Blood Hemoglobin Substantially Modulates the Impact of Gender, Morbid Obesity, and Hyperglycemia on COVID-19 Death Risk: A Multicenter Study in Italy and Spain.

Background: Hyperglycemia and obesity are associated with a worse prognosis in subjects with COVID-19 independently. Their interaction as well as the potential modulating effects of additional confounding factors is poorly known. Therefore, we aimed to identify and evaluate confounding factors affecting the prognostic value of obesity and hyperglycemia in relation to mortality and admission to the intensive care unit (ICU) due to COVID-19. Methods: Consecutive patients admitted in two Hospitals from Italy (Bologna and Rome) and three from Spain (Barcelona and Girona) as well as subjects from Primary Health Care centers. Mortality from COVID-19 and risk for ICU admission were evaluated using logistic regression analyses and machine learning (ML) algorithms. Results: As expected, among 3,065 consecutive patients, both obesity and hyperglycemia were independent predictors of ICU admission. A ML variable selection strategy confirmed these results and identified hyperglycemia, blood hemoglobin and serum bilirubin associated with increased mortality risk. In subjects with blood hemoglobin levels above the median, hyperglycemic and morbidly obese subjects had increased mortality risk than normoglycemic individuals or non-obese subjects. However, no differences were observed among individuals with hemoglobin levels below the median. This was particularly evident in men: those with severe hyperglycemia and hemoglobin concentrations above the median had 30 times increased mortality risk compared with men without hyperglycemia. Importantly, the protective effect of female sex was lost in subjects with increased hemoglobin levels. Conclusions: Blood hemoglobin substantially modulates the influence of hyperglycemia on increased mortality risk in patients with COVID-19. Monitoring hemoglobin concentrations seem of utmost importance in the clinical settings to help clinicians in the identification of patients at increased death risk.

Is it time to use real-world data from primary care in Alzheimer's disease?

BACKGROUND: The analysis of real-world data in clinical research is rising, but its use to study dementia subtypes has been hardly addressed. We hypothesized that real-world data might be a powerful tool to update AD epidemiology at a lower cost than face-to-face studies, to estimate the prevalence and incidence rates of AD in Catalonia (Southern Europe), and to assess the adequacy of real-world data routinely collected in primary care settings for epidemiological research on AD. METHODS: We obtained data from the System for the Development of Research in Primary Care (SIDIAP) database, which contains anonymized information of > 80% of the Catalan population. We estimated crude and standardized incidence rates and prevalences (95% confidence intervals (CI)) of AD in people aged at least 65 years living in Catalonia in 2016. RESULTS: Age- and sex-standardized prevalence and incidence rate of AD were 3.1% (95%CI 2.7-3.6) and 4.2 per 1000 person-years (95%CI 3.8-4.6), respectively. Prevalence and incidence were higher in women and in the oldest people. CONCLUSIONS: Our incidence and prevalence estimations were slightly lower than the recent face-to-face studies conducted in Spain and higher than other analyses of electronic health data from other European populations. Real-world data routinely collected in primary care settings could be a powerful tool to study the epidemiology of AD.

How well can electronic health records from primary care identify Alzheimer's disease cases?

BACKGROUND: Electronic health records (EHR) from primary care are emerging in Alzheimer's disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias. PATIENTS AND METHODS: This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity. RESULTS: Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7-94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1-76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7-73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6-73.0) and 83.3 (95% CI: 81.8-84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively. CONCLUSIONS: PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.

Epidemiology of dementia: prevalence and incidence estimates using validated electronic health records from primary care.

PURPOSE: Updated estimates of incidence and prevalence of dementia are crucial to ensure adequate public health policy. However, most of the epidemiological studies in the population in Spain were conducted before 2010. This study assessed the validity of dementia diagnoses recorded in electronic health records contained in a large primary-care database to determine if they could be used for research purposes. Then, to update the epidemiology of dementia in Catalonia (Spain), we estimated crude and standardized prevalence and incidence rates of dementia in Catalonia in 2016. METHODS: The System for the Development of Research in Primary Care (SIDIAP) database contains anonymized information for >80% of the Catalan population. Validity of dementia codes in SIDIAP was assessed in patients at least 40 years old by asking general practitioners for additional evidence to support the diagnosis. Crude and standardized incidence and prevalence (95% CI) in people aged ≥65 years were estimated assuming a Poisson distribution. RESULTS: The positive predictive value of dementia diagnoses recorded in SIDIAP was estimated as 91.0% (95% CI 87.5%-94.5%). Age-and sex-standardized incidence and prevalence of dementia were 8.6/1,000 person-years (95% CI 8.0-9.3) and 5.1% (95% CI 4.5%-5.7%), respectively. CONCLUSION: SIDIAP contains valid dementia records. We observed incidence and prevalence estimations similar to recent face-to-face studies conducted in Spain and higher than studies using electronic health data from other European populations.