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In observational studies, researchers must select a method to control for confounding. Options include propensity score (PS) methods and regression. It remains unclear how dataset characteristics (size, overlap in PSs, and exposure prevalence) influence the relative performance of the methods. A simulation study to evaluate the role of dataset characteristics on the performance of PS methods, compared to logistic regression, for estimating a marginal odds ratio was conducted. Dataset size, overlap in PSs, and exposure prevalence were varied. Regression showed poor coverage for small sample sizes, but with large sample sizes was relatively robust to imbalance in PSs and low exposure prevalence. PS methods displayed suboptimal coverage as overlap in PSs decreased, which was exacerbated at larger sample sizes. Power of matching methods was particularly affected by a lack of overlap, low exposure prevalence, and small sample size. The advantage of regression for large data size was reduced in sensitivity analysis with a complementary log–log outcome generation mechanism and unmeasured confounding, with superior bias and error but inferior coverage to matching methods. Dataset characteristics influence performance of methods for confounder adjustment. In many scenarios, regression may be the preferable option. •Key features of a dataset (size, exposure prevalence, and imbalance in propensity scores) affect the performance of several approaches aiming to address confounding.•Multiple logistic regression is relatively robust to low exposure prevalence and imbalance in the propensity score (PS), except in very small samples (N = 100).•For large sample sizes (N = 10,000 or more), multiple logistic regression performed better, whereas PS methods performed poorly as imbalance in PS distributions increased.•Although in some unmeasured confounding scenarios the cumulative coverage and power performance were higher in the nearest neighbor and 1-to-1 propensity score matching, this was driven by much larger standard errors, and the absolute error and bias in the point estimate were lower with multiple regression.•In large observation studies of national registries or primary care electronic health records, multiple regression estimation may often be the optimal choice in terms of simplicity and performance.

Background: An increasing number of diagnostic tests and biomarkers have been validated during the last decades, and this will still be a prominent field of research in the future because of the need for personalized medicine. Strict evaluation is needed whenever we aim at validating any potential diagnostic tool, and the first requirement a new testing procedure must fulfill is diagnostic accuracy. Summary: Diagnostic accuracy measures tell us about the ability of a test to discriminate between and/or predict disease and health. This discriminative and predictive potential can be quantified by measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, overall accuracy and diagnostic odds ratio. Some measures are useful for discriminative purposes, while others serve as a predictive tool. Measures of diagnostic accuracy vary in the way they depend on the prevalence, spectrum and definition of the disease. In general, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually over- or underestimate the indicators of test performance and limit the applicability of the results of the study. Key Messages: The testing procedure should be verified on a reasonable population, including people with mild and severe disease, thus providing a comparable spectrum. Sensitivities and specificities are not predictive measures. Predictive values depend on disease prevalence, and their conclusions can be transposed to other settings only for studies which are based on a suitable population (e.g. screening studies). Likelihood ratios should be an optimal choice for reporting diagnostic accuracy. Diagnostic accuracy measures must be reported with their confidence intervals. We always have to report paired measures (sensitivity and specificity, predictive values or likelihood ratios) for clinically meaningful thresholds. How much discriminative or predictive power we need depends on the clinical diagnostic pathway and on misclassification (false positives/negatives) costs.

Background Assuming a linear relationship between continuous predictors and outcomes in clinical prediction models is often inappropriate, as true linear relationships are rare, potentially resulting in biased estimates and inaccurate conclusions. Our research group addressed a single U-shaped independent variable before. Multiple U-shaped predictors can improve predictive accuracy by capturing nuanced relationships, but they also introduce challenges like increased complexity and potential overfitting. This study aims to extend the applicability of our previous research results to more common scenarios, thereby facilitating more comprehensive and practical investigations. Methods In this study, we proposed a novel approach called the Recursive Gradient Scanning Method (RGS) for discretizing multiple continuous variables that exhibit U-shaped relationships with the natural logarithm of the odds ratio (ln OR ) . The RGS method involves a two-step approach: first, it conducts fine screening from the 2.5th to 97.5th percentiles of the ln OR . Then, it utilizes an iterative process that compares AIC metrics to identify optimal categorical variables. We conducted a Monte Carlo simulation study to investigate the performance of the RGS method. Different correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships were considered in the simulation process. To compare the RGS method with other common approaches (such as median, Q 1 - Q 3 , minimum P -value method), we assessed both the predictive ability (e.g., AUC ) and goodness of fit (e.g., AIC ) of logistic regression models with variables discretized at different cut-points using a real dataset. Results Both simulation and empirical studies have consistently demonstrated the effectiveness of the RGS method. In simulation studies, the RGS method showed superior performance compared to other common discretization methods in discrimination ability and overall performance for logistic regression models across various U-shaped scenarios (with varying correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships). Similarly, empirical study showed that the optimal cut-points identified by RGS have superior clinical predictive power, as measured by metrics such as AUC , compared to other traditional methods. Conclusions The simulation and empirical study demonstrated that the RGS method outperformed other common discretization methods in terms of goodness of fit and predictive ability. However, in the future, we will focus on addressing challenges related to separation or missing binary responses, and we will require more data to validate our method.

Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed, such as sensitivity and specificity. Using paired indicators can be a disadvantage in comparing the performance of competing tests, especially if one test does not outperform the other on both indicators. Here we propose the use of the odds ratio as a single indicator of diagnostic performance. The diagnostic odds ratio is closely linked to existing indicators, it facilitates formal meta-analysis of studies on diagnostic test performance, and it is derived from logistic models, which allow for the inclusion of additional variables to correct for heterogeneity. A disadvantage is the impossibility of weighing the true positive and false positive rate separately. In this article the application of the diagnostic odds ratio in test evaluation is illustrated.

IMPORTANCE: Ventricular septal rupture (VSR) is a rare but life-threatening mechanical complication of acute myocardial infarction associated with high mortality despite prompt treatment. Surgery represents the standard of care; however, only small single-center series or national registries are usually available in literature, whereas international multicenter investigations have been poorly carried out, therefore limiting the evidence on this topic. OBJECTIVES: To assess the clinical characteristics and early outcomes for patients who received surgery for postinfarction VSR and to identify factors independently associated with mortality. DESIGN, SETTING, AND PARTICIPANTS: The Mechanical Complications of Acute Myocardial Infarction: an International Multicenter Cohort (CAUTION) Study is a retrospective multicenter international cohort study that includes patients who were treated surgically for mechanical complications of acute myocardial infarction. The study was conducted from January 2001 to December 2019 at 26 different centers worldwide among 475 consecutive patients who underwent surgery for postinfarction VSR. EXPOSURES: Surgical treatment of postinfarction VSR, independent of the technique, alone or combined with other procedures (eg, coronary artery bypass grafting). MAIN OUTCOMES AND MEASURES: The primary outcome was early mortality; secondary outcomes were postoperative complications. RESULTS: Of the 475 patients included in the study, 290 (61.1%) were men, with a mean (SD) age of 68.5 (10.1) years. Cardiogenic shock was present in 213 patients (44.8%). Emergent or salvage surgery was performed in 212 cases (44.6%). The early mortality rate was 40.4% (192 patients), and it did not improve during the nearly 20 years considered for the study (median [IQR] yearly mortality, 41.7% [32.6%-50.0%]). Low cardiac output syndrome and multiorgan failure were the most common causes of death (low cardiac output syndrome, 70 [36.5%]; multiorgan failure, 53 [27.6%]). Recurrent VSR occurred in 59 participants (12.4%) but was not associated with mortality. Cardiogenic shock (survived: 95 [33.6%]; died, 118 [61.5%]; P \\textless .001) and early surgery (time to surgery ≥7 days, survived: 105 [57.4%]; died, 47 [35.1%]; P \\textless .001) were associated with lower survival. At multivariate analysis, older age (odds ratio [OR], 1.05; 95% CI, 1.02-1.08; P = .001), preoperative cardiac arrest (OR, 2.71; 95% CI, 1.18-6.27; P = .02) and percutaneous revascularization (OR, 1.63; 95% CI, 1.003-2.65; P = .048), and postoperative need for intra-aortic balloon pump (OR, 2.98; 95% CI, 1.46-6.09; P = .003) and extracorporeal membrane oxygenation (OR, 3.19; 95% CI, 1.30-7.38; P = .01) were independently associated with mortality. CONCLUSIONS AND RELEVANCE: In this study, surgical repair of postinfarction VSR was associated with a high risk of early mortality; this risk has remained unchanged during the last 2 decades. Delayed surgery seemed associated with better survival. Age, preoperative cardiac arrest and percutaneous revascularization, and postoperative need for intra-aortic balloon pump and extracorporeal membrane oxygenation were independently associated with early mortality. Further prospective studies addressing preoperative and perioperative patient management are warranted to hopefully improve the currently suboptimal outcome.

Introduction: Epirubicin is widely used in many malignancies with good efficacy and tolerability. However, investigations about adverse events (AEs) using real-world information are still insufficient. Methods: We extracted Epirubicin-related reports submitted between the first quarter of 2014 and first quarter of 2023 from FAERS database. Four algorithms were utilized to evaluate whether there was a significant correlation between Epirubicin and AEs. Results: After de-duplicating, a total of 3919 cases were extracted. Among the 3919 cases, we identified 1472 AEs, 253 of which were found to be adverse drug reactions (ADRs) associated with Epirubicin. We analysed the occurrence of Epirubicin-induced ADRs and found several unexpected significant ADRs, such as hepatic artery stenosis, hepatic artery occlusion, intestinal atresia and so on. Interestingly, we found gait apraxia, a neurological condition, was also significantly associated with Epirubicin. To our knowledge, there haven't studies that have reported an association between gait disorders and the usage of epirubicin. Discussion: Our study identified new unexpected significant ADRs related to Epirubicin, providing new perspectives to the clinical use of Epirubicin.

The SARS-CoV-2 pandemic is one of the most concerning health problems around the globe. We reported the emergence of SARS-CoV-2 variant B.1.1.519 in Mexico City. We reported the effective reproduction number (Rt) of B.1.1.519 and presented evidence of its geographical origin based on phylogenetic analysis. We also studied its evolution via haplotype analysis and identified the most recurrent haplotypes. Finally, we studied the clinical impact of B.1.1.519. The B.1.1.519 variant was predominant between November 2020 and May 2021, reaching 90% of all cases sequenced in February 2021. It is characterized by three amino acid changes in the spike protein: T478K, P681H, and T732A. Its Rt varies between 0.5 and 2.9. Its geographical origin remain to be investigated. Patients infected with variant B.1.1.519 showed a highly significant adjusted odds ratio (aOR) increase of 1.85 over non-B.1.1.519 patients for developing a severe/critical outcome (p = 0.000296, 1.33–2.6 95% CI) and a 2.35-fold increase for hospitalization (p = 0.005, 1.32–4.34 95% CI). The continuous monitoring of this and other variants will be required to control the ongoing pandemic as it evolves.

Background Hypertension is the primary out-auditory adverse outcome caused due to occupational noise exposure. This study investigated the associations of noise exposure in an occupational setting with blood pressure and risk of hypertension. Methods A total of 1,390 occupational noise-exposed workers and 1399 frequency matched non-noise-exposed subjects were recruited from a cross-sectional survey of occupational noise-exposed and the general population, respectively. Blood pressure was measured using a mercury sphygmomanometer following a standard protocol. Multiple logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) of noise exposure adjusted by potential confounders. Results Noise-exposed subjects had significantly higher levels of systolic blood pressure(SBP) (125.1 ± 13.9 mm Hg) and diastolic blood pressure (DBP) (77.6 ± 10.7 mm Hg) than control subjects (SBP: 117.2 ± 15.7 mm Hg, DBP: 70.0 ± 10.5 mm Hg) ( P  < 0.001). Significant correlations were found between noise exposure and blood pressure (SBP and DBP) ( P  < 0.001). However, the linear regression coefficients with DBP appeared larger than those with SBP. The prevalence of hypertension was 17.8% in subjects with noise exposure and 9.0% in control group ( P  < 0.001). Compared with the control group, the subjects with noise exposure had the risk of hypertension with an OR of 1.941 (95% CI = 1.471– 2.561) after adjusting for age, sex, smoking, and drinking status. Dose–response relationships were found between noise intensity, years of noise exposure, cumulative noise exposure and the risk of hypertension (all P values < 0.05). No significant difference was found between subjects wearing an earplug and those not wearing an earplug, and between steady and unsteady noise categories ( P  > 0.05). Conclusions Occupational noise exposure was associated with higher levels of SBP, DBP, and the risk of hypertension. These findings indicate that effective and feasible measures should be implemented to reduce the risk of hypertension caused by occupational noise exposure.

Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.