Kramer Andrew A, Gershengorn Hayley B, Wunsch Hannah, Zimmerman Jack E
1Prescient Healthcare Consulting, Charlottesville, VA. 2Cerner Corporation, Vienna, VA. 3Department of Biostatistics, Kansas University Medical Center, Kansas City, MO. 4Division of Critical Care Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY. 5Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Departments of Anesthesia and Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada. 6Department of Anesthesiology and Critical Care Medicine, George Washington University Hospital, Washington, DC.
Crit Care Med. 2016 Jun;44(6):1042-8. doi: 10.1097/CCM.0000000000001636.
To develop a model that predicts the duration of mechanical ventilation and then to use this model to compare observed versus expected duration of mechanical ventilation across ICUs.
Retrospective cohort analysis.
Eighty-six eligible ICUs at 48 U.S. hospitals.
ICU patients receiving mechanical ventilation on day 1 (n = 56,336) admitted from January 2013 to September 2014.
None.
We developed and validated a multivariable logistic regression model for predicting duration of mechanical ventilation using ICU day 1 patient characteristics. Mean observed minus expected duration of mechanical ventilation was then obtained across patients and for each ICU. The accuracy of the model was assessed using R. We defined better performing units as ICUs that had an observed minus expected duration of mechanical ventilation less than -0.5 days and a p value of less than 0.01; and poorer performing units as ICUs with an observed minus expected duration of mechanical ventilation greater than +0.5 days and a p value of less than 0.01. The factors accounting for the majority of the model's explanatory power were diagnosis (71%) and physiologic abnormalities (24%). For individual patients, the difference between observed and mean predicted duration of mechanical ventilation was 3.3 hours (95% CI, 2.8-3.9) with R equal to 21.6%. The mean observed minus expected duration of mechanical ventilation across ICUs was 3.8 hours (95% CI, 2.1-5.5), with R equal to 69.9%. Among the 86 ICUs, 66 (76.7%) had an observed mean mechanical ventilation duration that was within 0.5 days of predicted. Five ICUs had significantly (p < 0.01) poorer performance (observed minus expected duration of mechanical ventilation, > 0.5 d) and 14 ICUs significantly (p < 0.01) better performance (observed minus expected duration of mechanical ventilation, < -0.5 d).
Comparison of observed and case-mix-adjusted predicted duration of mechanical ventilation can accurately assess and compare duration of mechanical ventilation across ICUs, but cannot accurately predict an individual patient's mechanical ventilation duration. There are substantial differences in duration of mechanical ventilation across ICU and their association with unit practices and processes of care warrants examination.
建立一个预测机械通气时长的模型，然后使用该模型比较各重症监护病房（ICU）机械通气的实际时长与预期时长。
回顾性队列分析。
美国48家医院的86个符合条件的ICU。
2013年1月至2014年9月期间入住ICU且第1天接受机械通气的患者（n = 56336）。
无。
我们利用ICU第1天患者的特征建立并验证了一个用于预测机械通气时长的多变量逻辑回归模型。然后计算了所有患者以及每个ICU的机械通气实际时长减去预期时长的平均值。使用R评估模型的准确性。我们将表现较好的单位定义为机械通气实际时长减去预期时长小于-0.5天且p值小于0.01的ICU；将表现较差的单位定义为机械通气实际时长减去预期时长大于+0.5天且p值小于0.01的ICU。占模型解释力大部分的因素是诊断（71%）和生理异常（24%）。对于个体患者，机械通气实际时长与平均预测时长的差异为3.3小时（95%CI，2.8 - 3.9），R值为21.6%。各ICU机械通气实际时长减去预期时长的平均值为3.8小时（95%CI，2.1 - 5.5），R值为69.9%。在86个ICU中，66个（76.7%）的机械通气实际平均时长在预测值的0.5天范围内。5个ICU表现显著较差（p < 0.01）（机械通气实际时长减去预期时长，> 0.5天），14个ICU表现显著较好（p < 0.01）（机械通气实际时长减去预期时长，< -0.5天）。
比较机械通气的实际时长与病例组合调整后的预测时长能够准确评估和比较各ICU的机械通气时长，但无法准确预测个体患者的机械通气时长。各ICU的机械通气时长存在显著差异，其与科室实践和护理流程的关联值得研究。
