Prevalence, predictors, and patient-reported outcomes of long COVID in hospitalized and non-hospitalized patients from the city of São Paulo, Brazil

BackgroundRobust data comparing long COVID in hospitalized and non-hospitalized patients in middle-income countries are limited.MethodsA retrospective cohort study was conducted in Brazil, including hospitalized and non-hospitalized patients. Long COVID was diagnosed at 90-day follow-up using WHO criteria. Demographic and clinical information, including the depression screening scale (PHQ-2) at day 30, was compared between the groups. If the PHQ-2 score is 3 or greater, major depressive disorder is likely. Logistic regression analysis identified predictors and protective factors for long COVID.ResultsA total of 291 hospitalized and 1,118 non-hospitalized patients with COVID-19 were included. The prevalence of long COVID was 47.1% and 49.5%, respectively. Multivariable logistic regression showed female sex (odds ratio [OR] = 4.50, 95% confidence interval (CI) 2.51–8.37), hypertension (OR = 2.90, 95% CI 1.52–5.69), PHQ-2 > 3 (OR = 6.50, 95% CI 1.68–33.4) and corticosteroid use during hospital stay (OR = 2.43, 95% CI 1.20–5.04) as predictors of long COVID in hospitalized patients, while female sex (OR = 2.52, 95% CI 1.95–3.27) and PHQ-2 > 3 (OR = 3.88, 95% CI 2.52–6.16) were predictors in non-hospitalized patients.ConclusionLong COVID was prevalent in both groups. Positive depression screening at day 30 post-infection can predict long COVID. Early screening of depression helps health staff to identify patients at a higher risk of long COVID, allowing an early diagnosis of the condition

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein’s telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources

Impact of screening and monitoring of capillary blood glucose in the detection of hyperglycemia and hypoglycemia in non-critical inpatients

Objective: To evaluate the impact of screening hyper and hypoglycemia measured by capillary glycemia and standard monitorization of  hyperglycemic patients hospitalized in regular care units of Hospital Israelita Albert Einstein. Methods: The capillary glycemia was  measured by the Precision PCx (Abbott) glucosimeter, using the PrecisionWeb (Abbott) software. The detection of hyper and hypoglycemia during the months of May/June were compared to those of March/April in 2009 and to the frequency of the diagnosis of diabetes in 2007. Rresults: There was an increase in the glycemia screening from 27.7 to 77.5% of hospitalized patients (p < 0.001), of hyperglycemia detection (from 9.3 to 12.2%; p < 0.001) and of hypoglycemia (from 1.5 to 3.3%; p < 0.001) during  the months of May/June  2009. According to this action 14 patients for each additional case of hyperglycemia and 26 cases for each case of hypoglycemia were identified. The detection of hyperglycemia was significantly higher (p < 0.001) than the frequency of registered diagnosis related do diabetes in the year of 2007. Cconclusions: the adoption of an institutional program of glycemia monitorization improves the detection of hyper and hypoglycemia and glycemia control in hospitalized patients in regular care units