Journal of Evidence‐Based Medicine最新文献

Cytomegalovirus (CMV) is an increasingly recognized complication of chimeric antigen receptor T-cell (CAR-T) and bispecific antibody (BsAb) therapies for hematologic malignancies, driven by therapy-related immunosuppression and cumulative exposure to lymphodepleting or steroid regimens. Given China's high adult CMV IgG seroprevalence (>90%), baseline risk, interpretation of low-level DNAemia, and operational thresholds differ from low-seroprevalence settings, requiring context-specific guidance. This China-adapted, evidence-graded consensus was developed by a multidisciplinary panel from major centers using a modified Delphi process and Oxford Centre for Evidence-Based Medicine levels to translate international guidance into a high-seroprevalence setting. Recommendations prioritize early risk stratification and pragmatic surveillance. We advise routine CMV monitoring by real-time quantitative PCR during the first 30 days after therapy, with risk-adapted extension thereafter. Interpretation and treatment triggers are anchored to WHO-traceable IU/mL and specified by specimen matrix to support comparability across assays. Consideration of prophylaxis is proposed for well-defined high-risk subgroups, acknowledging the need for prospective validation. Syndrome-based diagnostic and treatment algorithms are provided for tissue-invasive disease, including CMV pneumonia and encephalitis, with guidance on antiviral induction, step-down, and monitoring for virologic response and drug toxicity. This consensus explicitly adapts international recommendations to China's epidemiology, assay practice, and drug accessibility. By standardizing prevention, surveillance, and management in CAR T-cell and BsAb recipients, this consensus aims to lower non-relapse mortality and improve long-term outcomes. Priority research needs include harmonized viral-load thresholds, validation of risk-adapted prophylaxis strategies, and studies that clarify the significance of low-level DNAemia in this population.

Objective: Academic adjustment is essential for medical students facing rigorous academic demands. While individual and instructional factors have been well-studied, the role of family functioning remains underexplored. This study examined the association between family functioning and academic adjustment, explored the mediating role of coping styles, and compared these pathways between at-risk students (those who had failed at least one final examination) and controls.

Methods: A cross-sectional study was conducted using validated scales for assessment of academic adjustment, family functioning, and coping styles. Structural equation modeling and bootstrap analyses were used to test mediation effects.

Results: 1022 Chinese medical students (293 at-risk, 729 controls) were included. Family functioning was significantly and positively associated with academic adjustment (p < 0.001). Positive coping mediated this relationship in both groups (at-risk: β = 0.13, 95% confidence interval [CI] [0.068, 0.218]; controls: β = 0.21, 95% CI [0.182, 0.243]), while negative coping mediated the effect only in at-risk students (β = 0.12, 95% CI [0.090, 0.167]). At-risk students showed significantly lower academic adjustment (t = -6.56, p < 0.001, Cohen's d = -0.45) and relied on distinct coping mechanisms compared to controls.

Conclusions: This study reveals distinct mediating pathways of coping styles between at-risk and other students, deepening our understanding of family influences and providing practical guidance for targeted interventions in medical education.

Objective: Utilizing lung cancer risk prediction models at the screening stage can enhance the accuracy of identifying high-risk individuals eligible for lung cancer screening. However, there is a relative lack of research on such prediction models in China, particularly regarding machine learning algorithms.

Methods: A stratified random sampling method was employed to randomly divide the dataset into a training set (70%) and a validation set (30%). Key variables were screened using LASSO regression. Then logistic regression and XGBoost algorithm were utilized to construct a lung cancer risk prediction model in the training set and validate it in the validation set, respectively.

Results: A lung cancer risk prediction model was constructed using 11,708 participants enrolled in a prospective cohort, the Guangzhou Lung-Care Project Program. In the constructed lung cancer risk prediction models, the AUC of the logistic regression model in the validation set was 0.647 (95% CI: 0.574-0.720); in contrast, the AUC of the XGBoost model based on the machine learning algorithm in the validation set was 0.658 (95% CI: 0.589-0.727), demonstrating slightly better discriminative ability compared to the logistic regression model. In addition, this study found the important effect of childhood exposure to cooking fuels on the risk of lung cancer, which has been rarely considered in previous research.

Conclusion: The lung cancer risk prediction model constructed based on the XGBoost algorithm is better than the logistic regression algorithm in terms of prediction accuracy and robustness, aiding in the risk assessment of individuals undergoing screening.

Objective: To establish evidence-based guidance to standardize the clinical application of artificial iris implantation in patients with iris defects.

Methods: A systematic literature search was performed following evidence-based consensus development standards. Eighteen international experts participated in a Delphi process to define six core clinical issues. Evidence was screened, extracted, evaluated and integrated. Recommendations were formulated through iterative expert review.

Results: We established six key clinical issues related to artificial iris implantation and evidence-based recommendations to address critical gaps in clinical practice. Key outcomes included standardized criteria for indications, contraindications, and type of artificial iris selection, key aspects of surgeon-patient communication, surgical management principles and critical techniques, comprehensive perioperative care protocols, and strategies for managing long-term postoperative complications associated with artificial iris implantation.

Conclusions: This consensus standardizes artificial iris implantation through six evidence-based recommendations. It provides a standardized protocol for safe clinical implementation to restore visual function and cosmetic integrity in patients with iris defect.

Objective: The overwhelming majority of prediction models have not been applied. An evidence-based review is needed to show that the new research is justified. This study aimed to develop an assessment tool for researchers and peer reviewers to conduct a rapid and comprehensive evaluation on the necessity and feasibility of planning clinical prediction model before its startup.

Methods: The framework for developing quality assessment tools was followed to develop the necessity and Feasibility Assessment Tool of CLInical Prediction models for individual prognosis (FATCLIP). Firstly, the scope, framework, and item pool of the FATCLIP was identified by a steering group comprising 15 experts through a web-based meeting. Then, an iterative Delphi process was conducted to refine the FATCLIP, in which the Delphi group enrolled 34 experts from multidiscipline, including epidemiologists, statisticians, clinicians, evidence-based medicine specialists, health care administrators and academic journal editors.

Results: Through twice steering group meetings and 2 rounds of the Delphi process, the framework of FATCLIP was determined based on expert consensus, including 6 domains and 31 signaling questions. The six domains were as follows: prediction outcome, review of existing models, candidate predictors, data, development and validation, and application and extension. At the same time, the usage manual of FATCLIP was also presented.

Conclusions: The FATCILP aims to assist researchers and peer reviewers to detect potential challenges during the development and application of the clinical prediction model for individual prognosis before its start-up, so that the research of clinical prediction models could be efficient and avoid research waste.

Aim: To survey the physician's attention to the workload of combining clinical practice with Traditional Chinese Medicine (TCM) data collection.

Background: With the development of artificial intelligence technology in the medical field, the task of collecting diverse clinical data in TCM has increased. Based on the TCM's diagnostic and treatment principles, the collection of research data accompanying clinical practice is inevitable, which may have an impact on TCM clinical practice.

Method: A previous research was conducted to collect diverse instant TCM diagnostic and treatment data, and physicians and research designers proposed many suggestions focusing on the workload of combining clinical practice with TCM data collection. In this study, A 54-item questionnaire was developed based on the suggestions. Forty-eight participants with data-collection experience participated in a questionnaire survey, and they needed to grade each item, which reflected their attention to the workload of combining clinical practice with TCM data collection.

Results: The survey received 40 valid questionnaires, with 49 items scoring 4 or above. Three items in the content dimension (Q9, Q10, Q11) and two items in the spatial dimension (Q31, Q48) are scored lower. Additionally, 25 supplementary suggestions were collected during the study.

Conclusion: The workload of combining clinical practice with TCM data collection needs to be considered. The items in this survey could be regarded as a basis for developing a tool to consider the relationship between clinical practice and data collection.

Background: Percutaneous transhepatic cholangioscopy (PTCS) is a minimally invasive treatment for biliary diseases; however, postoperative biliary drainage can impair quality of life and cause complications. We developed a biodegradable blockage (BB) for tract embolization to replace drainage; this is the first study investigating this approach after PTCS.

Methods: In this prospective study, 10 patients with bile duct stones underwent PTCS with BB embolization (June-August 2024). Outcomes and complications were recorded over 3 months. A 1:1 propensity-matched control group from historical PTCS patients with standard drainage was established for comparison of hemoglobin levels and complications.

Results: BB placement was successful in all patients with no procedure-related deaths. No significant differences were found between the embolization and drainage groups in operative time, hemoglobin changes, or complication rates, though the small sample size warrants caution. One patient in the embolization group had a Grade II complication, versus three complications (two Grade I, one Grade II) in the drainage group. The embolization group had no serious adverse events during follow-up.

Conclusion: Tract embolization with BB appears to be a safe and feasible alternative to conventional drainage after PTCS. Larger randomized trials are needed for validation.

Objective: Integration of traditional Chinese and modern medicine (TCM-MM) aids rehabilitation of muscle strength among ischemic stroke (IS) survivors. However, it faces statistical challenges (e.g., multicollinearity, small sample) in the real-world setting. This study tried to provide an analytical framework for investigating linear causality with a retrospective small-sample case series.

Methods: Original data was sourced from hospital information system and processed by many means. Wilcoxon signed-rank test was utilized to execute a self-controlled before-and-after comparison, before multiple linear regression (MLR) models were established for exploring prognostic factors of muscle strength improvement. Afterward, Bayesian networks (BN), mediation analysis and between-subjects effects tests were undertaken the detection of underlying multicollinearity sources progressively. Both clinical interpretability and model performance, containing R² and mean squared error (MSE), served as the indices for modelling comparison.

Results: Muscle strength was significantly improved among 112 post-IS patients after accepting TCM-MM therapies (p < 0.01). Initially, MLR analysis with 11 explanatory variables (EVs) (MLR_1) revealed a probable multicollinearity-driven bias, resulting in reduced interpretability. Consequently, we traced collinearity among EVs using a BN structure that provided clues to mediating and mutual effects for establishing MLR with interactions embracing 11 EVs (MLR_2). Eventually, MLR_2 demonstrated superior model performance (ΔR² = 0.097, ΔMSE = -0.004), and better clinical interpretability. Whereas, we cannot deny a 1/3 probability of diminished statistical efficacy due to the small sample size.

Conclusion: Our study proposed a practically hybrid approach for exploring linear causality under multicollinearity using real-world small-sample data, which suggested that balancing model performance with clinical interpretability can resolve statistical trade-offs in modelling optimization.

Objective: To systematically identify immune cell phenotypes causally associated with oral lichen planus (OLP) susceptibility using Mendelian randomization (MR).

Methods: This two-sample MR study evaluated causal relationships between 731 immune cell phenotypes and OLP risk. Single nucleotide polymorphisms (SNPs) were linkage disequilibrium-clumped (r² < 0.001, 10,000 kb), filtered by F-statistic (>10), and harmonized across datasets (palindromic SNPs with intermediate allele frequencies removed). Inverse variance weighting was the primary method, complemented by MR-Egger, weighted median, and mode-based estimations. Heterogeneity (Cochran's Q), horizontal pleiotropy (MR-Egger intercept, MR pleiotropy residual sum, and outlier), and leave-one-out analyses were used for sensitivity checks. Associations passing multiple-testing correction were interpreted.

Results: Twenty-eight immune phenotypes demonstrated significant causal associations: 19 protective and 9 risk-increasing. Five of six regulatory T-cell (Treg) phenotypes showed protective effects, with odds ratios (ORs) ranging from 0.916 to 0.958, and CD3 on CD4 Tregs showing the strongest effect (OR = 0.916). CD8-bright leukocytes showed the strongest risk association (OR = 1.487). Eight B cell phenotypes conferred protection, particularly human leukocyte antigen DR (HLA DR) on B cells (OR = 0.889). Monocyte phenotypes exhibited divergent effects: Myeloid-derived suppressor cells were protective (OR = 0.840), whereas HLA DR-expressing monocytes increased risk (OR range: 1.276-1.281).

Conclusions: This study provides genetic evidence that OLP pathogenesis involves immunoregulatory imbalance between protective regulatory mechanisms and pathogenic effector responses. Findings support precision therapeutic strategies targeting specific immune pathways and offer insights for other oral autoimmune diseases.