BACKGROUND

Acute inflammatory disorders, including sepsis, are an exceedingly common source of excess morbidity and mortality in acute care. Early detection of the presence of acute inflammatory conditions in patients admitted to hospital often enables more effective therapeutic treatments and results in more favorable clinical outcomes than occur when such conditions are not detected until fully manifested. Unfortunately, the detection of symptoms and physiologic abnormalities is in many instances delayed, and such conditions may be relatively well established before computerized-decision support-aided detection or even clinical suspicion of the condition occurs. Acute systemic inflammatory conditions represent one class of conditions for which early diagnosis is particularly desirable, with sepsis being the most serious, and perhaps the most difficult to clinically diagnose.

Sepsis is the result of the interaction of a pathogenic microorganism and its metabolic products (such as lipopolysaccharide [LPS] or other endotoxin products) with a host's defense system that leads to acute systemic inflammation. Characterizing sepsis in a host, however, is made very complex by the number and heterogeneity of factors that play into the final outcome. For instance, the presence of underlying disease, a patient's genetically determined responses to inflammatory stimuli, the general status of his/her immune system, and the microbial mediators and virulence factors released by infectious organisms, among other factors, all contribute to the disease course. Moreover, the process by which this occurs is often remarkably rapid, leaving the clinician with little time or opportunity to identify the emergence of an acute inflammatory condition. While there have been attempts to provide a technological solution through decision support systems, these systems have significant drawbacks and cannot provide the reliability and accuracy of the systems and processes proposed in the present disclosure.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in isolation as an aid in determining the scope of the claimed subject matter.

Improved monitoring and decision support technology is provided for human patients who may be prone to an acute inflammatory condition, such as sepsis. In particular, a mechanism is provided, which may be embodied as a computerized decision support tool, to predict or determine the presence, identity, and/or severity of the acute inflammatory condition, based on a time series of monitored physiological values measured from a patient. Embodiments of the disclosure described herein may provide a forecasted risk for the emergence of an acute inflammatory disorder within a time horizon comprising a future time interval. In one embodiment, which may be used for sepsis, the future time interval is from 30 min to approximately 8 hours into the future, and may be dependent on the frequency of a patient's physiological measurements. In some instances, the mechanism further provides advanced notice to clinicians or caregivers when such a condition is detected or forecasted or modifies (or recommends modifying) treatment for the patient, according to the predicted likelihood.

A catastrophe-theoretic approach is provided, for forecasting emergence of the acute inflammatory disorder or event, which does not require patient measurements to be acquired on a regular or periodic basis. In particular, emerging alterations in the inter-relationships of cardiovascular time series and respiratory time series may be detected in a timely manner in cardiorespiratory patterns that form a “prodrome” that in many instances precedes the onset of systemic inflammatory response syndrome (SIRS) or sepsis. In one aspect, a vital signs time series is determined for a candidate patient. From the time series, a linear model and cusp catastrophe model is calculated, and goodness-of-fit measures are determined. A likelihood of future acute inflammatory disorder emergence is then determined within a future time interval, based on whether threshold for the smoothed cusp model is transgressed or, alternately, based on whether a threshold for the ratio of linear-to-cusp model values is exceeded. Based on the determined likelihood, a set of one or more actions may be initiated. One action comprises generating a notification that may be emitted or otherwise communicated to a provider clinician(s) responsible for the care of the patient. Another action that may be initiated, based on the determined likelihood of elevated risk of evergence, comprises a recommendation for modifying a care plan or treatment procedure associated with the patient. Yet another action that may be initiated, based on the determined likelihood, comprises automatically modifying computer code executed in a healthcare software program for treating the patient, thereby transforming the program at runtime.

Accordingly, one aim of embodiments of this disclosure is to improve upon conventional industry practices and decision support technology. For example, one aim of the embodiments of this disclosure is to reduce or eliminate the need for expensive laboratory tests and specialized machinery. The cost associated with conventional industry practice is due in part on its reliance on exotic variables, which can only be measured using expensive laboratory tests or specialized machinery. The expense and availability of these laboratory tests and machinery has a significant impact on treating a patient. The costs involved with these laboratory tests or machinery often leave diseases undiagnosed and under-treated. In some instances, the expense will cause a caregiver to hesitate in pursuing these laboratory tests and machinery. It is not unusual that these laboratory tests and machinery are only sought when the health of the patient has already dramatically declined. However, by employing the technologies described herein, the expense associated with the conventional industry practice can be eliminated as the embodiments described herein do not require these expensive laboratory tests or machinery. Moreover, the improvements offered by the embodiments described herein have a significant impact on the health of the patient as the instant technology can be employed at a lower expense and on a preventive basis, without any indication of a patient's decline in health. Consequently, embodiments of these technologies provide life-saving implications as the clinical decision support technology described herein can be employed prior to a decline in the patient's health. In addition, the embodiments described herein enable hospitals and medical centers to offer the instant clinical decision support technology without the significant investment in the additional laboratory equipment or machinery.

Another aim is to improve upon the accuracy of conventional industry practices and decision support technology. As described above, the expense involved with the conventional industry practice usually make it cost-prohibitive. However, even if these expensive laboratory tests and machinery are sought, they render less accurate results. For instance, the expensive laboratory tests and machinery typically produce a greater amount of noise, which decreases the rate of accuracy as there are higher rates of false positives and false negatives. As such, the outcome of these conventional technologies fail to be as reliable as embodiments of this disclosure. As described herein, conventional technologies are only capable of achieving a 35% sensitivity rate of certainty while the technology described herein is capable of achieving a 91% sensitivity rate. Accordingly, embodiments offer a significant improvement over the accuracy of achieved through conventional industry practices.

An additional aim is to enable a determination based on a leading indicator as opposed to a lagging indicator. Conventional decision support technology relies on late, lagging indicators. However, by employing the technologies described herein, the decision support technology does not need to rely on the lagging indicators that are typically required by conventional technology.

Other improvements relate to deriving more accurate predictive capabilities (versus conventional computerized-decision support technologies or medical practices) from even moderate-frequency, potentially-aperiodic time series such as that which may be readily obtained with monitoring in acute care environments. In this way, embodiments described herein overcome deficiencies in the prior art as they are robust (a) against temporary sensor artifacts or intermittent gaps or failures to perform periodic measurements, (b) against delays in uploading or synchronizing newly acquired patient physiological values with historical vital signs time series measured in the patient, and (c) against non-stationarity in the time series, such as may arise during periods when the patient's health deviates from predominant patterns, due to health conditions or physiologic phenomena that alter autonomic sympathetic-parasympathetic tone and neuroendocrine regulation, such as when the patient has systemic inflammatory response syndrome (SIRS) or sepsis. Moreover, forecasting one or more event occurrences within a near-term future interval, such as a 4-hour time horizon, provides a valuable benefit. Such a timeframe is long enough that an actionable occurrence is quite likely for many patients, but not so long that the risk of the event is omnipresent such that alert signals become annoying or a cause of “alert fatigue”.

