The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.
The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.
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DOI：
10.3389/fped.2022.1023539
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发表时间：
2022
影响因子：
2.6
通讯作者：
中科院分区：
文献类型：
--
作者：
临界指数-死亡率使用生理学、治疗和护理强度来计算儿科 ICU 患者的死亡风险。如果死亡风险计算的频率增加到每 3 小时一次，并且模型性能可以改善疾病严重程度的评估，则可以利用它来监测患者的重大死亡风险变化。使用临界指数-死亡率方法评估每 3 小时更新一次死亡风险的动态方法的性能，并确定对死亡风险预测有重要影响的变量。 2018年1月1日至2020年2月29日期间，共有8,399名儿童入住ICU，其中312例（3.7%）死亡。我们随机选择75%的患者进行培训，13%进行验证，12%进行测试。训练神经网络来预测入住 ICU 期间或之后的医院生存或死亡。变量包括年龄、性别、实验室检查、生命体征、药物类别和机械通气变量。使用非参数逻辑回归将神经网络校准为死亡风险。对所有时间段的歧视进行评估发现，AUROC 为 0.851 (0.841–0.862)，AUPRC 为 0.443 (0.417–0.467)。每 3 h 评估一次性能时，AUROC 的最小值为 0.778 (0.689–0.867)，最大值为 0.885 (0.841,0.862)； AUPRC 的最小值为 0.148 (0.058–0.328)，最大值为 0.499 (0.229–0.769)。校准图的截距为 0.011，斜率为 0.956，R2 为 0.814。观察到的死亡比例与预期死亡比例的比较显示，543 个风险区间中的 95.8% 没有统计学上的显着差异。通过死亡风险四分位数和 7 种高风险和低风险疾病分析的死亡和幸存者风险轨迹评估的构建有效性证实了对死亡和幸存者轨迹的先验临床预期。危重指数死亡率每 3 h 计算儿科 ICU 患者的死亡风险，其模型性能可以增强疾病严重程度的临床评估。整体临界指数-死亡率框架被有效地应用于开发机构特定的、临床相关的模型，用于儿科 ICU 患者的动态风险评估。
The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. Discrimination assessed across all time periods found an AUROC of 0.851 (0.841–0.862) and an AUPRC was 0.443 (0.417–0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689–0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058–0.328) and a maximum value of 0.499 (0.229–0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors. The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.
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影响因子：
8.8
作者：
Badawi, Omar;Liu, Xinggang;Swami, Sunil
通讯作者：
Swami, Sunil
影响因子：
3
作者：
Mohamadlou, Hamid;Panchavati, Saarang;Das, Ritankar
通讯作者：
Das, Ritankar
DOI：
10.1186/s13054-015-1054-y
发表时间：
2015-09-15
期刊：
Critical care (London, England)
影响因子：
--
作者：
Leteurtre S;Duhamel A;Deken V;Lacroix J;Leclerc F;Groupe Francophone de Réanimation et Urgences Pédiatriques
通讯作者：
Groupe Francophone de Réanimation et Urgences Pédiatriques
影响因子：
15.1
作者：
Booth, FV;Short, M;Levy, H
通讯作者：
Levy, H
影响因子：
8.8
作者：
Marcin, JP;Pollack, MM;Ruttimann, UE
通讯作者：
Ruttimann, UE