Analysis of the interval between submission and publication in genetics journals.

One of the main factors that attracts authors to choose a journal is the time interval between submission and publication, which varies between journals and subject matter. Here, we evaluated the time intervals between submission and publication according to journal impact factor and continent of author's affiliation, considering articles with authors from single or multiple continents. Altogether, 72 journals indexed in the Web of Science database within the subject matter "Genetics and Heredity", divided by impact factor into four quartiles and randomly selected were analyzed for time intervals from article submission to publication. Data from a total of 46,349 articles published from 2016 to 2020 were collected and analyzed considering the following time intervals: submission to acceptance (SA), acceptance to publication (AP) and submission to publication (SP). The median of the quartiles for the SP interval was 166 (IQR [118-225]) days for Q1, 147 (IQR [103-206]) days for Q2, 161 (IQR [116-226]) days for Q3 and 137 (IQR [69-264]) days for Q4, showing a significant difference among quartiles (p < 0.001). In Q4, median interval of time was shorter in interval SA but longer in interval AP, and overall, articles in Q4 had the shortest interval of time in SP. A potential association of the median time interval and authors' continent was analysed and no significant difference was observed between articles with authors from single versus multiple continents or between continents in articles with authors from only one continent. However, in journals from Q4, time from submission to publication was longer for articles with authors from North America and Europe than from other continents, although the difference was not significant. Finally, articles of authors from the African continent had the smallest representation in journals from Q1-Q3 and articles from Oceania were underrepresented in group Q4. The study provides a global analysis of the total time required for submission, acceptance and publication in journals in the field of genetics and heredity. Our results may contribute in the development of strategies to expedite the process of scientific publishing in the field, and to promote equity in knowledge production and dissemination for researchers from all continents.

Indicadores de Qualidade na condução de estudos clínicos / Quality indicators in the conduct of clinical studies

Introdução: A geração, análise e difusão de indicadores pertinentes é uma das estratégias fundamentais do processo de gestão de qualidade. Indicadores são mensurações que avaliam direta ou indiretamente os processos e desfechos da assistência ao participante de pesquisa. Padrões rigorosos de qualidade garantirão a validade dos dados obtidos nos estudos clínicos. Métodos: Estudo transversal descritivo e analítico, com dados coletados no Centro de Pesquisa Clínica de um Hospital Universitário, durante Novembro/2016 a Fevereiro/2019 quanto ao tempo de inclusão de dados no electronic case report form (IID), tempo de comunicação de desvios de protocolo ao Comitê de Ética em Pesquisa (ICD), tempo de resposta aos feasibilities recebidos (IRF) e tempo de resposta às pendências de monitoria (IRP). Resultados: Variações substanciais foram encontradas entre os escores de qualidade ao longo de 27 meses. O desempenho geral da equipe do Escritório de Projetos em Pesquisa alcançou a classificação excelente ou satisfatória em 61,40% das observações: 50,87% e 10,52% respectivamente. Entre 38,59% de observações críticas, 17,54% foram expressas pelo IRP, seguido de 12,28% por ICD e 7% atribuído ao IID que não atingiram a meta proposta. Conclusão: O Escritório de Projetos em Pesquisa elencou dois indicadores de qualidade, IID e IRP, que podem melhorar o tempo e a eficácia das entregas propostas da equipe. (AU)

Introduction: The generation, analysis and dissemination of relevant indicators is a fundamental strategy in the quality management process. Indicators are measurements that directly or indirectly evaluate the processes and outcomes of the care provided to research participants. Rigorous quality standards will guarantee the validity of the data obtained in clinical studies. Methods: This descriptive, analytical cross-sectional study collected data at the Clinical Research Center of a University Hospital, covering the period from November 2016 to February 2019, regarding the time of data inclusion in the electronic case report form (IID), communication of protocol deviations to the Research Ethics Committee (ICD), response time to received feasibilities (IRF) and response time to monitoring pending issues (IRP). Results: Substantial variations were found between quality scores in a period of 27 months. The overall performance of the Research Project Office team was excellent or satisfactory in 61.40% of the observations (50.87% and 10.52% respectively). Among critical observations (38.59%), 17.54% were expressed by IRP, 12.28% by ICD and 7% by IID, indicating those that did not reach the proposed target. Conclusions: The Research Project Office has listed two quality indicators, IID and IRP, which can improve the time and effectiveness of team deliveries.(AU)

Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification.

Ascertaining which patients are at highest risk of poor postoperative outcomes could improve care and enhance safety. This study aimed to construct and validate a propensity index for 30-day postoperative mortality. A retrospective cohort study was conducted at Hospital de Clínicas de Porto Alegre, Brazil, over a period of 3 years. A dataset of 13524 patients was used to develop the model and another dataset of 7254 was used to validate it. The primary outcome was 30-day in-hospital mortality. Overall mortality in the development dataset was 2.31% [n = 311; 95% confidence interval: 2.06-2.56%]. Four variables were significantly associated with outcome: age, ASA class, nature of surgery (urgent/emergency vs elective), and surgical severity (major/intermediate/minor). The index with this set of variables to predict mortality in the validation sample (n = 7253) gave an AUROC = 0.9137, 85.2% sensitivity, and 81.7% specificity. This sensitivity cut-off yielded four classes of death probability: class I, <2%; class II, 2-5%; class III, 5-10%; class IV, >10%. Model application showed that, amongst patients in risk class IV, the odds of death were approximately fivefold higher (odds ratio 5.43, 95% confidence interval: 2.82-10.46) in those admitted to intensive care after a period on the regular ward than in those sent to the intensive care unit directly after surgery. The SAMPE (Anaesthesia and Perioperative Medicine Service) model accurately predicted 30-day postoperative mortality. This model allows identification of high-risk patients and could be used as a practical tool for care stratification and rational postoperative allocation of critical care resources.