Diagnostics, Vol. 14, Pages 816: Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case–Control Matched Analysis

Diagnostics, Vol. 14, Pages 816: Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case–Control Matched Analysis

Diagnostics doi: 10.3390/diagnostics14080816

Authors: Dong Hyun Choi Hyunju Lee Hyunjin Joo Hyoun-Joong Kong Seung Bok Lee Sungwan Kim Sang Do Shin Ki Hong Kim

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case‒control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71–6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97–3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22–1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60–0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906–0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.