Objectives: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care,... Show moreObjectives: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes.Study Design and Setting: This retrospective external validation study included 14,092 older individuals of >=70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. Main Outcome Measure: In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. Results: All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large 1.45 to 7.46, calibration slopes 0.24e0.81, and C-statistic 0.55e0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of 2.35 to 0.15 indicating overestimation, calibration slopes of 0.24e0.81 indicating signs of overfitting, and C-statistic of 0.55e0.71. Conclusion: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic Show less
Roi-Teeuw, H.M. la; Luijken, K.; Blom, M.T.; Gussekloo, J.; Mooijaart, S.P.; Polinder-Bos, H.A.; ... ; Dries, C.J. van den 2024
BackgroundDuring the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of... Show moreBackgroundDuring the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19.MethodsFrom primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks.ResultsOf the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7–26% for the basic model including age and sex, changing to 4–41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 – 0.72) to 0.74 (95%CI 0.71 – 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions.ConclusionIn our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality. Show less
Klerk, J.A. de; Bijkerk, R.; Beulens, J.W.J.; Zonneveld, A.J. van; Muilwijk, M.; Harms, P.P.; ... ; Slieker, R.C. 2024
AimTo investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal... Show moreAimTo investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal.Materials and MethodsThe Hoorn Diabetes Care System cohort is a large prospective cohort consisting of individuals with type 2 diabetes from the northwest part of the Netherlands. In this cohort we assessed the association of baseline plasma levels of 172 metabolites with incident (Ntotal = 462/Ncase = 81) and prevalent (Ntotal = 1247/Ncase = 120) CKD using logistic regression. Additionally, replication in the UK Biobank, body mass index (BMI) mediation and causality of the association with Mendelian randomization was performed.ResultsElevated levels of total and individual branched-chain amino acids (BCAAs)-valine, leucine and isoleucine-were associated with an increased risk of incident CKD, but with reduced odds of prevalent CKD, where BMI was identified as an effect modifier. The observed inverse effects were replicated in the UK Biobank. Mendelian randomization analysis did not provide evidence for a causal relationship between BCAAs and prevalent CKD.ConclusionsOur study shows the intricate relationship between plasma BCAA levels and CKD in individuals with type 2 diabetes. While an association exists, its manifestation varies based on disease status and BMI, with no definitive evidence supporting a causal link between BCAAs and prevalent CKD. Show less
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale... Show moreThe application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. Show less
Introduction Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA... Show moreIntroduction Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information. Aim To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA. Methods The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA. Conclusion The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses. Show less
Aims Various drugs increase the risk of out-of-hospital cardiac arrest (OHCA) in the general population by impacting cardiac ion channels, thereby causing ventricular tachycardia/fibrillation (VTNF... Show moreAims Various drugs increase the risk of out-of-hospital cardiac arrest (OHCA) in the general population by impacting cardiac ion channels, thereby causing ventricular tachycardia/fibrillation (VTNF). Dihydropyridines block L-type calcium channels, but their association with OHCA risk is unknown. We aimed to study whether nifedipine and/or amlodipine, often-used dihydropyridines, are associated with increased OHCA risk, and how these drugs impact on cardiac electrophysiology.Methods and results We conducted a case-control study with VT/VF-documented OHCA cases with presumed cardiac cause from ongoing population-based OHCA registries in the Netherlands and Denmark, and age/sex/index date-matched nonOHCA controls (Netherlands: PHARMO Database Network, Denmark: Danish Civil Registration System). We included 2503 OHCA cases, 10 543 non-OHCA controls in Netherlands, and 8101 OHCA cases, 40 505 nonOHCA controls in Denmark. To examine drug effects on cardiac electrophysiology, we performed single-cell patch-clamp studies in human-induced pluripotent stem cell-derived cardiomyocytes. Use of high-dose nifedipine (>= 60 mg/day), but not low-dose nifedipine (<60 mg/day) or amlodipine (any-dose), was associated with higher OHCA risk than non-use of dihydropyridines [Netherlands: adjusted odds ratios (ORadj) 1.45 (95% confidence interval 1.02-2.07), Denmark: 1.96 (1.18-3.25)] or use of amlodipine [Netherlands: 2.31 (1.54-3.47), Denmark: 2.20 (1.32-3.67)]. Out-of-hospital cardiac arrest risk of (high-dose) nifedipine use was not further increased in patients using nitrates, or with a history of ischaemic heart disease. Nifedipine and amlodipine blocked L-type calcium channels at similar concentrations, but, at clinically used concentrations, nifedipine caused more L-type calcium current block, resulting in more action potential shortening.Conclusion High-dose nifedipine, but not low-dose nifedipine or any-dose amlodipine, is associated with increased OHCA risk in the general population. Careful titration of nifedipine dose should be considered. Show less
Sudden cardiac death from ventricular fibrillation during acute myocardial infarction is a leading cause of total and cardiovascular mortality. To our knowledge, we here report the first genome... Show moreSudden cardiac death from ventricular fibrillation during acute myocardial infarction is a leading cause of total and cardiovascular mortality. To our knowledge, we here report the first genome-wide association study for this trait, conducted in a set of 972 individuals with a first acute myocardial infarction, 515 of whom had ventricular fibrillation and 457 of whom did not, from the Arrhythmia Genetics in The Netherlands (AGNES) study. The most significant association to ventricular fibrillation was found at 21q21 (rs2824292, odds ratio = 1.78, 95% CI 1.47-2.13, P = 3.3 x 10(-10)). The association of rs2824292 with ventricular fibrillation was replicated in an independent case-control set consisting of 146 out-of-hospital cardiac arrest individuals with myocardial infarction complicated by ventricular fibrillation and 391 individuals who survived a myocardial infarction (controls) (odds ratio = 1.49, 95% CI 1.14-1.95, P = 0.004). The closest gene to this SNP is CXADR, which encodes a viral receptor previously implicated in myocarditis and dilated cardiomyopathy and which has recently been identified as a modulator of cardiac conduction. This locus has not previously been implicated in arrhythmia susceptibility. Show less
