Predicting Morbidity from Pediatric Critical Care

预测儿科重症监护的发病率

• 批准号: 8197201
• 负责人: Heidi J Dalton
• 金额: $ 27.34万
• 依托单位: PHOENIX CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-24 至 2014-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8197201
• 关键词: Acute; Admission activity; Adult; Affect; Age; Area; Blood Coagulation Disorders; Boston; Cardiovascular system; Caring; Case-Mix Adjustments; Cessation of life; Characteristics; Child; Childhood; Chronic; Chronic lung disease; Critical Care; Data; Decision Making; Development; Diagnosis; Exclusion; Foundations; Functional disorder; Funding; Health Status; Healthcare; Hospitals; Hour; Hypoglycemia; Image; Injury; Institution; Intensive Care; Intracranial Hemorrhages; Kidney; Lead; Linear Models; Linear Regressions; Logistic Regressions; Measures; Mechanical ventilation; Medical; Medicine; Metabolic; Methodology; Methods; Modeling; Morbidity - disease rate; Myopathy; Nature; Neonatal; Nervous System Trauma; Neurologic; New York; Outcome; Outcomes Research; Patients; Pediatric Hospitals; Pediatric Intensive Care Units; Perinatal; Physiological; Population Characteristics; Principal Investigator; Probability; Quality of Care; Recovery; Regression Analysis; Rehabilitation therapy; Research Methodology; Risk; Sample Size; Sampling; Severities; Severity of illness; Statistical Methods; Statistical Models; Steroids; System; Work; base; disability; functional disability; functional status; method development; mortality; outcome forecast; patient population; population based; programs; public health relevance; respiratory; success

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.

描述（由申请人提供）：重症监护具有根据死亡率校准的疾病严重程度的出色衡量标准，但严重程度可能会反映在随后的发病率和生存率中。适用于所有医疗结果和质量问题的重症监护结果研究的一个主要挑战是开发预测从正常到发病范围乃至死亡的全方位结果的方法。该提案的目的是开发和验证儿科重症监护的 3 种或更多结果状态的预测因子：死亡、生存一种或多种功能状态降低的状态，以及功能状态正常或不变的生存。初步研究证明了 a) 统计方法的可行性，以及 b) CPCCRN 和本 PI 为本提案的目的而开发的新功能状态评估方法（功能状态评分，FSS）的适用性和实用性。方法：将使用未从参与 PICUS 中排除的连续患者。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、功能正常和死亡为极端的多种功能状态的结果预测将包括“简单”线性模型（FSS贡献结果分级）以及针对3个或更多模型的多分类逻辑回归分析 离散的结果状态。统计模型将使用多达 12 个预测变量，包括不含神经学变量的 PRISM III 评分、仅神经学变量、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”线性回归，以正常到死亡的范围来概念化结果，其间有恶化的功能状态，以及利用 FSS 来定义除死亡之外的 2 个或更多结果状态的多分类逻辑回归。基于 4% 死亡率和 4% 新严重功能状态的样本量估计为 5067，但在选择单位时将重新估计。 公共卫生相关性：通过推进其概念和统计基础来改变严重性评估的范式将刺激变革。质量研究和方法、长期结果预测（包括 PICU 出院时的儿科残疾）以及根据入院严重程度纳入严重降低的功能状态概率和死亡率概率的决策可能会取得重要进展。