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Journal of Clinical Monitoring and Computing

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22.02.2017 | Original Research

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

verfasst von: Eliezer L. Bose, Gilles Clermont, Lujie Chen, Artur W. Dubrawski, Dianxu Ren, Leslie A. Hoffman, Michael R. Pinsky, Marilyn Hravnak

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 1/2018

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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.

Hravnak M, Edwards L, Clontz A, Valenta C, DeVita MA, Pinsky MR. Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system. Arch Intern Med. 2008;168(12):1300–8.CrossRefPubMedPubMedCentral

Buist MD, Moore GE, Bernard SA, Waxman BP, Anderson JN, Nguyen TV. Effects of a medical emergency team on reduction of incidence of and mortality from unexpected cardiac arrests in hospital: preliminary study. Bmj. 2002;324(7334):387–90.CrossRefPubMedPubMedCentral

Hillman K, Chen J, Cretikos M, Bellomo R, Brown D, Doig G, Finfer S, Flabouris A, Investigators MS. Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial. The Lancet. 2005;365(9477):2091–7.CrossRef

Bellomo R, Goldsmith D, Uchino S, Buckmaster J, Hart GK, Opdam H, Silvester W, Doolan L, Gutteridge G. A prospective before-and-after trial of a medical emergency team. Med J Aust. 2003;179(6):283–8.PubMed

Bristow PJ, Hillman KM, Chey T, Daffurn K, Jacques TC, Norman SL, Bishop GF, Simmons EG. Rates of in-hospital arrests, deaths and intensive care admissions: the effect of a medical emergency team. Med J Aust. 2000;173(5):236–40.PubMed

DeVita M, Braithwaite R, Mahidhara R, Stuart S, Foraida M, Simmons R. Use of medical emergency team responses to reduce hospital cardiopulmonary arrests. Quality Safety Health Care 2004;13(4):251–254.

Downey AW, Quach JL, Haase M, Haase-Fielitz A, Jones D, Bellomo R. Characteristics and outcomes of patients receiving a medical emergency team review for acute change in conscious state or arrhythmias*. Crit Care Med. 2008;36(2):477–81.CrossRefPubMed

Jones D, Duke G, Green J, Briedis J, Bellomo R, Casamento A, Kattula A, Way M. Medical emergency team syndromes and an approach to their management. Critical care. 2006;10(1):R30.CrossRefPubMedPubMedCentral

Jones D, Bates S, Warrillow S, Goldsmith D, Kattula A, Way M, Gutteridge G, Buckmaster J, Bellomo R. Effect of an education programme on the utilization of a medical emergency team in a teaching hospital. Intern Med J. 2006;36(4):231–6.CrossRefPubMed

10.

Alpaydin E. Introduction to machine learning. 2nd ed. Boston: MIT press; 2014

11.

Jang E-H, Park B-J, Kim S-H, Sohn J-H. Emotion recognition by machine learning algorithms using psychophysiological signals. Int J Eng Ind. 2012;3 (1).

12.

AlZoubi O, Mello SK, Calvo R. Detecting naturalistic expressions of nonbasic affect using physiological signals. IEEE Trans Affect Comput. 2012;3 (3):298–310.

13.

Martinez HP, Bengio Y, Yannakakis GN. Learning deep physiological models of affect. IEEE Comput Intell Mag. 2013;8 (2):20–33.

14.

Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM computing surv (CSUR). 1999;31 (3):264–323.

15.

Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc B. 2001;63(2):411–23.CrossRef

16.

Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences. 1998;95(25):14863–8.CrossRef

17.

Agrawal R, Srikant R Mining sequential patterns. In: Data Engineering, 1995. Proceedings of the Eleventh International Conference on, 1995. IEEE, pp 3–14.

18.

Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armañanzas R, Santafé G, Pérez A. Machine learning in bioinformatics. Brief Bioinform. 2006;7(1):86–112.CrossRefPubMed

19.

Sarstedt M, Mooi E. A concise guide to market research: the process, data, and methods using IBM SPSS statistics. Berlin: Springer; 2014.

20.

Ward JH Jr. Hierarchical grouping to optimize an objective function. Journal of the American statistical association. 1963;58(301):236–44.CrossRef

21.

Jain AK, Dubes RC. Algorithms for clustering data. New Jersey: Prentice-Hall, Inc; 1988.

22.

Murtagh F. A survey of recent advances in hierarchical clustering algorithms. The Computer Journal. 1983;26(4):354–9.CrossRef

23.

Steinley D. K-means clustering: a half-century synthesis. Br J Math Stat Psychol. 2006;59(1):1–34.CrossRefPubMed

24.

Jain AK. Data clustering: 50 years beyond K-means. Pattern recognition letters. 2010;31(8):651–66.CrossRef

25.

McLachlan G, Do K, Ambroise C. Analyzing microarray gene expression data. Vol. 422. New Jersey: John Wiley & Sons; 2005.

26.

Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9.CrossRefPubMed

27.

Brown H, Terrence J, Vasquez P, Bates DW, Zimlichman E. Continuous monitoring in an inpatient medical-surgical unit: a controlled clinical trial. Am J Med. 2014;127(3):226–32.CrossRefPubMed

28.

Hravnak M, Chen L, Dubrawski A, Clermont G, Bose E, Fiterau M, Wang D, Guillame-Bert M, Pinsky M. Supervised Machine Learning Can Classify Artifact In Multi-Signal Vital Sign Monitoring Data Erom Step-Down Unit (SDU) Patients. In: Intensive Care Medicine, 2014. p S28.

29.

Haldar P, Pavord ID, Shaw DE, Berry MA, Thomas M, Brightling CE, Wardlaw AJ, Green RH. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med. 2008;178(3):218–24.CrossRefPubMedPubMedCentral

30.

Cellucci T, Tyrrell PN, Twilt M, Sheikh S, Benseler SM. Distinct phenotype clusters in childhood inflammatory brain diseases: implications for diagnostic evaluation. Arthritis &amp. Rheumatology. 2014;66(3):750–6.

31.

Devaney M, Ram A Efficient feature selection in conceptual clustering. In: ICML, 1997. pp 92–97.

32.

Talavera L Feature selection as a preprocessing step for hierarchical clustering. In: International Conference of Machine Learning Proceedings (ICML), 1999. pp 389–397.

33.

Hastie T, Tibshirani R, Friedman J. Unsupervised learning. In: The Elements of Statistical Learning. Second, editor. Edition. New York: Springer; 2009. pp. 485–585.

34.

Arai K, Barakbah AR (2007) Hierarchical K-means: an algorithm for centroids initialization for K-means. Reports of the Faculty of Science and Engineering 36 (1):25–31.

35.

Mooi E, Sarstedt M. Cluster analysis. In: A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics. Berlin: Springer-Verlag; 2011. pp. 273–98.CrossRef

36.

Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985;50(2):159–79.CrossRef

37.

Hawkins MA, Schaefer JT, Gunstad J, Dolansky MA, Redle JD, Josephson R, Moore SM, Hughes JW. What is your patient’s cognitive profile? Three distinct subgroups of cognitive function in persons with heart failure. Appl Nurs Res. 2015;28(2):186–91.CrossRefPubMed

38.

Stuetzle W, Nugent R. A generalized single linkage method for estimating the cluster tree of a density. Journal of Computational Graphical Statistics. 2012;19(2):397–418.CrossRef

39.

Clarke B, Fokoue E, Zhang HH. Principles and theory for data mining and machine learning. New York: Springer Science & Business Media; 2009.CrossRef

40.

Wagstaff K, Cardie C, Rogers S, Schrödl S Constrained k-means clustering with background knowledge. In: International Conference on Machine Learning (ICML), 2001. pp 577–584.

41.

Schaffer C A conservation law for generalization performance. In: Proceedings of the 11th international conference on machine learning, 1994. pp 259–265.

42.

Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2012.

43.

Clermont G, Sileanu F, Pinsky M, Ogundele O. Physiologic Variable Analysis Between Hemodynamic Stable And Unstable Patients. Am J Respir Crit Care Med. 2012;185:A4630.

44.

Clermont G, Sileanu F, Pinsky M, Ogundele O. Use Of Derived Physiologic Variables To Predict Individual Patients’ Probability Of Hemodynamic Instability. Am J Respir Crit Care Med. 2013;187:A5067.

45.

Smith GB, Prytherch DR, Schmidt PE, Featherstone PI, Higgins B. A review, and performance evaluation, of single-parameter “track and trigger” systems. Resuscitation. 2008;79(1):11–21.CrossRefPubMed

46.

Hravnak M, DeVita MA, Clontz A, Edwards L, Valenta C, Pinsky MR. Cardiorespiratory instability before and after implementing an integrated monitoring system. Crit Care Med. 2011;39(1):65.CrossRefPubMedPubMedCentral

47.

Honaker J, King G. What to do about missing values in time-series cross-section data. American Journal of Political Science. 2010;54(2):561–81.CrossRef

48.

Honaker J, King G, Blackwell M. Amelia II: A program for missing data. Journal of Statistical Software. 2011;45(7):1–47.CrossRef

Titel: Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change
verfasst von: Eliezer L. Bose
Gilles Clermont
Lujie Chen
Artur W. Dubrawski
Dianxu Ren
Leslie A. Hoffman
Michael R. Pinsky
Marilyn Hravnak
Publikationsdatum: 22.02.2017
Verlag: Springer Netherlands
Erschienen in: Journal of Clinical Monitoring and Computing / Ausgabe 1/2018
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI: https://doi.org/10.1007/s10877-017-0001-7

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