Approximately 5-10% of hospitalized patients suffer significant clinical deterioration after admission, resulting in either transfer to the intensive care unit (ICU) or a code event (i.e., cardiac or pulmonary arrest). Delayed identification of these events result in increased morbidity and mortality. Unfortunately, existing prediction models result in multiple false alarms for every true positive alarm that they generate. In addition with every passing year, new monitoring systems are introduced that generate more false alarms, resulting in alarm fatigue which has been associated with patient deaths. The objective of this mentored career development proposal is to develop and assess novel computational algorithms that can predict the clinical deterioration of hospitalized patients earlier and more accurately than clinicians or conventional early warning systems, thereby allowing for timely intervention. Building upon our experience in the hematologic malignancy subpopulation of hospitalized patients, this new effort: 1) provides a foundation upon which to combine newer machine learning (ML) methods and clinical informatics to improve the capabilities of the model for an individual patient or specific subgroup; 2) assesses the impact and value of different variables from the electronic medical record (EMR) as part of the predictive model; and 3) broadens the evaluation of this approach to additional real-world patient populations, enabling insight into the translation of the models to clinical usage.
The specific aims of this project are thus:
Specific Aims Aim 1 To identify and extract model variables (features) from the EMR, evaluating different feature selection methods to optimize different predictive criterion and their impact on ML algorithms.
Aim 2 To develop an ML approach that handles multiple asynchronous data streams of longitudinal information from the EMR, providing predictions on clinical deterioration in real-time.
Aim 3 To explore clinician and rapid response team responses to early prediction of clinical deterioration. With successful completion of this proposal, the prediction model will be integrated into the EMR system. Future direction as part of a R01 proposal will involve external validation at other institutions and assessment of clinical impact on patient care.
Clinical deterioration in the hospital is often unexpected and is associated with increased mortality and morbidity, with the CDC reporting 17.2 million hospital admissions through the emergency department in 2010 suggesting that approximately 1 million people annually are at risk of clinical deterioration during their hospitalization. Existing early warning systems have not been able to accurately predict these events without creating an overwhelming amount of additional false positive alarms, resulting in alarm fatigue and potential harm. Our project, utilizing machine learning techniques with the large amount of available clinical data, will serve to develop a predictive model that can predict clinical deterioration earlier with a higher positive predictive value than current algorithms/models which, when implemented, will be able to provide hospitalized patients with an additional safety net.
