title: Poor prognosis for existing monitors in the intensive care unit.
abstract: OBJECTIVE
  To identify areas requiring the most urgent improvement in the intensive care unit (ICU); and to accurately determine the positive predictive value of routine critical care patient monitoring alarms, as well as the common causes for false-positive alarms.
  
  
  METHODS
  Prospective, observational study.
  
  
  METHODS
  A multidisciplinary ICU in a university-affiliated children's hospital (excluding children with primary heart disease).
  
  
  METHODS
  The occurrence rate, cause, and appropriateness of all alarms from tracked monitors were recorded by a trained observer and validated by the bedside nurse over a 10-wk period for a single bedspace at a time.
  
  
  RESULTS
  After 298 monitored hrs, 86% of a total 2,942 alarms were found to be false-positive alarms, while an additional 6% were classified as clinically irrelevant true alarms. Only 8% of all alarms tracked during the study period were determined to be true alarms with clinical significance. Alarms were also classified according to whether they were clearly associated with a "patient intervention" (18%), were clearly not associated with a patient intervention (74%), or had unclear association to interventions (8%). While 11% of "nonpatient intervention" alarms were clinically significant true alarms, only 2% of "patient intervention" alarms were so. Positive predictive values for the various devices ranged from < 1% for the pulse oximeter's heart rate signal to 74% for the arterial catheter's mean systemic blood pressure signal during periods free from patient interventions. The pulse oximeter caused false-positive alarms most frequently, with common reasons being bad data format/bad connection and poor contact.
  
  
  CONCLUSIONS
  Efforts to develop intelligent monitoring systems have more potential to deliver significantly improved patient care by initially targeting especially weak areas in ICU monitoring, such as pulse oximetry reliability.
authors.affiliation: MIT Laboratory for Computer Science, Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge 02139, USA.
authors.forename: C L
authors.initials: CL
authors.lastname: Tsien
authors.forename: J C
authors.initials: JC
authors.lastname: Fackler
mesh_terms: D003422:Critical Care / Q000379:methods / Q000592:standards*; D004868:Equipment Failure / Q000706:statistics & numerical data; D005189:False Positive Reactions; D006776:Hospitals, Pediatric; D006784:Hospitals, Teaching; D006801:Humans; D015278:Intensive Care Units, Pediatric / Q000592:standards*; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000592:standards*; D010092:Oximetry / Q000295:instrumentation / Q000592:standards; D011237:Predictive Value of Tests; D011446:Prospective Studies; D014481:United States