Predicting Morbidity from Pediatric Critical Care

预测儿科重症监护的发病率

• 批准号: 8601311
• 负责人: Heidi J Dalton
• 金额: $ 26.58万
• 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/8601311
• 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.

描述（由申请人提供）：重症监护具有针对死亡率校准的疾病严重程度的良好测量，但严重程度可能反映在随后的发病率以及生存率中。重症监护结局研究的一个主要挑战，适用于所有的医疗结局和质量问题是预测从正常到发病率范围以及死亡的全方位结局的方法的发展。本提案的目的是开发和验证儿科重症监护中3种或以上结局状态的预测因子：死亡、一种或多种功能状态降低的生存期以及功能状态正常或不变的生存期。初步研究证明了a）统计方法的可行性和B）CPCCRN和本PI为本提案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中排除的连续性患者。核心数据将包括生理数据、诊断、年龄和其他人口统计信息，（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，具有正常功能和死亡的多种功能状态的结局预测将包括两个“简单”线性模型，其中FSS贡献结局的分级，以及用于3个或更多离散结果状态的模型的多分类逻辑回归分析。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、仅含神经学变量、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，其在正常至死亡的量表上概念化结局，其中功能状态恶化介于两者之间，以及多分类逻辑回归，其利用FSS定义除死亡外的2种或更多种结局状态。基于4%死亡率和4%新发重度功能状态的样本量估计值为5067，但在选择单位时将重新估计。

项目成果

Pediatric mechanical circulatory support: future directions.

儿科机械循环支持：未来方向。

• DOI: 10.1097/pcc.0b013e318292e562
• 发表时间: 2013
• 期刊: Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
• 作者: Dalton,HeidiJ;Dodge-Khatami,Ali;MacLaren,Graeme
• 通讯作者: MacLaren,Graeme