Search

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Background The World Falls Guidelines (WFG) propose an algorithm that classifies patients as low-, intermediate-, and high-risk. We evaluated different operationalizations of the WFG algorithm and compared its predictive performance to other screening tools for falls, namely: the American Geriatrics Society and British Geriatrics Society (AGS/BGS) algorithm, the 3KQ on their own and fall history on its own. Methods We included data from 1509 adults aged ≥65 years from the population-based Longitudinal Aging Study Amsterdam. The outcome was ≥1 fall during 1-year follow-up, which was ascertained using fall calendars. The screening tools' items were retrospectively operationalized using baseline measures, using proxies where necessary. Results Sensitivity ranged between 30.9–48.0% and specificity ranged between 77.0–88.2%. Operationalizing the algorithm with the 3KQ instead of fall history yielded a higher sensitivity but lower specificity, whereas operationalization with the Clinical Frailty Scale (CFS) classification tree instead of Fried's frailty criteria did not affect predictive performance. Compared to the WFG algorithm, the AGS/BGS algorithm and fall history on its own yielded similar predictive performance, whereas the 3KQ on their own yielded a higher sensitivity but lower specificity. Conclusion The WFG algorithm can identify patients at risk of a fall, especially when the 3KQ are included in its operationalization. The CFS and Fried's frailty criteria may be used interchangeably in the algorithm's operationalization. The algorithm performed similarly compared to other screening tools, except for the 3KQ on their own, which have higher sensitivity but lower specificity and lack clinical recommendations per risk category. [ABSTRACT FROM AUTHOR]

Aims: Although the neuroanatomical distribution of tau and amyloid‐β is well studied in Alzheimer's disease (AD) (non)‐amnestic clinical variants, that of neuroinflammation remains unexplored. We investigate the neuroanatomical distribution of activated myeloid cells, astrocytes, and complement alongside amyloid‐β and phosphorylated tau in a clinically well‐defined prospectively collected AD cohort. Methods: Clinical variants were diagnosed antemortem, and brain tissue was collected post‐mortem. Typical AD (n = 10), behavioural/dysexecutive AD (n = 6), posterior cortical atrophy (PCA) AD (n = 3), and controls (n = 10) were neuropathologically assessed for AD neuropathology, concurrent pathology including Lewy body disease, limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC), and vascular pathology. For quantitative assessment, we analysed the corticolimbic distribution of phosphorylated tau, amyloid‐β, CD68, MHC‐II, C4b, and glial fibrillary acidic protein (GFAP) using digital pathology. Results: Phosphorylated tau was distinctly distributed in each variant. In all variants, amyloid‐β was neocortical‐dominant, with a notable increase in the middle frontal cortex of behavioural/dysexecutive AD. Typical AD and PCA AD had no concurrent Lewy body disease, whereas three out of six cases with behavioural/dysexecutive AD did. LATE‐NC stage >0 was observed in three AD cases, two typical AD (stage 1/3), and one behavioural/dysexecutive AD (stage 2/3). Vascular pathology was present in each variant. In typical AD, CD68 and MHC‐II were hippocampal‐dominant. In behavioural/dysexecutive AD, C4b was elevated in the middle frontal and inferior parietal cortex. In PCA AD, MHC‐II was increased in the fusiform gyrus, and GFAP in parietal cortices. Correlations between AD neuropathology and neuroinflammation were distinct within variants. Conclusions: Our data suggests that different involvement of neuroinflammation may add to clinical heterogeneity in AD, which has implications for neuroinflammation‐based biomarkers and future therapeutics. [ABSTRACT FROM AUTHOR]

Objective: Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single‐nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA. Methods: Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297). Results: rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high‐density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model. Conclusion: Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms. [ABSTRACT FROM AUTHOR]

Objective: Patients with back pain (BP) and radiating leg pain have poorer clinical outcomes compared to patients with BP alone. We aimed to describe the 1‐year clinical course and to identify prognostic factors associated with non‐recovery in older BP patients with radiating leg pain. Design: Patients in the BACE cohort aged >55 years with a new episode of BP and radiating leg pain were included (n = 377). Data on clinical outcomes were collected until 1‐year follow‐up. Uni‐ and multivariable regression analyses were performed to investigate the association between potential prognostic factors and three non‐recovery outcomes at the 1‐year follow‐up. Results: More than half of the patients (65%) did not recover after 12 months. In multivariable analyses, poor self‐rated health (odds ratio [OR] 2.34, 95% CI: 1.20–4.56) and BP duration at baseline (OR 1.48, 1.12–1.96) were significantly associated with non‐recovery for BP as outcome; age (OR 1.04, 1.03–1.05), smoking (OR 1.14, 1.00–1.30), depressive symptomatology (OR 1.03, 1.02–1.04), kinesiophobia (OR 1.03, 1.02–1.04), poor self‐rated health (OR 2.09, 1.83–2.39), baseline disability (OR 1.16, 1.14–1.17), BP duration (OR 1.49, 1.41–1.57), leg pain (OR 1.52, 1.37–1.68), pain during rotation (OR 1.71, 1.53–1.90) and other musculoskeletal complaints (OR 1.34, 1.17–1.52) were associated with disability. No factors were associated with leg pain. Conclusions: Several prognostic factors were associated with non‐recovery in older patients with BP and radiating leg pain. Primary care clinicians should be aware of these factors in managing these patients. [ABSTRACT FROM AUTHOR]

Background: Unplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. Aim: To develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs. Design and setting: This was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months. Method: The dataset was geographically split into a development (n = 142 791/243 324, 58.7%) and validation (n = 100 533/243 324, 41.3%) sample to predict unplanned admissions to hospital within 6 months. The performance of three different models was evaluated with increasingly smaller selections of candidate predictors (optimal, readily available, and easy-to-use models). Logistic regression was used with backward selection for model development. The models were validated internally and externally. Predictive performance was assessed by area under the curve (AUC) and calibration plots. Results: In both samples, 7.6% (development cohort: n = 10 839/142 791, validation cohort: n = 7675/100 533) had ≥1 unplanned hospital admission within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior admissions to hospital, pulmonary emphysema, heart failure, and polypharmacy. Its discriminative ability after validation was AUC 0.72 (95% confidence interval = 0.71 to 0.72). Calibration plots showed good calibration. Conclusion: The models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

BackgroundAlzheimer's disease (AD) is a heterogeneous disorder.ObjectiveTo investigate whether cognitive AD subtypes are associated with different rates of disease progression.MethodsWe included 1,066 probable AD patients from the Amsterdam Dementia Cohort (n = 290), Alzheimer's Disease Neuroimaging Initiative (n = 268), Dementia Competence Network (n = 226), and University of California, San Francisco (n = 282) with available follow-up data. Patients were previously clustered into two subtypes based on their neuropsychological test results: one with most prominent memory impairment (n = 663) and one with most prominent non-memory impairment (n = 403). We examined associations between cognitive subtype and disease progression, as measured with repeated Mini-Mental State Examination (MMSE) and Clinical Dementia Rating scale sum of boxes (CDR sob), using linear mixed models. Furthermore, we investigated mortality risk associated with subtypes using Cox proportional hazard analyses.ResultsPatients were 71±9 years old; 541 (51%) were female. At baseline, pooled non-memory patients had worse MMSE scores (23.1±0.1) and slightly worse CDR sob (4.4±0.1) than memory patients (MMSE 24.0±0.1; p

Background Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. Methods Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. Results We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426–2766] versus 90 441 (IQR 56 442–128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5–11); for RCD-based models, it was 16 (IQR 11–26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. Conclusions Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality. [ABSTRACT FROM AUTHOR]