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Background: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures., Methods: The National (Nationwide) Inpatient Sample for 2015 was used to establish our model. We identified 417 respiratory failure from a total of 4,062 patients who underwent pulmonary lobectomy. Risk factors for respiratory failure were identified, analyzed, and used in novel machine learning models to predict respiratory failure., Results: Factors that contributed to increased odds of respiratory failure, such as preexisting chronic diseases, and intraoperative and postoperative events during hospitalization were identified. Two machine learning-based prediction models were generated and optimized by the knowledge accrued from the clinical course of postlobectomy patients. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making., Conclusion: We identified risk factors for respiratory failure after lobectomy and introduced 2 machine learning-based techniques to predict respiratory failure for quality review and clinical decision-making settings. Such techniques can be used to not only provide targeted support but also standardize quality peer review measures., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objectives/hypothesis: Malignant otitis externa (MOE) is a rare disorder that is not well studied in the inpatient setting. The Nationwide Inpatient Sample (NIS) database was utilized to analyze characteristics and predischarge outcomes of hospitalized MOE patients., Methods: MOE hospitalizations were identified in the 2002 to 2013 NIS. Patient demographics, length of hospital stay, hospital charges, concomitant diagnoses, treatment-related procedures, complications, and in-hospital mortality rates were examined, with comparisons made among age cohorts and between diabetes mellitus (DM) and non-DM groups., Results: A total of 8,300 cases of inpatient MOE were identified, with elderly DM patients compromising 22.7% of cases. Compared to adults, elderly patients had more inpatient procedures, longer hospitalizations (6.0 vs. 4.3 days), higher hospital charges ($26,712 vs. $19,047) (all P < 0.001), greater odds of in-hospital complications, and in-hospital mortality (odds ratio 14.435, 95% confidence interval 5.313-39.220). Adult/elderly patients with DM had more comorbidities, longer hospital stays (5.5 vs. 4.0 days), and higher hospital charges ($25,118 vs. $17,039) (all P < 0.001) than non-DM patients. However, DM was not associated with greater in-hospital mortality rates (0.6% vs. 0.5%; P = 0.640). Compared to the adult/elderly cohort, pediatric patients had higher rates of nonelective admissions (19.8% vs. 14.5%), shorter hospital stays (2.9 vs. 4.9 days), and lower hospital charges ($8,876 vs. $21,672) (all P < 0.05)., Conclusion: Elderly diabetic patients made up a smaller fraction of hospitalized MOE cases than anticipated. Elderly patients had greater in-hospital complications and mortality. Diabetes mellitus in adult/elderly patients was not associated with increased mortality. Pediatric patients fared well with low complications rates and no instances of in-hospital mortality., Level of Evidence: 2C. Laryngoscope, 127:2328-2336, 2017., (© 2016 The American Laryngological, Rhinological and Otological Society, Inc.)

The article discusses a study to evaluate the safety of training and assessment in operating theaters by assessing the performance of specialists and trainees. The findings revealed that no significant differences were observed between the procedural outcomes of trainees and specialists, thereby indicating that trainees are safe to operate under senior supervision. Several procedural outcomes have been found capable of evaluating the performance of clinicians and healthcare systems.