Acta diabetologica · 2025
Development and validation of prediction model for stage I patients with lower extremity atherosclerotic disease in type 2 diabetes mellitus in China.
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.
Zhu Rong, Cui Weifeng, Zhao Ruixia, Liu Huijuan, Yan Shuxun, Shao Mingyi, Yu Haibin, Fu Yu
Аннотация
AIMS: Lower extremity atherosclerotic disease (LEAD) is the primary cause of ulcers, gangrene, and amputations in patients with type 2 diabetes mellitus (T2DM), stage I is the crucial time for prevention and intervention to improve the prognosis of T2DM-LEAD. The purpose of this study was to develop and validate a personalized predictive model to determine the risk of outcomes in stage I patients with T2DM-LEAD.
METHODS: There were 1603 stage I patients with T2DM-LEAD at baseline in this retrospective study. Least absolute shrinkage and selection operator regression was applied to filter predictive variables. Cox regression was used to construct a nomogram prediction model. The model's 3-year and 5-year predictive performance was evaluated in terms of its discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve, calibration curve, decision curve analysis, respectively.
RESULTS: Patients were randomly divided into a development cohort (n = 1122) and a validation cohort (n = 481). Age, cerebrovascular diseases, diabetic kidney disease, diabetic retinopathy, low-density lipoprotein cholesterol, fibrinogen, D-dimer and anti-platelet drugs were selected as predictive factors. The model presented moderate discrimination in development and validation sets with AUCs of 70.3 (95% CI: 65.2-75.3) and 70.1 (95% CI: 64.5-75.7) for the 3-year prediction. Andthe AUC values for the 5-year prediction in development and validation sets were 72.8 (95% CI: 67.6-78.1) and 75.9 (95% CI: 69.0-82.8), respectively. The calibration curve for the 3-year and 5-year predictions demonstrated good agreement between the predicted and actual probabilities, and decision curve analysis showed a wide range of beneficial clinical utility.
CONCLUSION: The prediction model can identify the risk of stage I patients with T2DM-LEAD who are likely to develop outcomes events within 3 years and 5 years. It is valuable for clinical decisions and helps healthcare providers and policy makers to develop more personalized clinical treatment strategies, which has significant public health implications.
METHODS: There were 1603 stage I patients with T2DM-LEAD at baseline in this retrospective study. Least absolute shrinkage and selection operator regression was applied to filter predictive variables. Cox regression was used to construct a nomogram prediction model. The model's 3-year and 5-year predictive performance was evaluated in terms of its discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve, calibration curve, decision curve analysis, respectively.
RESULTS: Patients were randomly divided into a development cohort (n = 1122) and a validation cohort (n = 481). Age, cerebrovascular diseases, diabetic kidney disease, diabetic retinopathy, low-density lipoprotein cholesterol, fibrinogen, D-dimer and anti-platelet drugs were selected as predictive factors. The model presented moderate discrimination in development and validation sets with AUCs of 70.3 (95% CI: 65.2-75.3) and 70.1 (95% CI: 64.5-75.7) for the 3-year prediction. Andthe AUC values for the 5-year prediction in development and validation sets were 72.8 (95% CI: 67.6-78.1) and 75.9 (95% CI: 69.0-82.8), respectively. The calibration curve for the 3-year and 5-year predictions demonstrated good agreement between the predicted and actual probabilities, and decision curve analysis showed a wide range of beneficial clinical utility.
CONCLUSION: The prediction model can identify the risk of stage I patients with T2DM-LEAD who are likely to develop outcomes events within 3 years and 5 years. It is valuable for clinical decisions and helps healthcare providers and policy makers to develop more personalized clinical treatment strategies, which has significant public health implications.