Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer
Abstract Background Comorbidities may complicate medical situations and have an impact on the treatment decisions and poor survival of cancer patients. How comorbidities cluster together and ultimately affect patients’ outcomes in gastrointestinal tract cancer (GTC) is a poorly understood area. Meth...
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BMC
2025-02-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13517-1 |
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| author | Linna Gao Tian Yao Shaohua Ge Jiuwei Cui Wei Li Zengqing Guo Hongxia Xu Min Weng Suyi Li Qinghua Yao Wen Hu Lan Zhou Junqiang Chen Xianghua Wu Qingchuan Zhao Hongli Li Hanping Shi Yi Ba He Huang The Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) Group |
| author_facet | Linna Gao Tian Yao Shaohua Ge Jiuwei Cui Wei Li Zengqing Guo Hongxia Xu Min Weng Suyi Li Qinghua Yao Wen Hu Lan Zhou Junqiang Chen Xianghua Wu Qingchuan Zhao Hongli Li Hanping Shi Yi Ba He Huang The Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) Group |
| author_sort | Linna Gao |
| collection | DOAJ |
| description | Abstract Background Comorbidities may complicate medical situations and have an impact on the treatment decisions and poor survival of cancer patients. How comorbidities cluster together and ultimately affect patients’ outcomes in gastrointestinal tract cancer (GTC) is a poorly understood area. Methods In a multicenter prospective observational study from 2012 to 2021, we grouped the comorbidities of patients with GTC by latent class analysis, obtaining two comorbidity classes. Cox regression models were initially used to predict mortality. LASSO techniques were used to reduce the dimension. The final model included the comorbidity classes and nine more predictors. Additionally, the performance of different simple multimorbidity measures were compared using the Bayesian information criterion (BIC), ROC curves and C-index. Finally, the performance of the final model was analyzed using ROC curves, calibration curves and decision curves. The nomogram was drawn to evaluate the model. Results We included 10,019 patients and obtained two comorbidity classes. Class 2 patients have a higher incidence of comorbidities, and a lower survival rate compared to Class 1 (P < 0.001). Compared to models containing the number of comorbidities or only a single comorbidity, the final model with the comorbidity classes has the highest AUC and C-index, as well as the lowest BIC, indicating this model has the best predictive performance. Conclusion We identified two classes of comorbidities that were associated with overall survival in patients with GTC. The combination of different comorbidities class plays a vital role in the prognosis of GTC. |
| format | Article |
| id | doaj-art-11f15172b1ca461aa577797a158f9ac4 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-11f15172b1ca461aa577797a158f9ac42025-02-09T12:41:47ZengBMCBMC Cancer1471-24072025-02-0125111010.1186/s12885-025-13517-1Effect of comorbidity classes on survival of patients with gastrointestinal tract cancerLinna Gao0Tian Yao1Shaohua Ge2Jiuwei Cui3Wei Li4Zengqing Guo5Hongxia Xu6Min Weng7Suyi Li8Qinghua Yao9Wen Hu10Lan Zhou11Junqiang Chen12Xianghua Wu13Qingchuan Zhao14Hongli Li15Hanping Shi16Yi Ba17He Huang18The Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) Group19Department of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, The First Clinical Medical College of Shanxi Medical UniversityDepartment of Gastrointestinal Surgery, Center of Clinical Epidemiology and Evidence Based Medicine, First Hospital of Shanxi Medical University, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical UniversityTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for CancerCancer Center, The First Hospital of Jilin UniversityCancer Center, The First Hospital of Jilin UniversityFujian Cancer HospitalDepartment of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University)Department of Clinical Nutrition, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Oncology, Anhui Provincial Cancer HospitalThe Second Affiliated Hospital of Zhejiang Chinese Medical University, Xinhua Hospital of Zhejiang ProvinceDepartment of Clinical Nutrition, Sichuan University West China HospitalDepartment of Clinical Nutrition, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical UniversityDepartment of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical UniversityXijing Hospital, Air Force Medical UniversityTianjin Medical University Cancer Institute and HospitalDepartment of Gastrointestinal Surgery and Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical UniversityPeking Union Medical College HospitalDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, The First Clinical Medical College of Shanxi Medical UniversityDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, The First Clinical Medical College of Shanxi Medical UniversityAbstract Background Comorbidities may complicate medical situations and have an impact on the treatment decisions and poor survival of cancer patients. How comorbidities cluster together and ultimately affect patients’ outcomes in gastrointestinal tract cancer (GTC) is a poorly understood area. Methods In a multicenter prospective observational study from 2012 to 2021, we grouped the comorbidities of patients with GTC by latent class analysis, obtaining two comorbidity classes. Cox regression models were initially used to predict mortality. LASSO techniques were used to reduce the dimension. The final model included the comorbidity classes and nine more predictors. Additionally, the performance of different simple multimorbidity measures were compared using the Bayesian information criterion (BIC), ROC curves and C-index. Finally, the performance of the final model was analyzed using ROC curves, calibration curves and decision curves. The nomogram was drawn to evaluate the model. Results We included 10,019 patients and obtained two comorbidity classes. Class 2 patients have a higher incidence of comorbidities, and a lower survival rate compared to Class 1 (P < 0.001). Compared to models containing the number of comorbidities or only a single comorbidity, the final model with the comorbidity classes has the highest AUC and C-index, as well as the lowest BIC, indicating this model has the best predictive performance. Conclusion We identified two classes of comorbidities that were associated with overall survival in patients with GTC. The combination of different comorbidities class plays a vital role in the prognosis of GTC.https://doi.org/10.1186/s12885-025-13517-1Gastrointestinal tract cancerComorbidityPredictive modelOverall survival |
| spellingShingle | Linna Gao Tian Yao Shaohua Ge Jiuwei Cui Wei Li Zengqing Guo Hongxia Xu Min Weng Suyi Li Qinghua Yao Wen Hu Lan Zhou Junqiang Chen Xianghua Wu Qingchuan Zhao Hongli Li Hanping Shi Yi Ba He Huang The Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) Group Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer BMC Cancer Gastrointestinal tract cancer Comorbidity Predictive model Overall survival |
| title | Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| title_full | Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| title_fullStr | Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| title_full_unstemmed | Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| title_short | Effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| title_sort | effect of comorbidity classes on survival of patients with gastrointestinal tract cancer |
| topic | Gastrointestinal tract cancer Comorbidity Predictive model Overall survival |
| url | https://doi.org/10.1186/s12885-025-13517-1 |
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