Sequence Patterns in the Resolution of Clinical Instabilities in Community-Acquired Pneumonia and Association with Outcomes
In patients hospitalized with community-acquired pneumonia (CAP), indicators of clinical instability at discharge (fever, tachycardia, tachypnea, hypotension, hypoxia, decreased oral intake and altered mental status) are associated with poor outcomes. It is not known whether the order of indicator stabilization is associated with outcomes.
To describe variation in the sequences, including whether and in what order, indicators of clinical instability resolve among patients hospitalized with CAP, and to assess associations between patterns of stabilization and patient-level outcomes.
DESIGN / PARTICIPANTS / MAIN MEASURES
Chart review ascertained whether and when indicators stabilized and other data for 1,326 adult CAP patients in six U.S. academic medical centers. The sequences of indicator stabilization were characterized using sequence analysis and grouped using cluster analysis. Associations between sequence patterns and 30-day mortality, length of stay (LOS), and total costs were modeled using regression analysis.
We found 986 unique sequences of indicator stabilization. Sequence analysis identified eight clusters of sequences (patterns) derived by the order or speed in which instabilities resolved or remained at discharge and inpatient mortality. Two of the clusters (56 % of patients) were characterized by almost complete stabilization prior to discharge alive, but differing in the rank orders of four indicators and time to maximum stabilization. Five other clusters (42 % of patients) were characterized by one to three instabilities at discharge with variable orderings of indicator stabilization. In models with fast and almost complete stabilization as the referent, 30-day mortality was lowest in clusters with slow and almost complete stabilization or tachycardia or fever at discharge [OR = 0.73, 95 % CI = (0.28–1.92)], and highest in those with hypoxia with instabilities in mental status or oral intake at discharge [OR = 3.99, 95 % CI = (1.68–9.50)].
Sequences of clinical instability resolution exhibit great heterogeneity, yet certain sequence patterns may be associated with differences in days to maximum stabilization, mortality, LOS, and hospital costs.
KEY WORDScommunity-acquired pneumonia hospitalization social sequence analysis process of care hospital discharge
The Multicenter Hospitalist Study was supported by grant RO1 HS10597 AHRQ from the Agency for Healthcare Research and Quality. Dr. Hougham and Dr. Ruhnke are supported by a research and training grant (KM1 CA156717) from the National Cancer Institute. Dr. Arora is supported by National Institute on Aging K23AG033763. Dr. Meltzer is supported by a Mid-career K24 Career Development Award from the National Institute on Aging (K24 AG031326-01). The content of this publication is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health. The authors acknowledge statistical programming assistance from Prof. Hyo Jung Tak. Prof. Andrew Abbott, Prof. John A. Goldsmith, Dr. Elbert S. Huang, and Dr. Michael David read an earlier version of the manuscript and made helpful suggestions.
An earlier version of this work was presented as a poster at the Society of Hospital Medicine’s Annual Meeting in San Diego, CA, April 2012.
Conflict of Interest
The authors declare that they do not have any conflicts of interest.
- 7.Niederman MS, Mandell LA, Anzueto A, Bass JB, Broughton WA, Campbell GD, et al. Guidelines for the management of adults with community-acquired pneumonia—Diagnosis, assessment of severity, antimicrobial therapy, and prevention. Am J Respir Crit Care Med. 2001;163(7):1730–54.PubMedCrossRefGoogle Scholar
- 8.Menendez R, Torres A, Rodriguez de Castro F, Zalacain R, Aspa J, Martin Villasclaras JJ, et al. Reaching stability in community-acquired pneumonia: the effects of the severity of disease, treatment, and the characteristics of patients. Clin Infect Dis. 2004;39(12):1783–90.PubMedCrossRefGoogle Scholar
- 15.Jurafsky D, Martin JH. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 2nd ed. Upper Saddle River, N.J.: Pearson Prentice Hall; 2009.Google Scholar
- 19.Tan P-N, Steinbach M, Kumar V. Cluster analysis: basic concepts and algorithms. Introduction to data mining. 1st ed. Boston: Pearson Addison Wesley; 2006:487–568.Google Scholar
- 20.Gabadinho A, Ritschard G, Muller NS, Studer M. Analyzing and visualizing state sequences in R with TraMineR. J Stat Softw. 2011;40(4):1–37.Google Scholar
- 27.Brzinsky-Fay C, Kohler U, Luniak M. Sequence analysis with Stata. Stata J. 2006;6(4):435–60.Google Scholar
- 29.Studer M. Le manuel de la librairie WeightedCluster: Un guide pratique pour la création de typologies de trajectoires en sciences sociales avec R. In: Studer M, ed. Étude des Inégalités de Genre en Début de Carrière Académique à L'aide de Méthodes Innovatrices d'Analyse de Données Séquentielles. Geneva: Thèse SES 777, Faculté des sciences économiques et sociales, Université de Genève; 2012.Google Scholar