Variability in Partial Nephrectomy Outcomes: Does Your Surgeon Matter?

Dagenais, Julien; Bertolo, Riccardo; Garisto, Juan; Maurice, Matthew J.; Mouracadé, Pascal; Kara, Önder; Chavali, Jaya Sai; Li, Jianbo; Nelson, Ryan J.; Fergany, Amr; Abouassaly, Robert; Kaouk, Jihad

doi:10.1016/j.eururo.2018.10.046

Cited by 60 publications

(32 citation statements)

References 28 publications

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“…Although surgeon volume itself is considered as one of the important factors for partial nephrectomy outcomes as mentioned by the Dagenais et al, other measured and unmeasured surgeon factors may account for 18-100% of variability of peri-and postoperative variables such as years in practice, surgical approach, fellowship and modular training, etc. Results of the current study are also consistent with the one reported above (24).…”

Section: Discussionsupporting

confidence: 93%

‘Trifecta’ outcomes of robot-assisted partial nephrectomy: Results of the ‘low volume’ surgeon

Başataç

Akpınar

2020

Int. braz j urol.

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Objective: There is limited data regarding surgeon volume and partial nephrectomy outcomes. The aim of this study is to report trifecta outcomes of robot-assisted partial nephrectomy (RAPN) performed by the low volume surgeon. Materials and Methods: Thirty-nine patients with clinical T1-2 renal tumors who underwent RAPN between 2012 and 2018 were included in this study. Trifecta was defined as negative surgical margins, warm ischemia time ≤20 minutes, and no operative complications. Patient demographics, R.E.N.A.L. nephrometry score, operation time, estimated blood loss, warm ischemia time, length of hospital stay, renal functions, and oncological outcomes were analyzed retrospectively. Complications were graded based on the modified Clavien-Dindo classification system. Results: The median R.E.N.A.L. nephrometry score was 6 (4-10). RAPN was successfully performed in all but one patient. The median operation time was 180 (90-240) minutes. Warm ischemia was performed only by segmental renal artery control in 35 and, by main renal artery control in three patients. The off-clamp technique was used in two patients. The median warm ischemia time was 16 (0-31) minutes. Seven patients had a warm ischemia time of longer than 20 minutes. Three patients had postoperative complications. The surgical margin was positive in one patient. As a result, the trifecta was achieved in 30 of the 39 patients (77%). Conclusion: RAPN is a safe and effective minimally invasive alternative in the treatment of renal masses. The present study suggests that reasonable trifecta rates can be achieved even by low volume surgeons.

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Section: Discussionsupporting

confidence: 93%

‘Trifecta’ outcomes of robot-assisted partial nephrectomy: Results of the ‘low volume’ surgeon

Başataç

Akpınar

2020

Int. braz j urol.

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“…However, differences were demonstrated for 16% of PN and 9.4% of RP patients, which is still remarkable. Furthermore, the CCI might be used to further analyze the impact of the surgeon's experience [21] and the role of hospital volume [22,23] on perioperative outcomes. These factors are increasingly considered and might help to predict the potential impact of centralization after major cancer surgery in urology [24] Aside from the more comprehensive overall assessment, an additional benefit of the CCI is the reduction of required sample sizes in clinical trials.…”

Section: Discussionmentioning

confidence: 99%

The comprehensive complication index (CCI): proposal of a new reporting standard for complications in major urological surgery

et al. 2020

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Purpose The comprehensive complication index (CCI) is a new tool for reporting the cumulative burden of postoperative complications on a continuous scale. This study validates the CCI for urological surgery and its benefits over the Clavien-Dindo-Classification (Clavien). Material and methods Data from a prospectively maintained data base of all consecutive patients at a university care-center was analyzed. Complications after radical cystectomy (RC), radical prostatectomy (RP), and partial nephrectomy (PN) were classified using the CCI and Clavien system. Differences in complications between the CCI and the Clavien were assessed and correlation analyses performed. Sample size calculations for hypothetical clinical trials were compared between CCI and Clavien to evaluate whether the CCI would reduce the number of required patients in a clinical trial. Results 682 patients (172 RC, 297 RP, 213 PN) were analyzed. Overall, 9.4-46.6% of patients had > 1 complication cumulatively assessed with the CCI resulting in an upgrading in the Clavien classification for 2.4-32.4% of patients. Therefore, scores between the systems differed for RC: CCI (mean ± standard deviation) 26.

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“…Robot‐assisted surgery has gained popularity among surgeons [1], but is associated with an overall complication rate of up to 30% and a major complications (Clavien–Dindo grade ≥3) rate of 3–6% [2]. The morbidity of partial nephrectomy is attributed to the combination of tumour complexity, patient‐related comorbidities [3], tumour surroundings [4], and surgeon experience [5,6]. Significant efforts have been made to develop tools to individualize patient risk profiles for better surgical planning.…”

Section: Introductionmentioning

confidence: 99%

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

Bhandari

Nallabasannagari²,

Reddiboina³

et al. 2020

BJU International

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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; the best model had an AUC‐ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR‐AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC‐ROC of 0.875 (95% CI 0.834, 0.913) and a PR‐AUC 0.706 (95% CI, 0.610, 0.790). Conclusions The performance of the ML models in the present study was encouraging. Further validation in a multi‐institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

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Variability in Partial Nephrectomy Outcomes: Does Your Surgeon Matter?

Cited by 60 publications

References 28 publications

‘Trifecta’ outcomes of robot-assisted partial nephrectomy: Results of the ‘low volume’ surgeon

‘Trifecta’ outcomes of robot-assisted partial nephrectomy: Results of the ‘low volume’ surgeon

The comprehensive complication index (CCI): proposal of a new reporting standard for complications in major urological surgery

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

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