ArticleMedical science monitor : international medical journal of experimental and clinical research2019
Predicting Selection Preference of Robotic Pancreaticoduodenectomy (RPD) in a Chinese Single Center Population: Development and Assessment of a New Predictive Nomogram.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Navigating the Intricacies of Robotic Pylorus-Preserving Pancreaticoduodenectomy Using the da Vinci SP (Single Port) System.Journal of clinical medicine · 2025Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Robotic pancreaticoduodenectomy (RPD) is a novel type of minimally invasive surgery to treat tumors located at the head of the pancreas. This study aimed to construct a novel prediction model for predicting selection preference for RPD in a Chinese single medical center population. MATERIAL AND METHODS The clinical data from 451 pancreatic ductal adenocarcinoma patients were collected and analyzed from January 2013 to December 2016. Twenty-three items affecting clinical strategies were optimized by LASSO (least absolute shrinkage and selection operator) regression analysis and then were incorporated in multivariable logistic regression analysis. C-index was used for evaluating the discriminative ability. Decision curve was applied to determine clinical application of this model and the calibration of this nomogram was evaluated by calibration plot. The model was internally validated through bootstrapping validation. RESULTS Clinicopathological factors included in the model were age, history of diabetes mellitus, history of hypertension, history of heart, brain and kidney disease, history of abdominal surgery, symptoms (jaundice, accidental discovery and weight loss), anemia, elevated carcinoembryonic antigen (CEA), smoking, alcohol intake, American Society of Anesthesiologists (ASA) scores, vascular invasion, overweight, preoperative lymph node metastasis and tumor size >3.5 cm. A C-index of 0.831 indicated good discrimination and calibration of this model. Interval validation generated an acceptable C-index of 0.787. When surgical approach was determined at the threshold of preference possibility less than 63%, decision curve analysis indicated that this model had good clinical application value in this range. CONCLUSIONS This new nomogram could be conveniently used to predict the selection preference of robotic surgery for patients with pancreatic head cancer.
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