Effect of comorbidity assessed by the Charlson Comorbidity Index on the length of stay, costs, and mortality among colorectal cancer patients undergoing colorectal surgery

Health Care Use Among Cancer Patients With Diabetes, National Health and Nutrition Examination Survey, 2017-2020

Introduction

Diabetes is a common comorbidity among people with cancer. The objective of our study was to examine patterns of health care use among patients with cancer and either type 2 diabetes or prediabetes.

Methods

We used data from the National Health and Nutrition Examination Survey (NHANES) for 2017-2020. The study population included US adults aged 18 years or older who were diagnosed with any cancer and type 2 diabetes or prediabetes (established by self-report and/or hemoglobin A1c measurement). We used Poisson and multivariate logistic regression models to determine the effect of comorbidity on health care use, defined as health care visits and overnight stays in a hospital.

Results

Of 905 cancer patients representing 27,180,715 people in the US, 24.4% had a type 2 diabetes diagnosis, and 25.8% had a prediabetes diagnosis. Patients with cancer and prediabetes had a significantly higher rate of health care visits (incidence rate ratio = 1.11; 95% CI, 1.01-1.22; P = .03) than patients with cancer only. We found no significant association between having cancer and type 2 diabetes and the number of health care visits or overnight hospital stays compared with patients with cancer only.

Conclusion

More emphasis should be placed on optimal care coordination among people with cancer and other conditions, such as diabetes and prediabetes, to reduce the impact of comorbidity on health care use. Interventions integrated with technology to provide timely access to education on preventing or managing diabetes and prediabetes among cancer patients are warranted.

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study

Aims

As people's standard of living improves, the incidence of colorectal cancer is increasing, and colorectal cancer hospitalization costs are relatively high. Therefore, predicting the cost of hospitalization for colorectal cancer patients can provide guidance for controlling healthcare costs and for the development of related policies.

Methods

This study used the first page of medical record data on colorectal cancer inpatient cases of a tertiary first-class hospital in Shenzhen from 2018 to 2022. The impacting factors of hospitalization costs for colorectal cancer were analyzed. Random forest and support vector regression models were used to establish predictive models of the cost of hospitalization for colorectal cancer patients and to compare and evaluate.

Results

In colorectal cancer inpatients, major procedures, length of stay, level of procedure, Charlson comorbidity index, age, and medical payment method were the important influencing factors. In terms of the test set, the R2 of the Random forest model was 0.833, the R2 of the Support vector regression model was 0.824; the root mean square error (RMSE) of the Random forest model was 0.029, and the RMSE of the Support vector regression model was 0.032. In the Random Forest model, the weight of the major procedure was the highest (0.286).

Conclusion

Major procedures and length of stay have the greatest impacts on hospital costs for colorectal cancer patients. The random forest model is a better method to predict the hospitalization costs for colorectal cancer patients than the support vector regression.

The predictive value of comorbidities on postoperative complication rates and overall survival in left-sided oncological colorectal resections: a multicentre cohort study

INTRODUCTION

Surgical- and nonsurgical complications significantly worsen postoperative outcomes, and identification of patients at risk is crucial to improve care. This study investigated whether comorbidities, graded by the Charlson Comorbidity Index (CCI), impact complication rates and impair long-term outcome in a cohort of left-sided colorectal resections.

METHODS

Retrospective analysis of patients undergoing oncological left-sided colorectal resections due to colorectal cancer between 01/2015 and 12/2020 in two referral centers in Austria using electronic medical records and national statistical bureau survival data. Patients with recurrent disease, peritoneal carcinomatosis, and emergency surgeries were excluded. Comorbidities were assessed using the CCI, and complication severity was defined by the Clavien-Dindo classification (CDC). Logistic regression analysis was performed to identify factors influencing the risk for postoperative complications, and overall survival was assessed using data from the national statistics bureau.

RESULTS

A total of 471 patients were analyzed. Multinominal logistic regression analysis identified a CCI greater than or equal to 6 ( P =0.049; OR 1.59, 95% CI: 1.10-2.54) and male sex ( P =0.022; OR 1.47, 95% CI: 1.21-2.98) as independent risk factors for major complications. While patients with a high CCI had the worst postoperative survival rates, perioperative complications only impacted on overall survival in patients with low CCIs, but not in patients with high CCIs.

CONCLUSION

Although a high CCI is a risk factor for major postoperative complications, the presence of comorbidities should not result in withholding surgery.

Postoperative clinical outcomes of patients with thymic epithelial tumors after over-3-year follow-up at a single-center

BACKGROUND

To evaluate postoperative clinical outcomes and analyze influencing factors for patients with thymic epithelial tumors over 3 years after operation.

METHODS

Patients with thymic epithelial tumors (TETs) who underwent surgical treatment in the Department of Thoracic Surgery at Beijing Hospital from January 2011 to May 2019 were retrospectively enrolled in the study. Basic patient information, clinical, pathological, and perioperative data were collected. Patients were followed up by telephone interviews and outpatient records. Statistical analyses were performed using SPSS version 26.0.

RESULTS

A total of 242 patients (129 men, 113 women) with TETs were included in this study, of which 150 patients (62.0%) were combined with myasthenia gravis (MG) and 92 patients (38.0%) were not. 216 patients were successfully followed up and their complete information was available. The median follow-up period was 70.5 months (range, 2-137 months). The 3-year overall survival (OS) rate of the whole group was 93.9%, and the 5-year OS rate was 91.1%. The 3-year relapse-free survival (RFS) rate of the whole group was 92.2%, and the 5-year relapse-free survival rate was 89.8%. Multivariable COX regression analysis indicated that recurrence of thymoma was an independent risk factor for OS. Younger age, Masaoka-Koga stage III + IV, and TNM stage III + IV were independent risk factors for RFS. Multivariable COX regression analysis indicated that Masaoka-Koga staging III + IV, WHO type B + C were independent risk factors for postoperative improvement of MG. For patients with MG, the postoperative complete stable remission (CSR) rate was 30.5%. And the result of multivariable COX regression analysis showed that thymoma patients with MG with Osserman staging IIA + IIB + III + IV were not prone to achieving CSR. Compared with patients without MG, MG was more likely to develop in patients with WHO classification type B, and patients with myasthenia gravis were younger, with longer operative duration, and more likely to develop perioperative complications.

CONCLUSIONS

The 5-year overall survival rate of patients with TETs was 91.1% in this study. Younger age and advanced stage were independent risk factors for RFS of patients with TETs, and recurrence of thymoma were independent risk factors for OS. In patients with MG, WHO classification type B and advanced stage were independent predictors of poor outcomes of MG treatment after thymectomy.