Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, WA.
Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA.
Pediatr Crit Care Med. 2020 Aug;21(8):e491-e501. doi: 10.1097/PCC.0000000000002425.
Pediatric protocols to guide allocation of limited resources during a disaster lack data to validate their use. The 2011 Pediatric Emergency Mass Critical Care Task Force recommended that expected duration of critical care be incorporated into resource allocation algorithms. We aimed to determine whether currently available pediatric illness severity scores can predict duration of critical care resource use.
Retrospective cohort study.
Seattle Children's Hospital.
PICU patients admitted 2016-2018 for greater than or equal to 12 hours (n = 3,206).
None.
We developed logistic and linear regression models in two-thirds of the cohort to predict need for and duration of PICU resources based on Pediatric Risk of Mortality-III, Pediatric Index of Mortality-3, and serial Pediatric Logistic Organ Dysfunction-2 scores. We tested the predictive accuracy of the models with the highest area under the receiver operating characteristic curve (need for each resource) and R (duration of use) in a validation cohort of the remaining one of three of the sample and among patients admitted during one-third of the sample and among patients admitted during surges of respiratory illness. Pediatric Logistic Organ Dysfunction score calculated 12 hours postadmission had higher predictive accuracy than either Pediatric Risk of Mortality or Pediatric Index of Mortality scores. Models incorporating 12-hour Pediatric Logistic Organ Dysfunction score, age, Pediatric Overall Performance Category, Pediatric Cerebral Performance Category, chronic mechanical ventilation, and postoperative status had an area under the receiver operating characteristic curve = 0.8831 for need for any PICU resource (positive predictive value 80.2%, negative predictive value 85.9%) and area under the receiver operating characteristic curve = 0.9157 for mechanical ventilation (positive predictive value 85.7%, negative predictive value 89.2%) within 7 days of admission. Models accurately predicted greater than or equal to 24 hours of any resource use for 78.9% of patients and greater than or equal to 24 hours of ventilation for 83.1%. Model fit and accuracy improved for prediction of resource use within 3 days of admission, and was lower for noninvasive positive pressure ventilation, vasoactive infusions, continuous renal replacement therapy, extracorporeal membrane oxygenation, and length of stay.
A model incorporating 12-hour Pediatric Logistic Organ Dysfunction score performed well in estimating how long patients may require PICU resources, especially mechanical ventilation. A pediatric disaster triage algorithm that includes both likelihood for survival and for requiring critical care resources could minimize subjectivity in resource allocation decision-making.
儿科协议缺乏指导灾难期间有限资源分配的数据,以验证其使用。2011 年儿科急诊大规模关键护理工作组建议将预期的关键护理持续时间纳入资源分配算法。我们旨在确定目前可用的儿科疾病严重程度评分是否可以预测关键护理资源的使用时间。
回顾性队列研究。
西雅图儿童医院。
2016 年至 2018 年入住 PICU 超过 12 小时的患者(n = 3206)。
无。
我们在队列的三分之二部分开发了逻辑和线性回归模型,以根据儿科死亡率风险 III、儿科死亡率指数 3 和连续儿科逻辑器官功能障碍 2 评分预测对 PICU 资源的需求和持续时间。我们使用验证队列中的剩余三分之一样本和呼吸道疾病发作期间入院的患者以及在此期间入院的患者中的最高接受者操作特征曲线(每种资源的需求)和 R(使用持续时间)来测试模型的预测准确性。入院后 12 小时计算的儿科逻辑器官功能障碍评分比儿科死亡率或儿科死亡率指数评分具有更高的预测准确性。纳入 12 小时儿科逻辑器官功能障碍评分、年龄、儿科总体表现类别、儿科大脑表现类别、慢性机械通气和术后状态的模型对任何 PICU 资源的需求具有 0.8831 的接受者操作特征曲线下面积(阳性预测值 80.2%,阴性预测值 85.9%)和机械通气的接受者操作特征曲线下面积 = 0.9157(阳性预测值 85.7%,阴性预测值 89.2%)在入院后 7 天内。该模型准确预测了 78.9%的患者需要使用任何资源超过 24 小时,83.1%的患者需要使用机械通气超过 24 小时。模型在入院后 3 天内预测资源使用的拟合度和准确性提高,而对无创正压通气、血管活性输注、连续肾脏替代治疗、体外膜氧合和住院时间的预测准确性较低。
纳入 12 小时儿科逻辑器官功能障碍评分的模型在估计患者可能需要多长时间 PICU 资源方面表现良好,尤其是机械通气。一种包含生存可能性和对关键护理资源需求的儿科灾难分诊算法可以最大限度地减少资源分配决策中的主观性。