Implementation of a multimodal opioid-sparing enhanced recovery pathway for robotic-assisted radical prostatectomy.

The purpose of the study is to evaluate the impact of a multimodal Enhanced Recovery After Surgery (ERAS) protocol on perioperative opioid consumption and hospital length of stay (LOS) after robotic-assisted radical prostatectomy (RARP). We compared the first 176 patients enrolled in the protocol (ERAS group) with the previous 176 patients (non-ERAS group) at a single quaternary institution from December 2017 to June 2019. The ERAS protocol included a multimodal opioid-sparing regimen utilizing acetaminophen, gabapentin, celecoxib, and liposomal bupivacaine. Demographic data, co-morbidities, post-operative pain scores, post-operative opiate consumption measured by morphine milligram equivalents (MME), operating time, and LOS were collected. The two groups were compared using chi-squared, Fisher exact, or Student t tests as appropriate. Multivariable logistic regression analysis was performed to identify predictors of prolonged LOS (> 1 day). The ERAS and non-ERAS groups were equivalent in terms of baseline characteristics and pathological data. The ERAS group had lower post-operative pain scores, post-operative opiate consumption (MME 15 vs. 46, p < 0.01), and LOS (1.2 vs. 1.7 days, p < 0.01) compared to the non-ERAS group. Only 22% in the ERAS cohort had a prolonged LOS compared to 39% of the non-ERAS group (p < 0.01). The ERAS protocol was a negative predictor of prolonged LOS on multivariable logistic regression analysis (odds ratio 0.39, 95% confidence interval 0.22-0.70, p < 0.01). A limitation of this study is its single-center retrospective design. The implementation of a multimodal opioid-sparing ERAS protocol was associated with improved pain control, reduced perioperative opioid usage, and shorter LOS after RARP.

Comparison of clinical outcomes and automated performance metrics in robot-assisted radical prostatectomy with and without trainee involvement.

PURPOSE: In this study, we investigate the effect of trainee involvement on surgical performance, as measured by automated performance metrics (APMs), and outcomes after robot-assisted radical prostatectomy (RARP). METHODS: We compared APMs (instrument tracking, EndoWrist® articulation, and system events data) and clinical outcomes for cases with varying resident involvement. Four of 12 standardized RARP steps were designated critical ("cardinal") steps. Comparison 1: cases where the attending surgeon performed all four cardinal steps (Group A) and cases where a trainee was involved in at least one cardinal step (Group B). Comparison 2, where Group A is split into Groups C and D: cases where attending performs the whole case (Group C) vs. cases where a trainee performed at least one non-cardinal step (Group D). Mann-Whitney U and Chi-squared tests were used for comparisons. RESULTS: Comparison 1 showed significant differences in APM profiles including camera movement time, third instrument usage, dominant instrument moving time, velocity, articulation, as well as non-dominant instrument moving time and articulation (all favoring Group A p < 0.05). There was a significant difference in re-admission rates (10.9% in Group A vs 0% in Group B, p < 0.02), but not for post-operative outcomes. Comparison 2 demonstrated a significant difference in dominant instrument articulation (p < 0.05) but not in post-operative outcomes. CONCLUSIONS: Trainee involvement in RARP is safe. The degree of trainee involvement does not significantly affect major clinical outcomes. APM profiles are less efficient when trainees perform at least one cardinal step but not during non-cardinal steps.

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

PURPOSE: Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). MATERIALS AND METHODS: We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. RESULTS: The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation. CONCLUSION: To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.