Evidence map›Paper›PMID 29801732›Full record

ArticleSurgery2018

Commercial quality "awards" are not a strong indicator of quality surgical care.

Adrienne N Cobb, Taylor R Erickson, Anai N Kothari, Emanuel Eguia, Sarah A Brownlee, Weiwei Yao, Hyunyou Choi, Victoria Greenberg, Joy Mboya, Michael Voss and 3 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Data resource profile: State Inpatient Databases.International journal of epidemiology · 2019
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Adrienne N CobbLoyola University Medical Center, Department of Surgery, Maywood, IL; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, Maywood, IL.
Taylor R EricksonLoyola University Medical Center, Department of Surgery, Maywood, IL.
Anai N KothariLoyola University Medical Center, Department of Surgery, Maywood, IL; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, Maywood, IL.
Emanuel EguiaLoyola University Medical Center, Department of Surgery, Maywood, IL; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, Maywood, IL.
Sarah A BrownleeLoyola University Medical Center, Department of Surgery, Maywood, IL.
Weiwei YaoUniversity of South Florida, Department of Surgery, Tampa, FL.
Hyunyou ChoiUniversity of South Florida, Department of Surgery, Tampa, FL.
Victoria GreenbergUniversity of South Florida, Department of Surgery, Tampa, FL.
Joy MboyaUniversity of South Florida, Department of Surgery, Tampa, FL.
Michael VossUniversity of South Florida, Department of Surgery, Tampa, FL.
Daniela Stan RaicuUniversity of South Florida, Department of Surgery, Tampa, FL.
Raffaella Settimi-WoodsUniversity of South Florida, Department of Surgery, Tampa, FL.
Paul C KuoOne:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, Maywood, IL; DePaul University, College of Computing and Digital Media, Chicago, IL . Electronic address: paulkuo@health.usf.edu.

Funding

TRAINING IN TRAUMA AND BURN RESEARCHT32GM008750 · NIGMS · LOYOLA UNIVERSITY CHICAGO · PI CHOUDHRY, MASHKOOR A, GONZALEZ, RICHARD PAUL · 2000 to 2023
$2.9M
NIGMS NIH HHS T32 GM008750
6 · The paper itself

Abstract

backgroundThis study aimed to determine whether publicized hospital rankings can be used to predict surgical outcomes.

methodsPatients undergoing one of nine surgical procedures were identified, using the Healthcare Cost and Utilization Project State Inpatient Database for Florida and New York 2011-2013 and merged with hospital data from the American Hospital Association Annual Survey. Nine quality designations were analyzed as possible predictors of inpatient mortality and postoperative complications, using logistic regression, decision trees, and support vector machines.

resultsWe identified 229,657 patients within 177 hospitals. Decision trees were the highest performing machine learning algorithm for predicting inpatient mortality and postoperative complications (accuracy 0.83, P<.001). The top 3 variables associated with low surgical mortality (relative impact) were Hospital Compare (42), total procedure volume (16) and, Joint Commission (12). When analyzed separately for each individual procedure, hospital quality awards were not predictors of postoperative complications for 7 of the 9 studied procedures. However, when grouping together procedures with a volume-outcome relationship, hospital ranking becomes a significant predictor of postoperative complications.

conclusionHospital quality rankings are not a reliable indicator of quality for all surgical procedures. Hospital and provider quality must be evaluated with an emphasis on creating consistent, reliable, and accurate measures of quality that translate to improved patient outcomes.

Indexed as

Awards and PrizesHospitalsQuality of Health CareFloridaHospitalizationHospital MortalityHumansMachine LearningNew YorkPostoperative ComplicationsSensitivity and SpecificitySurgical Procedures, Operative

Identifiers

PMID29801732
PMCPMC6108898

What OpenQuestion holds

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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.