Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.
Diagnostics (Basel)
; 14(8)2024 Apr 14.
Article
in En
| MEDLINE
| ID: mdl-38667462
ABSTRACT
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.
Full text:
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Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Diagnostics (Basel)
Year:
2024
Document type:
Article