ObjectiveTo determine the effect of introducing prospective monitoring of outcomes using control charts and regular feedback on indicators to surgical teams on major adverse events in patients.DesignNational, parallel, cluster randomised trial embedding a difference-in-differences analysis.Setting40 surgical departments of hospitals across France.Participants155 362 adults who underwent digestive tract surgery. 20 of the surgical departments were randomised to prospective monitoring of outcomes using control charts with regular feedback on indicators (intervention group) and 20 to usual care only (control group).InterventionsProspective monitoring of outcomes using control charts, provided in sets quarterly, with regular feedback on indicators (intervention hospitals). To facilitate implementation of the programme, study champion partnerships were established at each site, comprising a surgeon and another member of the surgical team (surgeon, anaesthetist, or nurse), and were trained to conduct team meetings, display posters in operating rooms, maintain a logbook, and devise an improvement plan.Main outcome measuresThe primary outcome was a composite of major adverse events (inpatient death, intensive care stay, reoperation, and severe complications) within 30 days after surgery. Changes in surgical outcomes were compared before and after implementation of the programme between intervention and control hospitals, with adjustment for patient mix and clustering.Results75 047 patients were analysed in the intervention hospitals (37 579 before and 37 468 after programme implementation) versus 80 315 in the control hospitals (41 548 and 38 767). After introduction of the control chart, the absolute risk of a major adverse event was reduced by 0.9% (95% confidence interval 0.4% to 1.4%) in intervention compared with control hospitals, corresponding to 114 patients (70 to 280) who needed to receive the intervention to prevent one major adverse event. A significant decrease in major adverse events (adjusted ratio of odds ratios 0.89, 95% confidence interval 0.83 to 0.96), patient death (0.84, 0.71 to 0.99), and intensive care stay (0.85, 0.76 to 0.94) was found in intervention compared with control hospitals. The same trend was observed for reoperation (0.91, 0.82 to 1.00), whereas severe complications remained unchanged (0.96, 0.87 to 1.07). Among the intervention hospitals, the effect size was proportional to the degree of control chart implementation witnessed. Highly compliant hospitals experienced a more important reduction in major adverse events (0.84, 0.77 to 0.92), patient death (0.78, 0.63 to 0.97), intensive care stay (0.76, 0.67 to 0.87), and reoperation (0.84, 0.74 to 0.96).ConclusionsThe implementation of control charts with feedback on indicators to surgical teams was associated with concomitant reductions in major adverse events in patients. Understanding variations in surgical outcomes and how to provide safe surgery is imperative for improvements.Trial registrationClinicalTrials.gov NCT02569450.
The coronavirus disease 2019 (COVID-19) pandemic has challenged hospital organizations worldwide, not only because of the novelty of the disease, but also because of the high volume of patients in need of critical care over a short time period [1]. ICU mortality of COVID-19 patients depends on patient-related and caregiver-related factors in addition to organizational aspects of the unit, where those patients are hospitalized. We sought to identify various organizational factors associated with ICU mortality among COVID-19 patients. We performed a nationwide study based on the medical information system from all public and private hospitals in France. All adults admitted to a French ICU for severe COVID-19 acute respiratory failure, with SAPS II greater than 15 and who received invasive ventilation, between January 1, 2020, and April 26, 2020 were included. The primary outcome was all-cause mortality during the ICU stay. We computed a modified Poisson regression model to estimate the influence on patient mortality of organizational factors including a potential weekend effect (death probability among patients discharged from ICU on Saturday or Sunday compared to other weekdays), hospital location in French regions, and ICU team experience over time (cumulative number of COVID-19 patients already admitted to the ICU) [2]. A total of 9809 patients from 350 hospitals were analyzed, with a median of 17 severe COVID-19 patients (range 1-230) and 4 related deaths (0-97) per ICU. Patients mean age was 63.2 years (SD 11.6), SAPS II was 45.4 (16.9) and ICU length of stay 20.5 days (16.1). Overall, 3069 (31.3%) patients died in ICU. After adjusting for
ObjectiveTo identify the determinants of operative time for thyroidectomy and quantify the relative influence of preoperative and intra-operative factors.BackgroundAnticipation of operative time is key to avoid both waste of hospital resources and dissatisfaction of the surgical staff. Having an accurate and anticipated planning would allow a rationalized operating room use and may improve patient flow and staffing level.MethodsWe conducted a prospective, cross-sectional study between April 2008 and December 2009. The operative time of 3454 patients who underwent thyroidectomy performed by 28 surgeons in five academic hospitals was monitored. We used multilevel linear regression to model determinants of operative time while accounting for the interplay of characteristics specific to surgeons, patients, and surgical procedures. The relative impact of each variable on operative time was estimated.ResultsOverall, 86% (99% CI 83 to 89) of operative time variation was related to preoperative variables. Surgeon characteristics accounted for 32% (99% CI 29 to 35) of variation, center location for 29% (99% CI 25 to 33), and surgical procedure or patient variables for 24% (99% CI 20 to 27). Operative time was significantly lower among experienced surgeons having practiced from 5–19 years (-21.8 min, P<0.05), performing at least 300 thyroidectomies per year (-28.8 min, P<0.05), and with increasing number of thyroidectomies performed the same day (-11.7min, P<0.001). Conversely, operative time increased in cases of procedure supervision by a more experienced surgeon (+20.0 min, P<0.001). The remaining 13.0% of variability was attributable to unanticipated technical difficulties at the time of surgery.ConclusionsVariation in thyroidectomy duration is largely explained by preoperative factors, suggesting that it can be accurately anticipated. Prediction tools allowing better regulation of patient flow in operating rooms appears feasible for both working conditions and cost management.
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