17-Hour Deterioration Prediction Lead from Wearables

Predictive Model DevelopmentPredictive External Validation

Authors:Scheid MRl; Friedmann B; Oppenheim M

Journal:Nature Communications

Continuous-wearable RNN forecasting flags ward deterioration ~17 hours before MEWS alerts (AUROC 0.89), shifting pharmacists from reactive RRT support to preventive medication management—earlier antibiotics, holding sedatives, staging emergency meds. With PPV only 0.11–0.22, impact depends on CDS-integrated, human-triaged escalation focused on high-risk patients to avoid alert fatigue.

November 3, 2025

Quick Take

A wearable-based recurrent neural network (RNN with LSTM) predicted inpatient deterioration with strong discrimination (AUROC ≈ 0.89 ± 0.03; PR AUC ≈ 0.58 ± 0.14) and a median lead time of ~17 hours before Modified Early Warning Score (MEWS) alerts and ~16.5 hours before hard outcomes.

Operational caveat for pharmacy: the multi‑hour lead time creates a proactive medication‑review window (earlier antibiotics, holding sedatives, staging emergency meds) but the model’s positive predictive value (PPV) is low (0.11–0.22 → ~80% false positives), so human triage and workflow redesign are required to avoid alert fatigue and wasted FTE effort.

Why it Matters

Intermittent ward vital checks miss early decline — continuous wearable monitoring detected ~9× more MEWS>6 events than episodic EHR (electronic health record) charting, with continuous respiratory rate (RR) and heart rate (HR) trends accounting for most previously missed alerts.

For inpatient pharmacy this shifts the problem from 'catching collapses' to managing a higher volume of earlier warnings — but operational PPV (~0.11–0.22) implies ~80% false positives, so a human triage layer and tuned thresholds are essential to avoid alert fatigue and inefficient use of pharmacist time.

Successful deployment requires clinical decision support (CDS) integration, clear triage tiers, and stewardship-aligned thresholds and staffing so scarce pharmacist FTEs focus on actionable, high‑risk patients rather than raw model alerts.

What They Did

Collected continuous wearable and EHR data from 888 adult non‑ICU inpatients (2,897 patient‑days) across four hospitals using two chest‑worn devices (VitalConnect Device #1 and Biobeat Device #2); dataset included demographics and charted vitals.

Built an LSTM recurrent neural network (RNN) using nine inputs (age, BMI, systolic blood pressure, heart rate, respiratory rate, temperature, SpO2, MEWS, movement) on 5‑hour sequences to predict forthcoming MEWS>6 alerts (≥30 min) 0.5–24 hours ahead.

Benchmarked against logistic regression and EHR‑only models and validated in three stages: retrospective holdout (Device #1), prospective data at a different hospital (Device #1), and external testing on a second device (Device #2); preprocessing included artifact rejection, 1‑minute resampling, simple imputation, z‑scoring, patient‑level splits, and training‑set rebalancing.

What They Found

Model performance: RNN discrimination was strong — retrospective AUROC ≈ 0.89 (±0.03) and PR AUC ≈ 0.58 (±0.14); prospective testing showed ROC 0.90 / PR 0.60; external‑device testing showed ROC 0.84 / PR 0.37.

Timing and hard outcomes: median lead time ≈ 17 hours for predicted MEWS>6 alerts and ≈ 16.5 hours for hard outcomes; retrospectively the model detected 5/6 (83%) unplanned ICU transfers, ~50% of RRT calls, and all intubations/arrests/deaths in the limited hard‑outcome sample.

Operational tradeoffs: Negative predictive value (NPV) was high (~0.99) but PPV was low (0.11–0.22 → ≈80% false positives), indicating the need for a central human triage layer before escalation to bedside teams; pharmacy implication — use the multi‑hour window for proactive medication review (earlier antibiotics, holding sedatives), staging emergency meds, and pre‑positioning ICU/respiratory therapies to reduce reactive code workload.

Mechanism: continuous wearable data produced ~9× more MEWS>6 alerts than episodic charting, driven mainly by continuous respiratory rate (~71% of missed alerts) and heart rate (~16%) signals — continuous RR/HR trends enabled earlier detection independent of motion artifacts.

Takeaways

Stand up a central triage layer to screen wearable‑model alerts; target an operational PPV ≈ 0.2 for escalations and route only human‑vetted, high‑credence cases to the bedside nurse and unit pharmacist for a pre‑RRT medication safety check.

Integrate continuous vitals and the risk score into the enterprise EHR and ward dashboards (show 24‑hour RR/HR trends with the alert); route escalations via secure messaging or an inbasket triage navigator to keep reviews within workflow.

Operationalize the 8–24‑hour window: define rapid assessment ownership, a pharmacist checklist for likely iatrogenic contributors (opioids/benzodiazepines, sedatives, electrolytes, antimicrobial gaps), and escalation aligned with existing MEWS pathways; train staff to interpret trend‑based alerts and document actions.

Treat the system like weather radar — a probabilistic forecast requiring human confirmation: maintain governance that monitors PPV/NPV, recalibrates the model as needed, and requires human triage before treatment changes are made.

Strengths and Limitations

Strengths:

Three‑stage validation (retrospective holdout, prospective separate hospital, external device) demonstrating reproducible, device‑agnostic performance across hospitals and sensors.

Rigorous signal processing (artifact rejection, movement analysis, sampling‑rate experiments) and post‑validation recalibration improved validity and supported RR/HR‑driven early detection.

Limitations:

Very few hard outcomes (11 total) in the dataset and non‑targeted patient patching limit training and definitive validation for ICU‑level endpoints, reducing certainty about claimed hard‑outcome performance.

Device measurement variability, model miscalibration/overconfidence, and low operating PPV (≈0.11–0.22) require a human triage layer and local recalibration before deployment; operational burden (triage FTEs, integration) must be planned.

Bottom Line

The wearable‑based continuous monitoring and RNN model are promising for enabling proactive pharmacy interventions by providing a multi‑hour prediction window, but the low PPV, few hard outcomes, and integration/triage needs mean this is not deployment‑ready. Pilot with human triage, local validation, threshold tuning, and EHR/CDS integration is required before broader rollout.