Evidence map›Paper›PMID 25469987›Full record

Trial reportPloS one2014

The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs.

Mary Charlson, Martin T Wells, Ralph Ullman, Fionnuala King, Celia Shmukler

Registry-linked trialAbstract readClinical Trial
In one paragraph

Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01761253 (Assessing Costs & Cost-variability Among Enrollees of Health Insurance Programs Utilizing Charlson's Comorbidity Index and Administrative Databases), which is not on this map. Cited by 117 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 1 pooled it
–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.

NCT01761253 completednot on this map

Assessing Costs & Cost-variability Among Enrollees of Health Insurance Programs Utilizing Charlson's Comorbidity Index and Administrative Databases

TypeobservationalSponsorWeill Medical College of Cornell UniversityRan2006 to 2010Enrolled241,000ConditionsMultiple Chronic Illnesses
3 · Its place in the literature

Who cites it

117 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  7. Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
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  8. Fever of unknown origin: cost of illness study.Open medicine (Warsaw, Poland) · 2026
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57 more citing papers are in PubMed but not listed here.

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

5 authors.

Mary CharlsonCenter for Integrative Medicine, Weill Cornell Medical College, New York, NY, United States of America.
Martin T WellsDepartment of Statistical Science, Cornell University, Ithaca, NY, United States of America.
Ralph Ullman1199SEIU Benefit and Pension Funds, New York, NY, United States of America.
Fionnuala King1199SEIU Benefit and Pension Funds, New York, NY, United States of America.
Celia Shmukler1199SEIU Benefit and Pension Funds, New York, NY, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReducing health care costs requires the ability to identify patients most likely to incur high costs. Our objective was to evaluate the ability of the Charlson comorbidity score to predict the individuals who would incur high costs in the subsequent year and to contrast its predictive ability with other commonly used predictors.

methodsWe contrasted the prior year Charlson comorbidity index, costs, Diagnostic Cost Group (DCG) and hospitalization as predictors of subsequent year costs from claims data of fund that provides comprehensive health benefits to a large union of health care workers. Total costs in the subsequent year was the principal outcome.

resultsOf the 181,764 predominantly Black and Latino beneficiaries, 70% were adults (mean age 45.7 years; 62% women). As the comorbidity index increased, total yearly costs increased significantly (P<.001). At lower comorbidity, the costs were similar across different chronic diseases. Using regression to predict total costs, top 5th and 10th percentile of costs, the comorbidity index, prior costs and DCG achieved almost identical explained variance in both adults and children. CONCLUSIONS AND RELEVANCE: The comorbidity index predicted health costs in the subsequent year, performing as well as prior cost and DCG in identifying those in the top 5% or 10%. The comorbidity index can be used prospectively to identify patients who are likely to incur high costs.

trial registrationClinicalTrials.gov NCT01761253.

Indexed as

AdolescentChildChild, PreschoolChronic DiseaseComorbidityFemaleHealth Care CostsHospitalizationHumansInfantMaleMiddle AgedModels, EconomicProspective StudiesRegression AnalysisYoung Adult

Identifiers

PMID25469987
PMCPMC4254512

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Registered trials

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.