Araştırma Çıktıları

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Author: search.filters.author.Abaza, Ronney

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    Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study
    (WILEY, 2020-01-01) Bhandari, Mahendra; Nallabasannagari, Anubhav Reddy; Reddiboina, Madhu; Porter, James R.; Jeong, Wooju; Mottrie, Alexandre; Dasgupta, Prokar; Challacombe, Ben; Abaza, Ronney; Rha, Koon Ho; Parekh, Dipen J.; Ahlawat, Rajesh; Capitanio, Umberto; Yuvaraja, Thyavihally B.; Rawal, Sudhir; Moon, Daniel A.; Buffi, Nicolo M.; Sivaraman, Ananthakrishnan; Maes, Kris K.; Porpiglia, Francesco; Gautam, Gagan; Turkeri, Levent; Meyyazhgan, Kohul Raj; Patil, Preethi; Menon, Mani; Rogers, Craig
    Objective To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. Materials and Methods The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). Results The rates of IOEs and POEs were 5.62\% and 20.98\%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables
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    Perioperative outcomes following robot-assisted partial nephrectomy for complex renal masses: A Vattikuti Collective Quality Initiative database study
    (WOLTERS KLUWER MEDKNOW PUBLICATIONS, 2022-01-01) Sharma, Gopal; Shah, Milap; Ahluwalia, Puneet; Dasgupta, Prokar; Challacombe, Benjamin J.; Bhandari, Mahendra; Ahlawat, Rajesh; Rawal, Sudhir; Buffi, Nicolo M.; Sivaraman, Ananthakrishnan; Porter, James R.; Rogers, Craig; Mottrie, Alexandre; Abaza, Ronney; Rha, Khoon Ho; Moon, Daniel; Thyavihally, Yuvaraja B.; Parekh, Dipen J.; Capitanio, Umberto; Maes, Kris K.; Porpiglia, Francesco; Turkeri, Levent; Gautam, Gagan
    Introduction: Outcomes of robot-assisted partial nephrectomy (RAPN) depend on tumor complexity, surgeon experience and patient profile among other variables. We aimed to study the perioperative outcomes of RAPN for patients with complex renal masses using the Vattikuti Collective Quality Initiative (VCQI) database that allowed evaluation of multinational data. Methods: From the VCQI, we extracted data for all the patients who underwent RAPN with preoperative aspects and dimensions used for an anatomical (PADUA) score of >= 10. Multivariate logistic regression was conducted to ascertain predictors of trifecta (absence of complications, negative surgical margins, and warm ischemia times {[}WIT] <25 min or zero ischemia) outcomes. Results: Of 3,801 patients, 514 with PADUA scores >= 10 were included. The median operative time, WIT, and blood loss were 173 (range 45-546) min, 21 (range 0-55) min, and 150 (range 50-3500) ml, respectively. Intraoperative complications and blood transfusions were reported in 2.1\% and 6\%, respectively. In 8.8\% of the patients, postoperative complications were noted, and surgical margins were positive in 10.3\% of the patients. Trifecta could be achieved in 60.7\% of patients. Clinical tumor size, duration of surgery, WIT, and complication rates were significantly higher in the group with a high (12 or 13) PADUA score while the trifecta was significantly lower in this group (48.4\%). On multivariate analysis, surgical approach (retroperitoneal vs. transperitoneal) and high PADUA score (12/13) were identified as predictors of the trifecta outcomes. Conclusion: RAPN may be a reasonable surgical option for patients with complex renal masses with acceptable perioperative outcomes.