Literature DB >> 17208120

Present-at-admission diagnoses improved mortality risk adjustment among acute myocardial infarction patients.

George J Stukenborg1, Douglas P Wagner, Frank E Harrell, M Norman Oliver, Steven W Heim, Amy L Price, Caroline Kim Han, Andrew M D Wolf, Alfred F Connors.   

Abstract

OBJECTIVE: Hospital mortality outcomes for acute myocardial infarction (AMI) patients are a focus of quality improvement programs conducted by government agencies. AMI mortality risk-adjustment models using administrative data typically adjust for baseline differences in mortality risk with a limited set of common and definite comorbidities. In this study, we present an AMI mortality risk-adjustment model that adjusts for comorbid disease and for AMI severity using information from secondary diagnoses reported as present at admission for California hospital patients. STUDY DESIGN AND
SETTING: AMI patients were selected from California hospital administrative data for 1996 through 1999 according to criteria used by the California Hospital Outcomes Project Report on Heart Attack Outcomes, a state-mandated public report that compares hospital mortality outcomes. We compared results for the new model to two mortality risk-adjustment models used to assess hospital AMI mortality outcomes by the state of California, and to two other models used in prior research.
RESULTS: The model using present-at-admission diagnoses obtained substantially better discrimination between predicted survival and inpatient death than the other models we considered.
CONCLUSION: AMI mortality risk-adjustment methods can be meaningfully improved using present-at-admission diagnoses to identify comorbid disease and conditions related closely to AMI.

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Mesh:

Year:  2006        PMID: 17208120     DOI: 10.1016/j.jclinepi.2006.05.014

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  4 in total

1.  Statin use and postoperative atrial fibrillation after major noncardiac surgery.

Authors:  Prashant D Bhave; L Elizabeth Goldman; Eric Vittinghoff; Judith H Maselli; Andrew Auerbach
Journal:  Heart Rhythm       Date:  2011-09-09       Impact factor: 6.343

2.  The accuracy of present-on-admission reporting in administrative data.

Authors:  L Elizabeth Goldman; Philip W Chu; Dennis Osmond; Andrew Bindman
Journal:  Health Serv Res       Date:  2011-08-11       Impact factor: 3.402

3.  Incidence, predictors, and outcomes associated with postoperative atrial fibrillation after major noncardiac surgery.

Authors:  Prashant D Bhave; L Elizabeth Goldman; Eric Vittinghoff; Judith Maselli; Andrew Auerbach
Journal:  Am Heart J       Date:  2012-10-26       Impact factor: 4.749

4.  The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data.

Authors:  Ji Hwan Bang; Soo-Hee Hwang; Eun-Jung Lee; Yoon Kim
Journal:  BMC Med Inform Decis Mak       Date:  2013-11-20       Impact factor: 2.796

  4 in total

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