Background: The learning curve for robotic colorectal surgery is ill-defined. This study aimed to investigate the learning curve of experienced laparoscopic rectal surgeons when starting with robotic total mesorectal excision (TME) using cumulative sum (CUSUM) charts.
Methods: This retrospective case series analysed patients who underwent curative and elective laparoscopic or robotic TMEs for rectal cancer performed by two surgeons. The first consecutive robotic TME cases of each surgeon were 1:1 propensity score matched to their laparoscopic TME cases using age, body mass index, American Society of Anesthesiologists grade, T stage (AJCC) and tumour location height. The matched laparoscopic cases defined individual standards for the quality indicators: operating time, R stage, lymph node harvest, length of hospital stay and major complications (Clavien-Dindo grade 3-5). Deviation of more than a quarter of a standard deviation from the mean for the continuous indicators, or exceeding the observed risk for the binary indicators was defined as off-target with an upward inflection in the CUSUM curve.
Results: From 2006 to 2015, 384 (294 laparoscopic; 90 robotic) TMEs met the inclusion criteria. Surgeon A performed 206 (70.1%) of the laparoscopic and 43 (47.8%) of the robotic cases. Surgeon B performed 88 (29.9%) of the laparoscopic and 47 (52.2%) of the robotic cases. After matching, no covariate exhibited an absolute standardised mean difference >0.25. For surgeon A, the CUSUM curves showed no apparent learning process compared to his laparoscopic standards. For surgeon B, a learning process for operation time, lymph node harvest and major complications was demonstrated by an initial upward inflection of the CUSUM curves; after 15 cases, all quality indicators were generally on target.
Conclusions: For experienced laparoscopic colorectal surgeons, the formal learning process for robotic TME may be short to reach a similar performance level as obtained in conventional laparoscopy.
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http://dx.doi.org/10.1007/s00464-017-5453-9 | DOI Listing |
Exp Biol Med (Maywood)
December 2024
Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients.
View Article and Find Full Text PDFJAMIA Open
February 2025
Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, United States.
Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.
Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.
Front Immunol
December 2024
Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Background: Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear.
View Article and Find Full Text PDFFront Oncol
December 2024
NeuroRadiology Unit, Ospedale del Mare, Azienda Sanitaria Locale Napoli 1 Centro (ASL NA1 Centro), Naples, Italy.
Introduction: Precision medicine refers to managing brain tumors according to each patient's unique characteristics when it was realized that patients with the same type of tumor differ greatly in terms of survival, responsiveness to treatment, and toxicity of medication. Precision diagnostics can now be advanced through the establishment of imaging biomarkers, which necessitates quantitative image acquisition and processing. The VASARI (Visually AcceSAble Rembrandt Images) manual annotation methodology is an ideal and suitable way to determine the accurate association between genotype and imaging phenotype.
View Article and Find Full Text PDFBackground: Diagnosis of cardiac amyloidosis (CA) is often missed or delayed due to confusion with other causes of increased left ventricular wall thickness. Conventional transthoracic echocardiographic measurements like global longitudinal strain (GLS) has shown promise in distinguishing CA, but with limited specificity. We conducted a study to investigate the performance of a computer vision detection algorithm in across multiple international sites.
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