PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Target trial emulation”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

4 recordsLinked to original sources
PubMed2025

Donor HLA Class I Evolutionary Divergence and Late Allograft Rejection After Liver Transplantation in Children: An Emulated Target Trial.

HLA evolutionary divergence (HED), a continuous metric quantifying the differences between each amino acid of two homologous HLA alleles, reflects the importance of the immunopeptidome presented to T lymphocytes. It has been associated with rejection after liver transplantation. This retrospective cohort study aimed to analyse the potential effect of donor or recipient HED on liver transplant rejection in a new series of patients transplanted during childhood and followed in adulthood. The study included 120 children who had been transplanted between 1991 and 2010 and were followed by routine biopsies and histological evaluations with a median of 14.1 years post-LT. Liver biopsies were performed routinely 1, 5, 10 and 20 years after transplantation and in the event of liver dysfunction. HED was calculated using the physicochemical Grantham distance for donor and recipient Class I (HLA-A, -B, -C) and Class II (HLA-DRB1, -DQB1) alleles. The influence of HED on rejection was analysed using inverse probability weighting (IPW) and target trial emulation using the g method. Based on the IPW score, donor HED class I was correlated with the occurrence of late (> 90 days) rejection (HR, 1.19, 95% CI: 1.01-1.40) independently of HLA mismatches, donor age and initial induction. The emulated target trial confirmed that donor HED Class I has a causal effect on liver graft rejection and this relationship was observed long-term.

Humans↗
PubMed2026

Urate-lowering effects of losartan: a meta-analysis of randomised controlled trials and target trial emulation.

Common in hypertensive patients, hyperuricaemia is often exacerbated by guideline-recommended antihypertensive therapies such as thiazide diuretics. Losartan is known as an angiotensin II receptor type 1 antagonist with a uricosuric effect, but the magnitude of its efficacy in lowering urate has not been quantified versus a placebo-reference. We sought to quantify the urate-lowering effect of losartan versus placebo using two complementary approaches. We first conducted a meta-analysis of randomised controlled trials (RCTs) to quantify the effect of losartan on serum urate levels versus placebo. We then applied a target trial emulation framework in the UK Biobank as a secondary source of data and a replication experiment. The meta-analysis of six prospective randomised controlled trials (RCTs), including 1119 losartan-treated patients and 1093 controls, demonstrated that losartan reduced serum urate levels by approximately 0.29 mg/dL relative to placebo (95% CI: -0.46 to -0.12, p = 0.0009). In the target trial emulation, 23 losartan-treated individuals showed a reduction in serum urate of approximately 0.35 mg/dL (95% CI: -0.66 to -0.03, p = 0.03) when compared with 92 matched controls, and after accounting for 12 potential confounders including established urate-lowering therapies. Our results provide evidence for a modest yet consistent urate-lowering potential of losartan. This modest biochemical effect (~0.3 mg/dL) may be an attractive option to mitigate the diuretic-induced urate elevation. Thus, losartan can be considered as a favourable antihypertensive choice for patients managed with diuretics or those who are at risk of hyperuricaemia/gout.

Losartan↗
PubMed2026

Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults.

BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.

Humans↗
PubMed2026

Immune dysregulation in depression and psychosis: summary of current evidence and future perspectives.

Despite compelling epidemiological, genetic and cellular evidence linking immune dysregulation to depression and schizophrenia (and other psychotic disorders), causality remains contested and no immune biomarker has yet demonstrated robust clinical utility. Emerging methodological approaches - from target trial emulation on observational data to functional genomics - offer a potential path towards precision immunopsychiatry and stratified immunomodulatory treatment.

Neuroimmunology↗