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The age-adjusted Charlson comorbidity index (ACCI) is a useful measure of comorbidity to standardize the evaluation of elderly patients and has been reported to predict mortality in various cancers. To our best knowledge, no studies have examined the relationship between the ACCI and survival of elderly patients with cancer. Therefore, the primary objective of this study was to investigate the relationship between the ACCI and survival of elderly patients with cancer. A total of 64 elderly patients (>80 years) with cancer between 2011 and 2021 were enrolled in this study. According to the ACCI, the age-adjusted comorbidity index was calculated by weighting individual comorbidities; patients with ACCI<11 were considered the low-ACCI group, whereas those with ACCI≥11 were considered the high-ACCI group. The correlations between the ACCI score and survival outcomes were statistically analyzed. There was a significant difference in overall survival (OS) and progression-free survival (PFS) between the high-ACCI group and the low-ACCI group (P<0.001). The median OS time of the high-ACCI group and the low-ACCI group were 13.9 (10.5-22.0) months and 51.9 (34.1-84.0) months, respectively. The 2-, 3-, and 5-year survival rates of the high-ACCI group were 28.1%, 18.8%, and 4.2%, respectively, whereas the 2-, 3-, and 5-year survival rates of the low-ACCI group were 77.3%, 66.4%, and 39.1%, respectively. Multivariate analysis showed that ACCI was independently associated with OS (HR=1.402, 95% CI: 1.226-1.604, P < 0.05) and PFS (HR=1.353, 95% CI: 1.085-1.688, P = 0.0073). The ACCI score is a significant independent predictor of prognosis in elderly patients with cancer.

Purpose In patients with early gastric cancer (EGC) who undergo endoscopic submucosal dissection (ESD) with endoscopic curability (eCura) C-2, the risk of non-gastric cancer mortality should be evaluated before receiving further gastrectomy. Charlson comorbidity index (CCI) is often used to estimate prognosis based on patient’s background before treatment. We identified the long-term risk of mortality from other causes associated with comorbidities in CCI and applied it to the creation of EGC specific CCI (GCCI). Methods A total of 1810 patients with EGC from 3 centers were included from January 2015 to February 2023. We used Cox proportional risk models to determine the risk of non-gastric cancer mortality related to comorbidities and used these hazard ratios to reweight the Charlson index to establish GCCI. Results The Cox model suggested that moderate to severe liver disease, metastatic solid tumors, severe to very severe chronic obstructive pulmonary disease (COPD), and leukemia had the highest risk of non-gastric cancer mortality [hazard ratio (HR) > 5)]. Survival analysis showed that the 5-year non-gastric cancer mortality rates in low-risk group (GCCI score 0–1), medium-risk group (GCCI score 2–4), and high-risk group (GCCI score 5–13) were 3%, 10%, and 52%, respectively. Conclusions GCCI could identify patients with EGC who have higher non-gastric cancer mortality. The GCCI could be used to help patients with EGC make medical decisions.

Objective To compare the predictive values of Charlson comorbidity index (CCI), modified Charlson comorbidity index kidney transplant (mCCI-KT) and recipient risk score (RRS) indices in prediction of patient and graft survival in kidney transplant patients. Methods In this retrospective study, all patients who underwent a live-donor KT from 2006 to 2010, were included. Demographic data, comorbidities and survival time after KT were extracted and the association between above indices with patient and graft survival were compared. Results In ROC curve analysis of 715 included patients, all three indicators were weak in predicting graft rejection with the area under curve (AUC) less than 0.6. The best models for predicting the overall survival were mCCI-KT and CCI with AUC of 0.827 and 0.780, respectively. Sensitivity and specificity of mCCI-KT at cut point of 1 were 87.2 and 75.6. Sensitivity and specificity of CCI at cut point of 3 were 84.6 and 68.3 and for RRS at cut point of 3 were 51.3 and 81.2, respectively. Conclusion The mCCI-KT index followed by the CCI index provided the best model in predicting the 10-year patient survival; however, they were poor in predicting graft survival and this model can be used for better stratifying transplant candidates prior to surgery.

BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.

IntroductionPrevalence of aortic stenosis and comorbidity burden correlates with advancing age. The Charlson Comorbidity Index (CCI) is a widely validated tool that predicts outcomes in a range of conditions and settings. We used CCI scores to assess the impact on 30-days, 6-month and 1-year outcomes following TAVI intervention. Our secondary aim was to review current practice of frailty screening in this group.MethodsWe analysed 38 eligible patients referred for CT TAVI at our institution between August 2021 to December 2022 and calculated their CCI score to study its impact on symptoms, procedural complications and mortality at 30-days, 6-months and 1-year post TAVI. Evidence of frailty screening was determined using retrospective case note review.ResultsThirty-eight (38) patients were referred for consideration of TAVI with mean age 77.9 and mean CCI score 4.5. Twenty-seven patients (71%) underwent TAVI with a mean age of 77.5 years and a mean CCI score of 5.2. The commonest comorbidities were previous myocardial infarction (47%), congestive heart failure (21%) and COPD (34%).At 30-days, 41% of patients (mean CCI 4.3) had objective improvement in exercise tolerance, 33% (mean CCI 5) reported subjective improvement and 7% (mean CCI 7) experienced no change in symptoms. Complications occurred in 2 patients with mean CCI 4.5. The benefit persisted in 15 out of 18 patients at 6 months. At 1-year, 3 out of 6 reported sustained benefit (mean CCI = 4.6) and 3 reported worsening symptoms (mean CCI = 5.6) due to progression of mitral valve disease (1), new diagnosis of possible cancer (1) and worsening ankle swelling/poor mobility (1). Frailty screening was not routinely done.ConclusionThe CCI tool is reliable in predicting outcomes for patients undergoing TAVI. It can be quickly performed using a web-based calculator in a cardiology out-patient clinic at time of assessment. We observed good 30-day outcomes with CCI scores 4–5 but this benefit seemed to lessen at 6-months and 1-year when CCI score >5.6. CCI score did not predict complications. Our results reflect the European Society of Cardiology (ESC) guidance with CCI score >5 conferring poorer prognosis. Futility of TAVI was predicted by CCI score >7 in our group. We recommend using the CCI score in the cardiology clinic to measure comorbid burden that may impact on recovery. Frailty status plays an important role in TAVI considerations. Incorporating frailty screening in adults suspected of living with frailty can be achieved in clinic using the widely validated Rockwood Clinical Frailty scale (CFS). This web-based tool is also available as a phone app. Mild-moderate frailty denoted by CFS 5/6 can be used to triage patients who may benefit from more comprehensive elderly care assessment. Intervention in severe frailty CFS >7 would likely confer more risk than benefit. These rapid web-based tools can help identify patients with potential barriers to recovery and rehab following TAVI.Abstract 30 Figure 1Number receiving TAVI by CCI scoreAbstract 30 Figure 2Post TAVI outcomes by mean CCI scoreConflict of InterestNone

The Charlson Comorbidity Index (CCI) is a highly cited and well established tool for measuring comorbidity in clinical research, but there are problems with its use in practice. Like most comorbidity summary measures, the CCI was developed to adjust for prognostic comorbidities in statistical models, particularly those exploring associations between a risk of death or survival time and other patient-related and disease-related factors. Despite this, the CCI is often used in cancer research to measure all comorbidity, or as a multimorbidity measure, and CCI scores are often used to assess the prognostic importance of multiple health conditions. In the latter case, it is not at all surprising that researchers report a significant association between CCI scores and a risk of death or survival times because CCI scores provide a summary of the presence or absence of a set of prognostic comorbidities. Advances in multimorbidity research require specific attention to the methods used to develop relevant indices. Published literature on the association between the comorbidity and risk of death or survival time should be interpreted with caution, especially if the CCI was used to provide a measure of comorbidities.

Background Several contemporary risk stratification tools are now being used since the development of the Charlson Comorbidity Index (CCI) in 1987. The purpose of this systematic review and meta-analysis was to compare the utility of commonly used co-morbidity indices in predicting surgical outcomes. Methods A comprehensive review was performed to identify studies reporting an association between a pre-operative co-morbidity measurement and an outcome (30-day/in-hospital morbidity/mortality, 90-day morbidity/mortality, and severe complications). Meta-analysis was performed on the pooled data. Results A total of 111 included studies were included with a total cohort size 25,011,834 patients. The studies reporting the 5-item Modified Frailty Index (mFI-5) demonstrated a statistical association with an increase in the odds of in-hospital/30-day mortality (OR:1.97,95%CI: 1.55–2.49, p  < 0.01). The pooled CCI results demonstrated an increase in the odds for in-hospital/30-day mortality (OR:1.44,95%CI: 1.27–1.64, p  < 0.01). Pooled results for co-morbidity indices utilizing a scale-based continuous predictor were significantly associated with an increase in the odds of in-hospital/30-day morbidity (OR:1.32, 95% CI: 1.20–1.46, p  < 0.01). On pooled analysis, the categorical results showed a higher odd for in-hospital/30-day morbidity (OR:1.74,95% CI: 1.50–2.02, p  < 0.01). The mFI-5 was significantly associated with severe complications (Clavien-Dindo ≥ III) (OR:3.31,95% CI:1.13–9.67, p  < 0.04). Pooled results for CCI showed a positive trend toward severe complications but were not significant. Conclusion The contemporary frailty-based index, mFI-5, outperformed the CCI in predicting short-term mortality and severe complications post-surgically. Risk stratification instruments that include a measure of frailty may be more predictive of surgical outcomes compared to traditional indices like the CCI.

Introduction: Functional status is one of the surrogates of advanced age, an established risk factor for Clostridioides difficile infection (CDI). We aimed to investigate the usefulness of functional status in the clinical management of CDI. Methods: We enrolled all hospitalized adult patients receiving antibiotics from a retrospective hospital-based cohort in Japan between 2016 and 2020. Using the Barthel index (BI), which is an objective scale of functional status, we investigated the association of BI with developing CDI and its impact on inhospital mortality in patients with CDI. Results: We enrolled 17,131 patients with 100 cases of CDI. Multivariable analysis revealed that lower BI (≤25) was an independent risk factor for developing CDI (adjusted odds ratio, 4.11; 95% confidence interval, 2.62–6.46). Furthermore, a combination of BI and Charlson comorbidity index (CCI) showed an adjusted odds ratio of 36.40 (95% confidence interval, 17.30–76.60) in the highest risk group. A high-risk group according to the combination of BI and CCI was estimated to have significantly higher inhospital mortality in patients with CDI using the Kaplan-Meier method (p = 0.017). A combination of lower BI and higher CCI was an independent predictor of inhospital mortality even in the multivariable Cox regression model (adjusted hazard ratio, 3.00; 95% confidence interval, 1.01–8.88). Conclusions: Assessment of functional status, especially combined with comorbidities, was significantly associated with developing CDI and may also be useful in predicting inhospital mortality.

Previous studies proposed comorbidity-based prediction tools to facilitate patient-level assessment of mortality risk, which are essential for confounder adjustment in epidemiologic studies. We compared established comorbidity indices using real-world administrative data of a broad surgical population. Adult patients undergoing surgical or interventional procedures between January 2005 and June 2020 at a tertiary academic medical center in Massachusetts, USA, were included. The Elixhauser Comorbidity Index (van Walraven modification), Combined Comorbidity Score, and Charlson Comorbidity Index were compared regarding the prediction of 30-day mortality. Age and sex were included in all models. Discriminative ability was quantified by the area under the receiver operating characteristic curve (AUROC), and calibration was assessed using the Brier score and reliability plots. A total of 514,282 patients were included, of which 5849 (1.1%) died within 30 days. A model including age and sex alone had an AUROC of 0.73 (95% CI 0.72-0.74). The Elixhauser Comorbidity Index–based model showed the best discriminative ability with an AUROC of 0.86 (95% CI 0.86-0.87) compared to models, including the Combined Comorbidity Score (AUROC, 0.85 [95% CI 0.84-0.85]) and the Charlson Comorbidity Index (AUROC, 0.82 [95% CI 0.81-0.83], P < .001, respectively). The Brier score was 0.011 for all scores. Overall, score performances were similar or improved after the implementation of the 10th Revision International Classification of Diseases (Clinical Modification) coding system. The primary findings were confirmed for in-hospital, 7-day, 90-day, 180-day, and 1-year mortality and when including score comorbidities as separate indicator variables (P < .001, respectively). Patient and procedural characteristics were predictive of mortality (AUROC, 0.91 [95% CI 0.91-0.91]), with confirmatory findings and slightly improved performances when adding comorbidity scores (AUROC, 0.93 [95% CI 0.93-0.93] for the Elixhauser Comorbidity Index; AUROC, 0.93 [95% CI 0.93-0.93] for the Combined Comorbidity Score; AUROC, 0.92 [95% CI 0.92-0.93] for the Charlson Comorbidity Index, P < .001, respectively). All 3 comorbidity indices predicted mortality with excellent discrimination; however, they showed only slightly improved performance when incorporated into a model including patient and procedural characteristics. When surgical data are unavailable and in surgical setting–specific subgroups, the Elixhauser Comorbidity Index consistently performed best. [Display omitted] •Comorbidity-based prediction tools enable patient-level assessment of mortality risk.•We compared established prediction tools using electronic health records.•Among 514,282 surgical patients, the Elixhauser Comorbidity Index performed best.•The Elixhauser Comorbidity Index may be used preferably for mortality prediction in broad surgical populations.