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Despite its prevalence, hemodialysis headache has been poorly studied, thus making it difficult to understand the pathophysiological mechanisms involved in its genesis. Current clinical management practices are therefore necessarily empiric with minimal to no evidence base.

Background: Headache is one of the most frequent symptoms that occur during hemodialysis sessions. Despite the high prevalence of dialysis headache, it has been little studied. Objective: To evaluate the characteristics, impact and factors associated with dialysis headache. The behavior of the cerebral vasculature was also compared between patients with and without dialysis headache. Methods: This was a cross-sectional study. Consecutive patients who underwent hemodialysis were assessed through a semi-structured questionnaire, the Headache Impact Test (HIT-6), the Hospital Anxiety and Depression Scale and the Short Form-36 Health Survey (SF-36). Transcranial Doppler ultrasonography was performed in the first and fourth hours of hemodialysis. Results: A total of 100 patients were included; 49 of them had dialysis headache. Women (OR=5.04; 95%CI 1.95-13.04), younger individuals (OR=1.05; 95%CI 1.01-1.08), individuals with higher schooling levels (OR=3.86; 95%CI 1.4-10.7) and individuals who had spent longer times on dialysis programs (OR=0.99; 95%CI 0.98-1) had more dialysis headache (logistic regression). Individuals with dialysis headache had worse quality of life in the domains of pain and general state of health (56.9 versus 76.4, p=0.01; 49.7 versus 60.2, p=0.03, respectively). Dialysis headache was associated with significantly greater impact on life (OR=24.4; 95%CI 2.6-226.6; logistic regression). The pulsatility index (transcranial Doppler ultrasonography) was lower among patients with dialysis headache than among those without them. Conclusions: Dialysis headaches occur frequently and are associated with worse quality of life and patterns of cerebral vasodilatation.

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients’ data were obtained through hospital records. Hospitals’ data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding ( β = − 0.37; 95% CI − 0.71 to − 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita ( β = − 0.40; 95% CI − 0.72 to − 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists ( β = − 0.59; 95% CI − 0.98 to − 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality ( β = 0.40; 95% CI 0.11–0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality. Supplementary Information The online version contains supplementary material available at 10.1007/s11739-022-03092-9.

Background. Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients’ longevity and quality of life. Objectives. The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT. Method. Unicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death. Results. 18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences (P>0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher (P = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m2) versus elderlies (53.3 ± 21.1 mL/min/1.73 m2). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m2 presented a statistical significance for age ≥60 years (P = 0.01) and reduction in serum haemoglobin (P = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F1 score: 0.79). Conclusion. Renal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

Background Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. Methods This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results The median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). Conclusions The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

Background: Acute kidney injury (AKI) is frequently associated with COVID–19 and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalized COVID–19 patients. Methods: This study is part of the multicentre cohort, the Brazilian COVID–19 Registry. A total of 5,212 adult COVID–19 patients were included between March/2020 and September/2020. We evaluated four categories of predictor variables: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) the need for mechanical ventilation at any time during hospitalization. Variable selection was performed using generalized additive models (GAM) and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. The accuracy was assessed using the area under the receiver operating characteristic curve (AUCROC). Risk groups were proposed based on predicted probabilities: non-high (up to 14.9%), high (15.0 to 49.9%), and very high risk (≥ 50.0%). Results: The median age of the model–derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalization. The validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in–hospital mortality. Thirty–two variables were tested and four important predictors of the need for KRT during hospitalization were identified using GAM: need for mechanical ventilation, male gender, higher creatinine at admission, and diabetes. The MMCD score had excellent discrimination in derivation (AUROC = 0.929; 95% CI 0.918–0.939) and validation (AUROC = 0.927; 95% CI 0.911–0.941) cohorts an good overall performance in both cohorts (Brier score: 0.057 and 0.056, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). Conclusion: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalized COVID–19 patients who may require more intensive monitoring, and can be useful for resource allocation.

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