Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 8 appraisals
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Differential effects of hormone therapy formulations on Parkinson's disease risk: a systematic review and meta-analysis
BACKGROUND: The relationship between menopausal hormone therapy (HT) and Parkinson's disease (PD) risk remains controversial, with inconsistent findings potentially driven by differences in hormonal formulations. METHODS: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. MEDLINE/PubMed and EMBASE were searched between January 8 and March 14, 2026. Observational studies evaluating the association between menopausal HT and PD risk were included. Effect estimates were pooled using random-effects models. Subgroup analyses were performed according to HT formulation (estrogen-only vs. combined estrogen-progestin therapy). A multilevel meta-analysis was conducted to account for within-study dependence. RESULTS: Fourteen studies including 753,749 participants (4,433 PD cases) were analyzed. Overall, HT was not significantly associated with PD risk (RR 1.08; 95% CI 0.94-1.23; I² = 49.4%). In subgroup analyses, combined therapy was associated with an increased PD risk (RR 1.40; 95% CI 1.07-1.82), whereas estrogen-only therapy showed no significant association (RR 1.01; 95% CI 0.81-1.27). Multilevel analysis yielded consistent results (combined: RR 1.33; 95% CI 1.00-1.77; estrogen-only: RR 1.03; 95% CI 0.83-1.27), with no statistically significant interaction between formulations (p = 0.12). CONCLUSIONS: Combined menopausal HT was associated with an increased risk of PD, while estrogen-only therapy showed no significant association. Although differences between formulations were not statistically significant, these findings suggest that hormone composition may influence neurological outcomes and warrant further investigation into individualized HT strategies.
23 July 2026
Read appraisal →Nature communications
Empowering clinical trial design with agentic intelligence and real-world data
Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electronic health records (EHR), encodes practice-based evidence that is of tremendous value to CTD. In recent years, many machine learning methods have been developed to extract such real-world evidence (RWE) from the RWD to inform CTD, but they still need to be communicated with the domain experts extensively in an iterative manner to be further refined and ultimately useful. In this paper, we introduce EmulatRx, an agentic framework that derives RWE for helping with CTD. Through the iterative conversation and analysis across agents with different roles, EmulatRx can autonomously refine trial protocols and finally generate a robust report containing insights that inform better CTD. We applied EmulatRx on the CTD process for both acute diseases (e.g., septic shock, acute heart failure, acute pulmonary edema, and acute kidney injury) using the MIMIC-IV data and chronic diseases (e.g., Alzheimer's disease and Parkinson's disease) using the INSIGHT Network across five New York City health systems. The results demonstrate EmulatRx's capabilities in facilitating and accelerating the CTD process.
9 July 2026
Read appraisal →Journal of the American College of Clinical Pharmacy : JACCP
Research and Scholarly Methods: Using Artificial Intelligence
Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.
1 July 2026
Read appraisal →BMJ open
Development of phenotype algorithms for the detection of adverse events in electronic health record data: a multicentre study
OBJECTIVES: To develop phenotype algorithms for the detection of adverse events (AEs) or AE-proxies in electronic health records (EHRs), accounting for varying data availability. DESIGN: Multicentre study conducted as part of the Use Case POLAR_MI (POLypharmacy, drug interActions, Risks) of the German Medical Informatics Initiative (MII). SETTING: Germany. PARTICIPANTS: Multidisciplinary teams from 10 German university sites within the MII. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Literature- and consensus-based development and operationalisation of AE algorithms using structured EHR data, including a standardised, multicentre expert review process. Data categories used: International Classification of Diseases, 10th Revision (ICD-10) codes for diagnoses; Anatomical Therapeutic Chemical (ATC) codes and 'Pharmazentralnummern' (PZN; German eight-digit identification code for pharmaceutical products) for medications (used in the treatment of AEs); Logical Observation Identifiers Names and Codes (LOINC) for laboratory values and medical findings; and 'Operationen- und Prozedurenschlüssel' (OPS; German procedure classification) codes for medical and surgical procedures. RESULTS: We developed 82 algorithms for 48 AEs. Algorithms for the same AE varied by data categories or code selections. At the AE level, 31 AEs were covered exclusively by newly developed algorithms, and 17 AEs by at least one modified algorithm.Overall, 52 algorithms were based on a single data category, while 30 required multiple categories. ICD-10 codes were most commonly used (n=65 AE algorithms), followed by LOINC (n=27), ATC codes (n=18), OPS codes (n=11) and PZN (n=2). All phenotype algorithms were semantically modelled and can be executed using the publicly available Terminology- and Ontology-based Phenotyping (TOP) Framework, which supports export in various formats. CONCLUSION: We present a peer-reviewed set of algorithms for a large number of AEs, which can be implemented in structured routine electronic data sources and (pending validation studies) may support pharmacoepidemiologic research. The algorithms will be implementable across all 39 participating sites of the German MII. As a next step, we will empirically validate the algorithms against all information (including free text) contained in EHRs. TRIAL REGISTRATION: Not applicable.
24 June 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Training a Smoking Status Probabilistic Model Using Cotinine Levels in a Large Claims Database
INTRODUCTION: Smoking status is an important confounder for many epidemiologic studies, yet it is not well documented in common sources of real-world data, including administrative claims. Probabilistic models can be used to create a proxy for smoking status, yet most published models have been trained using self-reported data. The objective of this study was to train a smoking status probabilistic model using cotinine values available in a large claims database. METHODS: Beneficiaries were included if they had at least one cotinine measurement and were categorized as a "current smoker" if their serum or plasma cotinine value was ≥5 ng/mL or urine cotinine value was ≥30 ng/mL. Predictors were collected across one year prior to the cotinine assessment date. The model was fit using logistic regression with stepwise forward selection. Model performance was assessed using discrimination and calibration. RESULTS: The final model yielded an area under the receiver operating characteristic curve of 0.77 (95%CI:0.75-0.78) and was well calibrated across most prediction deciles. The strongest predictors included diagnosis codes for smoking and drug abuse, and number of medications. The model was found to be highly specific, yet not sensitive at probability cutoffs ≥0.2. CONCLUSIONS: A smoking status model was developed and internally validated for application in claims data, using available cotinine values to define smoking status and found to have acceptable discrimination and calibration. The model is based on 26 predictors, fewer than other similar published smoking status models. External validation of the model should be a next step toward utilizing the model for epidemiological research. IMPLICATIONS: This study tests the utility of cotinine values to validate a smoking status probabilistic model, which has not been done in the literature to date. The results were robust to various cotinine levels used to define smoking status, per current guidance. The final model uses only 26 factors to predict smoking status, simplifying the application of the model in other claims databases.
25 May 2026
Read appraisal →BMJ (Clinical research ed.)
Concordance between target trial emulation and randomised controlled trials: systematic review and meta-analysis
OBJECTIVES: To assess the concordance of results between paired target trial emulations and their benchmarking randomised controlled trials and to explore determinants of emulation success. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline, EMBASE, Scopus, PsycINFO, and Web of Science (2010 to 2025). STUDY SELECTION: Observational studies that explicitly aimed to emulate target randomised controlled trials of a health intervention were included. DATA EXTRACTION: Data were extracted on study domain, target population, intervention, comparator, and main outcomes for each emulation and corresponding randomised controlled trial. MAIN OUTCOME MEASURES: For 21 predefined emulation design features, concordance was assessed using Pearson correlation coefficients, standardised difference agreement, and ratio of ratios, with subgroup and regression analyses of study characteristics and concordance. RESULTS: Among 107 target trial emulation-randomised controlled trial pairs, the Pearson correlation coefficient was 0.59 (95% confidence interval (CI) 0.45 to 0.70), standardised difference agreement was 79% (85/107), and summary ratio of ratios was 0.96 (95% CI 0.92 to 1.01; I2=36%). In 63 pairs with closer emulation of trial design, a higher level of agreement was observed, with a Pearson correlation coefficient of 0.83 (0.73 to 0.89) and standardised difference agreement of 87% (55/63). In subgroup analysis, trial emulations tended to systematically underestimate treatment effects from randomised controlled trials for specific outcomes related to venous thromboembolism and major adverse cardiovascular events, while overestimating those for respiratory outcomes, with pooled ratio of ratios of 0.76 (0.58 to 1.00; I2=40%), 0.91 (0.86 to 0.96; I2=32%), and 1.20 (1.03 to 1.40; I2=40%), respectively. Moreover, emulations based on claims data tended to underestimate the treatment effects (ratio of ratios 0.90, 0.82 to 0.99; I2=38%). In univariate regression analyses, poorer target trial emulation-randomised controlled trial concordance was associated with emulation designs with imbalanced baseline population characteristics, treatment started in hospital, and lower outcome emulation quality. CONCLUSIONS: Current target trial emulations achieve moderate concordance when replicating their corresponding randomised controlled trials. Concordance could be improved by better emulation design, including better baseline and outcome emulation and leveraging multisource linked databases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024619811.
20 May 2026
Read appraisal →Clinical pharmacology and therapeutics
Off-the-Shelf Large Language Models for Guiding Pharmacoepidemiological Study Design
This study aimed to assess the ability of two off-the-shelf large language models, ChatGPT and Gemini, to support the design of pharmacoepidemiological studies. We assessed 48 study protocols of pharmacoepidemiological studies published between 2018 and 2024, covering various study types, including disease epidemiology, drug utilization, safety, and effectiveness. The coherence (i.e., "Is the response coherent and well-formed, or is it difficult to understand?") and relevance (i.e., "Is the response relevant and informative, or is it lacking in substance?") of the large language models' responses were evaluated by human experts across seven key study design components. Coding accuracy was assessed. Both large language models demonstrated high coherence, with over 90% of study components rated as "Strongly agree" by experts for most categories. ChatGPT achieved the highest coherence for "Index date" (97.9%) and "Study design" (95.8%). Gemini excelled in "Study outcome" (93.9%) and "Study exposure" (95.9%). Relevance, however, was more variable, with ChatGPT aligning with expert recommendations in over 90% of cases for "Index date" and "Study design" but showing lower agreement for covariates (65%) and follow-up (70%). Coding agreement percentages reveal varying levels of concordance, with the Anatomical Therapeutic Chemical classification system coding system demonstrating the highest agreement at 50% with experts. In contrast, the Current Procedural Terminology and International Classification of Diseases systems showed agreements of 22.2% and 20%, respectively. While ChatGPT and Gemini show promise in certain tasks supporting pharmacoepidemiological study design, their limitations in relevance and coding accuracy highlight the need for critical oversight by domain experts.
4 May 2026
Read appraisal →Cancer causes & control : CCC
Comparing endocrine therapy adherence across EHR and claims data by race and ethnicity, language, and insurance.
PURPOSE: We compared adherence measurements across electronic health records (EHRs) and claims data for 79 HR + breast cancer survivors, considering their race, ethnicity, language, and insurance status. METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation. RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status. CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.
5 Apr 2026
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