National ICU Registries as Enablers of Clinical Research and Quality Improvement.

OBJECTIVES: Clinical quality registries (CQRs) have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. This narrative review describes the challenges, proposed solutions, and evidence generated by National ICU registries as facilitators for research and quality improvement. DATA SOURCES: English language articles were identified in PubMed using phrases related to ICU registries, CQRs, outcomes, and case-mix. STUDY SELECTION: Original research, review articles, letters, and commentaries, were considered. DATA EXTRACTION: Data from relevant literature were identified, reviewed, and integrated into a concise narrative review. DATA SYNTHESIS: CQRs have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. The initial experience in European countries and in Oceania ensured that through locally generated data, ICUs could assess their performances by using risk-adjusted measures and compare their results through fair and validated benchmarking metrics with other ICUs contributing to the CQR. The accomplishment of these initiatives, coupled with the increasing adoption of information technology, resulted in a broad geographic expansion of CQRs as well as their use in quality improvement studies, clinical trials as well as international comparisons, and benchmarking for ICUs. CONCLUSIONS: ICU registries have provided increased knowledge of case-mix and outcomes of ICU patients based on real-world data and contributed to improve care delivery through quality improvement initiatives and trials. Recent increases in adoption of new technologies (i.e., cloud-based structures, artificial intelligence, machine learning) will ensure a broader and better use of data for epidemiology, healthcare policies, quality improvement, and clinical trials.

Days of the week and 90-day mortality after esophagectomy: analysis of 33,980 patients from the National Clinical Database.

PURPOSE: The effect of the days of the week on the short-term outcomes after elective surgeries has been suggested; however, such data on esophagectomies remain limited. This study aimed to investigate the association between the day of the week and mortality rates after elective esophagectomy using a large-scale clinical database in Japan. METHODS: The data of elective esophagectomies, registered in the National Clinical Database in Japan, for esophageal cancer treatment between 2012 and 2017 were analyzed. We hypothesized that the later days of the week could have higher odds ratios of death after elective esophagectomy. With 22 relevant clinical variables and days of surgery, 90-day mortality was evaluated using hierarchical logistic regression modeling. RESULTS: Ninety-day mortality rates among 33,980 patients undergoing elective esophagectomy were 1.8% (range, 1.5-2.1%). Surgeries were largely concentrated on earlier days of the week, whereas esophagectomies performed on Fridays accounted for only 11.1% of all cases. Before risk adjustment, lower odds ratios of 90-day mortality were found on Tuesday and a tendency towards lower odds ratios on Thursday. In the hierarchical logistic regression model, 21 independent factors of 90-day mortality were identified. However, the adjusted odds ratios of 90-day mortality for Tuesday, Wednesday, Thursday, and Friday were 0.87, 1.09, 0.85, and 0.88, respectively, revealing no significant difference. CONCLUSION: The results imply that the variation in 90-day mortality rates after esophagectomy on different days of the week may be attributed to differing preoperative risk factors of the patient group rather than the disparity in medical care provided.

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic.

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.