Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study
Background The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.Methods Retrospective data regarding dialysis patients were obtained from two hospitals. Patie...
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| Format: | Article |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2322039 |
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| author | Jingcan Wu Xuehong Li Hong Zhang Lin Lin Man Li Gangyi Chen Cheng Wang |
| author_facet | Jingcan Wu Xuehong Li Hong Zhang Lin Lin Man Li Gangyi Chen Cheng Wang |
| author_sort | Jingcan Wu |
| collection | DOAJ |
| description | Background The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.Methods Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).Results The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.Conclusions The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients. |
| format | Article |
| id | doaj-art-b4924cd85cb84f699240b558d534c9f8 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-b4924cd85cb84f699240b558d534c9f82025-01-23T04:17:47ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2322039Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort studyJingcan Wu0Xuehong Li1Hong Zhang2Lin Lin3Man Li4Gangyi Chen5Cheng Wang6Department of Nephrology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, ChinaDepartment of Nephrology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, ChinaDepartment of Nephrology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, ChinaDepartment of Nephrology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, ChinaGuangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, ChinaDepartment of Nephrology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, ChinaDepartment of Nephrology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, ChinaBackground The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.Methods Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).Results The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.Conclusions The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2322039Maintenance dialysisall-cause mortalitypleural effusionprediction model |
| spellingShingle | Jingcan Wu Xuehong Li Hong Zhang Lin Lin Man Li Gangyi Chen Cheng Wang Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study Renal Failure Maintenance dialysis all-cause mortality pleural effusion prediction model |
| title | Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study |
| title_full | Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study |
| title_fullStr | Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study |
| title_full_unstemmed | Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study |
| title_short | Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study |
| title_sort | development and validation of a prediction model for all cause mortality in maintenance dialysis patients a multicenter retrospective cohort study |
| topic | Maintenance dialysis all-cause mortality pleural effusion prediction model |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2322039 |
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