We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness, and irregular sampling rates typically associated with real clinical data. The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters. We show how to scale the computations associated with the Gaussian process in a manner so that the entire system can be discriminatively trained end-to-end using backpropagation. In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 19.4 and 55.5 improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently used by our hospital.
There is a large body of works on the development and validation of early warning scores to predict clinical deterioration and other related outcomes. For instance, the MEWS score @cite_23 and NEWS score @cite_15 are two of the more common scores used to assess overall clinical deterioration. In addition, the SIRS score for systemic inflammatory response syndrome was part of the original clinical definition of sepsis @cite_8 , although other scores designed for sepsis such as SOFA @cite_24 and qSOFA @cite_5 have been more popular in recent years. A more sophisticated regression-based approach called the Rothman Index @cite_17 is also in widespread use for detecting overall deterioration. Finally, @cite_19 used a Cox regression to predict sepsis using clinical time series data, although they do not account for temporal structure since they simply create feature and event-time pairs from raw data.
@cite_8 @cite_24 @cite_19 @cite_23 @cite_5 @cite_15 @cite_17
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Objective. —To develop customized versions of the Simplified Acute Physiology Score II (SAPS II) and the 24-hour Mortality Probability Model II (MPM II24) to estimate the probability of mortality for intensive care unit patients with early severe sepsis. Design and Setting. —Logistic regression models developed for patients with severe sepsis in a database of adult medical and surgical intensive care units in 12 countries. Patients. —Of 11 458 patients in the intensive care unit for at least 24 hours, 1130 had severe sepsis based on criteria of the American College of Chest Physicians and the Society of Critical Care Medicine (systemic inflammatory response syndrome in response to infection, plus hypotension, hypoperfusion, or multiple organ dysfunction). Results. —In patients with severe sepsis, mortality was higher (48.0 vs 19.6 among other patients) and 28-day survival was lower. The customized SAPS II was well calibrated (P=.92 for the goodness-of-fit test) and discriminated well (area under the receiver operating characteristic [ROC] curve, 0.78). Performance in the validation sample was equally good (P=.85 for the goodness-of-fit test; area under the ROC curve, 0.79). The customized MPM II24was well calibrated (P=.92 for the goodness-of-fit test) and discriminated well (area under the ROC curve, 0.79). Performance in the validation sample was equally good (P=.52 for the goodness-of-fit test; area under the ROC curve, 0.75). The models are independent of each other; either can be used alone to estimate the probability of mortality of patients with severe sepsis. Conclusions. —Customization provides a simple technique to apply existing models to a subgroup of patients. Accurately assessing the probability of hospital mortality is a useful adjunct for clinical trials. (JAMA. 1995;273:644-650) Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed “TREWScore,” a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95 confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95 CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality. Graphical abstractDisplay Omitted New method to estimate patient condition during a hospital visit.Patient condition is computed by summing risks measured in each of 26 variables.Leverages data already in the EMR: vital signs, lab results, nursing assessments.Rothman Index, a measure of patient condition, is independent of diagnosis and spans all acuity levels.May help clinicians to improve continuity of care and to detect trends. Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients.A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model's validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals.Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of Â?0.92when separating hospice deceased from all other discharge categories, an AUC of Â?0.93 when predicting 24-h mortalityand an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomesreflective of patient condition across the acuity spectrum indicates utility in both medical-surgical unitsand critical care units. The model output, which we call the Rothman Index, may provide clinicians witha longitudinal view of patient condition to help address known challenges in caregiver communication,continuity of care, and earlier detection of acuity trends. Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals1–3, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients1,4–6. To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the Artificial Intelligence (AI) Clinician, which extracted implicit knowledge from an amount of patient data that exceeds by many-fold the life-time experience of human clinicians and learned optimal treatment by analyzing a myriad of (mostly suboptimal) treatment decisions. We demonstrate that the value of the AI Clinician’s selected treatment is on average reliably higher than human clinicians. In a large validation cohort independent of the training data, mortality was lowest in patients for whom clinicians’ actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.