Background: Thoracoscopic repair of esophageal atresia with tracheoesophageal fistula (EA/TEF) remains technically challenging due to the rarity of these procedures. The aim of this study is to report our experience with thoracoscopic repair of type C EA/TEF and to evaluate the learning curve based upon the surgeon's skill level.
Methods: We retrospectively reviewed data of thoracoscopic EA/TEF repair performed in our center between October 2008 and May 2019. The learning curve was evaluated using the cumulative sum (CUSUM) method based on operative time.
Results: Of the 50 consecutive cases evaluated, the mean birth weight was 2634 ± 608 g and the median age at operation was 3 days (range, 1-29 days). The mean operation time was 144 ± 65 min. Anastomosis leakage occurred in 3 cases (6%) and strictures requiring balloon dilatations occurred in 16 cases (32%). The CUSUM analysis evaluated a learning curve of approximately 10 cases of thoracoscopic type C EA/TEF repair. A lower gestational age was associated with longer operation time.
Conclusions: Thoracoscopic repair of type C EA/TEF is a feasible and safe procedure. The number of procedures required to achieve a stable learning curve was 10. The learning phase may be shortened by adequate set-up under the supervision of an expert endoscopic surgeon.
Type Of Study: Retrospective Comparative Treatment Study.
Level Of Evidence: III.
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http://dx.doi.org/10.1016/j.jpedsurg.2020.06.005 | DOI Listing |
Diagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFKnee Surg Relat Res
January 2025
Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Background: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA.
Remote, digital cognitive testing on an individual's own device provides the opportunity to deploy previously understudied but promising cognitive paradigms in preclinical Alzheimer's disease (AD). The Boston Remote Assessment for NeuroCognitive Health (BRANCH) captures a personalized learning curve for the same information presented over seven consecutive days. Here, we examined BRANCH multi-day learning curves (MDLCs) in 167 cognitively unimpaired older adults (age = 74.
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