Evidence map›Paper›PMID 37923993›Full record

ArticleBMC medical research methodology2023

Comparison of seven comorbidity scores on four-month survival of lung cancer patients.

Hélène Pluchart, Sébastien Bailly, Sébastien Chanoine, Denis Moro-Sibilot, Pierrick Bedouch, Anne-Claire Toffart

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

6 authors at 2 institutions in 1 country.

Hélène PluchartPôle Pharmacie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France. helene.pluchart@sfr.fr.ORCID 0000-0003-2438-6481
Sébastien BaillyUniv. Grenoble Alpes, Inserm, Centre Hospitalier Universitaire Grenoble Alpes, HP2, Grenoble, 38000, France.
Sébastien ChanoinePôle Pharmacie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Denis Moro-SibilotUniversité Grenoble Alpes, Grenoble, France.
Pierrick BedouchPôle Pharmacie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Anne-Claire ToffartUniversité Grenoble Alpes, Grenoble, France.
Centre National de la Recherche Scientifique · FRInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best reflects their impact on survival. Here, we compared seven usable comorbidity scores (Charlson Comorbidity Index, Age adjusted Charlson Comorbidity Index, Charlson Comorbidity Index adapted to lung cancer, National Cancer Institute combined index, National Cancer Institute combined index adapted to lung cancer, Elixhauser score, and Elixhauser adapted to lung cancer) with coded administrative data according to the tenth revision of the International Statistical Classification of Diseases and Related Health Problems to select the best prognostic index for predicting four-month survival. MATERIALS AND

methodsThis cohort included every patient with a diagnosis of lung cancer hospitalized for the first time in the thoracic oncology unit of our institution between 2011 and 2015. The seven scores were calculated and used in a Cox regression method to model their association with four-month survival. Then, parameters to compare the relative goodness-of-fit among different models (Akaike Information Criteria, Bayesian Information Criteria), and discrimination parameters (the C-statistic and Harrell's c-statistic) were calculated. A sensitivity analysis of these parameters was finally performed using a bootstrap method based on 1,000 samples.

resultsIn total, 633 patients were included. Male sex, histological type, metastatic status, CCI, CCI-lung, Elixhauser score, and Elixhauser-lung were associated with poorer four-month survival. The Elixhauser score had the lowest AIC and BIC and the highest c-statistic and Harrell's c-statistic. These results were confirmed in the sensitivity analysis, in which these discrimination parameters for the Elixhauser score were significantly different from the other scores.

conclusionsBased on this cohort, the Elixhauser score is the best prognostic comorbidity score for predicting four-month survival for hospitalized lung cancer patients.

Indexed as

Lung NeoplasmsBayes TheoremComorbidityHospital MortalityHumansMalePatientsPrognosisComorbidityLung cancerSurvival

Identifiers

PMID37923993
PMCPMC10623755
OpenAlexW4388293798

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Registered trials

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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.