Full
Outline
To develop and validate a high-precision,interpretable sarcopenia risk prediction model for peritoneal dialysis patients.
A retrospective study included 441 peritoneal dialysis patients from two tertiary hospitals in Anhui province from January 2023 to June 2025 as the training set,and 198 patients as the external validation set.Core predictors were screened using univariate analysis,Logistic regression,and LASSO regression.Eight machine learning algorithms were used to build models.Internal validation employed Bootstrap sampling,and external validation used an independent dataset.The optimal model was interpreted using the Shapley additive explanations(SHAP).
Six core predictors were identified:sex,dialysis vintage,and diabetes,prealbumin、interleukin-6、25-hydroxyvitamin D.The support vector machine(SVM) model showed the best performance,with an area under the receiver operating characteristic curve(AUC) of 0.958 in the internal test set and 0.968 in the external validation set.Accuracy,sensitivity,and specificity all exceeded 0.9.SHAP analysis revealed the contribution of each predictor and their nonlinear relationships.
The developed machine learning model demonstrates high accuracy,generalizability,and interpretability.It can provide intelligent decision support for early identification and precise intervention of sarcopenia in peritoneal dialysis patients,showing potential for clinical translation.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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