BMC medical informatics and decision making · 2026
Development and temporal validation of a machine learning-based model for predicting renal impairment in multiple myeloma: a single-center retrospective cohort study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhou Manli, Feng Sisi
Аннотация
BACKGROUND: Multiple myeloma (MM) is a hematopoietic system malignancy characterized by clonal proliferation of abnormal plasma cells, commonly presenting with renal impairment (RI) that significantly impacts patient's quality of life. The objective of this study was to develop a predictive model for assessing the risk of MM with RI.
METHODS: This retrospective study included 1029 patients with MM, who were categorized into two groups: MM without RI group and MM with RI group. Specifically, the modeling dataset (Cohort 1) consisted of 792 patients (401 without RI and 391 with RI), and the validation dataset (Cohort 2) consisted of 237 patients (119 without RI and 118 with RI). Eight machine learning (ML) algorithms were assessed using the modeling dataset (n = 792), and model validation was conducted on the validation dataset (n = 237). The predictive performance of ML models was evaluated through the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis.
RESULTS: The optimal model among the eight ML algorithms was XGBoost. The predictive model constructed based on the XGBoost algorithm, RI-Lab6, incorporated six features: NLR, LDH, Hb, ACa, AGR and β2 microglobulin. In the modeling dataset (cohort 1), RI-Lab6 demonstrated an AUC of 0.99 (95% CI, 0.98-0.99), while in the validation dataset (cohort 2), it achieved an AUC value of 0.98 (95% CI, 0.96-0.99).
CONCLUSIONS: These results indicated that the RI-Lab6 model possessed reliable predictive value for RI risk in MM patients and facilitated early identification of RI in these individuals.
METHODS: This retrospective study included 1029 patients with MM, who were categorized into two groups: MM without RI group and MM with RI group. Specifically, the modeling dataset (Cohort 1) consisted of 792 patients (401 without RI and 391 with RI), and the validation dataset (Cohort 2) consisted of 237 patients (119 without RI and 118 with RI). Eight machine learning (ML) algorithms were assessed using the modeling dataset (n = 792), and model validation was conducted on the validation dataset (n = 237). The predictive performance of ML models was evaluated through the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis.
RESULTS: The optimal model among the eight ML algorithms was XGBoost. The predictive model constructed based on the XGBoost algorithm, RI-Lab6, incorporated six features: NLR, LDH, Hb, ACa, AGR and β2 microglobulin. In the modeling dataset (cohort 1), RI-Lab6 demonstrated an AUC of 0.99 (95% CI, 0.98-0.99), while in the validation dataset (cohort 2), it achieved an AUC value of 0.98 (95% CI, 0.96-0.99).
CONCLUSIONS: These results indicated that the RI-Lab6 model possessed reliable predictive value for RI risk in MM patients and facilitated early identification of RI in these individuals.