Clinical Epidemiology最新文献

Purpose: We applied a Bayesian approach to further investigate the association of sodium-glucose cotransporter-2 inhibitors (SGLT2i) with the composite outcome of Major Adverse Cardiovascular Event and Heart Failure hospitalization (MACE+HF) and its individual components leveraging the ability of a Bayesian approach to incorporate prior clinical information and to make probability statements about the parameters.

Methods: We use a Bayesian time-to-event model, where the covariates are directly modeled in the hazard function. Following propensity score matching, we fit three Bayesian models; one with a relatively flat, normal prior on the SGLT2i coefficient (Uninformative) and 2 with informative priors from a meta-analysis (based on a cohort with no history of cardiovascular disease [No CVD] and cohorts with a history of CVD [CVD]). We estimate the posterior distribution for the hazard ratio (HR) using a Hamiltonian Monte Carlo algorithm. It allows us to estimate the probability of a meaningful protective association (HR < 0.90) in addition to point and interval estimates.

Results: The posterior means and 95% credible intervals for the HR suggested a protective association for SGLT2i versus dipeptidyl peptidase 4 inhibitors (DPP4i) for the MACE+HF outcome: No CVD: 0.82 (0.68, 0.96), CVD: 0.82 (0.71, 0.94), and Uninformative: 0.79 (0.65, 0.94). The probability of a meaningful protective association for the No CVD, CVD, and Uninformative priors were 88%, 92%, and 93%, respectively. The probability of a meaningful protective association for the HF hospitalization, CVD hospitalization and CVD death components of MACE+HF were 95%, 67%, and 93%, respectively.

Conclusion: The Bayesian analysis allowed for the incorporation of prior information via an informative prior and further investigation of the association between SGLT2 and the components of the MACE+HF composite outcome. It allowed for the calculation of an easily interpretable summary measure, the probability of a meaningful protective association.

Purpose: Information on severity of hidradenitis suppurativa (HS) is not available in administrative claims databases. Accurately identifying HS severity in claims data is important for identifying treatment effect modification by severity. We sought to develop and validate a claims-based algorithm to identify patients with mild, moderate, or severe HS.

Methods: Mass General Brigham (MGB) electronic health records (EHR) were linked to Medicaid claims data in the US from October 2016 through December 2019 to identify 350 patients aged 10 years and older with an ICD-10 diagnosis code for HS (L73.1). Chart review determined HS severity. A multinomial LASSO regression within a 70% training sample determined the most influential claims-based variables out of 30 candidates associated with mild, moderate, or severe HS. This model was used to calculate the positive predictive values (PPVs) for each level of HS within the hold-out testing sample.

Results: The study cohort was predominantly female (81%) aged 18-45 years (74%) with 26% White and 21% Black patients. We identified 72 patients with mild/uncertain HS, 173 with moderate HS, and 105 with severe HS. One ICD-10 diagnosis of HS had a PPV of 89%, which was further improved to 100% when also requiring the concurrent use of a systemic medication for HS. The PPV was 20% for mild/uncertain, 54% for moderate and 67% for severe HS. When combining severity into mild/moderate versus severe the PPV was 71%, indicating that among those classified as severe, 71% were truly severe.

Conclusion: The claims-based algorithm has a reasonable performance in identifying severe HS but had limitations distinguishing moderate and mild HS. The algorithm performed best at distinguishing severity when combining mild and moderate versus severe HS.

Background: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition characterized by insufficient levels of alpha-1 antitrypsin and elevated risk of lung and liver disease. We aimed to estimate the prevalence and incidence of diagnosed AATD (dAATD) in Norway, and mortality after dAATD.

Methods: A nationwide cohort of individuals diagnosed with AATD between 2008 and 2019 was identified from the Norwegian Patient Registry. Prevalence and incidence rates were standardized to the Norwegian population as of December 31, 2019. Mortality rate ratios (MRRs) were calculated to compare mortality between patients with dAATD and a randomly sampled, 10:1 matched general population comparison cohort.

Results: A total of 827 patients with dAATD were identified during the study period. The prevalence of dAATD in Norway at the end of 2019 was 10.7 per 100,000 people (95% confidence interval [CI]: 9.8-11.5), and the incidence rate was 1.4 per 100,000 person-years (95% CI: 1.3-1.5). Mortality among patients with dAATD was markedly elevated, with an MRR of 6.2 (95% CI: 5.3-7.2) with respect to the general population comparison cohort. Among patients with AATD, 26.8% had an emphysema diagnosis, 12.5% had an asthma diagnosis, and 6.4% had a liver disease diagnosis.

Conclusion: This study provides the first national estimates of dAATD frequency and mortality in Norway. The mortality for patients with dAATD was found to be markedly elevated compared with their age and sex matched peers, indicating a substantial disease burden and suggesting the need for improved diagnosis and earlier therapeutic interventions.

Background: In the United Kingdom (UK) primary care electronic health records (EHR), key demographic, clinical, and lifestyle variables such as ethnicity, social deprivation, body mass index and smoking status are often incomplete. This incompleteness can compromise research validity by introducing bias and reducing statistical power. There are a number of frequently used approaches to handling missing data, including complete records analysis (CRA), missing indicator method and multiple imputation (MI), however it is not clear to what extent these are used in primary care EHR analyses or whether their use is appropriate. This study examines current practice for applying methodologies and reporting of missing data, in one of the largest UK primary care EHR databases, the Clinical Practice Research Datalink (CPRD).

Methods: A random ~10% sample of observational studies from the CPRD bibliography, published between 01 January 2013 and 31 December 2023, was selected. Article screening and data extraction for each paper was completed by two reviewers, who used pre-prepared pro-forma to independently extract reporting and methods for handling missing data.

Results: From 2,481 publications during the study period, a random 220 were selected for detailed review. Missing data were reported in 163 (74%) studies. CRA was applied in 50 studies (23%), missing indicator method was used in 44 studies (20%), MI in 18 studies (8%), and alternative methods such as reclassification and mean imputation, in 15 studies (6%).

Conclusion: Many studies fail to follow published best practice, often relying on flawed methods like the missing indicator method. Greater transparency, rigorous missing data techniques, and clearer reporting are needed. Improved guidance with practical examples would enhance research quality. Without methodological consistency and scrutiny, the risk of bias and misinterpretation remains high, making it essential to integrate missing data considerations into study design and analysis.

Quasi-experimental approaches are used routinely in clinical epidemiological research to enable causal treatment/intervention effect estimation from observational data. One such approach is the regression discontinuity design (RDD): a method to estimate a causal effect in situations when a treatment or intervention is assigned to individuals according to an externally defined decision rule, based on a continuous, individual-level "assignment variable". RDDs were developed originally for use in econometrics but their use in clinical epidemiology is increasing, particularly with the widening availability of electronic health records and the use of rule-based treatment/intervention decisions. In particular, an RDD can be a useful method to assess the effectiveness of clinical decision making. In this paper, we provide an overview of the RDD, describing the method and key assumptions that permit its use in observational clinical data. We outline the common continuity-based and local randomisation RDDs and demonstrate how these can be fitted in both R and Stata. A worked example is presented of an RDD to estimate the treatment effect of statins on low density lipoprotein (LDL) cholesterol level, when statins are prescribed according to a rule based on a cardiovascular disease risk score.

Purpose: Directed acyclic graphs (DAGs) are critical in epidemiology and public health research for guiding study design and minimizing bias. Yet, developing DAGs for causal inference requires substantial domain knowledge. Given the vast amounts of training data for large language models (LLMs), this study assesses the effectiveness of prompt engineering for LLMs to generate DAGs that depict causal relationships in population health using OpenAI's GPT-4o and GPT-o1.

Methods: We consider a hypothetical study on statins vs no treatment for prevention of cardiovascular disease in a general adult population. We assessed four types of prompt engineering strategies: zero-shot, one-shot, instruction based, and chain of thought (CoT) prompts. Generated DAGs were assessed based on consistency, acyclicity, accuracy of sources, completeness (based on ASCVD risk score criteria), and adherence to the prompt.

Results: We found that all generated DAGs were acyclic, except for one run using the instruction-based prompt. Additionally, more than half of the DAGs included 6/7 of the ASCVD criteria, though race was absent from all. Overall, CoT resulted in the most complete DAGs and one-shot provided the most consistency across runs and adherence to the task in the prompt. The zero-shot prompt performed notably better on GPT-o1 compared to GPT-4o, consistently providing justifications and sources for variable inclusion.

Conclusion: While the findings suggest that LLMs have a baseline capacity to generate DAGs that adhere to basic epidemiological conventions, we also found several limitations including lack of justification, systematic omission of race, and frequent source hallucination, highlighting the need for human oversight and expertise. We conclude that contemporary LLMs cannot replace a domain expert's judgment but may serve as a brainstorming or pre-analysis tool for DAG development when guided by well-engineered prompts.

Introduction: Comorbidity between disorders is pervasive, and its relationship to the main conditions under investigation needs to be addressed for robust causal inference. However, many clinical etiologic studies still fail to capitalize on the theoretical advancements and improved recommendations regarding covariate adjustment in this context. Specifically, studies often lack explicit causal assumptions about the role of comorbidity in exposure-outcome relationships, potentially leading to inappropriate accounting for comorbid conditions and resulting in biased effect estimates. This study aims to explore common causal structures involving comorbidity and provide guidance for handling it in etiologic research.

Methods: We use Directed Acyclic Graphs (DAGs) to depict six causal scenarios involving comorbidity as a confounder, mediator, collider, or consequence of the exposure or outcome, illustrated with real-world clinical examples. Simulations were conducted across 5,000 iterations for each scenario, assessing the impact of conditioning on comorbidity under four effect measures (risk difference, odds ratio, risk ratio, and mean difference). Bias was evaluated by comparing adjusted and unadjusted effect estimates to the true values.

Results: The impact of conditioning on comorbidity varied by its causal role. Adjusting for comorbidity mitigated bias when it acted as a confounder but introduced bias when it was a mediator or collider. In instances where comorbidity was a consequence of either the exposure or outcome, the decision to adjust depended on the research objectives and could vary across effect measures.

Discussion: Explicit causal assumptions are essential for selecting appropriate analytical strategies in etiologic research. This study provides practical guidance on analytical handling of the measures of comorbidity, highlighting the need for study design and analysis to align with research objectives. Future work should address more complex causal structures and other methodological challenges.

This manuscript describes the design and protocol of the Biobank for Metabolic Syndrome Consequences (BMSC), a prospective cohort study in Northwest China. Metabolic syndrome (MetS) is characterized by a group of interrelated disorders, including abdominal obesity, hyperglycemia, hypertension, and dyslipidemia. The presence of three or more of these conditions markedly increases the risk of multiple chronic diseases and mortality. The pathophysiology and natural course of MetS and its consequences are insufficiently understood. To improve our understanding, longitudinal research that combines biomarkers with longitudinal data measured over multiple time points is imperative. The BMSC, launched in August 2021 and still ongoing, is a prospective observational study of 2000 Chinese participants aged 18 to 75 years with MetS or relevant disorders living in the Northwest of China. At baseline survey, data on sociodemography, disease history, behavior and lifestyle, and mental health are collected by a structured questionnaire. The anthropometry is conducted by trained researchers. Fasting peripheral venous blood, urine, stool, and hair samples are collected according to standardized protocols. Extensive physical examinations are conducted in specific subgroups. Participants will be followed up every 3 months for at least 5 years for the incidence of MetS-related outcomes, such as cardiovascular disease, with clinical data and biological samples being collected at intervals similar to the baseline. These findings may contribute to improved prevention, early diagnosis, and personalized treatment of MetS-related conditions.

Purpose: To investigate the impact of light-moderate (up to 20 g/day) alcohol consumption on incidence of postmenopausal breast, kidney, lung, pancreatic, colorectal, postmenopausal ovarian and postmenopausal endometrial cancer among women.

Methods: Participants were 70,932 women aged 41-70 years, randomly recruited in the Norwegian Women and Health (NOWAC) cohort study from 1996 to 2004. We included women who reported that they consumed alcohol. Only postmenopausal women (N = 32,735) were included in the analyses for female cancers. Multivariable Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI).

Results: The mean follow-up was 19 years. The estimated hazard ratio (HR) from each additional 12g/day of alcohol consumption for postmenopausal breast cancer was 1.20 (95% confidence intervals CI: 1.03 to 1.41), and for kidney cancer 0.42 (95% CI: 0.24 to 0.75). The corresponding estimates for postmenopausal breast cancer among women who used menopausal hormone therapy (MHT) were HR = 1.27, 95% CI: 1.05 to 1.54, and among women who never used MHT were HR = 1.12, 95% CI: 0.86 to 1.47. Compared to alcohol consumption of <3.5 g/day, consumption of 3.5-10 g/day revealed for lung cancer inverse association with risk of lung cancer among women who consumed primarily wine (HR = 0.65, 95% CI; 0.43 to 0.88), but not among other drinkers (HR = 1.10, 95% CI; 0.88 to 1.31). No associations were confined for pancreatic, colorectal, ovarian and endometrial cancers.

Conclusion: Women drinking light-moderate alcohol level had a higher risk of postmenopausal breast cancer and a lower risk of kidney cancer incidence. Our results do not support the threshold of up to 1 drink/day as a safe limit for breast cancer, especially for postmenopausal women who use MHT. The inverse relationship found for lung cancer could be explained by the healthier lifestyle correlated with this light-moderate drinking.

Purpose: This study aimed to investigate the association between the use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and the risk of developing depression in patients with type 2 diabetes mellitus.

Patients and methods: This study used Taiwan's National Health Insurance Database and an active comparator new-user design to evaluate depression risk among 551,917 patients initiating SGLT2i or DPP4i between 2016 and 2018. The primary outcome was depression incidence, assessed over a three-year follow-up. Stratified Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) between groups.

Results: Among new SGLT2i users, 3255 cases of depression occurred (7.18 per 1000 person-years) versus 7190 cases among DPP4i users (10.12 per 1000 person-years). After adjustment for demographic and clinical covariates, SGLT2i use was consistently associated with a lower risk of depression in both the full cohort (adjusted HR = 0.77; 95% CI: 0.73-0.80) and the propensity score-matched cohort (adjusted HR = 0.77; 95% CI: 0.74-0.81). The association remained robust in multiple sensitivity analyses and across clinical subgroups.

Conclusion: SGLT2i use was associated with a reduced risk of depression among individuals with type 2 diabetes mellitus. These findings suggest potential neuropsychiatric benefits of SGLT2i and support further investigation into their broader therapeutic implications.