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Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change

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Abstract

Cardiorespiratory instability (CRI) in monitored step-down unit (SDU) patients has a variety of etiologies, and likely manifests in patterns of vital signs (VS) changes. We explored use of clustering techniques to identify patterns in the initial CRI epoch (CRI1; first exceedances of VS beyond stability thresholds after SDU admission) of unstable patients, and inter-cluster differences in admission characteristics and outcomes. Continuous noninvasive monitoring of heart rate (HR), respiratory rate (RR), and pulse oximetry (SpO2) were sampled at 1/20 Hz. We identified CRI1 in 165 patients, employed hierarchical and k-means clustering, tested several clustering solutions, used 10-fold cross validation to establish the best solution and assessed inter-cluster differences in admission characteristics and outcomes. Three clusters (C) were derived: C1) normal/high HR and RR, normal SpO2 (n = 30); C2) normal HR and RR, low SpO2 (n = 103); and C3) low/normal HR, low RR and normal SpO2 (n = 32). Clusters were significantly different based on age (p < 0.001; older patients in C2), number of comorbidities (p = 0.008; more C2 patients had ≥ 2) and hospital length of stay (p = 0.006; C1 patients stayed longer). There were no between-cluster differences in SDU length of stay, or mortality. Three different clusters of VS presentations for CRI1 were identified. Clusters varied on age, number of comorbidities and hospital length of stay. Future study is needed to determine if there are common physiologic underpinnings of VS clusters which might inform clinical decision-making when CRI first manifests.

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Acknowledgements

This study was funded with grant support from the United States National Institutes of Health, National Institute of Nursing Research RO1 NR13912 and the National Science Foundation NSF 1320347.

Funding

This study was funded with grant support from the United States National Institutes of Health, National Institute of Nursing Research RO1 NR13912 and the National Science Foundation NSF 1,320,347. The funding bodies approved the study design as submitted in the grant application proposal, but had no role in the data collection, analyses, or interpretation, manuscript preparation, or decision to submit the manuscript for publication.

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Authors and Affiliations

  1. The University of Texas at Austin, 1710 Red River St., Austin, TX, 78701, USA

    Eliezer L. Bose

  2. Department of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, USA

    Gilles Clermont & Michael R. Pinsky

  3. Auton Lab, Carnegie Mellon University Robotics Institute, Pittsburgh, USA

    Lujie Chen & Artur W. Dubrawski

  4. Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, Pittsburgh, USA

    Dianxu Ren, Leslie A. Hoffman & Marilyn Hravnak

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  1. Eliezer L. Bose
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  2. Gilles Clermont
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  3. Lujie Chen
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  4. Artur W. Dubrawski
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  7. Michael R. Pinsky
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  8. Marilyn Hravnak
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Correspondence to Eliezer L. Bose.

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Ethical approval

The study was approved and has current active approval from the University of Pittsburgh Institutional Review Board (PRO12070002). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required, and was approved with consent waiver.

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The authors have no commercial conflicts of interest to report.

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Bose, E.L., Clermont, G., Chen, L. et al. Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change. J Clin Monit Comput 32, 117–126 (2018). https://doi.org/10.1007/s10877-017-0001-7

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  • DOI: https://doi.org/10.1007/s10877-017-0001-7

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