Literature DB >> 22797643

Comparison of 30-day mortality models for profiling hospital performance in acute ischemic stroke with vs without adjustment for stroke severity.

Gregg C Fonarow1, Wenqin Pan, Jeffrey L Saver, Eric E Smith, Mathew J Reeves, Joseph P Broderick, Dawn O Kleindorfer, Ralph L Sacco, DaiWai M Olson, Adrian F Hernandez, Eric D Peterson, Lee H Schwamm.   

Abstract

CONTEXT: There is increasing interest in reporting risk-standardized outcomes for Medicare beneficiaries hospitalized with acute ischemic stroke, but whether it is necessary to include adjustment for initial stroke severity has not been well studied.
OBJECTIVE: To evaluate the degree to which hospital outcome ratings and potential eligibility for financial incentives are altered after including initial stroke severity in a claims-based risk model for hospital 30-day mortality for acute ischemic stroke. DESIGN, SETTING, AND PATIENTS: Data were analyzed from 782 Get With The Guidelines-Stroke participating hospitals on 127,950 fee-for-service Medicare beneficiaries with ischemic stroke who had a score documented for the National Institutes of Health Stroke Scale (NIHSS, a 15-item neurological examination scale with scores from 0 to 42, with higher scores indicating more severe stroke) between April 2003 and December 2009. Performance of claims-based hospital mortality risk models with and without inclusion of NIHSS scores for 30-day mortality was evaluated and hospital rankings from both models were compared. MAIN OUTCOMES MEASURES: Model discrimination, hospital 30-day mortality outcome rankings, and value-based purchasing financial incentive categories.
RESULTS: Across the study population, the mean (SD) NIHSS score was 8.23 (8.11) (median, 5; interquartile range, 2-12). There were 18,186 deaths (14.5%) within the first 30 days, including 7430 deaths (5.8%) during the index hospitalization. The hospital mortality model with NIHSS scores had significantly better discrimination than the model without (C statistic, 0.864; 95% CI, 0.861-0.867, vs 0.772; 95% CI, 0.769-0.776; P < .001). Among hospitals ranked in the top 20% or bottom 20% of performers by the claims model without NIHSS scores, 26.3% were ranked differently by the model with NIHSS scores. Of hospitals initially classified as having "worse than expected" mortality, 57.7% were reclassified to "as expected" by the model with NIHSS scores. The net reclassification improvement (93.1%; 95% CI, 91.6%-94.6%; P < .001) and integrated discrimination improvement (15.0%; 95% CI, 14.6%-15.3%; P < .001) indexes both demonstrated significant enhancement of model performance after the addition of NIHSS. Explained variance and model calibration was also improved with the addition of NIHSS scores.
CONCLUSION: Adding stroke severity as measured by the NIHSS to a hospital 30-day risk model based on claims data for Medicare beneficiaries with acute ischemic stroke was associated with considerably improved model discrimination and change in mortality performance rankings for a substantial portion of hospitals.

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Year:  2012        PMID: 22797643     DOI: 10.1001/jama.2012.7870

Source DB:  PubMed          Journal:  JAMA        ISSN: 0098-7484            Impact factor:   56.272


  55 in total

1.  Predicting discharge mortality after acute ischemic stroke using balanced data.

Authors:  King Chung Ho; William Speier; Suzie El-Saden; David S Liebeskind; Jeffery L Saver; Alex A T Bui; Corey W Arnold
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

2.  Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

Authors:  Jacob V Spertus; Sharon-Lise T Normand; Robert Wolf; Matt Cioffi; Ann Lovett; Sherri Rose
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2016-11-08

3.  Preventable readmissions within 30 days of ischemic stroke among Medicare beneficiaries.

Authors:  Judith H Lichtman; Erica C Leifheit-Limson; Sara B Jones; Yun Wang; Larry B Goldstein
Journal:  Stroke       Date:  2013-10-30       Impact factor: 7.914

4.  Increased High-Sensitivity Troponin-T Levels Are Associated with Mortality After Ischemic Stroke.

Authors:  Asaf Maoz; Shai Rosenberg; Ronen R Leker
Journal:  J Mol Neurosci       Date:  2015-06-11       Impact factor: 3.444

5.  Neighborhood socioeconomic disadvantage and mortality after stroke.

Authors:  Arleen F Brown; Li-Jung Liang; Stefanie D Vassar; Sharon Stein Merkin; W T Longstreth; Bruce Ovbiagele; Tingjian Yan; José J Escarce
Journal:  Neurology       Date:  2013-01-02       Impact factor: 9.910

6.  Development and validation of a simplified Stroke-Thrombolytic Predictive Instrument.

Authors:  David M Kent; Robin Ruthazer; Carole Decker; Philip G Jones; Jeffrey L Saver; Erich Bluhmki; John A Spertus
Journal:  Neurology       Date:  2015-08-19       Impact factor: 9.910

7.  Race-Ethnic Disparities in 30-Day Readmission After Stroke Among Medicare Beneficiaries in the Florida Stroke Registry.

Authors:  Hannah Gardener; Erica C Leifheit; Judith H Lichtman; Kefeng Wang; Yun Wang; Carolina M Gutierrez; Maria A Ciliberti-Vargas; Chuanhui Dong; Mary Robichaux; Jose G Romano; Ralph L Sacco; Tatjana Rundek
Journal:  J Stroke Cerebrovasc Dis       Date:  2019-10-11       Impact factor: 2.136

8.  Variation in do-not-resuscitate orders for patients with ischemic stroke: implications for national hospital comparisons.

Authors:  Adam G Kelly; Darin B Zahuranec; Robert G Holloway; Lewis B Morgenstern; James F Burke
Journal:  Stroke       Date:  2014-02-12       Impact factor: 7.914

Review 9.  Electronic medical records and quality of cancer care.

Authors:  Thomas R Klumpp
Journal:  Curr Oncol Rep       Date:  2013-12       Impact factor: 5.075

10.  Do claims-based comorbidities adequately capture case mix for surgical site infections?

Authors:  Hilal Maradit Kremers; Laura W Lewallen; Brian D Lahr; Tad M Mabry; James M Steckelberg; Daniel J Berry; Arlen D Hanssen; Elie F Berbari; Douglas R Osmon
Journal:  Clin Orthop Relat Res       Date:  2014-12-06       Impact factor: 4.176

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