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Abstract：
OBJECTIVE: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU.
METHODS: A retrospective cohort study.
METHODS: Five university hospitals in the Netherlands between 2002 and 2015.
METHODS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.
METHODS: None.
RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model\'s generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality.
CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.
METHODS: A retrospective cohort study.
METHODS: Five university hospitals in the Netherlands between 2002 and 2015.
METHODS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.
METHODS: None.
RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model\'s generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality.
CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.
摘要：
目的：建立并验证急性进入ICU的恶性血液病患者1年死亡率的预测模型。
方法：回顾性队列研究。
方法：2002年至2015年间，荷兰有五所大学医院。
方法：共1097例恶性血液病患者急性进入ICU至少24小时。
方法：无。
结果：我们从22个潜在预测因子中创建了一个13变量模型。主要预测因素包括活动性疾病，年龄,以前的造血干细胞移植，机械通气,最低血小板计数,急性肾损伤，最大心率,和恶性肿瘤的类型。引导程序减少了过拟合，提高了模型的泛化性。这包括估计初始模型中的乐观情绪，并相应地缩小最终模型中的回归系数。我们使用中心内部-外部交叉验证评估了性能，并将其与急性生理学和慢性健康评估II模型进行了比较。此外,我们通过决策曲线分析评估了临床有用性.研究中观察到的1年总死亡率为62%(95%CI,59-65)。我们的13变量预测模型在跨中心的内部-外部验证中表现出可接受的校准和辨别（C统计量0.70；95％CI，0.63-0.77），优于急性生理学和慢性健康评估II模型（C统计量0.61；95％CI，0.57-0.65）。决策曲线分析表明在60-100%预测的1年死亡率的临床相关阈值概率范围内的总体净获益。
结论：我们新开发的13变量预测模型比急性生理学和慢性健康评估II模型更准确地预测了ICU收治的恶性血液病患者的1年死亡率。该模型可能有助于就ICU护理的延续和临终考虑做出共同的决策。
方法：回顾性队列研究。
方法：2002年至2015年间，荷兰有五所大学医院。
方法：共1097例恶性血液病患者急性进入ICU至少24小时。
方法：无。
结果：我们从22个潜在预测因子中创建了一个13变量模型。主要预测因素包括活动性疾病，年龄,以前的造血干细胞移植，机械通气,最低血小板计数,急性肾损伤，最大心率,和恶性肿瘤的类型。引导程序减少了过拟合，提高了模型的泛化性。这包括估计初始模型中的乐观情绪，并相应地缩小最终模型中的回归系数。我们使用中心内部-外部交叉验证评估了性能，并将其与急性生理学和慢性健康评估II模型进行了比较。此外,我们通过决策曲线分析评估了临床有用性.研究中观察到的1年总死亡率为62%(95%CI,59-65)。我们的13变量预测模型在跨中心的内部-外部验证中表现出可接受的校准和辨别（C统计量0.70；95％CI，0.63-0.77），优于急性生理学和慢性健康评估II模型（C统计量0.61；95％CI，0.57-0.65）。决策曲线分析表明在60-100%预测的1年死亡率的临床相关阈值概率范围内的总体净获益。
结论：我们新开发的13变量预测模型比急性生理学和慢性健康评估II模型更准确地预测了ICU收治的恶性血液病患者的1年死亡率。该模型可能有助于就ICU护理的延续和临终考虑做出共同的决策。
