Volatility: a new vital sign identified using a novel bedside monitoring strategy.
Volatility: a new vital sign identified using a novel bedside monitoring strategy.
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波动性:使用新颖的床边监测策略识别的新生命体征。
DOI:
10.1097/01.ta.0000151179.74839.98
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发表时间:
2005
期刊:
影响因子:
--
通讯作者:
MorrisJr,JohnA
中科院分区:
文献类型:
--
作者:
Grogan,EricL;Norris,PatrickR;Speroff,Theodore;Ozdas,Asli;France,DanielJ;Harris,PaulA;Jenkins,JudithM;Stiles,Renee;Dittus,RobertS;MorrisJr,JohnA
Background:SIMON (Signal Interpretation and Monitoring) monitors and archives continuous physiologic data in the ICU (HR, BP, CPP, ICP, CI, EDVI, S V O 2, S P O 2, 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.
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影响因子:
2.4
作者:
Bmca. Sayers
通讯作者:
Bmca. Sayers
影响因子:
4.2
作者:
R. Steinmeier;R. P. Hofmann;C. Bauhuf;U. Hübner;R. Fahlbusch
通讯作者:
R. Fahlbusch
DOI:
--
发表时间:
2002
期刊:
The American surgeon
影响因子:
--
作者:
K. Major;M. Shabot;S. Cunneen
通讯作者:
S. Cunneen
影响因子:
8.8
作者:
S. Tibby;H. Frndova;A. Durward;P. Cox
通讯作者:
P. Cox
影响因子:
2.4
作者:
T. Bardt;A. Unterberg;K. Kiening;Gerd;Wolfgang R. Lanksch
通讯作者:
Wolfgang R. Lanksch