Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deterioration.

Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hospital adverse events. Standard episodic inpatient assessment of vital signs can potentially miss changes in health status and delay recognition of elevated risk. To reduce the likelihood of this delayed recognition of risk, we developed a wearable-based deep learning model, using only 9 inputs, to identify the onset of deterioration earlier than traditional early warning systems. We showed this model could generalize to produce clinical alerts ahead of a broad class of significant adverse clinical outcomes, including rapid response team (RRT) interventions, unplanned intensive care unit (ICU) transfers, intubations, cardiac arrests, and in-hospital deaths. Using data from 888 adult non-ICU inpatient visits in four hospitals in New York and employing two different clinical grade wearable biosensors (4-8% data missingness, excluding SpO2), as part of a quality initiative, we trained a recurrent neural network (RNN) to predict both MEWS alerts and adverse clinical outcomes. Using multiple stages of validation, we showed in our retrospective, time-sequence duration optimized, prospective validation the RNN model was able to predict both periods of elevated MEWS scores (ROC AUC 0.89 +/- 0.3, PR AUC 0.58 +/- 0.14) and adverse clinical outcomes (accuracy: 81.8% on 11 events) up to an average of 17 hours in advance. Our results show that our wearable based RNN alert system outperforms traditional episodic clinical support tools in detecting early onset of inpatient deterioration; enabling timely interventions that can improve outcomes and reduce hospital costs for patients in early stages of deterioration.