Evidence map›Paper›PMID 26595225›Full record

ArticleMedical care2016

Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

Hemalkumar B Mehta, Francesca Dimou, Deepak Adhikari, Nina P Tamirisa, Eric Sieloff, Taylor P Williams, Yong-Fang Kuo, Taylor S Riall

Abstract read
In one paragraph

Article in Medical care, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hemalkumar B Mehta*Department of Surgery, The University of Texas Medical Branch, Galveston, TX †Department of Surgery, The University of South Florida, Tampa, FL ‡Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX §Department of Surgery, University of Arizona, Tucson, AZ.
Francesca Dimou
Deepak Adhikari
Nina P Tamirisa
Eric Sieloff
Taylor P Williams
Yong-Fang Kuo
Taylor S Riall

Funding

UTMB Clinical and Translational Science AwardUL1TR000071 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI BRASIER, ALLAN R. · 2012 to 2014
$14.4M
Patient Centered Outcomes Research in the ElderlyR24HS022134 · AHRQ · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2013 to 2017
$4.8M
SURGICAL TRAINING IN GASTROINTESTINAL ENDOCRINOLOGYT32DK007639 · NIDDK · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI RADHAKRISHNAN, RAVI S. · 1992 to 2022
$3.6M
AHRQ HHS 1R24HS022134AHRQ HHS R24 HS022134NCATS NIH HHS UL1 TR000071NCATS NIH HHS UL1TR000071NIDDK NIH HHS 5T32DK007639NIDDK NIH HHS T32 DK007639
6 · The paper itself

Abstract

introductionThe optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established.

objectiveCompare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes.

methodsWe used 100% Texas Medicare data (2006-2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement (N=39,616). The ability of diagnosis-based [Charlson comorbidity score, Elixhauser comorbidity score, Combined Comorbidity Score, Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC)] versus prescription-based Chronic disease score in predicting 30-day mortality, 1-year mortality, 30-day readmission, complications, and FTR were compared using c-statistics (c) and integrated discrimination improvement (IDI).

resultsThe overall 30-day mortality was 5.8%, 1-year mortality was 17.7%, 30-day readmission was 14.1%, complication rate was 39.7%, and FTR was 14.5%. CMS-HCC performed the best in predicting surgical outcomes (30-d mortality, c=0.797, IDI=4.59%; 1-y mortality, c=0.798, IDI=9.60%; 30-d readmission, c=0.630, IDI=1.27%; complications, c=0.766, IDI=9.37%; FTR, c=0.811, IDI=5.24%) followed by Elixhauser comorbidity index/disease categories (30-d mortality, c=0.750, IDI=2.37%; 1-y mortality, c=0.755, IDI=5.82%; 30-d readmission, c=0.629, IDI=1.43%; complications, c=0.730, IDI=3.99%; FTR, c=0.749, IDI=2.17%). Addition of prescription-based scores to diagnosis-based scores did not improve performance.

conclusionsThe CMS-HCC had superior performance in predicting surgical outcomes. Prescription-based scores, alone or in addition to diagnosis-based scores, were not better than any diagnosis-based scoring system.

Indexed as

ComorbidityAge FactorsFailure to Rescue, Health CareFemaleHumansInsurance Claim ReviewMaleMedicareMedicare Part DPatient ReadmissionPostoperative ComplicationsPrognosisRisk AdjustmentSex FactorsSurgical Procedures, OperativeUnited States

Identifiers

PMID26595225
PMCPMC4713334

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.