Clinical InvestigationEarly inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization
Section snippets
Funding
The portions of this work conducted at Baylor Scott & White Health were supported by CMS/CMMI Contract #HHSM-500-2012-0024C. No other extramural funding was used to support this work. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the manuscript, and its final contents.
Study populations
HF patients who were admitted as inpatients to any of the 23 Intermountain Healthcare hospitals in Utah and southern Idaho from 2005 to 2012 were
Overview
Selected baseline characteristics of the two Intermountain patient sets and the Baylor Scott & White validation set are provided in Table I. The incidence of 30-day readmission and of 30-day mortality were discordant (Figure 1), with more patients readmitted than those who died for females (1.6-fold more readmissions) and males (2.1-fold more readmissions). A much smaller number of individuals experienced both outcomes. Despite this, among those who survived to 30 days after index
Summary of findings
Sex-specific iHF risk scores were derived for efficient early use during an inpatient admission (within 24 hours of hospitalization) and effectively predicted post-discharge 30-day readmission in independent validation populations from Intermountain Healthcare and Baylor Scott & White Health (North Region). The primary models utilized data from inexpensive, commonly-ordered, electronically-available laboratory tests. Of substantial import, the reduced-variable iHF lab models had predictive
Acknowledgments
Author Contributions:
Authorship contributions include the following: conception and design: BDH, DB, ALM, DLL; acquisition of data: JB, GC, AB, TLB; analysis and interpretation of data: BDH, DB, ALM, LAS, RM, CAR, KDR, RA, AGK, BCJ, DLL; drafting of the manuscript or revising it critically for important intellectual content: BDH, DB, ALM, LAS, JB, GC, AB, RM, TLB, CAR, KDR, RA, AGK, BCJ, DLL; final approval of the manuscript: BDH, DB, ALM, LAS, JB, GC, AB, RM, TLB, CAR, KDR, RA, AGK, BCJ, DLL.
References (25)
- et al.
Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients
J Am Coll Cardiol Heart Fail
(2013) - et al.
Exceptional mortality prediction by risk scores from common laboratory tests
Am J Med
(2009) - et al.
Usefulness of in-hospital prescription of statin agents after angiographic diagnosis of coronary artery disease in improving continued compliance and reduced mortality
Am J Cardiol
(2001) - et al.
Correlates of early hospital readmission or death in patients with congestive heart failure
Am J Cardiol
(1997) - et al.
Prediction of hospital readmission for heart failure: development of a simple risk score based on administrative data
J Am Coll Cardiol
(1999) - et al.
Predictors of readmission among elderly survivors of admission with heart failure
Am Heart J
(2000) - et al.
Prediction of rehospitalization and death in severe heart failure by physicians and nurses of the ESCAPE trial
J Card Fail
(2007) - et al.
Effect of living alone on patient outcomes after hospitalization for acute myocardial infarction
Am J Cardiol
(2011) - et al.
Dispelling the myths: Calling for sex-specific reporting of trial results
Mayo Clin Proc
(2008) - et al.
An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure
Circ Cardiovasc Qual Outcomes
(2008)
Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community
Can Med Assoc J
An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data
Med Care
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