[To what extent does the eGFR reflects renal function?] / In hoeverre weerspiegelt de eGFR de nierfunctie?

Glomerular filtration rate (GFR) serves as a marker for various renal functions. Different formulas are available to calculate an estimated GFR (eGFR), which are commonly based on serum creatinine, age, and sex. However, the eGFR merely reflects GFR under specific conditions. Due to the multitude of functions of the kidney, it is not possible to capture all aspects in one value. To diagnose renal diseases comprehensively, not only eGFR but also urine analysis and clinical context should be considered. Interpretation of eGFR for renal function monitoring requires careful consideration of factors such as (blood pressure) medication, diabetes, obesity, and pregnancy. Combining various laboratory parameters with a patient's clinical context provides an overview of the different functions of the kidney and its consequences for the patient.

Opportunistic screening versus usual care for diagnosing atrial fibrillation in general practice: a cluster randomised controlled trial.

BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke, heart failure, and all-cause mortality. AF may be asymptomatic and therefore remain undiagnosed. Devices such as single-lead electrocardiographs (ECGs) may help GPs to diagnose AF. AIM: To investigate the yield of opportunistic screening for AF in usual primary care using a single-lead ECG device. DESIGN AND SETTING: A clustered, randomised controlled trial among patients aged ≥65 years with no recorded AF status in the Netherlands from October 2014 to March 2016. METHOD: Fifteen intervention general practices used a single-lead ECG device at their discretion and 16 control practices offered usual care. The follow-up period was 1 year, and the primary outcome was the proportion of newly diagnosed cases of AF. RESULTS: In total, 17 107 older people with no recorded AF status were eligible to participate in the study. In the intervention arm, 10.7% of eligible patients (n = 919) were screened over the duration of the study year. The rate of newly diagnosed AF was similar in the intervention and control practices (1.43% versus 1.37%, P = 0.73). Screened patients were more likely to have comorbidities, such as hypertension (60.0% versus 48.7%), type 2 diabetes (24.3% versus 18.6%), and chronic obstructive pulmonary disease (11.3% versus 7.4%), than eligible patients not screened in the intervention arm. Among patients with newly diagnosed AF in intervention practices, 27% were detected by screening, 23% by usual primary care, and 50% by a medical specialist or after stroke/transient ischaemic attack. CONCLUSION: Opportunistic screening with a single-lead ECG at the discretion of the GP did not result in a higher yield of newly detected cases of AF in patients aged ≥65 years in the community than usual care. For higher participation rates in future studies, more rigorous screening methods are needed.

Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: a multicountry patient-level meta-analysis of 141,220 screened individuals.

BACKGROUND: The precise age distribution and calculated stroke risk of screen-detected atrial fibrillation (AF) is not known. Therefore, it is not possible to determine the number needed to screen (NNS) to identify one treatable new AF case (NNS-Rx) (i.e., Class-1 oral anticoagulation [OAC] treatment recommendation) in each age stratum. If the NNS-Rx is known for each age stratum, precise cost-effectiveness and sensitivity simulations can be performed based on the age distribution of the population/region to be screened. Such calculations are required by national authorities and organisations responsible for health system budgets to determine the best age cutoffs for screening programs and decide whether programs of screening should be funded. Therefore, we aimed to determine the exact yield and calculated stroke-risk profile of screen-detected AF and NNS-Rx in 5-year age strata. METHODS AND FINDINGS: A systematic review of Medline, Pubmed, and Embase was performed (January 2007 to February 2018), and AF-SCREEN international collaboration members were contacted to identify additional studies. Twenty-four eligible studies were identified that performed a single time point screen for AF in a general ambulant population, including people ≥65 years. Authors from eligible studies were invited to collaborate and share patient-level data. Statistical analysis was performed using random effects logistic regression for AF detection rate, and Poisson regression modelling for CHA2DS2-VASc scores. Nineteen studies (14 countries from a mix of low- to middle- and high-income countries) collaborated, with 141,220 participants screened and 1,539 new AF cases. Pooled yield of screening was greater in males across all age strata. The age/sex-adjusted detection rate for screen-detected AF in ≥65-year-olds was 1.44% (95% CI, 1.13%-1.82%) and 0.41% (95% CI, 0.31%-0.53%) for <65-year-olds. New AF detection rate increased progressively with age from 0.34% (<60 years) to 2.73% (≥85 years). Neither the choice of screening methodology or device, the geographical region, nor the screening setting influenced the detection rate of AF. Mean CHA2DS2-VASc scores (n = 1,369) increased with age from 1.1 (<60 years) to 3.9 (≥85 years); 72% of ≥65 years had ≥1 additional stroke risk factor other than age/sex. All new AF ≥75 years and 66% between 65 and 74 years had a Class-1 OAC recommendation. The NNS-Rx is 83 for ≥65 years, 926 for 60-64 years; and 1,089 for <60 years. The main limitation of this study is there are insufficient data on sociodemographic variables of the populations and possible ascertainment biases to explain the variance in the samples. CONCLUSIONS: People with screen-detected AF are at elevated calculated stroke risk: above age 65, the majority have a Class-1 OAC recommendation for stroke prevention, and >70% have ≥1 additional stroke risk factor other than age/sex. Our data, based on the largest number of screen-detected AF collected to date, show the precise relationship between yield and estimated stroke risk profile with age, and strong dependence for NNS-RX on the age distribution of the population to be screened: essential information for precise cost-effectiveness calculations.

Cost-effectiveness of screening for atrial fibrillation in primary care with a handheld, single-lead electrocardiogram device in the Netherlands.

Aims: Atrial fibrillation (AF) is the most common arrhythmia and prevalence increases with age. Patients with AF have a high risk of stroke, and screening for AF is recommended in all people aged 65 years or older to identify patients eligible for stroke prevention. A handheld, single-lead electrocardiogram (ECG) device can be used for systematic screening in the population at risk. The objective of this study is to estimate the cost-effectiveness of screening for AF in primary care with the MyDiagnostick® during seasonal influenza vaccination in the Netherlands. Methods and results: Lifetime costs and effects of a single screening session for AF detection were assessed from a societal perspective with a decision analytic model consisting of a straightforward decision tree and a joining Markov model. The decision model simulated all patients aged 65 years and over attending the seasonal influenza vaccination in the Netherlands. Event probabilities were derived from clinical trials. Sensitivity analyses were performed to assess the impact of important model assumptions as well as determining the relative effect of individual parameters. Screening for AF with the MyDiagnostick® in all patients older than 65 years that attend seasonal influenza vaccination in the Netherlands would decrease the overall costs by €764 and increase the quality-adjusted life-years (QALYs) by 0.27 years per patient. Early detection of AF would prevent strokes and leads to beneficial health effects with subsequent cost savings. This screening method would have an estimated probability of 99.8% for being cost-effective at a conservative willingness-to-pay of €20 000/QALY. Conclusion: Screening for AF in primary care with a handheld, single-lead ECG during seasonal influenza vaccination is very likely to be cost saving for identifying new cases of AF in the Dutch population aged 65 years and over. Active screening for AF with a single-lead, handheld ECG device during seasonal influenza vaccination could be implemented in primary care.

Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination.

AIMS: To assess the yield of screening for atrial fibrillation (AF) with a hand-held single-lead electrocardiogram (ECG) device during influenza vaccination in primary care in the Netherlands. METHODS AND RESULTS: We used the MyDiagnostick to screen for AF in persons who participated in influenza vaccination sessions of ten Dutch primary care practices. In case of suspected AF detection by the stick, the recorded 1-min ECG registrations were analysed by a cardiologist. We scrutinized electronic medical files of the general practitioners to obtain information about the cases screened. Multivariable logistic regression analysis was performed to predict the relation between patient characteristics and a new screen-detected diagnosis of AF. In total, 3269 persons were screened for AF during the influenza vaccination sessions of 10 general practitioner practices. As a result, 37 (1.1%) new cases of AF were detected. Prior transient ischeamic attack or stroke (OR 6.05; 95%CI 1.93-19.0), and age (OR 1.09 per year; 95% CI 1.05-1.14) were independent predictors for such newly screen-detected AF. Of the 37 screen-detected AF cases, 2.7% had a CHA2DS2-VASc of 0, 18.9% a score of 1, and 78.4% a score of 2 or more. The majority needed oral anticoagulant therapy. CONCLUSIONS: Screening seems feasible with an easy to use single-lead, hand-held ECG device with automatic AF detection during influenza vaccination in primary care and results in a '1-day' yield of 1.1% new cases of AF. TRIAL REGISTRATION CLINICALTRIALSGOV: NCT02006524.