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Ann Am Thorac Soc. 2017 Mar;14(3):384-391. doi: 10.1513/AnnalsATS.201611-905OC.

Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms.

Author information

1
1 Auton Laboratory, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
2
2 LifeBridge Critical Care, Sinai Hospital Baltimore, Baltimore, Maryland.
3
3 Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pennsylvania; and.
4
4 Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania.

Abstract

RATIONALE:

Cardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored.

OBJECTIVES:

To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients.

METHODS:

Using a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after step-down unit admission, and again during the 4 hours immediately before the CRI event, between cases (ever had a CRI) and control subjects (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk.

MEASUREMENTS AND MAIN RESULTS:

Estimated risk was significantly higher for cases (918) than control subjects (1,053; P ≤ 0.001) during the initial 4-hour stable periods. Among cases, the aggregated nonpersonalized risk trend increased 2 hours before the CRI, whereas the personalized risk trend became significantly different from control subjects 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for nonpersonalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset).

CONCLUSIONS:

Insights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI, taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful in the dynamic allocation of technological and clinical personnel resources in acute care hospitals.

KEYWORDS:

early warning score; finite mixture model; instability; machine learning; physiologic monitoring

PMID:
28033032
PMCID:
PMC5427723
DOI:
10.1513/AnnalsATS.201611-905OC
[Indexed for MEDLINE]
Free PMC Article

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