Evidence map›Paper›PMID 34974828›Full record

ArticleBMC health services research2022

Exploring relationships between in-hospital mortality and hospital case volume using random forest: results of a cohort study based on a nationwide sample of German hospitals, 2016-2018.

Martin Roessler, Felix Walther, Maria Eberlein-Gonska, Peter C Scriba, Ralf Kuhlen, Jochen Schmitt, Olaf Schoffer

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
8.9field-weighted citation impact, top 1% of its field
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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
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

7 authors at 3 institutions in 1 country.

Martin RoesslerCenter for Evidence-based Healthcare, University Hospital Carl Gustav Carus and Medical Faculty at the Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany. martin.roessler@uniklinikum-dresden.de.
Felix WaltherCenter for Evidence-based Healthcare, University Hospital Carl Gustav Carus and Medical Faculty at the Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
Maria Eberlein-GonskaQuality and Medical Risk Management, University Hospital Carl Gustav Carus Dresden, Dresden, Germany.
Peter C ScribaIQM Initiative Qualitätsmedizin e.V., Berlin, Germany.
Ralf KuhlenIQM Initiative Qualitätsmedizin e.V., Berlin, Germany.
Jochen SchmittCenter for Evidence-based Healthcare, University Hospital Carl Gustav Carus and Medical Faculty at the Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
Olaf SchofferCenter for Evidence-based Healthcare, University Hospital Carl Gustav Carus and Medical Faculty at the Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
University Hospital Carl Gustav Carus · DEInstitut für Qualität im Management (Germany) · DETU Dresden · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRelationships between in-hospital mortality and case volume were investigated for various patient groups in many empirical studies with mixed results. Typically, those studies relied on (semi-)parametric statistical models like logistic regression. Those models impose strong assumptions on the functional form of the relationship between outcome and case volume. The aim of this study was to determine associations between in-hospital mortality and hospital case volume using random forest as a flexible, nonparametric machine learning method.

methodsWe analyzed a sample of 753,895 hospital cases with stroke, myocardial infarction, ventilation > 24 h, COPD, pneumonia, and colorectal cancer undergoing colorectal resection treated in 233 German hospitals over the period 2016-2018. We derived partial dependence functions from random forest estimates capturing the relationship between the patient-specific probability of in-hospital death and hospital case volume for each of the six considered patient groups.

resultsAcross all patient groups, the smallest hospital volumes were consistently related to the highest predicted probabilities of in-hospital death. We found strong relationships between in-hospital mortality and hospital case volume for hospitals treating a (very) small number of cases. Slightly higher case volumes were associated with substantially lower mortality. The estimated relationships between in-hospital mortality and case volume were nonlinear and nonmonotonic.

conclusionOur analysis revealed strong relationships between in-hospital mortality and hospital case volume in hospitals treating a small number of cases. The nonlinearity and nonmonotonicity of the estimated relationships indicate that studies applying conventional statistical approaches like logistic regression should consider these relationships adequately.

Indexed as

HospitalsModels, StatisticalCohort StudiesHospital MortalityHumansLogistic ModelsCohort studyHospital mortalityNonparametric modellingRandom ForestRisk factorsVolume-outcome relationship

Identifiers

PMID34974828
PMCPMC8722027
OpenAlexW4206185598

What OpenQuestion holds

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LicenceCC BY
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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.