Dementia risk scores are commonly used tools to estimate the risk of developing Alzheimer's disease and dementia. We lack an overview of what risk scores are used for, what is claimed they ought to be used for, and whether they are suitable for these applications. To address this, we use the ‘Lifestyle for Brain Health’ (LIBRA) score as a representative example risk score and conduct a literature review to study its applications. The goals of this study are (1) to create an overview of how the LIBRA score has been utilized in scientific articles, (2) to record other applications that these same articles mention, and (3) to critically assess whether LIBRA is suitable for these applications. Of the 66 articles included in our review, 36 involved analyzing associations of LIBRA with dementia, cognition, or other outcomes. We also identified several other applications, with 32 articles mentioning LIBRA as an estimate of ‘dementia prevention potential’, 6 articles used LIBRA as a surrogate outcome for their trial or intervention, and 7 articles mentioned that it could help support clinician decisions in practice. Although there is a clear need for tools that can be used for these applications, the amount of evidence supporting the suitability of dementia risk scores for many of these applications is limited. We recommend that researchers transparently report the purposes of these dementia risk scores, which may include causal tasks, and that research is done to evaluate whether it is valid to use these scores in this way.

Objective: Osteoarthritis (OA) is typically studied in isolated joints, but humans are interconnected systems. This raises the question of how multi-joint OA manifests, and whether it forms a distinct subgroup. This study aimed to investigate whether individuals with OA worsening in both the hip and the knee exhibit unique clinical, structural, or demographic characteristics compared to those with isolated OA worsening or no worsening. Design: We conducted a retrospective analysis using data from the Osteoarthritis Initiative, including 1958 participants with radiographic assessments of hip and knee joints at baseline and 48-month follow-up. Participants were categorized into four groups based on joint space narrowing: no worsening, hip-only worsening, knee-only worsening, or combined worsening in 48 months. Univariate comparisons and multivariate logistic regression analyses were performed to compare the combined worsening group to the other groups. Results: Combined worsening occurred in 12.5% of participants. Compared to those with no worsening, the combined worsening group had more severe baseline radiographic knee OA (aOR: 1.38 (1.15–1.64)). Compared to hip-only OA worsening, the combined group had more severe knee OA (aOR: 1.36 (1.11–1.67)). Compared to those with knee-only OA worsening, combined OA worsening was associated with female sex (aOR: 1.92 (1.31–2.76)). Conclusions: Our findings show differences between individuals with combined or isolated OA worsening, which may reflect accumulation of single-joint risk factors rather than a distinct trajectory. This research provides a foundation for large-scale investigations into multi-joint OA subtypes to improve patient stratification and inform targeted interventions.

Aims We aimed to compare performances of conventional survival models with machine learning (ML) survival models for incident heart failure (HF) in men and women without prevalent HF, cardiomyopathy (CM) or ischaemic heart disease (IHD), and to identify potential high-risk precursors overlooked by conventional survival models. Methods and results We predicted 10-year risk of incident HF in 266 306 women (2894 events) and 212 061 men (4213 events). We constructed multivariable Cox models, first using ∼ 400 baseline characteristics, and subsequently only those remaining after LASSO stability selection. We also used Random Survival Forest (RSF) and eXtreme Gradient Survival Boosting (XGBoost). Performances were assessed using internal cross validation and hold-out sets, with C-indices, calibration curves and net-benefit analyses. Model performances were comparable during internal validation: XGBoost (C-index ± SE) (men: 0.79 ± 0.0040, women: 0.83 ± 0.0023) showed similar performance to the multivariable Cox model (men: 0.80 ± 0.0031, women: 0.83 ± 0.0022) and Cox models after LASSO stability selection, while RSF showed numerically slightly lower performance (men: 0.78 ± 0.0025, women: 0.81 ± 0.0015). Findings were similar in the hold-out sets. Age, cystatin-C, lifetime treatments/medications, other heart disease, systolic blood pressure, and spirometry measures were identified as high-risk factors in both model types for both sexes. Additionally, sex-specific and model-specific risk factors were identified. Conclusion Machine learning models and Cox proportional hazard models performed well and similarly for 10-year incident HF risk prediction in the general population. However, sex-specific and model-specific risk predictors were found. Spirometry measures, rarely included in existing models, were identified as important risk factors. Our results suggest that ML models for HF prediction in the general population reveal insights that would otherwise remain unnoticed.

Background: Despite several randomised controlled trials (RCTs) on the use of adjuvant treatment with corticosteroids in patients with community-acquired pneumonia (CAP), the effect of this intervention on mortality remains controversial. We aimed to evaluate heterogeneity of treatment effect (HTE) of adjuvant treatment with corticosteroids on 30-day mortality in patients with CAP. Methods: In this individual patient data meta-analysis, we included RCTs published before July 1, 2024, comparing adjuvant treatment with corticosteroids versus placebo in patients hospitalised with CAP. The primary endpoint was 30-day all-cause mortality, collected across all trials, and analyses followed the intention-to-treat principle. We analysed HTE using risk and effect modelling. For risk modelling, patients were classified as having less severe or severe CAP based on the pneumonia severity index (PSI), comparing PSI class I–III versus class IV–V. For effect modelling, we trained a corticosteroid-effect model on six trials and externally validated it using data from two trials, received after model preregistration. This model classified patients into two groups: no predicted benefit and predicted benefit from adjuvant treatment with corticosteroids. The literature search was registered on PROSPERO, CRD42022380746. Findings: We included eight RCTs with 3224 patients. Across all eight trials, 246 (7·6%) patients died within 30 days (106 [6·6%] of 1618 in the corticosteroid group vs 140 [8·7%] of 1606 in the placebo group; odds ratio [OR] 0·72 [95% CI 0·56–0·94], p=0·017). The corticosteroid-effect model, which selected C-reactive protein (CRP), showed significant HTE during external validation in the two most recent trials. In these trials, 154 (11·4%) of 1355 patients died within 30 days (88 [13·1%] of 671 in the placebo group vs 66 [9·6%] of 684 in the corticosteroid group; OR 0·71 [95% CI 0·50–0·99], p=0·044). Among patients predicted to have no benefit (CRP ≤204 mg/L, n=725), no significant effect was observed (OR 0·98 [95% CI 0·63–1·50]), whereas for those with predicted benefit (CRP >204 mg/L, n=630), 39 (13·0%) of 301 patients died in the placebo group compared with 20 (6·1%) of 329 in the corticosteroid group (0·43 [0·25–0·76], p_interaction=0·026). No significant HTE was found between less severe CAP (PSI class I–III, n=229) and severe CAP (PSI class IV–V, n=1126). Corticosteroid therapy significantly increased hyperglycaemia risk (44 [12·8%] of 344 in the placebo group vs 84 [24·8%] of 339 in the corticosteroid group; OR 2·50 [95% CI 1·63–3·83], p<0·0001) and hospital re-admission risk (30 [3·7%] of 814 in the placebo group vs 57 [7·0%] of 819 in the corticosteroid group; 1·95 [1·24–3·07], p=0·0038). Interpretation: Overall, adjuvant therapy with corticosteroids significantly reduces 30-day mortality in patients hospitalised with CAP. The treatment effect varied significantly among subgroups based on CRP concentrations, with a substantial mortality reduction observed only in patients with high baseline CRP. Funding: None.

Background: – Reports regarding the relationship between tacrolimus exposure and the risk of acute kidney allograft rejection are conflicting. This may be explained by the previous use of methodological approaches that disregarded important factors in the analysis of longitudinal measurements and time-to-event data. Therefore, in this study, joint models were used to investigate the relationship between repeated measurements of tacrolimus predose concentrations (C ₀) and time to acute biopsy-proven acute rejection (BPAR).Methods: – This was a post hoc analysis of a randomized controlled trial in which living-donor kidney transplant recipients (KTR) received either a standard, bodyweight-based or CYP3A5 genotype-based tacrolimus starting dose. Joint modeling was performed by coupling a mixed-effects model for tacrolimus C ₀ with a Cox proportional hazards model for the risk of rejection. Only the first episode of rejection was considered.Results: – A total of 229 KTRs were included, of whom the incidence of BPAR was 10.5% (n = 24 KTRs) in the first 3 months posttransplant. A total of 3069 tacrolimus measurements were available for the analysis. A joint model adjusted for recipient age and peak panel reactive antibodies demonstrated that tacrolimus C ₀ was associated with risk of rejection. A 1-unit increase in the time-normalized area under the curve for logarithmically (log)-transformed C ₀ represented a change of −2.65 in the log of the relative hazard (95% credible interval: −5.05 to −0.36, P = 0.022).Conclusions: – A negative association between the cumulative effect of tacrolimus C ₀ and BPAR was observed using joint modeling. This demonstrated that KTRs with lower tacrolimus exposure were at a higher risk of rejection.

A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mechanisms to appear dependent. Building on this observation, we develop a novel two-stage procedure that detects these dependencies with high statistical power while controlling false positives. The algorithm does not require access to randomized data and, in contrast to other falsification approaches, functions even under transportability violations when the environment has a direct effect on the outcome of interest. To showcase the practical relevance of our approach, we show that our method is able to efficiently detect confounding on both simulated and semi-synthetic data.