Reduced heart rate volatility: An early predictor of death in trauma patients

Eric L. Grogan, John A. Morris, Patrick R. Norris, Daniel J. France, Asli Ozdas, Renée A. Stiles, Paul A. Harris, Benoit M. Dawant, Theodore Speroff, Robert J. Winchell, Richard Mullins, David B. Hoyt, Gregory J. Jurkovich, Basil A. Pruitt, John A. Morris

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

Objective: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. Background: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. Methods: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. Results: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. Conclusions: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.

Original languageEnglish (US)
Pages (from-to)547-556
Number of pages10
JournalAnnals of Surgery
Volume240
Issue number3
DOIs
StatePublished - Sep 2004
Externally publishedYes

Fingerprint

Volatilization
Heart Rate
Wounds and Injuries
Logistic Models
Injury Severity Score
Vital Signs
Autonomic Nervous System
Critical Illness
Ventilation
Databases
Sensitivity and Specificity
Survival

Keywords

  • Computerized monitoring
  • Dense data capture
  • Heart rate
  • Heart rate variability
  • Intensive care unit
  • Variability
  • Volatility

ASJC Scopus subject areas

  • Surgery

Cite this

Grogan, E. L., Morris, J. A., Norris, P. R., France, D. J., Ozdas, A., Stiles, R. A., ... Morris, J. A. (2004). Reduced heart rate volatility: An early predictor of death in trauma patients. Annals of Surgery, 240(3), 547-556. https://doi.org/10.1097/01.sla.0000137143.65540.9c

Reduced heart rate volatility : An early predictor of death in trauma patients. / Grogan, Eric L.; Morris, John A.; Norris, Patrick R.; France, Daniel J.; Ozdas, Asli; Stiles, Renée A.; Harris, Paul A.; Dawant, Benoit M.; Speroff, Theodore; Winchell, Robert J.; Mullins, Richard; Hoyt, David B.; Jurkovich, Gregory J.; Pruitt, Basil A.; Morris, John A.

In: Annals of Surgery, Vol. 240, No. 3, 09.2004, p. 547-556.

Research output: Contribution to journalArticle

Grogan, EL, Morris, JA, Norris, PR, France, DJ, Ozdas, A, Stiles, RA, Harris, PA, Dawant, BM, Speroff, T, Winchell, RJ, Mullins, R, Hoyt, DB, Jurkovich, GJ, Pruitt, BA & Morris, JA 2004, 'Reduced heart rate volatility: An early predictor of death in trauma patients', Annals of Surgery, vol. 240, no. 3, pp. 547-556. https://doi.org/10.1097/01.sla.0000137143.65540.9c
Grogan EL, Morris JA, Norris PR, France DJ, Ozdas A, Stiles RA et al. Reduced heart rate volatility: An early predictor of death in trauma patients. Annals of Surgery. 2004 Sep;240(3):547-556. https://doi.org/10.1097/01.sla.0000137143.65540.9c
Grogan, Eric L. ; Morris, John A. ; Norris, Patrick R. ; France, Daniel J. ; Ozdas, Asli ; Stiles, Renée A. ; Harris, Paul A. ; Dawant, Benoit M. ; Speroff, Theodore ; Winchell, Robert J. ; Mullins, Richard ; Hoyt, David B. ; Jurkovich, Gregory J. ; Pruitt, Basil A. ; Morris, John A. / Reduced heart rate volatility : An early predictor of death in trauma patients. In: Annals of Surgery. 2004 ; Vol. 240, No. 3. pp. 547-556.
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abstract = "Objective: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. Background: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. Methods: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. Results: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. Conclusions: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.",
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AU - Morris, John A.

AU - Norris, Patrick R.

AU - France, Daniel J.

AU - Ozdas, Asli

AU - Stiles, Renée A.

AU - Harris, Paul A.

AU - Dawant, Benoit M.

AU - Speroff, Theodore

AU - Winchell, Robert J.

AU - Mullins, Richard

AU - Hoyt, David B.

AU - Jurkovich, Gregory J.

AU - Pruitt, Basil A.

AU - Morris, John A.

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N2 - Objective: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. Background: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. Methods: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. Results: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. Conclusions: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.

AB - Objective: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. Background: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. Methods: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. Results: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. Conclusions: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.

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