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The Charlson Comorbidity Index as an Independent Prognostic Factor in Older Colorectal Cancer Patients.

ABSTRACT: High-age patients have higher rates of comorbidity that are associated with a poor prognosis. It is important to correctly evaluate their preoperative status to avoid mortality. The aim of this study was to clarify whether the Charlson comorbidity index (CCI) was useful for predicting postoperative outcomes. This retrospective study collected data from 250 consecutive patients over 75 years of age. The CCI takes into account 19 comorbid conditions. Inflammation-based scores, including the Glasgow prognostic score (GPS) and the platelet to lymphocyte ratio (PLR), are other preoperative scoring systems. The relationships among these scores and postoperative outcomes were evaluated. The patients were classified according to their vital status (dead, n = 30 or alive, n = 220). Comorbidities, the presence of double cancer, and lymph node metastases were significantly different between the groups (p < 0.01, p = 0.01, and p < 0.01). In regard to the scoring systems, the CCI, GPS, and PLR were significantly different (p = 0.02, p = 0.03, and p = 0.05). Multivariate analysis identified CCI ? 2 (hazard ratio (HR) = 5.24, 95 % confidence interval (CI) = 1.30-12.1, p = 0.01) as a significant determinant of postoperative outcome (p < 0.01). The overall survival tended to be lower in patients with high CCI scores group (p = 0.03). The CCI was useful to predict postoperative outcomes in high-age colorectal cancer patients.

SUBMITTER: Tominaga T

PROVIDER: S-EPMC5866797 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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The Charlson Comorbidity Index as an Independent Prognostic Factor in Older Colorectal Cancer Patients.

Tominaga Tetsuro T Nonaka Takashi T Takeshita Hiroaki H Kunizaki Masaki M Sumida Yorihisa Y Hidaka Shigekazu S Sawai Terumitsu T Nagayasu Takeshi T

The Indian journal of surgery 20160824 1

High-age patients have higher rates of comorbidity that are associated with a poor prognosis. It is important to correctly evaluate their preoperative status to avoid mortality. The aim of this study was to clarify whether the Charlson comorbidity index (CCI) was useful for predicting postoperative outcomes. This retrospective study collected data from 250 consecutive patients over 75 years of age. The CCI takes into account 19 comorbid conditions. Inflammation-based scores, including the Glasgo ...[more]

PMID: 29581686

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Project description:BackgroundIdentifying a high-risk group of older people before surgical procedures is very important. The study aimed to explore the association between the age-adjusted Charlson comorbidity index (ACCI) and all-cause mortality and readmission among older Chinese surgical patients (age ≥65 years).MethodsA large-scale cohort study was performed in 25 general public hospitals from six different geographic regions of China. Trained registered nurses gathered data on clinical and sociodemographic characteristics. All-cause mortality was recorded when patients died during hospitalization or during the 90-day follow-up period. Readmission was also tracked from hospital discharge to the 90-day follow-up. The ACCI, in assessing comorbidities, was categorized into two groups (≥5 vs. <5). A multiple regression model was used to examine the association between the ACCI and all-cause mortality and readmission.ResultsThere were 3,911 older surgical patients (mean = 72.46, SD = 6.22) in our study, with 1,934 (49.45%) males. The average ACCI score was 4.77 (SD = 1.99), and all-cause mortality was 2.51% (high ACCI = 5.06% vs. low ACCI = 0.66%, P < 0.001). After controlling for all potential confounders, the ACCI score was an independent risk factor for 90-day hospital readmission (OR = 1.18, 95% CI: 1.14, 1.23) and 90-day all-cause mortality (OR = 1.26, 95% CI: 1.16-1.36). Furthermore, older surgical patients with a high ACCI (≥5) had an increased risk of all-cause mortality (OR = 6.13, 95% CI: 3.17, 11.85) and readmission (OR = 2.13, 95% CI: 1.78, 2.56) compared to those with a low ACCI (<5). The discrimination performance of the ACCI was moderate for mortality (AUC:0.758, 95% CI: 0.715-0.80; specificity = 0.591, sensitivity = 0.846) but poor for readmission (AUC: 0.627, 95% CI: 0.605-0.648; specificity = 0.620; sensitivity = 0.590).ConclusionsThe ACCI is an independent risk factor for all-cause mortality and hospital readmission among older Chinese surgical patients and could be a potential risk assessment tool to stratify high-risk older patients for surgical procedures.

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

The Charlson Comorbidity Index as an Independent Prognostic Factor in Older Colorectal Cancer Patients.

Publications

The Charlson Comorbidity Index as an Independent Prognostic Factor in Older Colorectal Cancer Patients.

Similar Datasets

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