Continuous vital sign monitoring for predicting hospital length of stay: a feasibility study in chronic obstructive pulmonary disease and chronic heart failure patients.

Abstract

Background: Vital signs monitoring provides clinicians with real-time information regarding patients' current medical condition. We hypothesize that applying comprehensive analytical methods to underutilized, routinely collected vital signs data can yield valuable insights to support clinical decision-making. In this study, we present a novel approach for vital signs time series analysis applied to hospitalization length of stay (LOS) prediction in chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF) patients.

Methods: Heart rate (HR), respiratory rate (RR) and peripheral oxygen saturation (SpO₂) were continuously monitored during the first 24 h of hospital admission in COPD and CHF patients admitted to general, non-ICU hospital wards. The resulting time series were submitted to a comprehensive analysis through a highly comparative, massive feature extraction. We identified key patterns associated with hospitalization length of stay (LOS). Finally, we developed a predictive model for hospitalization LOS combining predictive features from the three vital signs time series.

Results: A total of 101 patients were enrolled in the study, 74 of whom were eligible for analysis (39 COPD and 35 CHF patients). Periodicity and self-correlation in HR and RR time series were associated to hospitalization LOS. In SpO₂ time series, short-term fluctuations and local dynamics were associated to hospitalization LOS. The predictive model for hospitalization LOS was built using nineteen predictive features and achieved an area under the curve (AUC) of 0.975, an accuracy of 0.944, a sensitivity of 0.979, and a specificity of 0.900 in 10-fold cross-validation.

Conclusion: Through a comprehensive feature-based analysis, we identified key patterns in HR, RR, and SpO₂ time series associated with hospitalization LOS in COPD and CHF patients and a compact set of features that can accurately predict LOS in COPD and CHF patients using only routinely collected data from the first 24 h of admission.