BMC Medical Research Methodology最新文献

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with a five-fold increased risk of stroke. Early prediction of AF onset could improve care for at-risk patients. Existing predictive models often rely on clinical risk scores or machine learning approaches using, for instance, heart rate variability (HRV) features. Among these, sample entropy (SampEn), a quantitative measure of signal complexity, has shown promise as a predictor of AF onset. In this study, we proposed a joint modeling approach that incorporates both baseline covariate and the longitudinal trajectory of SampEn to estimate the risk of AF onset within the next 12 hours.

Methods: We developed several joint models using varying model structural complexity, particularly in modeling the longitudinal process. We evaluated models performance using bootstrap replications on the publicly available IRIDIA-AF dataset. We used time-dependent area under the curve (AUC), sensitivity, and specificity, together with other calibration and accuracy measures to assess predictive performance. Additionally, we illustrated individual prediction profiles for selected patient records.

Results: The best-performing model, which used natural cubic splines in the longitudinal submodel, achieved an AUC of 64.4% and a sensitivity of 77.63%. A simpler model using a linear longitudinal trajectory achieved the highest specificity of 77.94%.

Conclusions: These results demonstrate the potential of joint models for short-term AF risk prediction, providing not only binary classification but also dynamic, individualized risk estimates over time. They enable updated predictions and personalized monitoring of patient risk as new longitudinal data become available.

Background: Accurate prediction of operative duration is essential for efficient scheduling and resource allocation in surgical settings. In assisted reproductive technology (ART), procedures are brief yet highly time-sensitive, such that even small timing errors can propagate delays across tightly coupled workflows. This study aimed to develop and validate interpretable prediction models for operative duration in ART procedures using routinely available electronic medical record (EMR) data.

Methods: We retrospectively analyzed 763 operative cases from a high-volume fertility clinic in South Korea. Operative duration was defined using EMR-recorded start and end timestamps. Predictors included procedure type, patient age, reservation characteristics, attending physician, and day of surgery. We evaluated representative models from three commonly used predictive modeling paradigms-linear models, tree-based ensembles, and kernel/neural-network approaches-within a unified preprocessing and five-fold cross-validation framework. Model performance was assessed using standard error-based metrics and benchmarked against a commonly used moving-average heuristic.

Results: Among the evaluated approaches, regularized linear models-particularly ridge and Bayesian ridge regression-demonstrated stable and interpretable performance. After back-transformation to the original time scale, these models achieved a mean absolute error of approximately 3.1 min, corresponding to a 7-8% reduction compared with the moving-average (MA5) baseline. Procedure type, patient age, and reservation type emerged as the most influential predictors of operative duration.

Conclusions: Using routinely available EMR data, interpretable linear models demonstrated consistent performance gains over a simple operational baseline. These results highlight the value of transparent and well-validated modeling strategies for operative duration prediction in high-throughput clinical settings.

Trial registration: Not applicable (retrospective study using de-identified EMR data).

Background: Analyses of craniofacial morphology are essential for various medical and research applications, including the study of midfacial development, dysmorphologies, and planning surgical interventions. Incomplete CT scans often due to patient movement, imaging artifacts, or obscured landmarks which can result in missing data. If not properly addressed, such missingness may bias conclusions and weaken statistical power.

Objective: This paper evaluates imputation techniques to identify the most suitable method for handling missing completely at random values in small, high-dimensional, and highly correlated craniofacial morphometric datasets.

Methods: 42 craniofacial variables were measured from 32 observations. The missing data structure was set to be at random with 268 (20%) missing values. Five common imputation techniques namely Mean/Median imputation, k-Nearest Neighbors (kNN), Multiple Imputation by Chained Equations (MICE), Random Forest (RF), and Decision Tree, were considered. The performance of the imputation technique was quantified using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Variance Preservation.

Results: RF Imputation demonstrated the best overall performance, with the lowest RMSE (1.3987) and MAE (0.4902), indicating a high level of accuracy in imputing missing values. It also maintained a relatively close to 1 variance preservation (0.8961), suggesting its effectiveness in retaining the original variability in the dataset. MICE present lower accuracy with high RMSE (3.0869) and MAE (1.1246) however appear to have the closest variance preservation to 1 (1.0580).

Conclusion: The findings emphasize the importance of choosing suitable imputation techniques for small, high-dimensional, and correlated datasets such as those in craniofacial morphometry. RF emerged as the most effective method, offering a strong balance between accuracy and variance preservation.

Background: Clinical trial participants have a right to know the results of the trials in which they participate. Trial results are often not shared directly with participants and concerns with privacy and resource constraints may prevent researchers from contacting participants after trial completion.

Questions/purposes: The objectives of this cross-sectional study were to explore the feasibility of contacting orthopaedic fracture trial participants after trial completion and to determine the preferences and priorities of the participants who wished to know the results.

Patients/methods: Following the publication of the primary manuscript, we attempted to contact participants from the completed PREPARE trial at Hamilton Health Sciences to determine if they would like to know the trial results. We asked participants about their preferences for receiving trial results, their experiences upon learning them, and if they wished to learn which treatment they received.

Results: Twenty-eight percent (181/641) of PREPARE trial participants contacted agreed to participate in this study. We found that 95.5% (95% CI 91.0%-97.9%) of respondents wished to know the trial results and the preferred method was through viewing summary posters via an online link (78.2%; 95% CI 71.1%-84.0%). Most felt satisfied after learning the trial results (67.8%; 95% CI 59.5%- 75.2%) and 82.2% (95% CI 75.2%-87.5%) wanted to know which treatment they received. Fifty-one percent (95% CI 42.7%-58.7%) reported that learning the results increased their likelihood of participating in a future trial.

Conclusions: Although it was challenging to both contact and re-engage participants after completing an orthopaedic trial that involved minimal participant burden, our study findings suggest that learning the trial results may have a positive impact on individual participants and the research community. Given the limited understanding of results among our respondents, researchers should have processes in place to engage participants meaningfully throughout the trial and proactively discuss with them how the results will be shared once the trial is complete.

Level of evidence: IV.