To determine the risk of COVID-19 transmission during minimally invasive surgical (MIS) procedures MethodsSurgical society statements regarding the risk of COVID transmission during MIS procedures were reviewed. In addition, the available literature on COVID-19 and other viral transmission in CO2 pneumoperitoneum, as well as the presence of virus in the plume created by electrocautery during MIS was reviewed. The society recommendations were compared to the available literature on the topic to create our review and recommendations to mitigate COVID-19 transmission. ResultsThe recommendations promulgated by various surgical societies evolved over time as more information became available on COVID-19 transmission. Review of the available literature on the presence of COVID-19 in CO2 pneumoperitoneum was inconclusive. There is no clear evidence of the presence of COVID-19 in plume created by electrocautery. Technologies to reduce CO2 pneumoperitoneum release into the operating room as well as filter viral particles are available and should reduce the exposure risk to operating room personnel. ConclusionThere is no clear evidence of COVID-19 virus in the CO2 used during MIS procedures or in the plume created by electrocautery. Until the presence or absence of COVID-19 viral particles has been clearly established, measures to mitigate CO2 and surgical cautery plume release into the operating room should be performed. Further study on the presence of COVID-19 in MIS pneumoperitoneum and cautery plume is needed.

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Background To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa). Methods We performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient’s age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging–Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants—which included 59 patients from external institutions—to validate the model. Results The study participants included 184 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient’s risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE−, with an area under the ROC curve (AUC) of 0.90 (86.0–95.8%), high sensitivity (86%), and moderate specificity (70%). Conclusions Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.

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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