Assessment of surgeon and hospital volume for robot-assisted and laparoscopic benign hysterectomy in Sweden

Effect of Uterine Weight on the Surgical Outcomes of Robot-Assisted Hysterectomy in Benign Indications

Background Uterine weight is an important factor in determining the complexity of a hysterectomy. Although greater uterine weight increases operative time and blood loss in open or laparoscopic surgery, it remains uncertain whether this applies to robot-assisted hysterectomy. This study aimed to investigate the effect of uterine weight on the surgical outcomes of robot-assisted hysterectomy. Methods We conducted a retrospective cohort study involving 872 patients who underwent robot-assisted hysterectomies at our institution between January 2019 and June 2022. Of these, 724 cases were analyzed and classified into four groups based on uterine weight: <250 g (377 patients), 250-500 g (253 patients), 500-750 g (69 patients), and ≥750 g (25 patients). We performed univariate analysis with the following endpoints: operation time, blood loss, postoperative hospital stay, complication rate, conversion to laparotomy rate, and blood transfusion rate. Results Operating time and blood loss increased significantly with greater uterine weight in the four groups (both p-values <0.01), but postoperative hospital stay and complication rate did not increase (p = 0.448, p = 0.679, respectively). None of the patients underwent conversion to laparotomy or blood transfusion. Conclusion Although the operating time for robot-assisted hysterectomy and blood loss increased with greater uterine weight, the complications and length of postoperative hospital stay were similar between groups. Robot-assisted hysterectomy is safe in cases of much uterine weight.

The learning curve of introduced robotic-assisted hysterectomy versus skilled laparoscopic hysterectomy for benign gynecologic diseases

AIM

This study aimed to compare introduced robotic-assisted hysterectomy (RAH) and skilled total laparoscopic hysterectomy (TLH) for the treatment of benign gynecological diseases.

METHODS

Patients who underwent RAH or TLH by two surgeons at the Tottori University Hospital between January 2018 and May 2022 were included in this retrospective study. Inclusion criteria were patients with 100-300 g of uterine weight. The exclusion criteria were patients with stage IV endometriosis. Mean operative time and learning curve were compared among the first-half RAH, second-half RAH, and TLH groups.

RESULTS

There were 40 eligible cases (first-half RAH: 20 cases, second-half RAH: 20 cases) in the RAH group and 44 cases in the TLH group. The total operative time (TOT) of the second half of RAH was significantly shorter than that of the first half of RAH (p = 0.021) and was comparable to that of the TLH group. The operative time (OT) of the second half of RAH was shorter than that of TLH (p = 0.023). The preparation time of TLH was shorter than that of the RAH group (p < 0.01). The learning curve of the TOT in RAH crossed that of TLH on the 31st case of RAH. In contrast, both curves of the OT crossed on the 11th case of RAH.

CONCLUSION

The TOT of the introduced RAH was equivalent to that of skilled TLH in approximately 30 cases since the first RAH. Furthermore, the OT of RAH was comparable to that of TLH in approximately 10 cases of surgery since the first RAH.

Robotic-assisted Gynecological Surgery in Older Patients - a Comparative Cohort Study of Perioperative Outcomes

Study design Because of current demographic developments, a hypothesis was proposed whereby older female patients aged > 65 years can be safely operated using minimally invasive, robotic-assisted surgery, despite having more preoperative comorbidities. A comparative cohort study was designed to compare the age group ≥ 65 years (older age group, OAG) with the age group < 65 years (younger age group, YAG) after robotic-assisted gynecological surgery (RAS) in two German centers. Patients and methods Consecutive RAS procedures performed between 2016 and 2021 at the Women's University Hospital of Jena and the Robotic Center Eisenach to treat benign or oncological indications were included in the study. The age groups were compared according to their preoperative comorbidities (ASA, Charlson comorbidity index [CCI], cumulative illness rating scale - geriatric version [CIRS-G]) and perioperative parameters such as Clavien-Dindo (CD) classification of surgical complications. Analysis was performed using Welch's t -test, chi ² test, and Fisher's exact test. Results A total of 242 datasets were identified, of which 63 (73 ± 5 years) were OAG and 179 were YAG (48 ± 10 years). Patient characteristics and the percentage of benign or oncological indications did not differ between the two age groups. Comorbidity scores and the percentage of obese patients were higher in the OAG group: CCI (2.7 ± 2.0 vs. 1.5 ± 1.3; p < 0.001), CIRS-G (9.7 ± 3.9 vs. 5.4 ± 2.9; p < 0.001), ASA class II/III (91.8% vs. 74.1%; p = 0.004), obesity (54.1% vs. 38.2%; p = 0.030). There was no difference between age groups, even grouped for benign or oncological indications, with regard to perioperative parameters such as duration of surgery (p = 0.088; p = 0.368), length of hospital stay (p = 0.786; p = 0.814), decrease in Hb levels (p = 0.811; p = 0.058), conversion rate (p = 1.000; p = 1.000) and CD complications (p = 0.433; p = 0.745). Conclusion Although preoperative comorbidity was higher in the group of older female patients, no differences were found between age groups with regard to perioperative outcomes following robotic-assisted gynecological surgery. Patient age is not a contraindication for robotic gynecological surgery.

Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty

PURPOSE

Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA).

METHODS

A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R² and percentage of predictions with an error < 5/10/15 min were computed.

RESULTS

Males, BMI > 40 kg/m² and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages.

CONCLUSIONS

This study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization.

LEVEL OF EVIDENCE

III.

Volume-outcome relationship in intra-abdominal robotic-assisted surgery: a systematic review

As robotic-assisted surgery (RAS) expands to smaller centres, platforms are shared between specialities. Healthcare providers must consider case volume and mix required to maintain quality and cost-effectiveness. This can be informed, in-part, by the volume-outcome relationship. We perform a systematic review to describe the volume-outcome relationship in intra-abdominal robotic-assisted surgery to report on suggested minimum volumes standards. A literature search of Medline, NICE Evidence Search, Health Technology Assessment Database and Cochrane Library using the terms: "robot*", "surgery", "volume" and "outcome" was performed. The included procedures were gynecological: hysterectomy, urological: partial and radical nephrectomy, cystectomy, prostatectomy, and general surgical: colectomy, esophagectomy. Hospital and surgeon volume measures and all reported outcomes were analysed. 41 studies, including 983,149 procedures, met the inclusion criteria. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale and the retrieved data was synthesised in a narrative review. Significant volume-outcome relationships were described in relation to key outcome measures, including operative time, complications, positive margins, lymph node yield and cost. Annual surgeon and hospital volume thresholds were described. We concluded that in centres with an annual volume of fewer than 10 cases of a given procedure, having multiple surgeons performing these procedures led to worse outcomes and, therefore, opportunities should be sought to perform other complimentary robotic procedures or undertake joint cases.