A nomogram to predict postoperative pulmonary complications after cardiothoracic surgery.

OBJECTIVE: The objective was to develop a novel scoring system that would be predictive of postoperative pulmonary complications in critically ill patients after cardiac and major vascular surgery. METHODS: A total of 17,433 postoperative patients after coronary artery bypass graft, valve, or thoracic aorta repair surgery admitted to the cardiovascular intensive care units at Cleveland Clinic Main Campus from 2009 to 2015. The primary outcome was the composite of postoperative pulmonary complications, including pneumonia, prolonged postoperative mechanical ventilation (>48 hours), or reintubation occurring during the hospital stay. Elastic net logistic regression was used on the training subset to build a prediction model that included perioperative predictors. Five-fold cross-validation was used to select an appropriate subset of the predictors. The predictive efficacy was assessed with calibration and discrimination statistics. Post hoc, of 13,353 adult patients, we tested the clinical usefulness of our risk prediction model on 12,956 patients who underwent surgery from 2015 to 2019. RESULTS: Postoperative pulmonary complications were observed in 1669 patients (9.6%). A prediction model that included baseline and demographic risk factors along with perioperative predictors had a C-statistic of 0.87 (95% confidence interval, 0.86-0.88), with a corrected Brier score of 0.06. Our prediction model maintains satisfactory discrimination (C-statistics of 0.87) and calibration (Brier score of 0.07) abilities when evaluated on an independent dataset of 12,843 recent adult patients who underwent cardiovascular surgery. CONCLUSIONS: A novel prediction nomogram accurately predicted postoperative pulmonary complications after major cardiac and vascular surgery. Intensivists may use these predictors to allow for proactive and preventative interventions in this patient population.

Prevalence of Burnout Among Respiratory Therapists Amidst the COVID-19 Pandemic.

Background: Burnout is a major challenge in health care, but its prevalence has not been evaluated in practicing respiratory therapist (RTs). The purpose of this study was to identify RT burnout prevalence and factors associated with RT burnout.Methods: An online survey was administered to 26 centers in the United States and between January and March 2021. Validated, quantitative, cross-sectional surveys were used to measure burnout and leadership domains. The survey was sent to department directors and distributed by the department directors to staff. Data analysis was descriptive and logistic regression analysis was performed to evaluate risk factors, expressed as odds ratios (OR), for burnout.Results: The survey was distributed to 3,010 RTs, and the response rate was 37%. Seventy-nine percent of respondents reported burnout, 10% with severe, 32% with moderate, and 37% with mild burnout. Univariate analysis revealed those with burnout worked more hours per week, worked more hours per week in the ICU, primarily cared for adult patients, primarily delivered care via RT protocols, reported inadequate RT staffing, reported being unable to complete assigned work, were more frequently exposed to COVID-19, had a lower leadership score, and fewer had a positive view of leadership. Logistic regression revealed burnout climate (OR 9.38, p<0.001), inadequate RT staffing (OR 2.08 to 3.19, p=0.004 to 0.05), being unable to complete all work (OR 2.14 to 5.57, p=0.003 to 0.20), and missing work for any reason were associated with increased risk of burnout (OR 1.96, p=0.007). Not providing patient care (OR 0.18, p=0.02) and a positive leadership score (0.55, p=0.02) were associated with decreased risk of burnout.Conclusion: Burnout was common among RTs in the midst of the COVID-19 pandemic. Good leadership was protective against burnout while inadequate staffing, inability to complete work, and burnout climate were associated with burnout.