Dataset Information

Charlson comorbidity index as a predictor of short-term outcomes after pulmonary resection.

ABSTRACT:

Background

The objective of this retrospective study is to evaluate the impact of the CCI on short-term outcomes in pulmonary resection.

Methods

We retrospectively analyzed 1,309 patients who underwent pulmonary surgery consecutively in our hospital.

Results

All patients were divided into complication group and non-complication group. CCI (P=0.012), blood loss (P=0.015) and type of surgery (P<0.001) were an independent risk factors for complications in multivariate analysis. Assuming a threshold of 3 for defining poor outcomes for pulmonary resection, the sensitivity and specificity were 87.9% and 44.2%, respectively. The area under the curve for CCI was 0.711 (P<0.001). There were 918 (70.1%) patients in the CCI ≤3 group and 391 (29.9%) patients in the CCI ≤3 group. The rate of poor outcome was 3.3% in the CCI ≤3 group, and 9.2% in the CCI >3 group (P<0.001).

Conclusions

The main finding of the present study was that CCI >3 was associated with a poor short-term outcome. For patients with CCI >3, it was suggested that the experienced surgical team should perform pulmonary resection in the shortest time and preserving the lung function as much as possible.

SUBMITTER: Shao W

PROVIDER: S-EPMC7711382 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Charlson comorbidity index as a predictor of short-term outcomes after pulmonary resection.

Shao Weipeng W Zhang Zhenrong Z Zhang Jun J Feng Hongxiang H Liang Chaoyang C Liu Deruo D

Journal of thoracic disease 20201101 11

<h4>Background</h4>The objective of this retrospective study is to evaluate the impact of the CCI on short-term outcomes in pulmonary resection.<h4>Methods</h4>We retrospectively analyzed 1,309 patients who underwent pulmonary surgery consecutively in our hospital.<h4>Results</h4>All patients were divided into complication group and non-complication group. CCI (P=0.012), blood loss (P=0.015) and type of surgery (P<0.001) were an independent risk factors for complications in multivariate analysis ...[more]

PMID: 33282368

Similar Datasets

Project description:BackgroundThe original Charlson Comorbidity Index (CCI) encompassed 19 categories of medical conditions that were identifiable in medical records. Subsequent publications provided scoring algorithms based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The recent adoption of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in the United States created a need for a new scoring scheme. In addition, a review of existing claims-based scoring systems suggested 3 areas for improvement: the lack of explicit identification of secondary diabetes, the lack of differentiation between HIV infection and AIDS, and insufficient guidance on scoring hierarchy. In addition, addressing the third need raised the issue of disease severity in renal disease.ObjectivesThis initiative aimed to create an expanded and refined ICD-9 scoring system for CCI, addressing the classification of issues noted above, create a corresponding ICD-10 system, assess the comparability of ICD-9- and ICD-10-based scores, and validate the new scoring scheme.MethodsWe created ICD-9 and ICD-10 code tables for 19 CCI medical conditions. The new scoring scheme was labeled CDMF CCI and was tested using claims-based data for individuals aged ≥65 years who participated in a Humana Medicare Advantage plan during at least 1 of 3 consecutive 12-month periods. Two 12-month periods were during the ICD-9 era and the third 12-month period was during the ICD-10 era. Because many individuals were counted in more than one 12-month period, we described the study population as comprising 3 panels. We used regression models to analyze the association between the CCI score and same-year inpatient admissions and near-term (90-day) mortality. Additional testing was done by comparing the mean CCI score or disease prevalence in the 3 subpopulations of people with HIV/AIDS, renal disease, or diabetes. Finally, we calculated area under the receiver operating characteristics (AUC-ROC) curve values by applying the Deyo system and our ICD-9 and ICD-10 scoring systems.ResultsThe CDMF ICD-9 and ICD-10 scoring scheme yielded comparable scores across the 3 panels, and inpatient admissions and mortality rates consistently increased in each panel as the CCI score increased. Comparisons of the performance of the Deyo system and our proposed CDMF ICD-9 system in the 3 key subpopulations showed that the CDMF ICD-9 system produced a lower CCI score in the presence of HIV infection without AIDS, achieved similar detection ability of diabetes, and allowed good differentiation between mild-to-moderate and severe renal disease. AUC-ROC values were similar between the CDMF ICD-9 coding system and the Deyo system.ConclusionOur results support the implementation of the CDMF CCI scoring instrument to triage individual patients for disease- and care-management programs. In addition, the CDMF scheme allows for a more precise understanding of chronic disease at a population level, thus allowing health systems and plans to design services and benefits to meet multifactorial clinical needs. Preliminary validation sets the stage for further testing using long-term follow-up data and for the adaptation of this coding scheme to a chart review instrument.

Dataset Information

Charlson comorbidity index as a predictor of short-term outcomes after pulmonary resection.

Background

Methods

Results

Conclusions

Publications

Charlson comorbidity index as a predictor of short-term outcomes after pulmonary resection.

Similar Datasets

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