In:
Journal of Investigative Medicine, SAGE Publications, Vol. 67, No. 6 ( 2019-08), p. 943-949
Abstract:
Recurrent hospitalizations are common in longitudinal studies; however, many forms of cumulative event analyses assume recurrent events are independent. We explore the presence of event dependence when readmissions are spaced apart by at least 30 and 60 days. We set up a comparative framework with the assumption that patients with emergency percutaneous coronary intervention (PCI) will be at higher risk for recurrent cardiovascular readmissions than those with elective procedures. A retrospective study of patients who underwent PCI (January 2008–December 2012) with their follow-up information obtained from a regional database for hospitalization was conducted. Conditional gap time (CG), frailty gamma (FG) and conditional frailty models (CFM) were constructed to evaluate the dependence of events. Relative bias (%RB) in point estimates using CFM as the reference was calculated for comparison of the models. Among 4380 patients, emergent cases were at higher risk as compared with elective cases for recurrent events in different statistical models and time-spaced data sets, but the magnitude of HRs varied across the models (adjusted HR [95% CI]: all readmissions [unstructured data] —CG 1.16 [1.09 to 1.22], FG 1.45 [1.33 to 1.57] , CFM 1.24 [1.16 to 1.32]; 30-day spaced—CG1.14 [1.08 to 1.21] , FG 1.28 [1.17 to 1.39], CFM 1.17 [1.10 to 1.26] ; and 60-day spaced—CG 1.14 [1.07 to 1.22], FG 1.23 [1.13 to 1.34] CFM 1.18 [1.09 to 1.26]). For all of the time-spaced readmissions, we found that the values of %RB were closer to the conditional models, suggesting that event dependence dominated the data despite attempts to create independence by increasing the space in time between admissions. Our analysis showed that independent of the intercurrent event duration, prior events have an influence on future events. Hence, event dependence should be accounted for when analyzing recurrent events and challenges contemporary methods for such analysis.
Type of Medium:
Online Resource
ISSN:
1081-5589
,
1708-8267
DOI:
10.1136/jim-2018-000873
Language:
English
Publisher:
SAGE Publications
Publication Date:
2019
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