OBJECTIVE: Lung protective ventilation reduces mortality in patients with acute lung injury, but underrecognition of acute lung injury has limited its use. We recently validated an automated electronic acute lung injury surveillance system in patients with major trauma in a single intensive care unit. In this study, we assessed the system's performance as a prospective acute lung injury screening tool in a diverse population of intensive care unit patients. DESIGN: Patients were screened prospectively for acute lung injury over 21 wks by the automated system and by an experienced research coordinator who manually screened subjects for enrollment in Acute Respiratory Distress Syndrome Clinical Trials Network (ARDSNet) trials. Performance of the automated system was assessed by comparing its results with the manual screening process. Discordant results were adjudicated blindly by two physician reviewers. In addition, a sensitivity analysis using a range of assumptions was conducted to better estimate the system's performance. SETTING: The Hospital of the University of Pennsylvania, an academic medical center and ARDSNet center (1994-2006). PATIENTS: Intubated patients in medical and surgical intensive care units. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 1270 patients screened, 84 were identified with acute lung injury (incidence of 6.6%). The automated screening system had a sensitivity of 97.6% (95% confidence interval, 96.8-98.4%) and a specificity of 97.6% (95% confidence interval, 96.8-98.4%). The manual screening algorithm had a sensitivity of 57.1% (95% confidence interval, 54.5-59.8%) and a specificity of 99.7% (95% confidence interval, 99.4-100%). Sensitivity analysis demonstrated a range for sensitivity of 75.0-97.6% of the automated system under varying assumptions. Under all assumptions, the automated system demonstrated higher sensitivity than and comparable specificity to the manual screening method. CONCLUSIONS: An automated electronic system identified patients with acute lung injury with high sensitivity and specificity in diverse intensive care units of a large academic medical center. Further studies are needed to evaluate the effect of automated prompts that such a system can initiate on the use of lung protective ventilation in patients with acute lung injury.
OBJECTIVE: Lung protective ventilation reduces mortality in patients with acute lung injury, but underrecognition of acute lung injury has limited its use. We recently validated an automated electronic acute lung injury surveillance system in patients with major trauma in a single intensive care unit. In this study, we assessed the system's performance as a prospective acute lung injury screening tool in a diverse population of intensive care unit patients. DESIGN:Patients were screened prospectively for acute lung injury over 21 wks by the automated system and by an experienced research coordinator who manually screened subjects for enrollment in Acute Respiratory Distress Syndrome Clinical Trials Network (ARDSNet) trials. Performance of the automated system was assessed by comparing its results with the manual screening process. Discordant results were adjudicated blindly by two physician reviewers. In addition, a sensitivity analysis using a range of assumptions was conducted to better estimate the system's performance. SETTING: The Hospital of the University of Pennsylvania, an academic medical center and ARDSNet center (1994-2006). PATIENTS: Intubated patients in medical and surgical intensive care units. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 1270 patients screened, 84 were identified with acute lung injury (incidence of 6.6%). The automated screening system had a sensitivity of 97.6% (95% confidence interval, 96.8-98.4%) and a specificity of 97.6% (95% confidence interval, 96.8-98.4%). The manual screening algorithm had a sensitivity of 57.1% (95% confidence interval, 54.5-59.8%) and a specificity of 99.7% (95% confidence interval, 99.4-100%). Sensitivity analysis demonstrated a range for sensitivity of 75.0-97.6% of the automated system under varying assumptions. Under all assumptions, the automated system demonstrated higher sensitivity than and comparable specificity to the manual screening method. CONCLUSIONS: An automated electronic system identified patients with acute lung injury with high sensitivity and specificity in diverse intensive care units of a large academic medical center. Further studies are needed to evaluate the effect of automated prompts that such a system can initiate on the use of lung protective ventilation in patients with acute lung injury.
Authors: Majid Afshar; Gordon S Smith; Michael L Terrin; Matthew Barrett; Matthew E Lissauer; Sahar Mansoor; Jean Jeudy; Giora Netzer Journal: J Trauma Acute Care Surg Date: 2014-06 Impact factor: 3.313
Authors: Francis Perry Wilson; Peter P Reese; Michael Gs Shashaty; Susan S Ellenberg; Yevgeniy Gitelman; Amar D Bansal; Richard Urbani; Harold I Feldman; Barry Fuchs Journal: Clin Trials Date: 2014-07-14 Impact factor: 2.486
Authors: Sahar Mansoor; Majid Afshar; Matthew Barrett; Gordon S Smith; Erik A Barr; Matthew E Lissauer; Michael T McCurdy; Sarah B Murthi; Giora Netzer Journal: Am J Emerg Med Date: 2014-12-09 Impact factor: 2.469
Authors: Craig R Rackley; Joseph E Levitt; Hanjing Zhuo; Michael A Matthay; Carolyn S Calfee Journal: J Intensive Care Med Date: 2012-06-24 Impact factor: 3.510
Authors: Majid Afshar; Cara Joyce; Anthony Oakey; Perry Formanek; Philip Yang; Matthew M Churpek; Richard S Cooper; Susan Zelisko; Ron Price; Dmitriy Dligach Journal: AMIA Annu Symp Proc Date: 2018-12-05