Abstract：
OBJECTIVE: This study aimed to develop and validate a nomogram to predict prolonged diabetes ketoacidosis (DKA) resolution time (DRT).
METHODS: We retrospectively extracted sociodemographic, clinical, and laboratory data from the electronic medical records of 394 adult patients with DKA admitted to Tawam Hospital between January 2017 and October 2022. Logistic regression stepwise model was developed to predict DRT ≥ 24 h. Model discrimination was evaluated using C-index and calibration was determined using calibration plot and Brier score.
RESULTS: The patients\' average age was 34 years; 54 % were female. Using the stepwise model, the final variables including sex, diabetes mellitus type, loss of consciousness at presentation, presence of infection at presentation, body mass index, heart rate, and venous blood gas pH at presentation were used to generate a nomogram to predict DRT ≥ 24 h. The C-index was 0.76 in the stepwise model, indicating good discrimination. Despite the calibration curve of the stepwise model showing a slight overestimation of risk at higher predicted risk levels, the Brier score for the model was 0.17, indicating both good calibration and predictive accuracy.
CONCLUSIONS: An effective nomogram was established for estimating the likelihood of DRT ≥ 24 h, facilitating better resource allocation and personalized treatment strategy.
METHODS: We retrospectively extracted sociodemographic, clinical, and laboratory data from the electronic medical records of 394 adult patients with DKA admitted to Tawam Hospital between January 2017 and October 2022. Logistic regression stepwise model was developed to predict DRT ≥ 24 h. Model discrimination was evaluated using C-index and calibration was determined using calibration plot and Brier score.
RESULTS: The patients\' average age was 34 years; 54 % were female. Using the stepwise model, the final variables including sex, diabetes mellitus type, loss of consciousness at presentation, presence of infection at presentation, body mass index, heart rate, and venous blood gas pH at presentation were used to generate a nomogram to predict DRT ≥ 24 h. The C-index was 0.76 in the stepwise model, indicating good discrimination. Despite the calibration curve of the stepwise model showing a slight overestimation of risk at higher predicted risk levels, the Brier score for the model was 0.17, indicating both good calibration and predictive accuracy.
CONCLUSIONS: An effective nomogram was established for estimating the likelihood of DRT ≥ 24 h, facilitating better resource allocation and personalized treatment strategy.
摘要：
目的：本研究旨在开发和验证用于预测延长的糖尿病酮症酸中毒（DKA）消退时间（DRT）的列线图。
方法：我们回顾性提取了社会人口统计学，临床,以及来自2017年1月至2022年10月Tawam医院收治的394例DKA成年患者的电子病历的实验室数据.建立Logistic回归逐步回归模型来预测DRT≥24h。使用C指数评估模型判别，并使用校准图和Brier评分确定校准。
结果：患者平均年龄为34岁；54%为女性。使用逐步模型，最后的变量包括性别,糖尿病类型，演示时失去意识，出现时存在感染，身体质量指数,心率,和呈现时的静脉血气pH值用于生成列线图以预测DRT≥24h。在逐步模型中，C指数为0.76，显示良好的歧视。尽管逐步模型的校准曲线显示，在较高的预测风险水平下，风险略有高估，模型的Brier得分为0.17,表明校准和预测准确性均较好.
结论：建立了有效的列线图来估计DRT≥24h的可能性，促进更好的资源配置和个性化治疗策略。
方法：我们回顾性提取了社会人口统计学，临床,以及来自2017年1月至2022年10月Tawam医院收治的394例DKA成年患者的电子病历的实验室数据.建立Logistic回归逐步回归模型来预测DRT≥24h。使用C指数评估模型判别，并使用校准图和Brier评分确定校准。
结果：患者平均年龄为34岁；54%为女性。使用逐步模型，最后的变量包括性别,糖尿病类型，演示时失去意识，出现时存在感染，身体质量指数,心率,和呈现时的静脉血气pH值用于生成列线图以预测DRT≥24h。在逐步模型中，C指数为0.76，显示良好的歧视。尽管逐步模型的校准曲线显示，在较高的预测风险水平下，风险略有高估，模型的Brier得分为0.17,表明校准和预测准确性均较好.
结论：建立了有效的列线图来估计DRT≥24h的可能性，促进更好的资源配置和个性化治疗策略。
