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Background: Chronic kidney disease (CKD) is a major global health problem and its early identification would allow timely intervention to reduce complications. We performed a systematic review and meta-analysis of multivariable prediction models derived and/or validated in community-based electronic health records (EHRs) for the prediction of incident CKD in the community. Methods: Ovid Medline and Ovid Embase were searched for records from 1947 to 31 January 2024. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST) and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Results: Seven studies met inclusion criteria, describing 12 prediction models, with two eligible for meta-analysis including 2 173 202 patients. The Chronic Kidney Disease Prognosis Consortium (CKD-PC) (summary c-statistic 0.847; 95% CI 0.827-0.867; 95% PI 0.780-0.905) and SCreening for Occult REnal Disease (SCORED) (summary c-statistic 0.811; 95% CI 0.691-0.926; 95% PI 0.514-0.992) models had good model discrimination performance. Risk of bias was high in 64% of models, and driven by the analysis domain. No model met eligibility for meta-analysis if studies at high risk of bias were excluded, and certainty of effect estimates was 'low'. No clinical utility analyses or clinical impact studies were found for any of the models. Conclusions: Models derived and/or externally validated for prediction of incident CKD in community-based EHRs demonstrate good prediction performance, but assessment of clinical usefulness is limited by high risk of bias, low certainty of evidence and a lack of impact studies.
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OBJECTIVE: The tapering of biologic disease-modifying antirheumatic drug (b-DMARD) therapy for patients with rheumatoid arthritis (RA) in stable remission is frequently undertaken, but specific guidance on how to successfully taper is lacking. The objective of this study is to identify predictors of flare in patients in stable b-DMARD-induced clinical remission, who did or did not follow structured b-DMARD tapering. METHODS: Patients with RA receiving b-DMARD treatment who had achieved sustained remission according to a Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) <2.6 for ≥6 months were offered tapering. Clinical, ultrasound (US) (total power Doppler [PD]/grayscale abnormalities), CD4+ T cell subsets, and patient-reported outcomes (PROs) were collected at inclusion. The primary endpoint was the occurrence of flare (loss of DAS28-CRP remission) over 12 months. Logistic regression analyses identified predictors of flare. Dichotomization into high/low-risk groups was based on 80% specificity using the area under the receiving operator curve (AUROC). RESULTS: Of 63 patients choosing tapering, 23 (37%) flared compared with 12 of 60 (20%) on stable treatment (P = 0.043). All patients who flared regained remission upon reinstating treatment. In the tapering group, flare was associated with lower regulatory T cell (Treg) (P < 0.0001) and higher CRP levels (P < 0.0001), erythrocyte sedimentation rate (P < 0.035), and inflammation-related cells (IRCs) (P = 0.054); stepwise modeling selected Tregs (odds ratio [OR] = 0.350, P = 0.004), IRCs (OR = 1.871, P = 0.007), and CRP level (OR = 1.577, P = 0.004) with 81.7% accuracy and AUROC = 0.890. In the continued therapy group, modeling retained the tender joint count, total PD, and visual analog scale pain score, with 82.1% accuracy and AUROC = 0.899. Most patients in the study were considered low risk of flare (80 of 123 patients [65%]). Only 5 of 37 (13.5%) of the low-risk patients who tapered flared, which was notable compared with the continued therapy group (20% flare). CONCLUSION: Flare on tapering b-DMARDs was predicted by lower Tregs and elevated inflammation biomarkers (IRCs/CRP level); flare on continued b-DMARDs was associated with raised pain parameters and US inflammation. Knowledge of these biomarkers should improve outcomes by targeted selection for tapering, and by increased monitoring of those on continued therapy predicted to flare.
RESUMO
AIMS: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS: From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.
