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

<p>Abstract</p> <p>Background</p> <p>Acute 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.</p> <p>Methods/Design</p> <p>In 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.</p> <p>Discussion</p> <p>This 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.</p

Association of Pre-Hospitalization Aspirin Therapy and Acute Lung Injury: Results of a Multicenter International Observational Study of At-Risk Patients

OBJECTIVE: To evaluate the association between prehospitalization aspirin therapy and incident acute lung injury in a heterogeneous cohort of at-risk medical patients. DESIGN: This is a secondary analysis of a prospective multicenter international cohort investigation. SETTING: Multicenter observational study including 20 US hospitals and two hospitals in Turkey. PATIENTS: Consecutive, adult, nonsurgical patients admitted to the hospital with at least one major risk factor for acute lung injury. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Baseline characteristics and acute lung injury risk factors/modifiers were identified. The presence of aspirin therapy and the propensity to receive this therapy were determined. The primary outcome was acute lung injury during hospitalization. Secondary outcomes included intensive care unit and hospital mortality and intensive care unit and hospital length of stay. Twenty-two hospitals enrolled 3855 at-risk patients over a 6-month period. Nine hundred seventy-six (25.3%) were receiving aspirin at the time of hospitalization. Two hundred forty (6.2%) patients developed acute lung injury. Univariate analysis noted a reduced incidence of acute lung injury in those receiving aspirin therapy (odds ratio [OR], 0.65; 95% confidence interval [CI], 0.46-0.90; p = .010). This association was attenuated in a stratified analysis based on deciles of aspirin propensity scores (Cochran-Mantel-Haenszel pooled OR, 0.70; 95% CI, 0.48-1.03; p = .072). CONCLUSIONS: After adjusting for the propensity to receive aspirin therapy, no statistically significant associations between prehospitalization aspirin therapy and acute lung injury were identified; however, a prospective clinical trial to further evaluate this association appears warranted

Electronic health record surveillance algorithms facilitate the detection of transfusion‐related pulmonary complications

BackgroundTransfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) are leading causes of transfusion-related mortality. Notably, poor syndrome recognition and underreporting likely result in an underestimate of their true attributable burden. We aimed to develop accurate electronic health record-based screening algorithms for improved detection of TRALI/transfused acute lung injury (ALI) and TACO.Study design and methodsThis was a retrospective observational study. The study cohort, identified from a previous National Institutes of Health-sponsored prospective investigation, included 223 transfused patients with TRALI, transfused ALI, TACO, or complication-free controls. Optimal case detection algorithms were identified using classification and regression tree (CART) analyses. Algorithm performance was evaluated with sensitivities, specificities, likelihood ratios, and overall misclassification rates.ResultsFor TRALI/transfused ALI detection, CART analysis achieved a sensitivity and specificity of 83.9% (95% confidence interval [CI], 74.4%-90.4%) and 89.7% (95% CI, 80.3%-95.2%), respectively. For TACO, the sensitivity and specificity were 86.5% (95% CI, 73.6%-94.0%) and 92.3% (95% CI, 83.4%-96.8%), respectively. Reduced PaO2 /FiO2 ratios and the acquisition of posttransfusion chest radiographs were the primary determinants of case versus control status for both syndromes. Of true-positive cases identified using the screening algorithms (TRALI/transfused ALI, n = 78; TACO, n = 45), only 11 (14.1%) and five (11.1%) were reported to the blood bank by physicians, respectively.ConclusionsElectronic screening algorithms have shown good sensitivity and specificity for identifying patients with TRALI/transfused ALI and TACO at our institution. This supports the notion that active electronic surveillance may improve case identification, thereby providing a more accurate understanding of TRALI/transfused ALI and TACO epidemiology

The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation

OBJECTIVE: Unplanned readmission of hospitalized patients to an intensive care unit (ICU) is associated with a worse outcome, but our ability to identify who is likely to deteriorate after ICU dismissal is limited. The objective of this study is to develop and validate a numerical index, named the Stability and Workload Index for Transfer, to predict ICU readmission. DESIGN: In this prospective cohort study, risk factors for ICU readmission were identified from a broad range of patients' admission and discharge characteristics, specific ICU interventions, and in-patient workload measurements. The prediction score was validated in two independent ICUs. SETTING: One medical and one mixed medical-surgical ICU in two tertiary centers. PATIENTS: Consecutive patients requiring >24 hrs of ICU care. INTERVENTIONS: None. MEASUREMENTS: Unplanned ICU readmission or unexpected death following ICU dismissal. RESULTS: In a derivation cohort of 1,131 medical ICU patients, 100 patients had unplanned readmissions, and five died unexpectedly in the hospital following ICU discharge. Predictors of readmission/unexpected death identified in a logistic regression analysis were ICU admission source, ICU length of stay, and day of discharge neurologic (Glasgow Coma Scale) and respiratory (hypoxemia, hypercapnia, or nursing requirements for complex respiratory care) impairment. The Stability and Workload Index for Transfer score predicted readmission more precisely (area under the curve [AUC], 0.75; 95% confidence interval [CI], 0.70-0.80) than the day of discharge Acute Physiology and Chronic Health Evaluation III score (AUC, 0.62; 95% CI, 0.56-0.68). In the two validation cohorts, the Stability and Workload Index for Transfer score predicted readmission similarly in a North American medical ICU (AUC, 0.74; 95% CI, 0.67-0.80) and a European medical-surgical ICU (AUC, 0.70; 95% CI, 0.64-0.76), but was less well calibrated in the medical-surgical ICU. CONCLUSION: The Stability and Workload Index for Transfer score is derived from information readily available at the time of ICU dismissal and acceptably predicts ICU readmission. It is not known if discharge decisions based on this prediction score will decrease the number of ICU readmissions and/or improve outcom