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Ageing has increased the use of health services, with a corresponding rise in avoidable hospitalizations. We aimed to assess and characterize the perceived risk of hospitalization in primary health care (PHC). 118 individuals aged ≥65 years, PHC patients, were assessed using the Community Risk Assessment Instrument by their General Practitioner, who identified their perceived risk of hospitalization, at one year. The instrument is composed of three domains (mental state, daily living activities (ADLs) state and medical state). Multivariate logistic regression was used to identify the best model to predict the risk of hospitalization. Four models were estimated, one for each domain and one with all the variables of the instrument. 58.5% were identified as being at risk of hospitalization. The best predictive models are those that include functionality assessment variables (ADL model and Community Assessment of Risk Instrument model). The model that includes all the variables of three domains presents the best predictive value. Mobility problems (Odds Ratio (OR) 16.18 [CI: 1.63–160.53]), meal preparation (OR 10.93 [CI: 1.59–75.13]), communication (OR 6.91 [CI: 1.37–34.80]) and palliative care (OR 4.84 [CI: 1.14–20.58]) are the best predictors of hospitalization risk. The use of multidimensional assessment tools can allow the timely identification of people at risk, contributing to a reduction in hospitalizations. [ABSTRACT FROM AUTHOR]

Background Vaccination programs are essential for the containment of the coronavirus disease 2019 pandemic, which has hit haemodialysis populations especially hard. Early reports suggest a reduced immunologic response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in dialysis patients, in spite of a high degree of seroconversion. We aimed to identify risk factors for a reduced efficacy of an mRNA vaccine in a cohort of haemodialysis patients. Method In a multicentre study, including 294 Portuguese haemodialysis patients who had received two doses of BNT162b2 with a 3-week interval, immunoglobulin G-class antibodies against the SARS-CoV-2 spike protein were determined 3 weeks after the first dose (M1) and 6 weeks after the second dose (M2). The threshold for seroconversion was 10 UR/mL. Demographic and clinical data were retrieved from a quality registry. Adverse events were registered using a questionnaire. Results At M2, seroconversion was 93.1% with a median antibody level of 197.5 U/mL (1.2–3237.0) and a median increase of 180.0 U/mL (−82.9 to 2244.6) from M1. Age [beta −8.9; 95% confidence interval (95% CI) −12.88 to −4.91; P < 0.0001], ferritin >600 ng/mL (beta 183.93; 95% CI 74.75–293.10; P = 0.001) and physical activity (beta 265.79; 95% CI 30.7–500.88; P = 0.03) were independent predictors of SARS-CoV-2 antibody levels after two vaccine doses. Plasma albumin >3.5 g/dL independently predicted the increase of antibody levels between both doses (odds ratio 14.72; 95% CI 1.38 to 157.45; P = 0.03). Only mild adverse reactions were observed in 10.9% of patients. Conclusions The SARS-CoV-2 vaccine BNT162b2 is safe and effective in haemodialysis patients. Besides age, iron status and nutrition are possible modifiable modulators of the immunologic response to SARS-CoV-2 mRNA vaccines. These data suggest the need for an early identification of populations at higher risk for diminished antibody production and the potential advantage of the implementation of oriented strategies to maximize the immune response to vaccination in these patients. [ABSTRACT FROM AUTHOR]

Background Experiences from the first wave of the 2019 coronavirus disease (COVID-19) pandemic can aid in the development of future preventive strategies. To date, risk prediction models for COVID-19-related incidence and outcomes in hemodialysis (HD) patients are missing. Methods We developed risk prediction models for COVID-19 incidence and mortality among HD patients. We studied 38 256 HD patients from a multi-national dialysis cohort between 3 March and 3 July 2020. Risk prediction models were developed and validated, based on predictors readily available in outpatient HD units. We compared mortality among patients with and without COVID-19, matched for age, sex and diabetes. Results During the observational period, 1259 patients (3.3%) acquired COVID-19. Of these, 62% were hospitalized or died. Mortality was 22% among COVID-19 patients with odds ratios 219.8 [95% confidence interval (CI) 80.6–359] to 342.7 (95% CI 60.6–13 595.1), compared to matched patients without COVID-19. Since the first wave of the pandemic affected most European countries during the study, the risk prediction model for incidence of COVID-19 was developed and validated in European patients only [ N  = 22 826 area under the ROC curve(AUC)Dev 0.64, AUCVal 0.69]. The model for prediction of mortality was developed in all COVID-19 patients (AUCDev 0.71, AUCVal 0.78). Angiotensin receptor blockers were independently associated with a lower incidence of COVID-19 in European patients. Conclusions We identified modifiable risk factors for COVID-19 incidence and outcome in HD patients. Our risk prediction tools can be readily applied in clinical practice. This can aid in the development of preventive strategies for future waves of COVID-19. [ABSTRACT FROM AUTHOR]

Ageing has increased the use of health services, with a corresponding rise in avoidable hospitalizations. We aimed to assess and characterize the perceived risk of hospitalization in primary health care (PHC). 118 individuals aged ≥65 years, PHC patients, were assessed using the Community Risk Assessment Instrument by their General Practitioner, who identified their perceived risk of hospitalization, at one year. The instrument is composed of three domains (mental state, daily living activities (ADLs) state and medical state). Multivariate logistic regression was used to identify the best model to predict the risk of hospitalization. Four models were estimated, one for each domain and one with all the variables of the instrument. 58.5% were identified as being at risk of hospitalization. The best predictive models are those that include functionality assessment variables (ADL model and Community Assessment of Risk Instrument model). The model that includes all the variables of three domains presents the best predictive value. Mobility problems (Odds Ratio (OR) 16.18 [CI: 1.63–160.53]), meal preparation (OR 10.93 [CI: 1.59–75.13]), communication (OR 6.91 [CI: 1.37–34.80]) and palliative care (OR 4.84 [CI: 1.14–20.58]) are the best predictors of hospitalization risk. The use of multidimensional assessment tools can allow the timely identification of people at risk, contributing to a reduction in hospitalizations. [ABSTRACT FROM AUTHOR]

Background and Aims: Brush cytology during endoscopic retrograde cholangiopancreatography (ERCP) is the most frequently used strategy for obtaining a tissue sample from an indeterminate biliary stricture. A recent study reported that age is a factor associated with positive yields, but further analysis of how age influences the results was lacking. We aimed to evaluate clinical effectiveness of biliary cytology and prognostic factors for a positive outcome, especially age., Methods: This study was a single-center, retrospective, clinical study of 77 consecutive patients who underwent brush cytology during ERCP to obtain a diagnosis of an indeterminate biliary stricture. We compared 2 routine cytology techniques: A (smear); B (centrifugation of the cytological material collected and the cut-off brush + cell block when sufficient amount of material was available). The data were collected aiming to compare the accuracy of the different techniques used and the prognostic factors affecting the outcome, with a particular focus on age. The yield for brush cytology was compared with the gold standard defined as either definitive histology or the long-term clinical course., Results: The overall accuracy of the 2 used methods was 75.3%. Sensitivity was 52.5%, specificity was 100%, positive predictive value was 100%, and negative predictive value was 66.1%. Although not statistically significant, there was a trend toward accuracy for method B compared with method A (80.4 vs. 65.4%; p = 0.153). Multiple logistic regression analysis showed that younger age was the only independent prognostic factor associated with a positive diagnosis (OR 0.95; 95% CI 0.90-0.99; p = 0.039). Receiver operating characteristic curves for age yielded an area under the curve value of 68.2%. On the basis of the Youden index, 69 years was found to be the optimal cutoff for age., Conclusions: In this series, the accuracy of routine biliary brush cytology was not equal for all methods and ages; in particular, younger patients (below 69 years) tended to have a higher probability of a correct diagnosis.