational age and Apgar at 5 minutes, all relating to the infant, where said three additional parameters have numerical weighting factors as follows: - normal moderate severe zone zone zone birth weight in grams: > lOOOgm 750 - 900 < 0 10 17 smallness for gestational age: > 3 rd < 3 rd 0 8 ; and : Apgar at 5 minutes: 7 - 10 < 7 0 18. 8 The method as m claim 7, including the step of obtaining a score compnsed of a score from claim 1 and adding a method score from said three additional parameters by way of 10 if the single optimal value of birth weight lies the range of (moderate birth weight m grams) -r OR 17, if the single optimal value of birth weight lies in the range of (severe birth weight m grams) -r 8 if the measured smallness for gestational age is m the range of (moderate smallness for gestational age) -r 18, if the measured Apgar at 5 mmutes is m the severe range for Apgar at 5 minutes, to obtain a SNAPPE-II score 9 The method, as m claim 7, wherein an on-l e determination is made at the time the infant is admitted into a neonatal intensive care unit, including the method steps of automatically (a) making a second on-lme determination of subsequent illness-seventy, dunng a predetermined time lapse from admission, and (b) making a companson of the illness-seventy at the time of admission with the said subsequent illness-seventy to generate an indication of a measure of progress of the patient 10 The method of making an on-lme determination of a patient illness-seventy as m claim 8 including the step of assessing a measure of mortality nsk of the patient m terms of probability of death, bv using a measured patient seventy illness score and a predetermined mortality nsk equation, wherein Probability of death = P(death) = l/ e R , where R = (SNAPPE-II x 0 783) - 5 1656 1 1 A system for making a computenzed on-lme determination of an lllness- seventy-score of a monitored neonatal patient by using measurement values of at least six on-line parameters from the monitored patient and a softw are program, said parameters relating to six different measurable physical conditions from the monitored patient, said system compnsmg (a) programmed means receiv ing user input for defining a complete range of possible measurement values for each said parameter, said complete range including an accepted range extending from known minimum to known maximum values in medical practice for each said parameter. (b) programmed means receiving user-mputs for dividing each said complete range from (a) into at least three contiguous range-zones of normal, moderate, and severe, said three range-zones when viewed collectiv ely forming each said complete range, (c) programmed means receiving user-mputs to attnbute a numencal weighting and multiplying factor to charactenze each zone for each measurable physical condition, (d) means for taking measurements of on-lme physical condition values for said at least six on-lme parameters from the patient being momtored and for choosing an optimal value from said measurements, (e) programmed means for determining from (c) above as to which range-zone each optimal value of a measured physical condition value falls withm, and for obtaining a modified partial weighted score for each parameter; and (f) on-lme adding means connected to receive and automatically sum modified partial weighted score values to generate an on-lme indication of the lllness-seventy-score of the monitored on-lme patient 12 The system as in claim 1 1 , wherein the monitored patient is an infant, and including means to make measurement values of at least three more parameters relating to birth weight, smallness measure for gestational age, and Apgar at 5 minutes, including means to generate a Pennatal Extended Score (SNAPPE-II), which is the sum of the illness seventy measure m step (f) of claim 1 together with the sum of (1) a weighting factor from step (c) abov e, based on a measured birth weight zone, (n) a weighting factor from step (c) above, based on a measured smallness for gestational age, and (in) a weighting factor from step (c) abov e, based on measured Apgar at 5 minutes to generate a measure of mortality 13 The system as in claim 12 including means to evaluate a probability of death as }7 P(death) = 1/(1 ÷ e R ) where (R = SNAPPE-II measure x 0.783) - 5.1656. 14. A method of making an on-line determination of an illness-severity of a neonatal patient being monitored by using measurement values of at least six on-line parameters from the patient in a predetermined time span, said parameters relating to six measurable predetermined physical conditions, said method comprising the steps of: (a) obtaining, from the patient being momtored, a plurality of measurements in a predetermined time span for each said measurable physical condition and choosing, using a program, an optimal value from said plurality of measured values, thus generating six optimal values for six said predetermined physical conditions in said predetermined time span; b) defining a range of possible measurement values for each said parameter, said range including a known minimum and extending to known maximum values of said parameter measurements in medical practice; (c) dividing, by using user-inputs, each said range into at least three contiguous zones of normal, moderate, and severe; (d) attributing, by using user-inputs and using said program, numerical weighting factors to characterize said zones; (e) determining a zone and its weighting factor for each said optimal value from step (a), as decided by in which zone an optimal value would fall; (f) producing a modified partial score for an optimal value of each measured parameter, thus generating six modified partial score values; and (g) automatically summing at least six said modified partial score values for the six measured parameters to obtain an on-line illness-severity which can be displayed. 15. The method as in claim 14, where said at least six on-line parameters relating to said optimal values of said measurable predetermined physical conditions comprise: ( 1 ) lowest mean blood pressure; (2) lowest semm pH; (3) lowest temperature (4) lowest pAOJFi0 2 ratio, i.e., the oxvgenation ratio; (5) urine output; and (6) presence of multiple seizures. 16. The method of claim 15, wherein said numerical weighting factors recited in step (c) above comprise values: normal moderate severe zone zone zone for lowest mean blood pressure: 0 9 19 for lowest temperature: 0 8 15 for oxygenation ratio: 0 5 16 for lowest serum pH: 0 7 16 for seizures: 0 - 19; and for urine output: 0 5 18. 17. The method of making an on-line determination of an illness-severity-score of a monitored patient, as in claim 14 including the step of using three additional measured optimized parameters, wherein the patient is a prematurely bom infant and wherein said three additional optimized parameters comprise birth weight, smallness for gestational age and Apgar at 5 minutes, all relating to the infant. 18. The method of making an on-line determination of a patient illness-severity as in claim 17 including the step of assessing a measure of mortality risk of the patient in terms of probability of death, by using a measured patient severity illness score and a predetermined mortality risk equation, wherein: Probability of death = P(death)=l/l÷e R , where R=(SNAPPE-II x 0.783)- 5.1656, where SNAPPE-II is an illness severity measurement obtained from the method recited in claim 17. 2

Inventors: ESCOBAR GABRIEL J, LEE SHOO, RICHARDSON DOUGLAS K, ESCOBAR, GABRIEL, J., LEE, SHOO, RICHARDSON, DOUGLAS, K.
Applicants: BETH ISRAEL HOSPITAL, CHILDREN S & WOMEN S HEALTH CE, KAISER FOUNDATION HEALTH PLAN, PERMANENT MEDICAL GROUP INC, UNIV BRITISH COLUMBIA, BETH ISRAEL DEACONESS MEDICAL CENTER, CHILDREN'S & WOMEN'S HEALTH CENTRE OF BRITISH COLUMBIA, KAISER FOUNDATION HEALTH PLAN, INC., THE PERMANENT MEDICAL GROUP, INC., UNIVERSITY OF BRITISH COLUMBIA
Application year: 1998
Main classification: G06F19
IPC codes: G06F19