Development and evaluation of predictive alerts for hemodynamic instability in ICU patients.
This paper describes an algorithm for identifying ICU patients that are likely to become hemodynamically unstable. The algorithm consists of a set of rules that trigger alerts. Unlike most existing ICU alert mechanisms, it uses data from multiple sources and is often able to identify unstable patients earlier and with more accuracy than alerts based on a single threshold. The rules were generated using a machine learning technique and were tested on retrospective data in the MIMIC II ICU database, yielding a specificity of approximately 0.9 and a sensitivity of 0.6.
Eshelman
Larry J
LJ
Philips Research North America, Briarcliff Manor, NY, USA.
Lee
K P
KP
Frassica
Joseph J
JJ
Zong
Wei
W
Nielsen
Larry
L
Saeed
Mohammed
M
D000465:Algorithms; D003422:Critical Care / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D007022:Hypotension / Q000175:diagnosis*; D008991:Monitoring, Physiologic / Q000379:methods*; D009102:Multiple Organ Failure / Q000175:diagnosis*; D009518:New York; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity