Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Department of Anaesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Crit Care Explor. 2024 May 24;6(6):e1093. doi: 10.1097/CCE.0000000000001093. eCollection 2024 Jun.
To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU.
A retrospective cohort study.
Five university hospitals in the Netherlands between 2002 and 2015.
A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.
None.
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 (-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (-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.
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.
开发和验证一种预测模型，用于预测血液恶性肿瘤患者 ICU 急性入院后 1 年的死亡率。
回顾性队列研究。
2002 年至 2015 年荷兰的五所大学医院。
共纳入 1097 例血液恶性肿瘤患者，这些患者 ICU 急性入院时间至少为 24 小时。
无。
我们从 22 个潜在预测因子中创建了一个 13 变量模型。关键预测因子包括活动疾病、年龄、先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类型。自举程序减少了过度拟合并提高了模型的通用性。这涉及估计初始模型中的乐观程度，并相应地缩小最终模型中的回归系数。我们通过中心的内部-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，我们还通过决策曲线分析评估了临床实用性。研究中观察到的总体 1 年死亡率为 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区分度（-统计量为 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（-统计量为 0.61；95%CI，0.57-0.65）。决策曲线分析表明，在临床相关阈值概率范围内（预测 1 年死亡率为 60-100%），总体净获益。
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 模型更准确地预测血液恶性肿瘤患者 ICU 急性入院后 1 年的死亡率。该模型可能有助于在 ICU 护理的继续和生命末期考虑方面进行共同决策。
