Evidence map›Paper›PMID 31002316›Full record

ArticleJAMA network open2019

Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality-Associated Readmission.

Laura A Graham, Hillary J Mull, Todd H Wagner, Melanie S Morris, Amy K Rosen, Joshua S Richman, Jeffery Whittle, Edith Burns, Laurel A Copeland, Kamal M F Itani and 1 more

Abstract readComparative Study
In one paragraph

Article in JAMA network open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Laura A GrahamHealth Services Research and Development Unit, Birmingham VA Medical Center, Birmingham, Alabama.
Hillary J MullCenter for Healthcare Organization and Implementation Research, Boston VA Healthcare System, Boston, Massachusetts.
Todd H WagnerVeterans Affairs, Palo Alto, Veterans Affairs Medical Center, Palo Alto, California.
Melanie S MorrisHealth Services Research and Development Unit, Birmingham VA Medical Center, Birmingham, Alabama.
Amy K RosenCenter for Healthcare Organization and Implementation Research, Boston VA Healthcare System, Boston, Massachusetts.
Joshua S RichmanHealth Services Research and Development Unit, Birmingham VA Medical Center, Birmingham, Alabama.
Jeffery WhittleMilwaukee Veterans Affairs Medical Center, Milwaukee, Wisconsin.
Edith BurnsMilwaukee Veterans Affairs Medical Center, Milwaukee, Wisconsin.
Laurel A CopelandVeterans Affairs Central Western Massachusetts Healthcare System, Leeds.
Kamal M F ItaniCenter for Healthcare Organization and Implementation Research, Boston VA Healthcare System, Boston, Massachusetts.
Mary T HawnVeterans Affairs, Palo Alto, Veterans Affairs Medical Center, Palo Alto, California.

Funding

HSRD VA IK6 HX002839
6 · The paper itself

Abstract

Importance: The existing readmission quality metric does not meaningfully distinguish readmissions associated with surgical quality from those that are not associated with surgical quality and thus may not reflect the quality of surgical care. Objective: To compare a quality metric that classifies readmissions associated with surgical quality with the existing metric of any unplanned readmission in a surgical population. Design, Setting, and Participants: Cohort study using US nationwide administrative data collected on 4 high-volume surgical procedures performed at 103 Veterans Affairs hospitals from October 1, 2007, through September 30, 2014. Data analysis was conducted from October 1, 2017, to January 24, 2019. Main Outcomes and Measures: Hospital-level rates of unplanned readmission (existing metric) and surgical readmissions associated with surgical quality (new metric) in the 30 days following hospital discharge for an inpatient surgical procedure. Results: The study population included 109 258 patients who underwent surgery at 103 hospitals. Patients were majority male (94.1%) and white (78.2%) with a mean (SD) age of 64.0 (10.0) years at the time of surgery. After case-mix adjustment, 30-day surgical readmissions ranged from 4.6% (95% CI, 4.5%-4.8%) among knee arthroplasties to 11.1% (95% CI, 10.9%-11.3%) among colorectal resections. The new surgical readmission metric was significantly correlated with facility-level postdischarge complications for all procedures, with ρ coefficients ranging from 0.33 (95% CI, 0.13-0.51) for cholecystectomy to 0.52 (95% CI, 0.38-0.68) for colorectal resection. Correlations between postdischarge complications and the new surgical readmission metric were higher than correlations between complications and the existing readmission metric for all procedures examined (knee arthroplasty: 0.50 vs 0.48; hip replacement: 0.44 vs 0.18; colorectal resection: 0.52 vs 0.42; and cholecystectomy: 0.33 vs 0.10). When compared with using the existing readmission metric, using the new surgical readmission metric could change hip replacement-associated payment penalty determinations in 28.4% of hospitals and knee arthroplasty-associated penalties in 26.0% of hospitals. Conclusions and Relevance: In this study, surgical quality-associated readmissions were more correlated with postdischarge complications at a higher rate than were unplanned readmissions. Thus, a metric based on such readmissions may be a better measure of surgical care quality. This work provides an important step in the development of future value-based payments and promotes evidence-based quality metrics targeting the quality of surgical care.

Indexed as

AgedArthroplasty, Replacement, HipArthroplasty, Replacement, KneeBenchmarkingCohort StudiesColorectal SurgeryFemaleHospitalsHumansMaleMiddle AgedPatient DischargePatient ReadmissionPostoperative ComplicationsProspective StudiesQuality of Health Care

Identifiers

PMID31002316
PMCPMC6481441

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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.