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This individual participant data analysis assesses the cross-sectional and longitudinal associations of baseline thyroid dysfunction with cognitive function and dementia.

BackgroundPhotoplethysmography (PPG) is a proven way to measure heart rate (HR). This technology is already available in smartphones, which allows measuring HR only by using the smartphone. Given the widespread availability of smartphones, this creates a scalable way to enable mobile HR monitoring. An essential precondition is that these technologies are as reliable and accurate as the current clinical (gold) standards. At this moment, there is no consensus on a gold standard method for the validation of HR apps. This results in different validation processes that do not always reflect the veracious outcome of comparison.ObjectiveThe aim of this paper was to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology.MethodsThe FibriCheck (Qompium) app was used in two separate prospective nonrandomized studies. In the first study, the HR of the FibriCheck app was consecutively compared with 2 different Food and Drug Administration (FDA)-cleared HR devices: the Nonin oximeter and the AliveCor Mobile ECG. In the second study, a next step in validation was performed by comparing the beat-to-beat intervals of the FibriCheck app to a synchronized ECG recording.ResultsIn the first study, the HR (BPM, beats per minute) of 88 random subjects consecutively measured with the 3 devices showed a correlation coefficient of .834 between FibriCheck and Nonin, .88 between FibriCheck and AliveCor, and .897 between Nonin and AliveCor. A single way analysis of variance (ANOVA; P=.61 was executed to test the hypothesis that there were no significant differences between the HRs as measured by the 3 devices. In the second study, 20,298 (ms) R-R intervals (RRI)–peak-to-peak intervals (PPI) from 229 subjects were analyzed. This resulted in a positive correlation (rs=.993, root mean square deviation [RMSE]=23.04 ms, and normalized root mean square error [NRMSE]=0.012) between the PPI from FibriCheck and the RRI from the wearable ECG. There was no significant difference (P=.92) between these intervals.ConclusionsOur findings suggest that the most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system, compared on the basis of beat-to-beat analysis. This approach could lead to more correct assessments of the accuracy of HR apps.

BackgroundHealth data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy.ObjectiveThis study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems.MethodsThe Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification.ResultsThe Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial.ConclusionsCollecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

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The authors of "Integration or Fragmentation of Health Care? Examining Policies and Politics in a Belgian Case Study" present a fresh perspective on the inertia of integrated care (IC) implementation. They conclude that the decisive power in Belgium is fragmented and undermines efforts towards IC. As researchers in integrated heart failure care and active primary health care professionals, we comment on the three policy initiatives evaluated by Martens et al from a bottom-up perspective. A Learning Health Care Network (LHCN) was established Sept 2019 to overcome fragmentation, the lack of evaluation and capacity loss each time a pilot project ends. This commentary wishes to illustrate that a LHCN can be a powerful meso-level mechanism to engage in alignment work and to overcome macro-level barriers that are often difficult to change and not supportive of IC.

Background Depression is a common mental disorder in family practice with an impact on global health. The aim of this study is to provide insight in the trends of epidemiological measures as well as pharmacological treatments and comorbidities of depression. Methods A study using data from INTEGO, a family practice registration network in Flanders, Belgium. Trends in age-standardized prevalence and incidence of depression from 2000 to 2019 as well as antidepressant prescriptions in prevalent depression cases were analyzed with join point regression. Comorbidity profiles were explored using the Cochran-Armitage test and the Jonckheere-Terpstra test. Results We identified 538 299 patients older than 15 years during the study period. We found an increasing trend in the age-standardized prevalence of depression from 6.73 % in 2000 to 9.20 % in 2019. For the incidence of depression, a decreasing trend was observed from 2000 to 2015 with an incidence of 9.42/1000 in 2000 and 6.89/1000 in 2015, followed by an increasing trend from 2015 to 2019 (incidence of 13.64/1000 in 2019). The average number of chronic diseases per patient with depression increased significantly during the study period (from 1.2 to 1.8), and the proportion of patients relative to the whole study population that received at least one antidepressant prescription per year increased between 2000 and 2019 from 26.44% to 40.16%. Conclusions The prevalence of depression increases while the incidence sharply rises, but only in recent years. Patients with depression tend to have more comorbidities, making a multi-faceted approach to these patients more important.

Background Early detection and treatment of chronic kidney disease (CKD) can prevent further deterioration and complications. Previous studies suggested that the diagnosis is often made when advanced renal failure occurs. The aims of this study were to describe the prevalence of unregistered CKD stages 3–5 in a Belgian General Practitioner population, to determine risk factors for under-registration and to investigate the diagnostic delay. Methods The analyses were carried out in the INTEGO database, a Flanders general practice-based morbidity registration network. The study used INTEGO data from the year 2018 for all patients ≥18 years old. CKD was defined as two consecutive eGFR laboratory measurements (eGFR <60 mL/min/1.73m2) at least three months apart during the baseline period. Registered CKD was characterised by a documented diagnosis of CKD (ICPC2 U99) during the ≥12-month lookback period before the first eGFR measurement and up to six months after the second eGFR in the EHR. The prevalence of unregistered CKD and the median time of diagnostic delay were estimated. Baseline characteristics were described. A multivariate cross-sectional logistic regression analysis was conducted to identify determinants of unregistered CKD. We estimated the odds ratios and their 95% confidence interval. Results Among included patients, there were 10 551 patients (5.5%) meeting the criteria of CKD. The prevalence of unregistered CKD was 68%. The mean diagnostic delay was 1.94 years (Standard deviation 0.93). Being a male, a concurrent diagnosis of diabetes, stroke, heart failure and hypertension, and more severe CKD (stages 3b, 4 and 5) independently increased the chance on registered CKD. Conclusion The proportion of patients who had no registered CKD code in the EHR was substantial. The differences between registered and unregistered patients make thinking about solutions to facilitate registration in the EHR imperative.