Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

Cumulative residual cholesterol predicts the risk of cardiovascular disease in the general population aged 45 years and older.

BACKGROUND: Numerous studies have affirmed a robust correlation between residual cholesterol (RC) and the occurrence of cardiovascular disease (CVD). However, the current body of literature fails to adequately address the link between alterations in RC and the occurrence of CVD. Existing studies have focused mainly on individual RC values. Hence, the primary objective of this study is to elucidate the association between the cumulative RC (Cum-RC) and the morbidity of CVD. METHODS: The changes in RC were categorized into a high-level fast-growth group (Class 1) and a low-level slow-growth group (Class 2) by K-means cluster analysis. To investigate the relationship between combined exposure to multiple lipids and CVD risk, a weighted quantile sum (WQS) regression analysis was employed. This analysis involved the calculation of weights for total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), which were used to effectively elucidate the RC. RESULTS: Among the cohort of 5,372 research participants, a considerable proportion of 45.94% consisted of males, with a median age of 58. In the three years of follow-up, 669 participants (12.45%) had CVD. Logistic regression analysis revealed that Class 2 individuals had a significantly reduced risk of developing CVD compared to Class 1. The probability of having CVD increased by 13% for every 1-unit increase in the Cum-RC according to the analysis of continuous variables. The restricted cubic spline (RCS) analysis showed that Cum-RC and CVD risk were linearly related (P for nonlinearity = 0.679). The WQS regression results showed a nonsignificant trend toward an association between the WQS index and CVD incidence but an overall positive trend, with the greatest contribution from TC (weight = 0.652), followed by LDL (weight = 0.348). CONCLUSION: Cum-RC was positively and strongly related to CVD risk, suggesting that in addition to focusing on traditional lipid markers, early intervention in patients with increased RC may further reduce the incidence of CVD.

Prevalence of dry eye and Meibomian gland dysfunction in Central and South America: a systematic review and meta-analysis.

BACKGROUND: Dry eye is one of the most common ophthalmic conditions and can significantly impact quality of life. Meibomian gland dysfunction (MGD) is a major cause of evaporative dry eye. We sought to conduct a systematic review and meta-analysis to estimate the prevalence and incidence of dry eye and MGD in Central and South America and to identify factors associated with disease burden. METHODS: Data sources Ovid MEDLINE and Embase. STUDY SELECTION: A search conducted on August 16, 2021, identified studies published between January 1, 2010, and August 16, 2021, with no restrictions regarding participant age or language of publication. Case reports, case series, case-control studies, and interventional studies were excluded. DATA EXTRACTION AND SYNTHESIS: The review was based on a protocol registered on PROSPERO (CRD42021256934). Risk of bias was assessed in duplicate using a risk of bias tool designed for the purposes of descriptive epidemiological studies. Data were extracted by one investigator and verified by another for accuracy. Prevalence of dry eye and MGD were grouped based on study participant characteristics. MAIN OUTCOMES AND MEASURES: Prevalence and incidence of dry eye and MGD in Central and South America. Summary estimates from meta-analysis with 95% confidence intervals (CI). RESULTS: Fourteen studies (11,594 total participants) were included. The population prevalence of dry eye was 13% (95% CI, 12%-14%) in Brazil and 41% (95% CI, 39%-44%) in Mexico based on one study each. Meta-analyses suggested that dry eye prevalence was 70% among indoor workers (95% CI, 56%-80%; I2, 82%; 3 studies), 71% among students (95% CI, 65%-77%; I2, 92%; 3 studies), and 83% in general ophthalmology clinics (95% CI, 77%-88%; I2, 88%; 2 studies). MGD prevalence ranged from 23% among indoor workers (95% CI, 16%-31%; 1 study) to 68% in general ophthalmology clinics (95% CI, 62%-72%; 1 study). No studies reported incidence of dry eye or MGD. CONCLUSIONS: This systematic review and meta-analysis demonstrated considerable variation in the published prevalence of dry eye and MGD among the general population and subpopulations in Central and South America. Local and subpopulation estimates of dry eye disease burden may be valuable to assist needs assessments and implementation of measures to mitigate the condition.

Association between relative muscle strength and cardiovascular disease among middle-aged and older adults in China.

BACKGROUND: The association between sarcopenia and cardiovascular disease (CVD) is well known. However, the clinical diagnosis of sarcopenia is complex and not suitable for early clinical identification and prevention of CVD. Relative muscle strength (RMS) is a relatively quantitative and straightforward indicator, but its association with CVD remains unclear. Hence, the objective of this research was to investigate the correlation between RMS and CVD incidence. METHODS: This was a cross-sectional study, using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011. CVD events were assessed through self-reported physician diagnoses. The RMS was determined by dividing the maximum grip strength by the appendicular skeletal muscle mass (ASM). This study used multivariate logistic regression and restricted cubic spline (RCS) curves to explore the correlation between RMS and CVD incidence. Additionally, we conducted subgroup analyses to provide additional evidence supporting the association between the two variables. RESULTS: A total of 8,733 people were included in our study, with 1,152 (13.19%) CVD patients and 7,581 (86.81%) non-CVD patients. When the data were grouped according to quartiles (Q) of RMS, the inverse association between CVD and RMS remained statistically significant even after controlling for all potential confounding factors. Compared with participants in Q1 of RMS, the ORs (95% CIs) of CVD among those in Q2-Q4 were 0.99 (0.83, 1.17), 0.81 (0.67, 0.98), and 0.70 (0.57, 0.85), respectively. Moreover, the RCS results showed a negative linear correlation between the RMS and CVD incidence (P for nonlinearity = 0.555). Subgroup analysis revealed no significant interaction in any of the groups except for the sex group (P for interaction = 0.046). CONCLUSION: Our study indicated a stable negative correlation between RMS and CVD incidence. RMS is helpful for the early identification and prevention of CVD.

The normality assumption on between-study random effects was questionable in a considerable number of Cochrane meta-analyses.

BACKGROUND: Studies included in a meta-analysis are often heterogeneous. The traditional random-effects models assume their true effects to follow a normal distribution, while it is unclear if this critical assumption is practical. Violations of this between-study normality assumption could lead to problematic meta-analytical conclusions. We aimed to empirically examine if this assumption is valid in published meta-analyses. METHODS: In this cross-sectional study, we collected meta-analyses available in the Cochrane Library with at least 10 studies and with between-study variance estimates > 0. For each extracted meta-analysis, we performed the Shapiro-Wilk (SW) test to quantitatively assess the between-study normality assumption. For binary outcomes, we assessed between-study normality for odds ratios (ORs), relative risks (RRs), and risk differences (RDs). Subgroup analyses based on sample sizes and event rates were used to rule out the potential confounders. In addition, we obtained the quantile-quantile (Q-Q) plot of study-specific standardized residuals for visually assessing between-study normality. RESULTS: Based on 4234 eligible meta-analyses with binary outcomes and 3433 with non-binary outcomes, the proportion of meta-analyses that had statistically significant non-normality varied from 15.1 to 26.2%. RDs and non-binary outcomes led to more frequent non-normality issues than ORs and RRs. For binary outcomes, the between-study non-normality was more frequently found in meta-analyses with larger sample sizes and event rates away from 0 and 100%. The agreements of assessing the normality between two independent researchers based on Q-Q plots were fair or moderate. CONCLUSIONS: The between-study normality assumption is commonly violated in Cochrane meta-analyses. This assumption should be routinely assessed when performing a meta-analysis. When it may not hold, alternative meta-analysis methods that do not make this assumption should be considered.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

The effect of aerobic exercise on oxidative stress in patients with chronic kidney disease: a systematic review and meta-analysis with trial sequential analysis.

PURPOSE: The purpose of this study was to investigate how aerobic exercise affects oxidative stress (OS) in patients with chronic kidney disease (CKD). METHODS: Retrieval dates range from the date the database was established to 19 July 2023, without languages being restricted. A meta-analysis and sensitivity analysis were conducted using RevMan 5.3 and Stata 16.0. RESULTS: The meta-analysis showed that, compared to usual activity or no exercise, aerobic exercise significantly reduced the oxidative markers malondialdehyde (MDA) (mean differences (MD) - 0.96 (95% CI -1.33, - 0.59); p < 0.00001), advanced oxidation protein product (AOPP) (MD - 3.49 (95% CI - 5.05, - 1.93); p < 0.00001), F2-isoprostanes (F2-iso) (MD - 11.02 (95% CI - 17.79, - 4.25); p = 0.001). Aerobic exercise also increased the antioxidant marker superoxide dismutase (SOD) in CKD patients (standardized mean differences (SMD) 1.30 (95% CI 0.56, 2.04); p = 0.0005). Subgroup analysis showed a significant increase in glutathione peroxidase (GPX) in patients aged ≥60 years (SMD 2.11 (95% CI 1.69, 2.54); p < 0.00001). The change in total antioxidant capacity (TAC) after aerobic exercise was insignificant in patients with CKD. The trial sequential analysis supported aerobic exercise's effectiveness in improving MDA, SOD, AOPP, and F2-iso in patients with CKD. CONCLUSION: The results of this review suggest that aerobic exercise improves OS indicators (MDA, SOD, AOPP, and F2-iso) in CKD patients compared to conventional treatment or no exercise and that the effects on GPX and TAC indicators need further confirmation. For better validation of benefits and exploration of the best aerobic exercise regimen to improve OS status with CKD, further studies with high methodological quality and large sample sizes are needed.

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environmental perturbations. With a large number of images generated by high-content microscopy while manual labeling is both labor-intensive and error-prone, machine learning offers a viable alternative for automatic labeling of subcellular localizations. Contrarily, in recent years applications of deep learning methods to large datasets in natural images and other domains have become quite successful. An appeal of deep learning methods is that they can learn salient features from complicated data with little data preprocessing. For such purposes, we applied several representative types of deep convolutional neural networks (CNNs) and two popular ensemble methods, random forests and gradient boosting, to predict protein subcellular localization with a moderately large cell image data set. We show a consistently better predictive performance of CNNs over the two ensemble methods. We also demonstrate the use of CNNs for feature extraction. In the end, we share our computer code and pretrained models to facilitate CNN's applications in genetics and computational biology.

Retrospective Analysis of the Effect of Postdischarge Telephone Calls by Hospitalists on Improvement of Patient Satisfaction and Readmission Rates.

OBJECTIVES: The influence of postdischarge telephone call interventions preventing hospital readmissions is unclear. A novel approach of the discharging hospitalist providing this intervention may improve overall patient satisfaction. Our objective was to assess the impact of postdischarge telephone calls from discharging hospitalists on readmissions and patients' ratings of hospital care and hospitalist communication. METHODS: Data were retrospectively collected from patients' electronic health records at a 167-bed hospital in Fridley, Minnesota and the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. Patients were 18 years old or older and diagnosed as having nonpsychiatric conditions. Telephone calls were made by the discharging hospitalist to adult patients discharged to home with or without home care services between February 28, 2015 and February 29, 2016. Multivariate logistic regression models were used to evaluate associations of postdischarge telephone calls with global hospital care rating and hospitalist communication from HCAHPS, and 30-day readmission rates from electronic health records. RESULTS: Of 4490 eligible patients, 1067 had completed telephone calls (23.8%). The intervention was associated with a statistically significant improvement in the responses to HCAHPS overall hospital rating and HCAHPS doctor communication questions (adjusted odds ratio 1.52, P = 0.04 and adjusted odds ratio 1.56, P = 0.021) that varied by patient age at first admission (P = 0.001 and P = 0.101). With longer inpatient lengths of stay, 30-day readmission rates improved after patients received a postdischarge telephone call, but this outcome was not statistically significant. CONCLUSIONS: This study revealed that postdischarge telephone calls from discharging hospitalists increased patient satisfaction. Further research is needed to understand the causal relationships among the intervention, 30-day hospital readmission rates, and inpatient length of stay.

Project Safe Guard: Challenges and opportunities of a universal rollout of peer-delivered lethal means safety counseling at a US military installation.

INTRODUCTION: The US Department of Defense recommends lethal means safety counseling (LMSC) to promote firearm injury prevention via secure storage of personal firearms. We describe the rollout of a universal, peer-delivered adaptation of Project Safe Guard (PSG)-a brief, single-session LMSC discussion-at a US Space Force installation. METHOD: Program evaluation data were collected via anonymous, voluntary, and online surveys. Of approximately 862 eligible active-duty service members and embedded civilians, 324 completed the preprogram survey and 68 and 37 completed the 1- and 2-month follow-ups, respectively. RESULTS: At preprogram, 69.1% agreed that peer-delivered LMSC is appropriate. After rollout, 100% of the 222 firearm locking devices available to service members were requested from the on-base Violence Prevention Integrator. The effectiveness of PSG was indeterminable due to the low survey response rates. CONCLUSIONS: Despite strong preprogram support for peer-delivered LMSC and behavioral indicators of secure firearm storage (e.g., firearm locking device requests), several challenges limited the uptake and evaluability of the PSG program in this naturalistic environment, including military survey fatigue and competing mission priorities. Additional work is needed to determine the effectiveness of peer-delivered LMSC in a military context. Sustained base support and military-civilian collaborations will be critical.

Predicting Mortality in Trauma Research: Evaluating the Performance of Trauma Scoring Tools in a South African Population.

Background Trauma is a leading cause of death and disability in low-resource settings, yet trauma severity scores are seldom validated in these contexts. There is a pressing need to better characterize and compare trauma scoring tools, especially within research frameworks. This study aimed to evaluate the performance of various trauma scoring tools in predicting in-hospital mortality among trauma patients in South Africa. Methods This study conducted a secondary analysis of existing data from the multicenter Epidemiology and Outcomes of Prolonged Trauma Care (EpiC) study, which included 13,548 adult trauma patients aged 18 years and older, collected between August 2021 and March 2024. The predictive ability of the scoring tools was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results The mortality rate was 2.5% (n = 298). The Kampala Trauma Score (KTS) demonstrated the highest predictive ability for seven-day in-hospital mortality, with an AUROC of 0.95 and an AUPRC of 0.53. Similarly, the Trauma and Injury Severity Score (TRISS) and the New Injury Severity Score (NISS) also exhibited strong predictive capabilities, with AUROC values of 0.96 and AUPRC values of 0.62 for TRISS and an AUROC of 0.96 and AUPRC of 0.53 for NISS. In contrast, the Revised Trauma Score and Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) showed lower predictive performance, with AUROC values of 0.87 (AUPRC = 0.51) and 0.86 (AUPRC = 0.47), respectively. Conclusions The KTS exhibited optimal performance characteristics for retrospectively predicting mortality in our cohort, outperforming other scoring tools. Notably, it is also the simplest scoring tool, featuring the fewest variables compared to other trauma severity assessments. These findings highlight the necessity for external validation of trauma scoring tools in resource-limited populations to ensure their applicability and effectiveness in trauma research across diverse healthcare settings.

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

Importance: During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics. Objective: To evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint. Design, Setting, and Participants: This retrospective cohort study used electronic health record data from October 1, 2021, to December 11, 2021, for the training cohort and data from June 1, 2021, to October 1, 2021, for the testing cohort. The cohorts included patients who had positive test results for SARS-CoV-2 and qualified for COVID-19 mAb therapy based on the US Food and Drug Administration's emergency use authorization criteria, ascertained from the patient electronic health record. Only some of the qualifying candidates received treatment with mAbs. Data were analyzed between from January 2023 to May 2024. Main Outcomes and Measures: The primary outcome was overall expected hospitalization, assessed as the potential reduction in overall expected hospitalization if the PLT-based allocation system was used. This was compared to observed allocation using risk differences. Results: Among 9542 eligible patients in the training cohort (5418 female [56.8%]; age distribution: 18-44 years, 4151 [43.5%]; 45-64 years, 3146 [33.0%]; and ≥65 years, 2245 [23.5%]), a total of 3862 (40.5%) received mAbs. Among 6248 eligible patients in the testing cohort (3416 female [54.7%]; age distribution: 18-44 years, 2827 [45.2%]; 45-64 years, 1927 [30.8%]; and ≥65 years, 1494 [23.9%]), a total of 1329 (21.3%) received mAbs. Treatment allocation using the trained PLT model led to an estimated 1.6% reduction (95% CI, -2.0% to -1.2%) in overall expected hospitalization compared to observed treatment allocation in the testing cohort. The visual assessment showed that the PLT-based point system had a larger reduction in 28-day hospitalization compared with the Monoclonal Antibody Screening Score (maximum overall hospitalization difference, -1.0% [95% CI, -1.3% to -0.7%]) in the testing cohort. Conclusions and Relevance: This retrospective cohort study proposes and tests a PLT method, which can be linked to a electronic health record data platform to improve real-time allocation of scarce treatments. Use of this PLT-based allocation method would have likely resulted in fewer hospitalizations across a population than were observed in usual care, with greater expected reductions than a commonly used point system.

An Evidence Map for Social and Structural Determinants for Maternal Morbidity and Mortality: A Systematic Review.

OBJECTIVE: To identify the social-structural determinants of health risk factors associated with maternal morbidity and mortality in the United States during the prenatal and postpartum periods. DATA SOURCES: We searched MEDLINE, CINAHL, and Social Sciences Citation Index through November 2022 for eligible studies that examined exposures related to social and structural determinants of health and at least one health or health care-related outcome for pregnant and birthing people. METHODS OF STUDY SELECTION: After screening 8,378 unique references, 118 studies met inclusion criteria. TABULATION, INTEGRATION, AND RESULTS: We grouped studies by social and structural determinants of health domains and maternal outcomes. We used alluvial graphs to summarize results and provide additional descriptions of direction of association between potential risk exposures and outcomes. Studies broadly covered risk factors including identity and discrimination, socioeconomic, violence, trauma, psychological stress, structural or institutional, rural or urban, environment, comorbidities, hospital, and health care use. However, these risk factors represent only a subset of potential social and structural determinants of interest. We found an unexpectedly large volume of research on violence and trauma relative to other potential exposures of interest. Outcome domains included maternal mortality, severe maternal morbidity, hypertensive disorders, gestational diabetes, cardiac and metabolic disorders, weathering depression, other mental health or substance use disorders, and cost per health care use outcomes. Patterns between risk factors and outcomes were highly mixed. Depression and other mental health outcomes represented a large proportion of medical outcomes. Risk of bias was high, and rarely did studies report the excess risk attributable to a specific exposure. CONCLUSION: Limited depth and quality of available research within each risk factor hindered our ability to understand underlying pathways, including risk factor interdependence. Although recently published literature showed a definite trend toward improved rigor, future research should emphasize techniques that improve the ability to estimate causal effects. In the longer term, the field could advance through data sets designed to fully ascertain data required to robustly examine racism and other social and structural determinants of health, their intersections, and feedback loops with other biological and medical risk factors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42022300617.

Real-world use of nirmatrelvir-ritonavir in COVID-19 outpatients during BQ.1, BQ.1.1., and XBB.1.5 predominant omicron variants in three U.S. health systems: a retrospective cohort study.

Background: Ritonavir-boosted Nirmatrelvir (NMV-r), a protease inhibitor with in vitro activity against SARS-CoV-2, can reduce risk of progression to severe COVID-19 among high-risk individuals infected with earlier variants, but less is known about its effectiveness against omicron variants BQ.1/BQ.1.1/XBB.1.5. We sought to evaluate effectiveness of NMV-r in BQ.1/BQ.1.1/XBB.1.5 omicron variants by comparing hospitalisation rates to NMV-r treated patients during a previous omicron phase and to contemporaneous untreated patients. Methods: We conducted a retrospective observational cohort study of non-hospitalised adult patients with SARS-CoV-2 infection using real-world data from three health systems in Colorado and Utah, and compared hospitalisation rates in NMV-r-treated patients in a BA.2/BA.2.12.1/BA.4/BA.5 variant-predominant (first) phase (April 3, 2022-November 12, 2022), with a BQ.1/BQ.1.1/XBB.1.5 variant-predominant (second) phase (November 13, 2022-March 7, 2023). In the primary analysis, we used Firth logistic regression with a two-segment (phase) linear time model, and pre-specified non-inferiority bounds for the mean change between segments. In a pre-specified secondary analysis, we inferred NMV-r effectiveness in a cohort of treated and untreated patients infected during the second phase. For both analyses, the primary outcome was 28-day all-cause hospitalisation. Subgroup analyses assessed treatment effect heterogeneity. Findings: In the primary analysis, 28-day all-cause hospitalisation rates in NMV-r treated patients in the second phase (n = 12,061) were non-inferior compared to the first phase (n = 25,075) (198 [1.6%] vs. 345 [1.4%], adjusted odds ratio (aOR): 0.76 [95% CI 0.54-1.06]), with consistent results among secondary endpoints and key subgroups. Secondary cohort analyses revealed additional evidence for NMV-r effectiveness, with reduced 28-day hospitalisation rates among treated patients compared to untreated patients during a BQ.1/BQ.1.1/XBB.1.5 predominant phase (198/12,061 [1.6%] vs. 376/10,031 [3.7%], aOR 0.34 [95% CI 0.30-0.38), findings robust to additional sensitivity analyses. Interpretation: Real-world evidence from major US healthcare systems suggests ongoing NMV-r effectiveness in preventing hospitalisation during a BQ.1/BQ.1.1/XBB.1.5-predominant phase in the U.S, supporting its continued use in similar patient populations. Funding: U.S. National Institutes of Health.

Triglyceride glucose index as a predictor of mortality in middle-aged and elderly patients with type 2 diabetes in the US.

Despite a wealth of research linking the triglyceride glucose index (TyG index) to metabolic diseases. However, little evidence links the TyG index to all-cause or CVD mortality in middle-aged and elderly individuals with type 2 diabetes (T2D). This study analyzed data from 2998 patients with T2D who participated in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018. The TyG index and mortality in middle-aged and elderly T2D patients were investigated using Cox regression models. The nonlinear association between the TyG index and mortality can be understood with the help of a restricted cubic spline (RCS). During a median follow-up period of 82 months, 883 fatalities were observed from all causes and 265 from CVD. The TyG index was found to have a U-shaped relationship with all-cause and CVD mortality in T2D, with cutoffs of 8.95 and 9, respectively, according to the RCS. After controlling for other factors, an increase of 1 unit in the TyG index was related to an increase of 33% in all-cause mortality and 50% in CVD mortality when TyG was ≥ 8.95 and 9. When TyG < 8.95 and 9, with the change in the TyG index, the change in all-cause and CVD death was insignificant. Patients with T2D who are middle-aged or older, especially elderly patients, have higher TyG levels associated with increased mortality. In middle-aged and elderly patients with T2D, the TyG index may predict the probability of death from any cause and death from CVD.

Meta-analysis methods for risk difference: A comparison of different models.

Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

Association between dietary omega-3 intake and coronary heart disease among American adults: The NHANES, 1999-2018.

BACKGROUND: Omega-3 has been extensively studied for its cardiovascular disease (CVD) benefits. However, the results of this evidence are inconsistent. Therefore, in this study, dietary omega-3 intake was investigated further in relation to coronary heart disease (CHD) risk among U.S. adults. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) database for people ages 20 years and older between 1999 and 2018 to conduct a cross-sectional survey. The Medical Condition Questionnaire (MCQ) was used to determine CHD status. We measured dietary omega-3 intake using two 24-hour dietary recall interviews. Multivariate logistic regression and subgroup analysis were used to explore the correlation between dietary omega-3 intake and CHD. The dose-response relationship between the two was analyzed with a restricted cubic spline (RCS). RESULTS: 31,184 study subjects were included, of whom 1,604 (5.14%) were patients with CHD. By quintile (Q) of dietary omega-3 intake, after adjusting for all confounding factors, compared with Q1, when total dietary omega-3, alpha-linolenic acid (ALA), docosapentaenoic acid (DPA), eicosatetraenoic acid (ETA), eicosapentaenoic acid (EPA), and docosahexenoic acid (DHA) intake reached Q5, the odds ratio (95% confidence interval, CI) of CHD were 0.76 (0.60, 0.96), 0.73 (0.57, 0.94), 0.70 (0.54, 0.92), 0.66 (0.50, 0.85), 0.84 (0.69, 1.02), and 0.83 (0.64, 1.07), respectively, while EPA and DHA were not significantly associated with the disease (Trend p > 0.05). Intake of omega-3 and CHD were linearly related (P for nonlinear = 0.603). No significant interactions were found within subgroups except for the age group (P for interaction = 0.001). Sensitivity analysis and multivariate logistic regression results are generally in agreement. CONCLUSIONS: Total dietary omega-3, ALA, DPA, and ETA intake were negatively associated with CHD risk. In contrast, EPA and DHA had no significant correlation with CHD.

Enhancing open clinical trials through blinded evaluations: an exploration with diabetic foot infections.

BACKGROUND: Blinding drugs through simulation techniques is an important means to control the subjective bias of investigators and subjects. However, clinical trials face significant challenges in the placebo production of drugs, and many trials cannot be double-blinded. OBJECTIVE: This study was conducted to ascertain the consistency between non-blind and blind evaluation results in clinical trials and to pioneer strategies to control information bias, particularly in trials where double-blinding is not feasible. METHODS: In this investigation, a randomized controlled trial (RCT) studying diabetic foot infections (DFIs) was utilized as a representative case. In this trial, the grading of DFIs, as per guidelines by the Infectious Disease Society of America (IDSA) and International Working Group on Diabetic Foot (IWGDF), was used as the primary efficacy indicator. A sample of sixteen patients was randomly chosen from the RCT, and DFI grading was assessed jointly by both non-blinded investigators and blinded center-reading investigators. A consistency test was then deployed to compare the evaluation results, forming the basis for our proposed strategies for effective blinded evaluation. In addition, other perspectives were collected at the end of this study, including with those involved in designing and conducting the recent blinded evaluation trial. RESULTS: Five subjects were excluded due to the quality of photos or the lack of post-treatment visits. The post-treatment IDSA/IWGDF grading results were compared in 11 subjects (experimental group=6, control group=5), and the consistency test showed inconsistent results between the non-blinded and center reading blinded evaluations (Kappa=0.248, p=0.384). In the experimental group, three cases were judged as grade 1 in the non-blinded evaluation and grade 2 in the central reading blinded evaluation; in the control group, three cases were judged as grade 2 in the non-blinded evaluation and grade 1 in the central reading blinded evaluation. The sum of these two cases in 22 post-treatment determinations was 27% (6/22). Furthermore, researchers propose several strategies for implementing blinded evaluations in clinical trials after this trial, which encompass aspects such as staff allocation, training, participant management, trial drug administration, efficacy indicator collection, and safety event management. CONCLUSIONS: The study highlighted that evaluations from non-blinded site investigators may potentially exaggerate the efficacy of the experimental group and that deep wounds can present challenges for observation via center-reading photos. These findings underline the vital necessity for objective assessment in open clinical trials, especially those where wound observation serves as the primary efficacy indicator. The study suggests the adoption of independent blinded investigators at each site, complemented by a comprehensive set of standard operating procedures for blinding evaluation. These measures could serve as an effective counterbalance to subjective bias, thereby augmenting the credibility and consistency of results in open clinical trials. The implications of these findings and recommendations could be of great significance for the design and execution of future open clinical trials, potentially bolstering the quality of clinical research in this area. TRIAL REGISTRATION: ChiCTR2000041443. Registered on December 2020.

Efficacy of Fufang E'jiao Jiang in the Treatment of Patients with Qi and Blood Deficiency Syndrome: A Real-World Prospective Multicenter Study with a Patient Registry.

Objective: This nationwide, multicenter prospective observational study with a patient registry was designed to evaluate the efficacy of Fufang E'jiao Jiang (FEJ) in Chinese patients with Qi and blood deficiency syndrome (QBDS). Methods: QBDS patients were consecutively recruited from 81 investigational sites in China from July, 2019, to December, 2020. Patients who met the eligibility criteria were enrolled in a prospective registry database. Baseline characteristics and changes in scores on the traditional Chinese medicine (TCM) symptom evaluation scale for Qi and blood deficiency, the clinical global impression (CGI) scale, the fatigue scale-14 (FS-14), and the Pittsburgh sleep quality index (PSQI) were analyzed to determine the clinical efficacy of FEJ. Results: A total of 3,203 patients were recruited. The average remission rate (i.e., the sum of the cure rate and improvement rate) of the 20 symptoms of QBDS was 92.49% after 4 weeks of FEJ treatment, which was higher than at baseline; the rate increased to 94.69% at 8 weeks. The CGI scale revealed that the number of total remissions at 4 and 8 weeks was 3,120 (97.41%) and 415 (100%), respectively. The total FS-14 scores decreased by 1.67 ± 4.11 (p < 0.001) at 4 weeks and 1.72 ± 3.09 (p < 0.001) at 8 weeks of treatment. The PSQI scores were 6.6 ± 4.7 and 6.52 ± 3.07 at 4 and 8 weeks, respectively, which were significantly lower than the baseline scores (p < 0.001; p = 0.0033). Both the subhealth fatigue (SF) and iron deficiency anemia (IDA) groups showed significantly improved clinical symptoms of QBDS (p < 0.01). Between-group comparisons revealed significantly greater improvements in FS-14 and PSQI scores in the SF group than in the IDA group (p < 0.05). A multivariate logistic regression analysis showed that disease course, FS-14 score at baseline, and four-week FEJ doses were independent risk factors for the degree of symptom relief in QBDS patients (p < 0.05). Conclusion: In real-world settings, FEJ has a promising effect in treating QBDS and can significantly improve the severity of its symptoms.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research.

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.