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Background: The optimal training program to transform a new resident into a competent and capable surgeon is constantly evolving. Competency-based evaluation represents a change in mindset from quantitative or chronologic metrics for graduate readiness. As surgery becomes more specialized, more dependent on technology, and more public, we must continue to improve our ability to pass on technical skills. Approaching surgery in a component-based fashion enables even the most complex operation to be broken down into smaller sets of steps that range the entire spectrum of complexity. Treating an operation through the lens of its components, emphasizing stepwise forward progression in a trainee's experience, may provide a way to train competent surgeons more efficiently. Current case-logging products do not provide adequate granularity to apply this methodology., Methods: Application design relied on the involvement of local surgeons from all specialties and subspecialties related to general surgical training. Individual interviews with multiple experts in each field were used to generate a list of most commonly performed operations. Once a consensus was reached, the same surgeons were queried on what they felt were the core steps that make up each operation. This information was utilized to create a novel mobile application which enables the user to record cases by date, attending surgeon, specific operation, and which portions of the operation they were able/allowed to perform., Conclusion: Component-based case logging through the Logix application may be a useful adjunct as we continue to implement competency-based surgical training. Future investigation will assess user experience and compare subjective and objective metrics of training progression between the Logix application and currently utilized products. The information provided by the application stands to benefit not just trainees, but educators, training programs, and regulatory bodies., Key Message: Component-based case logging via a novel mobile application stands to increase the efficiency of surgical training and more effectively assess trainee competency., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Inc.)

Objective: To generate a heart failure (HF) readmission prediction model using the Nationwide Readmissions Database to guide management and reduce HF readmissions., Patients and Methods: A retrospective analysis was performed for patients listed for HF admissions in the Nationwide Readmissions Database from January 1, 2010, to December 31, 2014. A Cox proportional hazards model for sample survey data for the prediction of readmission for all patients with HF was implemented using a derivation cohort (2010-2012). We generated receiver operating characteristic (ROC) curves and estimated area under the ROC curve at each time point (30, 60, 90, and 180 days) to assess the accuracy of our predictive model using the derivation cohort (2010-2012) and compared it with the validation cohort (2013-2014). A risk score was computed for the validation cohort. On the basis of the total risk score, we calculated the probability of readmission at 30, 60, 90, and 180 days., Results: Approximately 1,420,564 patients were admitted for HF, contributing to 1,817,735 total HF admissions. Of these, 665,867 patients had at least 1 readmission for HF. The 10 most common comorbidities for readmitted patients included hypertension, diabetes mellitus, renal failure, chronic pulmonary disease, deficiency anemia, fluid and electrolyte disorders, obesity, hypothyroidism, peripheral vascular disorders, and depression. The area under the ROC curve for the prediction model was 0.58 in the derivation cohort and 0.59 in the validation cohort., Conclusion: The prediction model will find clinical utility at point of care in optimizing the management of patients with HF and reducing HF readmissions., (© 2022 The Authors.)