Combining physician's subjective and physiology-based objective mortality risk predictions
Combining physician's subjective and physiology-based objective mortality risk predictions
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DOI：
10.1097/00003246-200008000-00050
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发表时间：
2000-08-01
影响因子：
8.8
通讯作者：
Ruttimann, UE
中科院分区：
文献类型：
--
作者：
Marcin, JP;Pollack, MM;Ruttimann, UE
目的：目前可用的基于生理学的死亡率风险预测模型都没有纳入医疗保健专业人员的主观判断，这是可以提高预测器性能并使此类系统更容易被医疗保健专业人员接受的额外信息来源。本研究比较了医生和护士主观死亡率估计的性能与基于生理学的方法，儿科死亡风险（PRISM）III，然后，医疗保健提供者的估计与PRISM III估计使用贝叶斯统计相结合。然后将贝叶斯模型的表现与最初的两个预测进行比较。设计：同期队列研究。设置：一所大学附属儿童医院的三级儿科重症监护室。患者：连续入住儿科重症监护室。干预措施：无。测量和主要结果：对于642名连续合格患者中的每一名，准确的死亡率估计和确定性程度从主治医师、研究员、住院医师和负责患者护理的护士处收集与估计相关的量表（从1到5的连续量表）。贝叶斯统计被用于结合联合收割机的PRISM III和确定性加权主观预测，以创建第三贝叶斯估计的死亡率。PRISM III能很好地区分幸存者和非幸存者（曲线下面积[AUC]，0.924），医生和护士也是如此（AUC主治医生，0.953;研究员，0.870;住院医生，0.923;护士，0.935）。尽管医疗保健提供者的AUC与PRISM III的AUC无显著差异，但贝叶斯AUC高于医疗保健提供者的AUC（所有p均小于或等于0.09）和PRISM III AUC。同样，贝叶斯估计的校准统计量是上级的校准统计量的医疗服务提供者和PRISM III models.Conclusions：本研究的结果表明，医疗服务提供者的主观死亡率预测和PRISM III死亡率预测同样表现良好。结合提供者和PRISM III死亡率预测的贝叶斯模型比单独提供者或PRISM III更准确，可能更容易被医生接受。使用主观结果预测的方法可能与个体患者决策支持更相关。
Objective: None of the currently available physiology-based mortality risk prediction models incorporate subjective judgements of healthcare professionals, a source of additional information that could improve predictor performance and make such systems more acceptable to healthcare professionals. This study compared the performance of subjective mortality estimates by physicians and nurses with a physiology-based method, the Pediatric Risk of Mortality (PRISM) III, Then, healthcare provider estimates were combined with PRISM III estimates using Bayesian statistics. The performance of the Bayesian model was then compared with the original two predictions.Design: Concurrent cohort study.Setting: A tertiary pediatric intensive care unit at a university affiliated children's hospital.Patients: Consecutive admissions to the pediatric intensive care unit.Interventions: None.Measurements and Main Results: For each of the 642 consecutive eligible patients, an exact mortality estimate and the degree of certainty (continuous scale from 1 to 5) associated with the estimate was collected from the attending, fellow, resident, and nurse responsible for the patient's care. Bayesian statistics were used to combine the PRISM III and certainty weighted subjective predictions to create a third Bayesian estimate of mortality. PRISM III discriminated survivors from nonsurvivors very well (area under curve [AUC], 0.924) as did the physicians and nurses (AUCs attendings, 0.953; fellows, 0.870; residents, 0.923; nurses, 0.935). Although the AUCs of the healthcare providers were not significantly different from the AUCs of PRISM III, the Bayesian AUCs were higher than both the healthcare providers' AUCs (p less than or equal to .09 for all) and PRISM III AUCs. Similarly, the calibration statistics for the Bayesian estimates were superior to the calibration statistics for both the healthcare providers and PRISM III models.Conclusions: The results of this study demonstrated that healthcare providers' subjective mortality predictions and PRISM III mortality predictions perform equally well. The Bayesian model that combined provider and PRISM III mortality predictions was more accurate than either provider or PRISM III alone and may be more acceptable to physicians. A methodology using subjective outcome predictions could be more relevant to individual patient decision support.