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

INTRODUCTION: The study aims to analyze differences between robot-assisted total laparoscopic hysterectomy (RATLH) and total laparoscopic hysterectomy (TLH) in benign indications, emphasizing surgeon and hospital volume. MATERIAL AND METHODS: All women in Sweden undergoing a total hysterectomy for benign indications with or without a bilateral salpingo-oophorectomy from January 1, 2015 to December 31, 2017 (n = 12 386) were identified from three national Swedish registers. Operative time, blood loss, conversion rate, complications, readmission, reoperation, length of hospital stays, and time to daily life activity were evaluated by univariable and multivariable regression models in RATLH and TLH. Surgeon and hospital volume were obtained from the Swedish National Quality Register of Gynecological Surgery and divided into subclasses. RESULTS: TLH was associated with a higher rate of intraoperative complications (adjusted odds ratios [aOR] 2.8, 95% CI 1.3-5.8) and postoperative bleeding complications (aOR 1.8, 95% CI 1.2-2.9) compared with RATLH. Intraoperative data showed a higher conversion rate (aOR 13.5, 95% CI 7.2-25.4), a higher blood loss (200-500 mL aOR 3.5, 95% CI 2.7-4.7; > 500 mL aOR 7.6, 95% CI 4.0-14.6) and a longer operative time (1-2 h aOR 16.7 95% CI 10.2-27.5; >2 h aOR 47.6, 95% CI 27.9-81.1) in TLH compared with RATLH. The TLH group had a lower caseload per year than the RATLH group. Higher surgical volume was associated with lower median blood loss, shorter operative time, a lower conversion rate, and a lower perioperative complication rate. Differences in conversion rate or operative time in RATLH were not affected by surgeon volume when compared with TLH. One year after surgery, patient satisfaction was higher in RATLH than in TLH (aOR 0.6, 95% CI 0.4-0.9). CONCLUSIONS: RATLH led to better perioperative outcome and higher patient satisfaction 1 year after surgery. These outcome differences were slightly more pronounced in very low-volume surgeons but persisted across all surgeon volume groups.

Association of clinical trial participation after myocardial infarction with socioeconomic status, clinical characteristics, and outcomes.

Aims: To investigate whether participants in clinical trials after myocardial infarction (MI) are representable for the post-MI population concerning characteristics, secondary prevention, and prognosis. Methods and results: Cohort study on 31 792 attendants to 1-year revisits after MI throughout Sweden (n = 2941 clinical trial participants) between 2008 and 2013 identified in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART). Individual-level data on socioeconomic status (SES) (disposable income, educational level, and marital status) and outcomes (first recurrent non-fatal MI, coronary heart disease death, fatal or non-fatal stroke until study end 2018) were linked from other national registries. Trial participants were more likely to be men [risk ratio 1.09; 95% confidence interval (CI) 1.07-1.11], and married (1.07; 1.04-1.10), have a highest-quintile income (1.42; 1.36-1.48), and post-secondary education (1.25; 1.18-1.33), while less likely to have a history of MI (0.88; 0.80-0.97), be persistent smokers (0.83; 0.75-0.92) and have left ventricular dysfunction (0.59; 0.44-0.79) compared to non-participants. During a mean 6.7-year follow-up, 5206 outcome events occurred. Risk was lower in trial participants (hazard ratio 0.80; 95% CI 0.72-0.89), also after adjusting for clinical characteristics and post-MI therapies (0.85; 0.77-0.94) and additionally for SES (0.88; 0.79-0.97). Conclusions: Clinical trial participants post-MI are more often male, have higher SES, a more advantageous risk profile, and better prognosis. Additional unmeasured participation bias was implied. Questionable external validity of post-MI trials highlights the importance of complementary studies using real-world data.