Predicting Morbidity from Pediatric Critical Care

描述(由申请人提供)：重症监护对疾病的严重程度有很好的衡量标准，以死亡率为标准，但严重程度可能反映在随后的发病率和存活率上。危重病护理结果研究的一个主要挑战，并适用于所有医疗结果和质量问题，是开发预测从正常到疾病和死亡的各种结果的方法。这项建议的目的是开发和验证儿科重症监护的3种或3种以上结局状态的预测因子：死亡，存活一个或多个功能状态降低的状态，以及功能状态正常或不变的生存状态。初步研究表明，a)统计方法的可行性，b)CPCCRN和该PI为本提案目的开发的新的功能状态评估方法(功能状态评分，FSS)的适用性和实用性。方法：连续的未被排除在PICUS之外的患者将被使用。核心数据将包括生理数据、诊断、年龄和其他人口统计信息、FSS(入院前、PICU出院、出院)、生存/死亡(PICU和医院)、影响功能状态的治疗、成像、对功能正常和死亡的多种功能状态的结果预测将包括两个“简单的”线性模型，其中FSS贡献结果的分级，以及对3个或更多离散结果状态的模型进行多分类Logistic回归分析。统计模型将使用最多12个预测变量，包括没有神经变量的PRISM III评分、仅有神经变量、ICU前护理区域、手术状态、诊断(最多6个)、年龄、基线FSS。统计方法将包括“简单的”线性回归，将结果概念化，范围从正常到死亡，其间的功能状态恶化，以及利用FSS定义除死亡外的两种更多结果状态的多分类Logistic回归。根据4%的死亡率和4%的新的严重功能状态估计的样本量为5067，但在选择单位时将重新估计。 公共卫生相关性：通过推进严重性评估的概念和统计基础来改变其范式，将刺激变革。在质量研究和方法、包括PICU出院时的儿科残疾在内的长期结果预测以及通过纳入严重降低的功能状态概率以及基于入院严重程度的死亡概率的决策方面，可能会出现重要的进展。

DESCRIPTION (provided by applicant): Critical care has excellent measures of severity of illness calibrated to mortality, but severity may be reflected in subsequent morbidity as well survival. A major challenge of critical care outcomes research and applicable to all medical outcomes and quality issues is the development of methods that predict the full range of outcomes from normal through the range of morbidities as well as death. The AIM of this proposal is to develop and validate a predictor of 3 or more outcome states from pediatric intensive care: death, survival one or more states of reduced functional status, and survival with normal or unchanged functional status. Preliminary Studies demonstrate a) the feasibility of the statistical approach and b) the applicability and utility of a new functional status assessment method (Functional Status Score, FSS) developed by the CPCCRN and by this PI for the purpose of this proposal. METHODS: Consecutive patients without exclusion from the participating PICUS will be utilized. Core data will consist of physiological data, diagnoses, age and other demographic information, FSS (pre-admission, PICU discharge, hospital discharge), survival/death (PICU and hospital), therapies affecting functional status, imaging, Outcome prediction for multiple functional states with normal function and death being the extreme will include both "simple" linear models with the FSS contributing the gradations of outcome, and polychotomous logistic regression analysis for models of 3 or more discrete outcome states. Statistical models will use up to 12 predictor variables including PRISM III score without neurological variables, neurological variables only, pre- ICU care area, operative status, diagnoses (up to 6), age, baseline FSS. Statistical methods will include "simple" linear regression conceptualizing outcome on a scale of normal to death with worsening functional states in between and polychotomous logistic regression utilizing the FSS to define 2 of more outcome states in addition to death. Sample size estimates based on a 4% mortality rate and a 4% new severe functional status are 5067 but will be re-estimated when units are selected. PUBLIC HEALTH RELEVANCE: Shifting the paradigm of severity assessment by advancing its conceptual and statistical foundations will stimulate change. Important advances could occur in quality research and methods, long-term outcome forecasting including pediatric disability at PICU discharge, and decision making by including severely decreased functional status probabilities as well as mortality probabilities based on admission severity.