Sweta Thakur

Acute lung injury prediction score: derivation and validation in a population-based sample.

Early recognition of patients at high risk of acute lung injury (ALI) is critical for successful enrolment of patients in prevention strategies for this devastating syndrome. We aimed to develop and prospectively validate an ALI prediction score in a population-based sample of patients at risk. In a retrospective derivation cohort, predisposing conditions for ALI were identified at the time of hospital admission. The score was calculated based on the results of logistic regression analysis. Prospective validation was performed in an independent cohort of patients at risk identified at the time of hospital admission. In a derivation cohort of 409 patients with ALI risk factors, the lung injury prediction score discriminated patients who developed ALI from those who did not with an area under the curve (AUC) of 0.84 (95% CI 0.80–0.89; Hosmer–Lemeshow p = 0.60). The performance was similar in a prospective validation cohort of 463 patients at risk of ALI (AUC 0.84, 95% CI 0.77–0.91; Hosmer–Lemeshow p = 0.88). ALI prediction scores identify patients at high risk for ALI before intensive care unit admission. If externally validated, this model will serve to define the population of patients at high risk for ALI in whom future mechanistic studies and ALI prevention trials will be conducted.

Limiting ventilator-induced lung injury through individual electronic medical record surveillance

Background:To improve the safety of ventilator care and decrease the risk of ventilator-induced lung injury, we designed and tested an electronic algorithm that incorporates patient characteristics and ventilator settings, allowing near-real-time notification of bedside providers about potentially injurious ventilator settings. Methods:Electronic medical records of consecutive patients who received invasive ventilation were screened in three Mayo Clinic Rochester intensive care units. The computer system alerted bedside providers via the text paging notification about potentially injurious ventilator settings. Alert criteria included a Pao2/Fio2 ratio of <300 mm Hg, free text search for the words “edema” or “bilateral + infiltrates” on the chest radiograph report, a tidal volume of >8 mL/kg predicted body weight (based on patient gender and height), a plateau pressure of >30 cm H2O, and a peak airway pressure of >35 cm H2O. Respiratory therapists answered a brief online satisfaction survey. Ventilator-induced lung injury risk was compared before and after the introduction of ventilator-induced lung injury alert. Findings:The prevalence of acute lung injury was 42% (n = 490) among 1,159 patients receiving >24 hrs of invasive ventilation. The system sent 111 alerts for 80 patients, with a positive predictive value of 59%. The exposure to potentially injurious ventilation decreased after the intervention from 40.6 ± 74.6 hrs to 26.9 ± 77.3 hrs (p = .004). Interpretations:Electronic medical record surveillance of mechanically ventilated patients accurately detects potentially injurious ventilator settings and is able to influence bedside practice at moderate costs. Its implementation is associated with decreased patient exposure to potentially injurious mechanical ventilation settings.

Towards the prevention of acute lung injury: a population based cohort study protocol

BackgroundAcute lung injury (ALI) is an example of a critical care syndrome with limited treatment options once the condition is fully established. Despite improved understanding of pathophysiology of ALI, the clinical impact has been limited to improvements in supportive treatment. On the other hand, little has been done on the prevention of ALI. Olmsted County, MN, geographically isolated from other urban areas offers the opportunity to study clinical pathogenesis of ALI in a search for potential prevention targets.Methods/DesignIn this population-based observational cohort study, the investigators identify patients at high risk of ALI using the prediction model applied within the first six hours of hospital admission. Using a validated system-wide electronic surveillance, Olmsted County patients at risk are followed until ALI, death or hospital discharge. Detailed in-hospital (second hit) exposures and meaningful short and long term outcomes (quality-adjusted survival) are compared between ALI cases and high risk controls matched by age, gender and probability of developing ALI. Time sensitive biospecimens are collected for collaborative research studies. Nested case control comparison of 500 patients who developed ALI with 500 matched controls will provide an adequate power to determine significant differences in common hospital exposures and outcomes between the two groups.DiscussionThis population-based observational cohort study will identify patients at high risk early in the course of disease, the burden of ALI in the community, and the potential targets for future prevention trials.

Alcohol consumption and development of acute respiratory distress syndrome: a population-based study.

This retrospective population-based study evaluated the effects of alcohol consumption on the development of acute respiratory distress syndrome (ARDS). Alcohol consumption was quantified based on patient and/or family provided information at the time of hospital admission. ARDS was defined according to American-European consensus conference (AECC). From 1,422 critically ill Olmsted county residents, 1,357 had information about alcohol use in their medical records, 77 (6%) of whom developed ARDS. A history of significant alcohol consumption (more than two drinks per day) was reported in 97 (7%) of patients. When adjusted for underlying ARDS risk factors (aspiration, chemotherapy, high-risk surgery, pancreatitis, sepsis, shock), smoking, cirrhosis and gender, history of significant alcohol consumption was associated with increased risk of ARDS development (odds ratio 2.9, 95% CI 1.3–6.2). This population-based study confirmed that excessive alcohol consumption is associated with higher risk of ARDS.