TY - JOUR
T1 - Volatility
T2 - A new vital sign identified using a novel bedside monitoring strategy
AU - Grogan, Eric L.
AU - Norris, Patrick R.
AU - Speroff, Theodore
AU - Ozdas, Asli
AU - France, Daniel J.
AU - Harris, Paul A.
AU - Jenkins, Judith M.
AU - Stiles, Renee
AU - Dittus, Robert S.
AU - Morris, John A.
AU - Thompson, Errington C.
AU - Cansfield,
AU - Trunkey, Donald D.
AU - Genuit, Thomas
AU - Malhotra, Ajai K.
AU - Enderson, Blaine L.
AU - Healey, Mark A.
AU - Kuhls, Deborah A.
PY - 2005/1
Y1 - 2005/1
N2 - Background: SIMON (Signal Interpretation and Monitoring) monitors and archives continuous physiologic data in the ICU (HR, BP, CPP, ICP, CI, EDVI, SvO2, SpO2, SVRI, PAP, and CVP). We hypothesized: heart rate (HR) volatility predicts outcome better than measures of central tendency (mean and median). Methods: More than 600 million physiologic data points were archived from 923 patients over 2 years in a level one trauma center. Data were collected every 1 to 4 seconds, stored in a MS-SQL 7.0 relational database, linked to TRACS, and de-identified. Age, gender, race, Injury Severity Score (ISS), and HR statistics were analyzed with respect to outcome (death and ventilator days) using logistic and Poisson regression. Results: We analyzed 85 million HR data points, which represent more than 71,000 hours of continuous data capture. Mean HR varied by age, gender and ISS, but did not correlate with death or ventilator days. Measures of volatility (SD, % HR > 120) correlated with death and prolonged ventilation. Conclusions: 1) Volatility predicts death better than measures of central tendency. 2) Volatility is a new vital sign that we will apply to other physiologic parameters, and that can only be fully explored using techniques of dense data capture like SIMON. 3) Densely sampled aggregated physiologic data may identify sub-groups of patients requiring new treatment strategies.
AB - Background: SIMON (Signal Interpretation and Monitoring) monitors and archives continuous physiologic data in the ICU (HR, BP, CPP, ICP, CI, EDVI, SvO2, SpO2, SVRI, PAP, and CVP). We hypothesized: heart rate (HR) volatility predicts outcome better than measures of central tendency (mean and median). Methods: More than 600 million physiologic data points were archived from 923 patients over 2 years in a level one trauma center. Data were collected every 1 to 4 seconds, stored in a MS-SQL 7.0 relational database, linked to TRACS, and de-identified. Age, gender, race, Injury Severity Score (ISS), and HR statistics were analyzed with respect to outcome (death and ventilator days) using logistic and Poisson regression. Results: We analyzed 85 million HR data points, which represent more than 71,000 hours of continuous data capture. Mean HR varied by age, gender and ISS, but did not correlate with death or ventilator days. Measures of volatility (SD, % HR > 120) correlated with death and prolonged ventilation. Conclusions: 1) Volatility predicts death better than measures of central tendency. 2) Volatility is a new vital sign that we will apply to other physiologic parameters, and that can only be fully explored using techniques of dense data capture like SIMON. 3) Densely sampled aggregated physiologic data may identify sub-groups of patients requiring new treatment strategies.
KW - And Intensive care unit
KW - Computerized monitoring
KW - Heart rate
KW - Variability
KW - Volatility
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U2 - 10.1097/01.TA.0000151179.74839.98
DO - 10.1097/01.TA.0000151179.74839.98
M3 - Article
C2 - 15674143
AN - SCOPUS:13544252679
SN - 0022-5282
VL - 58
SP - 7
EP - 14
JO - Journal of Trauma - Injury, Infection and Critical Care
JF - Journal of Trauma - Injury, Infection and Critical Care
IS - 1
ER -