Patent Description:
Infusion control systems available today include mechanisms for pressure measurement that involve invasive sensors in contact with the fluid. These systems, when used for medication infusion devices in healthcare applications, pose the threat of infections and contamination, thereby enhancing protocol costs for using, handling, and disposing of used items. Other systems using non-invasive pressure measurement protocols include sophisticated averaging algorithms and machine learning algorithms to predict when the source of a behavioral change in the infusion process is due to an occlusion. However, the predictability of these algorithms is degraded, especially when certain configuration conditions are changed (e.g., when a different tubing is used, or after pump reconfiguration). <CIT> discloses an occlusion detection system comprising:.

In a first embodiment, a system for detection of a fluid condition in an intravenous infusion tubing is provided. The system includes a fluid filled tube and a force sensor coupled to a wall of the tube via a restraining element and configured to obtain a value of a tubing force when the tube is deformed by the restraining element. The system also includes a memory storing instructions, and a processor configured to execute the instructions to determine parameters for fitting a curve, the curve comprising the value of the tubing force, to determine a fluid pressure value for a fluid in the tube based on the parameters for fitting the curve, and to activate an alarm responsive to the fluid pressure value and to the parameters for fitting the curve when an occlusion condition is identified in the tube, wherein the parameters for fitting the curve comprise a time-decaying parameter associated to the tubing force.

In a second embodiment, a method includes generating, with a fluid displacement system, a fluid flow in a tube and deforming a tube with a restraining element coupled with a wall of the tube, and collecting, with a force sensor including the restraining element, a value of a tubing force in response to a wall deformation caused by deforming the tube and determining, with a processor, parameters for fitting a curve, the curve comprising the value of the tubing force wherein the parameters for fitting the curve comprise a time-decaying parameter associated to the tubing force. The method also includes determining a fluid pressure value for a fluid in the tube based on the parameters for fitting the curve, identifying an occlusion condition in the tube based on the fluid pressure value and on the parameters for fitting the curve, and activating an alarm when the occlusion condition is identified in the tube.

In the figures, elements having the same or similar reference numeral have the same or similar functionality or configuration, unless expressly stated otherwise.

The disclosure is related to methods and systems for occlusion detection upstream (container-side) and downstream (patient-side) from infusion pumps in medication infusion applications. In a medication infusion system, occlusions may occur along conduits and tubes upstream (e.g., upstream occlusion, or USO) or downstream of a pump that directs the infusion fluid (the "infusate") from a container (e.g., a bag, a bottle, and the like) to the patient. An upstream occlusion may occur when the infusion fluid is unable to reach the pump (and thus to the patient) due to a blockage upstream of the pump. Such blockages may include a closed roller clamp, a blocked filter (e.g., a wetted filter), an inadvertent kink in the tubing, and the like. A downstream occlusion may occur when the infusion fluid is unable to reach the patient due to a blockage downstream of the pump. Such blockages may include a closed roller clamp, an inadvertent kink in the tubing, clotting or blockage of the fluid entry port, and the like. As a result of occlusions and other infusion anomalies, the medication may be under-infused to the patient, potentially creating life threatening emergencies.

Some of the advantages of embodiments consistent with the present disclosure include the use of a regression model that receives measurement from a simple force measurement device and quickly returns coefficients adapted for different types of tubing materials (and behavior) and settings used in medication infusion systems. Measurements as disclosed herein provide accurate and sensitive measurements of a fluid force, thereby reducing the occurrence of false positive and false negative events.

Embodiments as disclosed herein substantially reduce the number of false negative occlusion events by providing accurate modeling of the stress relaxation in the infusion tubing. Accordingly, embodiments as disclosed herein avoid falsely detecting an occlusion event, such as when a tubing has loss resiliency due to misuse or aging or variation due to manufacturing, thereby reducing a total force measured by the system. Further, accurate modeling of data collected by devices and systems as disclosed herein provide a distinction between soft and hard occlusion events. Some embodiments also incorporate data filtering into the modeling to provide assessment of the infusion process that is robust to noise.

Some embodiments include learning the properties of many infusion tubes and from these properties to predict what the wall force component will be at any time. The instantaneous measured force is compared to the model-estimate and a difference is formed. This difference may be used directly or may better be converted to an estimated fluid pressure simply by dividing the present difference by a sensitivity term with units (force per unit pressure). In some embodiments, a method as disclosed herein include performing a medication infusion of a fluid through infusion tubing fluidically coupled with an infusion pump, and performing continuous measurements of the total force (tubing force + fluid force) produced by the infusion tubing when compressed with an external force sensor. The method also includes activating an alarm when the total force is different from the expected force by a pre-determined error value or equivalently when the predicted pressure falls below a user-determined threshold. As time progresses and the wall force reduces due to stress relaxation, the regression model incorporates all prior measured forces to update the estimated tube parameters and thus produce a continually improving prediction of the true wall force over time. When the difference between the current measured force and the predicted force or equivalently the computed pressure exceeds a predetermined threshold, alerts and or alarms are produced. Additionally, the algorithm fits the shape of the curve in sequential windows to determine the local behavior of the signal (e.g., determine the slope of the curve in a specified time window and comparing the slopes of each time window); these values are used to augment the long term based estimates including stress relaxation.

In some embodiments, use previously stored values or ranges may be used as a check to verify the validity of the fitting algorithm. For example, when an estimate is out of the expected range for many tubes, the algorithm could either 'start over' or reject certain samples.

<FIG> illustrates infusion architecture <NUM> using an occlusion detection system <NUM>, according to some embodiments. Infusion architecture <NUM> includes a fluid container <NUM> fluidically coupled with a pump <NUM> through an upstream conduit or tubing <NUM>. Pump <NUM> is fluidically coupled with a patient <NUM> through a downstream conduit or tubing <NUM>, thereby providing the contents of fluid container <NUM> to patient <NUM>.

An occlusion <NUM> may occur at any point along either of upstream tubing <NUM> or downstream tubing <NUM>. Occlusion <NUM> may be a soft occlusion (e.g., a partial occlusion) or a hard occlusion that may block the fluid flow. A soft upstream occlusion event may be the result of a wetted filter upstream of the infusion line. A hard occlusion event may be caused by a kink in upstream tubing <NUM> or a closed roller clamp located on the upstream tubing <NUM>. A hard occlusion may include a sudden, complete blockage of the fluid flow.

Occlusion detection system <NUM> includes a force measurement device <NUM>. Occlusion detection system <NUM> may also include a processor <NUM> and a memory <NUM>. In some embodiments, memory <NUM> stores instructions which, when executed by processor <NUM>, cause occlusion detection system <NUM> to perform methods as disclosed herein. For example, force measurement device <NUM> may be configured to provide pressure measurement data to processor <NUM>, which may in turn determine, based on the fluid pressure measurement data, whether occlusion <NUM> has occurred. Pump <NUM> may also be communicably coupled with an alarm <NUM>. Accordingly, occlusion detection system <NUM> may be configured to activate alarm <NUM> when an occlusion is detected in upstream tubing <NUM> or in downstream tubing <NUM>. In some embodiments, alarm <NUM> may include a physical alarm generating a sound and a visual signal. In some embodiments, alarm <NUM> may include a communication to a centralized server for handling.

In some embodiments, pump <NUM> may be communicably coupled with a database <NUM> for retrieving, editing, and/or storing files including historical data of prior occlusion events. Accordingly, in some embodiments memory <NUM> may include machine learning algorithms, artificial intelligence algorithms, and neural network algorithms trained using historical information stored in database <NUM>. Further in some embodiments, occlusion detection system <NUM> provides recently collected medication infusion information to database <NUM> for further training of machine learning algorithms.

<FIG> illustrates a force measurement device <NUM>, according to some embodiments. Force measurement device <NUM> includes a vice <NUM> having a force sensor <NUM> in one of its compression members or jaws <NUM>-<NUM>, and <NUM>-<NUM> (hereinafter, collectively referred to as "jaws <NUM>"). Jaws <NUM> are configured to squeeze tubing <NUM> by adjusting a gap G <NUM> formed therebetween, so as to obtain a slight deformation of the cross section of tubing <NUM>, while pump <NUM> is in operation. As a result of the deformation, tubing <NUM> exerts a force <NUM> (F) against force sensor <NUM>. In general, force F <NUM> includes two components: a fluid force <NUM> (Ff), and a tube force <NUM> (Ft). Ft <NUM> is the resilient force that the material for tubing <NUM> (or tubing <NUM>) exerts against jaws <NUM> in vice <NUM> to oppose deformation. Ft <NUM> is the force exerted against jaws <NUM> by the pressure of fluid <NUM> inside tubing <NUM>.

In some embodiments, F <NUM>, Ft <NUM>, and Ft <NUM> may be related through the following mathematical expression <MAT>.

Accordingly, having a precise measure of force F <NUM>, it is desirable to have an accurate model for Ft <NUM> so that the fluid pressure may be determined from Ff. In some embodiments, Ff may be obtained from Eq. <NUM> as <MAT> wherein Pfluid is a fluid pressure (e.g., upstream pump <NUM>) as a function of a pumping rate, r, and time, t, and S is a sensitivity factor that relates the fluid pressure to the tube wall force (e.g., expressed in units of pressure/force and associated with the contact area between tubing <NUM> and force sensor <NUM>).

In some embodiments, the resiliency of the material in tubing <NUM> is not constant (e.g., through time, t). Furthermore, the plasticity of the material, which enables deformation of tubing <NUM> upon a certain stress (e.g., the pressure exerted by jaws <NUM>), implies that, during the time span of a measurement, force Ft <NUM> is expected to change (e.g., Ft is reduced as tubing <NUM> complies with a deformation). An accurate model for the function Ft(t) is therefore highly desirable. In some embodiments, it may be assumed that force Ft <NUM> decays logarithmically in time, following a mathematical as expression <MAT>.

In Eq. <NUM>, 'F(to)' is a constant associated with the initial resilient force at time t=<NUM> (in arbitrary units), and 'm' is the viscoelastic stress relaxation of the material in tubing <NUM>. In some embodiments, m is a negative number, as force Ft is expected to decrease with time (e.g., for a fixed gap G231 in vice <NUM>). The specific values of F(to) and m are dependent on the age of tubing <NUM>, on the specific material forming tubing <NUM> (e.g., Silicone and the like), and even on the specific handling of tubing <NUM>. Further, the specific value of F(to) may depend on the amount of deformation induced in the tubing by the force measurement device (e.g., G <NUM>). Accordingly, the value of F(to) may depend on the exact measurement configuration for tubing <NUM>, and may desirably be re-calibrated using Eq. <NUM> each time pump <NUM> is open-closed and restarted. In some embodiments, the initial resilient force, F(to), and the creep parameter, m, may be determined using a continuous regression on the tubing force measurements.

In some embodiments, a regression step including Eq. <NUM> is desirable because parameters F(to) and m may be determined with relatively high accuracy within the first few data points after t=<NUM>. The behavior of Ft expected may, in fact, remain as modeled by Eq. <NUM> for long periods of time, involving many cycles of pump <NUM> after the infusion process has started. Moreover, the regression model in Eq. <NUM> enables an accurate estimation of the initial tubing resiliency through initial force, F(to), which is desirable, as tubing resiliency is a highly varying parameter of infusion architecture <NUM> (cf. Moreover, in some embodiments different tubing materials may be used, thereby leading to substantially different initial force F(to) even when G231 is the same. Accordingly, an accurate and fast determination of F(to) as provided by Eq. <NUM> is desirable and advantageous over currently available systems. Another advantage of performing a regression of data points with Eq. <NUM> is that the regression is linear on the parameters F(to) and m, relative to the measurement values, Fmeas.

From Eq. <NUM>, it is seen that when the fluid pressure becomes negative (relative to the atmosphere surrounding vice <NUM>), then Ff is less than zero, and force F <NUM> drops below an expected value for Ft <NUM> (cf. Eq. <NUM>). Accordingly, a drop in F <NUM> below the expected value for Ft in Eq. <NUM> likely indicates an occlusion event along the line of tubing <NUM>. Such an occurrence (Ff<<NUM>) is commonly observed for an upstream occlusion.

In a downstream occlusion (DSO) event the fluid pressure increases beyond an expected value (relative to the atmosphere surrounding vice <NUM>), then Ff is more positive than expected and force F <NUM> shows a sudden jump above expected value for Ft <NUM> (cf. Eq. <NUM>). The magnitude of the jump may vary according to the flow rate in tubing <NUM>. Accordingly, Eq. <NUM> indicates that a sudden raise in F <NUM> above the expected value for Ft in Eq. <NUM> may be due to DSO event along the line of tubing <NUM>.

In other scenarios, pump <NUM> may have a reversal cycle in which for a transitory lapse the fluid is reversed from downstream tubing <NUM> to upstream tubing <NUM>. When force measurement device <NUM> is mechanically coupled with upstream tubing <NUM>, a pump reversal event will create a sudden, positive increase of Ff <NUM>. Therefore a sudden, positive increase of F <NUM> above an expected value for Ft <NUM> may indicate a USO, and may trigger an alarm.

Further, in scenarios where the fluid pressure remains constant, or almost constant (e.g., no infusion interruption events, or any other infusion anomalies) the total force F <NUM> measured is expected to follow the same behavior as tubing force Ft <NUM> (e.g., a logarithmic decay as in Eq. <NUM>). As the infusion progresses, it is expected that the contents of fluid container <NUM> will be slowly drained out, causing a slight decay in fluid pressure and therefor a natural decay in Ff <NUM> with time. In some embodiments, the natural decay of Ff <NUM> with time may be neglected relative to the logarithmic decay of Ft <NUM>, infusion anomalies, and the precision of force measurement device <NUM>.

<FIG> illustrates a force measurement chart 200B, according to some embodiments. The ordinate (Y-axis) in chart 200B indicates a force value that may be either a total force F <NUM> measured with force measurement device <NUM>, or an expected tube force Ft <NUM> as modeled (e.g., by Eq. <NUM>). The abscissa (X-axis) in chart 200B indicates time, normalized to arbitrary units, such that any measurement starts at time t=<NUM>. Chart 200B includes three curves <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM> (hereinafter, collectively referred to as "expected Ft curves <NUM>"), for different expected values of Ft <NUM> as modeled by Eq. <NUM>.

In a first curve <NUM>-<NUM>, an initial resilient force F(to)<NUM> is combined with a first creep parameter m<NUM>. In a second curve <NUM>-<NUM>, initial resilient force F(to)<NUM> is combined with a second creep parameter m<NUM> (wherein |m<NUM>|<|m<NUM>|). In a third curve <NUM>-<NUM>, an initial resilient force F(to)<NUM> is selected (wherein F(to)<NUM> <F(to)<NUM>).

<FIG> illustrate force measurement charts 300A-H, respectively. The ordinate (Y-axis) in charts 300A-H indicates a force value that may be either a total force F <NUM> measured with force measurement device <NUM> (e.g., data points Fmeas <NUM>), or an expected tube force Ft <NUM> as modeled (e.g., by Eq. <NUM>). The abscissa (X-axis) in charts 300A-G indicate time, normalized to arbitrary units, such that any measurement starts at time t=<NUM>.

<FIG> illustrates a hard upstream occlusion event <NUM> determined by force measurement device <NUM> in a medication infusion, according to some embodiments. Hard USO event <NUM> is determined when Fmeas <NUM> drops below a threshold curve <NUM> at point <NUM>. Threshold curve <NUM> is obtained by subtracting Ferror <NUM><NUM> from Ft expected curve <NUM>. The value of Ferror <NUM> may be selected by the user, or may be determined by a machine learning algorithm having access to a database including multiple data points <NUM> and recordings of prior hard USO events <NUM>.

<FIG> illustrates a soft upstream occlusion event <NUM> determined by force measurement device <NUM> in a medication infusion, according to some embodiments. Fmeas points <NUM> follow first Ft expected curve <NUM>-<NUM> until they start to slowly decrease below Ft expected curve <NUM>-<NUM> (as of point <NUM>). While Fmeas points <NUM> do not cross below threshold curve <NUM>, they become consistently aligned with Ft expected curve <NUM>-<NUM> having a creep parameter m<NUM> that is more negative than m<NUM> (cf. Occlusion detection system <NUM> may determine that a soft USO event <NUM> has occurred when a rate of change of parameter m is lower than a pre-selected threshold, - δ, (dm/dt < -δ < <NUM>).

Several data points <NUM>, departing from curve <NUM>-<NUM> before an alarm is set at point <NUM> is triggered and curve <NUM>-<NUM> is obtained in a new regression. Accordingly, in some embodiments a lag of the regression relative to the force measurement may be beneficial to ensure that the deviation from curve <NUM>-<NUM> is not a fluctuation.

<FIG> illustrates a pump reversal event <NUM> determined by force measurement device <NUM> at the start of a medication infusion, according to some embodiments. In embodiments as disclosed herein, pump reversal may be induced purposely to determine the existence of an USO, e.g., a rapid increase in pressure under pump reversal may indicate USO. Pump reversal event <NUM> may be characterized by a sudden increase in Fmeas <NUM> (dFmeas/dt > <NUM>), which in some embodiments may occur at the start of the medication infusion (cf. point <NUM>). In some embodiments, occlusion detection system <NUM> introduces a pump reversal event <NUM> at measurement point <NUM>, and when a rate of change of measured force dFmeas/dt exceeds a pre-selected threshold, Σ (dFmeas/dt > Σ) generates an alarm/alert. A value of dFmeas/dt in pump reversal event <NUM> may be enhanced dramatically when an occlusion (e.g., a soft occlusion) is present upstream of force measurement device <NUM>. Indeed, when such is the case, it is seen that when pump <NUM> operates in the forward direction there is a reduction in Fmeas as the fluid pressure drops (cf. Eq. <NUM>), which becomes an increase in Fmeas due to a sudden fluid pressure raise in pump reversal event <NUM>. Accordingly, in some embodiments, a pressure increase (as determined by a force Fmeas increase) during a pump reversal event <NUM> may also indicate the present of an upstream occlusion (e.g., soft or hard). Further, in some embodiments, at the start of the infusion process, a pump reversal event may be induced briefly to determine whether dFmeas/dt increases beyond a pre-determined threshold, thereby revealing the presence of an upstream occlusion. When the induced pump reversal reveals a dovetailing of Fmeas (dFmeas/dt less than the pre-selected threshold, or zero), it may be determined that no occlusion is present.

<FIG> illustrates pump reversal event <NUM> determined by force measurement device <NUM> in the middle of a medication infusion, according to some embodiments. Accordingly, Fmeas <NUM> associated with dFmeas/dt > +ε > <NUM> occurs further down the medication infusion process.

<FIG> illustrates pump reversal event <NUM> subsequent to soft USO event <NUM> determined by force measurement device <NUM> in the middle of a medication infusion, according to some embodiments. Soft USO event <NUM> occurs at measurement point <NUM>-<NUM> when dm/dt < -δ < <NUM>, so that measurement points <NUM> follow curve Ft-expected <NUM>-<NUM> up to measurement point <NUM>-<NUM>, when dFmeas/dt > +ε> <NUM>.

<FIG> illustrates an infusion interrupt event <NUM> determined by force measurement device <NUM> in a medication infusion, according to some embodiments. Infusion interrupt event <NUM> may be associated with an infusion malfunction or anomaly, or any interruption caused by medical personnel opening-closing and restarting the medication infusion process. During infusion interrupt event <NUM>, a large change in Fmeas value may be observed at point <NUM> over a short span of time. The change in Fmeas value may be positive (as illustrated) or negative. However, as chart 300F illustrates, measurement points <NUM> after infusion interrupt event <NUM> follow closely a new curve Ft expected <NUM> and remain well above a new threshold curve <NUM> (wherein threshold curve <NUM> is obtained by subtracting Ferror <NUM> from Ft expected <NUM>. In some embodiments, the system is configured to recalculate Ftexpected curve <NUM> using the first data point <NUM> (or the first few data points <NUM>) after infusion interrupt event <NUM>. In some embodiments, when the system detects a large change (e.g., an increase) in Fmeas, then curve Ft expected <NUM> is recalculated using a different (e.g., larger) value for initial force F(t<NUM>) and a re-calibrated value for time, t, while maintaining the same value for the stress relaxation (m). For example, a regression formula for curve Ft expected <NUM> may be: <MAT>.

Wherein t<NUM> is the time at which infusion interrupt event <NUM> took place.

In the case when infusion interrupt event <NUM> is due to an open-close and restart event, the initial force F(t<NUM>) may include a natural resiliency of the infusion tubing (e.g., upstream tubing <NUM>) to its un-deformed shape when released from vice <NUM>.

<FIG> illustrates an infusion interrupt event <NUM> followed by a pump reversal event <NUM> shortly after restart, determined by force measurement device <NUM> in the middle of a medication infusion, according to some embodiments. Accordingly, pump reversal event <NUM> illustrates an abnormal increase, (dF/dt > +ε > <NUM>), in Fmeas even after curves <NUM> and <NUM> are calculated.

<FIG> illustrates a downstream occlusion (DSO) event <NUM> determined by force measurement device <NUM> in a medication infusion, according to some embodiments. DSO event <NUM> is determined when Fmeas <NUM> jumps above a threshold curve <NUM> at point <NUM>. Threshold curve <NUM> is obtained by adding F error <NUM> to Ft expected curve <NUM>. The value of Ferror <NUM> may be selected by the user, or may be determined by recordings of prior DSO events <NUM>.

<FIG> illustrates a flowchart with steps in a method <NUM> for detecting an occlusion event in an infusion architecture, according to some embodiments. At least some of the steps in method <NUM> may be performed by a system having a processor executing commands stored in a memory of the computer (e.g., occlusion detection system <NUM>, processor <NUM>, and memory <NUM>). The system may include a force measurement device providing data to and receiving commands from the processor (e.g., force measurement device <NUM>). The force measurement device may be coupled to an infusion architecture including a container, an upstream tubing fluidically coupling the container with an infusion pump, and a downstream tubing fluidically coupling the infusion pump to a patient (e.g., infusion architecture <NUM>, upstream tubing <NUM>, infusion pump <NUM>, downstream tubing <NUM>, patient <NUM>). The force medication architecture may be communicably coupled with an alarm, so as to prompt medical personnel or equipment to handle an occlusion event upstream or downstream from the pump, or a pump reversal event (e.g., alarm <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Methods consistent with the present disclosure may include at least some, but not all of the steps illustrated in method <NUM>, performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method <NUM>, performed overlapping in time, or almost simultaneously.

Step <NUM> includes retrieving an expected force value and a stress relaxation value from the memory. The memory may include a regression model for the expected force value, the regression model being dependent on parameters such as the stress relaxation and an initial force value offset. In some embodiments, the regression model includes a mathematical expression for the expected tubing force as a function of time (cf. Eq. <NUM>). Accordingly, in some embodiments the regression includes a logarithmically decaying function of time, controlled by the stress relaxation of the tubing material.

Step <NUM> includes performing a medication infusion of a fluid through an infusion tubing fluidically coupled with the infusion pump. In some embodiments, the infusion tubing includes the upstream tubing and the downstream tubing, fluidically coupled with one another via the infusion pump.

Step <NUM> includes measuring a total force on the infusion tubing with a pressure sensor. In some embodiments, step <NUM> includes squeezing the infusion tubing against a pressure sensor in the force measurement device.

Step <NUM> includes determining whether the updated force value is less than the expected force value by a pre-determined error value (e.g., Ferror <NUM>, cf.

Step <NUM> includes updating the stress relaxation value with a regression model that incorporates the total force (cf. Eq. <NUM>), and determining a rate of change of the stress relaxation value (dm/dt).

Step <NUM> includes determining whether the rate of change in the stress relaxation is more negative than a pre-selected value, δ (dm/dt < -<NUM> < <NUM>).

When the measured force is no less than the expected force and the change in stress relaxation value is not negative according to step <NUM>, step <NUM> includes adding the updated force value and the updated stress relaxation value to the regression model. The method may be repeated from step <NUM> until the infusion process is complete.

When the measured force is less than the expected force, or when the change in stress relaxation is negative according to step <NUM>, step 410b includes activating the alarm.

<FIG> illustrates a flowchart with steps in a method <NUM> for detecting a upstream occlusion using the disclosed method and including intentional pump reversal in an infusion architecture, according to some embodiments. At least some of the steps in method <NUM> may be performed by a system having a processor executing commands stored in a memory of the computer (e.g., occlusion detection system <NUM>, processor <NUM>, and memory <NUM>). The system may include a force measurement device providing data to and receiving commands from the processor (e.g., force measurement device <NUM>). The force measurement device may be coupled to an infusion architecture including a container, an upstream tubing fluidically coupling the container with an infusion pump, and a downstream tubing fluidically coupling the infusion pump to a patient (e.g., infusion architecture <NUM>, upstream tubing <NUM>, infusion pump <NUM>, downstream tubing <NUM>, patient <NUM>). The force medication architecture may be communicably coupled with an alarm, so as to prompt medical personnel or equipment to handle an occlusion event upstream or downstream from the pump, or a pump reversal event (e.g., alarm <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Methods consistent with the present disclosure may include at least some, but not all of the steps illustrated in method <NUM>, performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method <NUM>, performed overlapping in time, or almost simultaneously.

Step <NUM> includes measuring a total force from the tubing to update a stress relaxation value and an expected force.

Step 504a includes storing the updated stress relaxation value and determining a rate of change of the stress relaxation value (dm/dt). Step 504b includes storing a filtered value for the measured force and determining a rate of change of the measured force (dF/dt). In some embodiments, step 504a includes filtering a force measurement provided by the force measurement device to smooth out system fluctuations. Some examples in step 504a may include determining a moving average of a selected number of measurement points, or applying a more sophisticated filter to the data, e.g., a Kalman filter, a digital filter, or any other predictor of a true force value given the measured force value Fmeas and a statistical analysis of prior measurement fluctuations.

Step 506a includes determining whether the absolute value of the rate of change of the stress relaxation value is greater than a pre-determined threshold, δ (dm/dt < -δ <<NUM>). Step 506b includes determining whether the measured total force from the tubing is lower than the expected force by a pre-determined threshold, Ferror (Fmeas < Fexpected-Ferror).

Step <NUM> includes confirming an occlusion due to a stop flow and reversal of flow when step 506a determines that |dm/dt| > δ, or when step 506b determines that Fmeas < Fexpected-Ferror.

Step 510a includes determining whether dF/dt is larger than a pre-determined threshold, ε (dF/dt > +ε><NUM>) when dm/dt < -δ <<NUM>, according to step 506a. Step 510b includes determining whether dF/dt is larger than ε (dF/dt > +ε > <NUM>), when Fmeas < Fexpected-Ferror, according to step 506b.

Step 512a includes generating a soft USO alarm when step 510a determines that, dF/dt > +ε><NUM>. Step 512b includes generating a hard USO alarm when step 510b determines that dF/dt > +ε><NUM>.

Step <NUM> includes determining whether the infusion process is complete when dF/dt > +ε according to steps 510a and 510b.

Step 516a includes updating the regression parameters and repeating method <NUM> from step <NUM> when the infusion process is not complete. Step 516b includes updating the database with infusion log information. In some embodiments, step <NUM> b includes storing the measured force and the stress relaxation value in the database, wherein the database includes multiple measured force values and multiple stress relaxation values associated with an infusion condition (e.g., upstream occlusion, downstream occlusion, pump reversal, infusion interrupt, and the like), and wherein updating the regression model comprises training the regression model on the multiple total force values and the multiple stress relaxation values.

<FIG> illustrates a "low detail" flowchart with steps in a method for detecting an upstream occlusion in an infusion architecture, according to some embodiments. At least some of the steps in method <NUM> may be performed by a system having a processor executing commands stored in a memory of the computer (e.g., occlusion detection system <NUM>, processor <NUM>, and memory <NUM>). The system may include a force measurement device providing data to and receiving commands from the processor (e.g., force measurement device <NUM>). The force measurement device may be coupled to an infusion architecture including a container, an upstream tubing fluidically coupling the container with an infusion pump, and a downstream tubing fluidically coupling the infusion pump to a patient (e.g., infusion architecture <NUM>, upstream tubing <NUM>, infusion pump <NUM>, downstream tubing <NUM>, patient <NUM>). The force medication architecture may be communicably coupled with an alarm, so as to prompt medical personnel or equipment to handle an occlusion event upstream or downstream from the pump, or a pump reversal event (e.g., alarm <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Methods consistent with the present disclosure may include at least some, but not all of the steps illustrated in method <NUM>, performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method <NUM>, performed overlapping in time, or almost simultaneously.

Steps 602a and 602b include loading and powering 'on' an IV infusion pump set including tubing, a pump, and a pressure sensor, as disclosed herein. Step <NUM> includes verifying that steps 602a and 602b have been completed. Step <NUM> includes measuring a force data point, Factual. Step <NUM> includes calculating and storing regression parameters such as Fexpected, Fo, and m. Step <NUM> includes verifying that the infusion process has started. If the infusion process has not started, the method returns to step <NUM>.

When the infusion process has started, step 612a includes calculating the difference Fexpected-Factual. Step <NUM> includes converting force difference to pressure difference (Pdifference). Step <NUM> includes determining whether Pdifference is greater than a pre-selected Plimit value. If Pdifference is greater than Plimit, step <NUM> includes activating the USO alarm, otherwise the method rturns to step <NUM>. When the infusion process has started, step 612b includes verifying whether dm/dt < limit, where "limit" is a pre-selected, negative, threshold. When dm/dt<limit, step <NUM> includes activating the USO alarm, otherwise, the method returns to step <NUM>.

<FIG> illustrates a more detailed flowchart with steps in a method for detecting an upstream occlusion in an infusion architecture, according to some embodiments. At least some of the steps in method <NUM> may be performed by a system having a processor executing commands stored in a memory of the computer (e.g., occlusion detection system <NUM>, processor <NUM>, and memory <NUM>). The system may include a force measurement device providing data to and receiving commands from the processor (e.g., force measurement device <NUM>). The force measurement device may be coupled to an infusion architecture including a container, an upstream tubing fluidically coupling the container with an infusion pump, and a downstream tubing fluidically coupling the infusion pump to a patient (e.g., infusion architecture <NUM>, upstream tubing <NUM>, infusion pump <NUM>, downstream tubing <NUM>, patient <NUM>). The force medication architecture may be communicably coupled with an alarm, so as to prompt medical personnel or equipment to handle an occlusion event upstream or downstream from the pump, or a pump reversal event (e.g., alarm <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Further, steps as disclosed in method <NUM> may include retrieving, editing, and/or storing files in a database that is part of, or is communicably coupled to, the computer (e.g., database <NUM>). Methods consistent with the present disclosure may include at least some, but not all of the steps illustrated in method <NUM>, performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method <NUM>, performed overlapping in time, or almost simultaneously.

Steps 702a and 702b include loading and powering 'on' an IV infusion pump set including tubing, a pump, and a pressure sensor, as disclosed herein. Step <NUM> includes verifying that steps 702a and 702b have been completed. Step <NUM> includes measuring a force at approximately 4hertz (Hz = <NUM> measurement per second).

Step 708a includes applying a low pass filter with a flow rate dependent frequency response to obtain Factual. Step 708b includes applying a filter at log2 of the flow rate using data from the entire sample window (e.g., all samples thus far collected). The time window/sample size may be variable. In some embodiments, a continuous sampling at Log2 rate is maintained until the tubing is removed from the pump (e.g., removed from the force sensor). Step 708c includes filter at log<NUM> of the flow rate using a sliding window. Step 710a includes calculating linear least square coefficients of the filtered log<NUM> flow rate data with the entire window (step 708b). Step 712a includes storing regression parameters from step 710a (e.g., Fo, m), and step 714a includes calculating Fexpected from the regression parameters of step 712a.

Step 710c includes calculating linear least square coefficients of the filtered log<NUM> flow rate sampling with a sliding window. Step 712c includes storing regression parameters (e.g., Fo, m) from step 710c, and step 714c includes calculating <MAT> using the regression parameters from step 712c.

Step <NUM> includes verifying whether the infusion process has started. When the infusion process has started, Step 718a includes calculating Pdifference = (Fexpected-Factual)/SensitivityTube and step 718c includes determining whether dm/dt is lower than a negative, pre-selected limit value ( <MAT>). When the infusion has not started, or <MAT> is not less than the negative, pre-selected limit value, step 720b includes calculating and storing the regression parameters.

Step 720a includes determining whether Pdifference from step 718a is less than a pre-selected Plimit value. If it is not, then the method proceeds with step 720b. If Pdifference is greater than Plimit (step 720a), or if <MAT> (step 720c), step <NUM> includes activating the USO alarm.

<FIG> is a block diagram illustrating an example computer system <NUM> with which the methods and steps illustrated in methods <NUM>-<NUM> can be implemented, according to some embodiments. In certain aspects, computer system <NUM> can be implemented using hardware or a combination of software and hardware, either in a dedicated server, integrated into another entity, or distributed across multiple entities.

Computer system <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and a processor <NUM> coupled with bus <NUM> for processing information. By way of example, computer system <NUM> can be implemented with one or more processors <NUM>. Processor <NUM> can be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information. In some embodiments, processor <NUM> may include modules and circuits configured as a 'placing' tool or engine, or a 'routing' tool or engine, to place devices and route channels in a circuit layout, respectively and as disclosed herein.

Computer system <NUM> includes, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory <NUM>, such as a Random Access Memory (RAM), a flash memory, a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device, coupled to bus <NUM> for storing information and instructions to be executed by processor <NUM>. Processor <NUM> and memory <NUM> can be supplemented by, or incorporated in, special purpose logic circuitry.

The instructions may be stored in memory <NUM> and implemented in one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, the computer system <NUM>, and according to any method well known to those of skill in the art, including, but not limited to, computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architectural languages (e.g., Java,. NET), and application languages (e.g., PHP, Ruby, Perl, Python). Instructions may also be implemented in computer languages such as array languages, aspect-oriented languages, assembly languages, authoring languages, command line interface languages, compiled languages, concurrent languages, curly-bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extension languages, fourth-generation languages, functional languages, interactive mode languages, interpreted languages, iterative languages, list-based languages, little languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multiparadigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, off-side rule languages, procedural languages, reflective languages, rule-based languages, scripting languages, stack-based languages, synchronous languages, syntax handling languages, visual languages, Wirth languages, embeddable languages, and xml-based languages. Memory <NUM> may also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor <NUM>.

A computer program as discussed herein does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code).

Computer system <NUM> further includes a data storage device <NUM> such as a magnetic disk or optical disk, coupled to bus <NUM> for storing information and instructions.

Computer system <NUM> is coupled via input/output module <NUM> to various devices. The input/output module <NUM> is any input/output module. Example input/output modules <NUM> include data ports such as USB ports. The input/output module <NUM> is configured to connect to a communications module <NUM>. Example communications modules <NUM> include networking interface cards, such as Ethernet cards and modems. In certain aspects, the input/output module <NUM> is configured to connect to a plurality of devices, such as an input device <NUM> and/or an output device <NUM>. Example input devices <NUM> include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a user can provide input to the computer system <NUM>. Other kinds of input devices <NUM> are used to provide for interaction with a user as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, tactile, or brain wave input. Example output devices <NUM> include display devices, such as a LED (light emitting diode), CRT (cathode ray tube), or LCD (liquid crystal display) screen, for displaying information to the user.

Methods as disclosed herein may be performed by computer system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in memory <NUM>. Such instructions may be read into memory <NUM> from another machine-readable medium, such as data storage device <NUM>. Execution of the sequences of instructions contained in main memory <NUM> causes processor <NUM> to perform the process steps described herein (e.g., as in methods <NUM>-<NUM>). One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory <NUM>. In alternative aspects, hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects of the present disclosure. Thus, aspects of the present disclosure are not limited to any specific combination of hardware circuitry and software.

Various aspects of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The communication network can include, for example, any one or more of a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a broadband network (BBN), the Internet, and the like. Further, the communication network can include, but is not limited to, for example, any one or more of the following network topologies, including a bus network, a star network, a ring network, a mesh network, a star-bus network, tree or hierarchical network, or the like. The communications modules can be, for example, modems or Ethernet cards.

Computing system <NUM> includes servers and personal computer devices. A personal computing device and server are generally remote from each other and typically interact through a communication network. Computer system <NUM> can be, for example, and without limitation, a desktop computer, laptop computer, or tablet computer. Computer system <NUM> can also be embedded in another device, for example, and without limitation, a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, a video game console, and/or a television set top box.

The term "machine-readable storage medium" or "computer readable medium" as used herein refers to any medium or media that participates in providing instructions or data to processor <NUM> for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical disks, magnetic disks, or flash memory, such as data storage device <NUM>. Volatile media include dynamic memory, such as memory <NUM>. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus <NUM>. Common forms of machine-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. The machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them.

In one aspect, a method may be an operation, an instruction, or a function and vice versa. In one aspect, a clause or a claim may be amended to include some or all of the words (e.g., instructions, operations, functions, or components) recited in other one or more clauses, one or more words, one or more sentences, one or more phrases, one or more paragraphs, and/or one or more claims.

To illustrate the interchangeability of hardware and software, items such as the various illustrative blocks, modules, components, methods, operations, instructions, and algorithms have been described generally in terms of their functionality. Whether such functionality is implemented as hardware, software or a combination of hardware and software depends upon the particular application and design constraints imposed on the overall system.

As used herein, the phrase "at least one of" preceding a series of items, with the terms "and" or "or" to separate any of the items, modifies the list as a whole, rather than each member of the list (e.g., each item).

In one aspect, a term field effect transistor (FET) may refer to any of a variety of multi-terminal transistors generally operating on the principals of controlling an electric field to control the shape and hence the conductivity of a channel of one type of charge carrier in a semiconductor material, including, but not limited to a metal oxide semiconductor field effect transistor (MOSFET), a junction FET (JFET), a metal semiconductor FET (MESFET), a high electron mobility transistor (HEMT), a modulation doped FET (MODFET), an insulated gate bipolar transistor (IGBT), a fast reverse epitaxial diode FET (FREDFET), and an ion-sensitive FET (ISFET).

To the extent that the term "include," "have," or the like is used in the description or the claims, such term is intended to be inclusive in a manner similar to the term "comprise" as "comprise" is interpreted when employed as a transitional word in a claim.

A reference to an element in the singular is not intended to mean "one and only one" unless specifically stated, but rather "one or more. " The term "some" refers to one or more. Underlined and/or italicized headings and subheadings are used for convenience only, do not limit the subject technology, and are not referred to in connection with the interpretation of the description of the subject technology.

The subject matter of this specification has been described in terms of particular aspects, but other aspects can be implemented and are within the scope of the following claims. For example, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. The actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Moreover, the separation of various system components in the aspects described above should not be understood as requiring such separation in all aspects, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Claim 1:
A system (<NUM>) for detection of a fluid condition in a flexible tube (<NUM>), the system (<NUM>) comprising:
a flexible tube (<NUM>);
a sensor (<NUM>) coupled to a wall of the tube (<NUM>) via a restraining element (<NUM>) and configured to obtain a value of a tubing force Ft (<NUM>) when the tube (<NUM>) is deformed by the restraining element (<NUM>);
a memory (<NUM>) storing instructions; and
a processor (<NUM>) configured to execute the instructions to determine parameters for fitting a force-time curve, the curve comprising the time-varying value of the tubing force Ft (<NUM>), to determine a fluid pressure value for a fluid in the tube (<NUM>) based on the parameters for fitting the curve, and to activate an alarm (<NUM>) responsive to the fluid pressure value and to the parameters for fitting the curve when an abnormal condition is identified in the fluid, wherein the parameters for fitting the force-time curve comprise at least one time-decaying parameter associated to the tubing force Ft (<NUM>),
characterized by wherein, to determine the fluid pressure value, the processor (<NUM>) is configured to determine a force Ft (<NUM>) on the wall of the tube (<NUM>) using an initial resilient force (F(t<NUM>)) plus a creep parameter (m) times a logarithmic function of time, ln(t), and to adjust one or more of the initial resilient force (F(t<NUM>)) and the creep parameter (m) based on a material, an age, and a handling of the tube (<NUM>).