EClinicalMedicine最新文献

Background: Pancreatic adenocarcinoma (PAC) has a poor prognosis and substantially impairs health-related quality of life (HRQoL). Large studies on longitudinal HRQoL in patients with PAC, taking patient treatment into account, are lacking. This study aimed to investigate HRQoL over time in patients with PAC undergoing various treatments.

Methods: This nationwide cohort study included patients diagnosed with PAC between 2015 and 2020. Data were collected from the Dutch Pancreatic Cancer Project (PACAP) and the Netherlands Cancer Registry. Patients were categorized into four groups based on treatment modality: resection (R-PAC), chemotherapy for localized disease (C-PAC), chemotherapy for metastatic disease (M1-C-PAC), and best supportive care (BSC). HRQoL was assessed using the EORTC QLQ-C30 and -PAN26 questionnaires at baseline, during treatment, and at 0-3 months and 3-6 months after treatment. Linear mixed models were used to analyze changes in HRQoL over time, with clinically relevant changes defined as a minimal mean difference of 10 points in absolute scores and reported with 95% confidence intervals.

Findings: Overall, 1496 patients were included (673 [45.0%] female), of whom 675 (45.1%) in R-PAC, 319 (21.3%) in C-PAC, 340 patients (22.7%) in M1-C-PAC, and 162 (10.8%) in BSC group. In R-PAC, hepatic symptoms and health care satisfaction improved while role and social functioning deteriorated and eating related problems, side effects and fear of future health increased during treatment. In C-PAC, insomnia, pancreatic pain, hepatic symptoms decreased while diarrhea, side effects and fear of future health increased. In M1-C-PAC, pain, insomnia, pancreatic pain, hepatic symptoms, ascites and constipation decreased, sexuality improved while fear of future health and side effects increased. In BSC, hepatic symptoms decreased and flatulence increased.

Interpretation: This nationwide study identified specific improvements and deteriorations in various HRQoL domains during 6 months follow-up. This information may be valuable in the clinical setting to inform patients on potential outcomes of the course of HRQoL during various treatment strategies.

Funding: None.

Background: Complete and unbiased reporting of clinical trial results is essential for evaluating medical advances, yet publication bias and reporting discrepancies in research on the clinical application of artificial intelligence (AI) remain unknown.

Methods: We conducted a comprehensive search of research publications and clinical trial registries focused on the application of AI in healthcare. Our search included publications in Dimensions.ai and pre-registered records from ClinicalTrials.gov and the EU Clinical Trials Registry before 31 December 2023. We linked registered trials to their corresponding publications, analysed the registration, reporting and different dissemination patterns of results, identified discrepancies between clinical trial registries and published literature, and assessed the use of these results in secondary research.

Findings: We identified 28,248 publications related to the use of AI in clinical settings and found 1863 publications that included a clinical trial registration ID. The clinical trial registry search identified 3710 trials evaluating the use of AI in clinical settings, of which 1106 trials are completed, yet only 101 trials have published results. By linking the trials to their corresponding publications, we found that 26 trials had results available from both registries and publications. There were more results in trial registries than in articles, but researchers showed a clear preference for rapid dissemination of results through peer-reviewed articles (37.6% published within one year) over trial registries (15.8%). Discrepancies and omissions of results were common, and no complete agreement was observed between the two sources. Selective reporting of publications occurred in 53.6% of cases, and the underestimation of the incidence of adverse events is alarming.

Interpretation: This research uncovers concerns with the registration and reporting of AI clinical trial results. While trial registries and publications serve distinct yet complementary roles in disseminating research findings, discrepancies between them may undermine the reliability of the evidence. We emphasise adherence to guidelines that promote transparency and standardisation of reporting, especially for investigator-initiated trials (IITs).

Funding: The authors declare no source of funding.

Background: With limited access to mRNA COVID-19 vaccines in lower income countries, and people living with HIV (PLWH) largely excluded from clinical trials, Part A of the multicentre CoVPN 3008 (Ubuntu) study aimed to assess the safety of mRNA-1273, the relative effectiveness of hybrid versus vaccine immunity, and SARS-CoV-2 viral persistence among PLWH in East and Southern Africa during the omicron outbreak.

Methods: Previously unvaccinated adults with HIV and/or other comorbidities associated with severe COVID-19 received either one (hybrid immunity) or two (vaccine immunity) 100-mcg doses of ancestral strain mRNA-1273 in the first month, depending on baseline evidence of prior SARS-CoV-2 infection. In a prospective cohort study design, we used covariate-adjusted Cox regression and counterfactual cumulative incidence methods to determine the hazard ratio and relative risk of COVID-19 and severe COVID-19 with hybrid versus vaccine immunity within six months. The ongoing Ubuntu study is registered on ClinicalTrials.gov (NCT05168813) and this work was conducted from December 2021 to March 2023.

Findings: Between December 2021 and September 2022, 14,237 participants enrolled, and 14,002 (83% PLWH, 69% SARS-CoV-2 seropositive) were included in the analyses. Vaccinations were safe and well tolerated. Common adverse events were pain or tenderness at the injection site (26.7%), headache (20.4%), and malaise (20.3%). Severe adverse events were rare (0.8% of participants after the first and 1.1% after the second vaccination), and none were life-threatening or fatal. Among PLWH, the median CD4 count was 635 cells/μl and 18.5% had HIV viraemia. The six-month cumulative incidences in the hybrid immunity and vaccine immunity groups were 2.02% (95% confidence interval [CI] 1.61-2.44) and 3.40% (95% CI 2.30-4.49) for COVID-19, and 0.048% (95% CI 0.00-0.10) and 0.32% (95% CI 0.59-0.63) for severe COVID-19. Among all PLWH the hybrid immunity group had a 42% lower hazard rate of COVID-19 (hazard ratio [HR] 0.58; 95% CI 0.44-0.77; p < 0.001) and a 73% lower hazard rate of severe COVID-19 (HR 0.27; 95% CI 0.07-1.04; p = 0.056) than the vaccine immunity group, but this effect was not seen among PLWH with CD4 counts <350 cells/μl or HIV viraemia. Twenty PLWH had persistent SARS-CoV-2 virus at least 50 days.

Interpretation: Hybrid immunity was associated with superior protection from COVID-19 compared to vaccine immunity with the ancestral mRNA-1273 vaccine. Persistent infections among immunocompromised PLWH may provide reservoirs for emerging variants.

Funding: National Institute of Allergy and Infectious Diseases.

Background: Duration of second stage of labor is crucial for fetal delivery, but the optimal length of this stage remains controversial. While extending the duration of second stage can reduce primary cesarean delivery rates, it may increase maternal and neonatal morbidities as the duration progresses. We aimed to develop a personalized machine learning (ML) model to predict the possible second-stage duration.

Methods: This multicenter, retrospective study was conducted at four tertiary hospitals in China from September 2013 to October 2022. Data from three hospitals in Guangdong Province was selected as derivation set, and a geographically independent dataset from Fujian Province as the external validation set. Singleton vaginal deliveries with term live birth in a cephalic position were included. The primary outcome was the duration of the second stage of labor. Since durations beyond 3 h were rare, we developed binary classification models with thresholds at 1 h and 2 h. After the optimal features selected by recursive feature elimination (RFE) method, four ML algorithms were employed to build the models. The best model would be selected with the predictive performance and interpreted with Shapley Additive exPlanations method. The study is registered in Clinical Trial (ChiCTR2400085338).

Findings: Electronic medical records of 79,381 vaginal deliveries were obtained, and 63,401 deliveries meeting the inclusion criteria were included in the final analysis. Eight risk features were selected through the RFE process. Gradient boosting machine implemented by decision tree models achieved the best performance, yielding areas under the curve for 1-h and 2-h models of 0.808 (95% confidence interval [CI] 0.797-0.819) and 0.824 (95% CI 0.804-0.843) in the testing set, and 0.862 (95% CI 0.854-0.870) and 0.859 (95% CI 0.843-0.875) in the external validation set, respectively.

Interpretation: An explainable and reliable ML model was developed to predict the probable second-stage duration, which could assist in individualized labor management. Factors such as first-stage duration and maternal age are potential predictors for the second stage.

Funding: National Natural Science Foundation of China (No.82371689, N0.81771602), and National Key Research and Development Program of China (No.2021YFC2700703).