Evidence map›Paper›PMID 26656501›Full record

ArticlePloS one2015

New Morbidity and Comorbidity Scores based on the Structure of the ICD-10.

Jürgen Stausberg, Stefan Hagn

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

26 citing papers in PubMed.

  1. Article
  2. Cancer Patients with Sepsis: Prognostic Insights from a Population-Based Cohort Study in Norway.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
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  7. Availability in ECMO Reduces the Failure to Rescue in Patients With Pulmonary Embolism After Major Surgery: A Nationwide Analysis of 2.4 Million Cases.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024
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  18. Hospital volume following major surgery for gastric cancer determines in-hospital mortality rate and failure to rescue: a nation-wide study based on German billing data (2009-2017).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2021
    Article
  19. Article
  20. 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

2 authors.

Jürgen StausbergInstitute for Medical Informatics, Biometry and Epidemiology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany.
Stefan HagnZuckerbacherweg 6, 84036 Landshut, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measures of morbidity and comorbidity are frequently used for the control of confounding, particularly in health services research. Several proposals for those measures are defined with ICD-coded diagnoses available in hospital routine data. However, a measure that makes use of the ICD structure is missing. Objective of this work was to elaborate the power of the ICD structure for defining morbidity and comorbidity measures. Routine data from three German hospitals with inpatients discharged 2008 were used for model development; routine data from 36 German hospitals with inpatients admitted and discharged 2010 were used for model evaluation. Two different risk models were developed, one based on ICD-10 chapters, the other based on ICD-10 groups. The models were transformed into sum scores using whole-number weights. Models and scores were compared with the Charlson Index and the Elixhauser Comorbidities using the receiver operating characteristic. Dependent variable was hospital death. Logistic regression was used to derive the new models. Charlson Index and Elixhauser Comorbidities were mapped to the German ICD-10. According to the receiver operating characteristic, the quality of the measures based on the structure of the ICD-10 was superior compared with the Charlson Index and the Elixhauser Comorbidities. The best result was achieved with the measure based on ICD-10-groups with an area under curve of 0.910 (95% confidence interval = 0.907-0.913). The sum scores showed a comparable performance. The developed new measures may be used to control for confounding.

Indexed as

ComorbidityComputer SimulationGermanyHealth Services ResearchHospital MortalityHumansInternational Classification of DiseasesModels, StatisticalMorbidityRisk AdjustmentRisk AssessmentROC Curve

Identifiers

PMID26656501
PMCPMC4677989

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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.