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Background Hemodialysis patients with COVID-19 have been reported to be at higher risk for death than the general population. Several prognostic factors have been identified in the studies from Asian, European or American countries. This is the first national Lebanese study assessing the factors associated with SARS-CoV-2 mortality in hemodialysis patients. Methods This is an observational study that included all chronic hemodialysis patients in Lebanon who were tested positive for SARS-CoV-2 from 31st March to 1st November 2020. Data on demographics, comorbidities, admission to hospital and outcome were collected retrospectively from the patients’ medical records. A binary logistic regression analysis was performed to assess risk factors for mortality. Results A total of 231 patients were included. Mean age was 61.46 ± 13.99 years with a sex ratio of 128 males to 103 females. Around half of the patients were diabetics, 79.2% presented with fever. A total of 115 patients were admitted to the hospital, 59% of them within the first day of diagnosis. Hypoxia was the major reason for hospitalization. Death rate was 23.8% after a median duration of 6 (IQR, 2 to 10) days. Adjusted regression analysis showed a higher risk for death among older patients (odds ratio = 1.038; 95% confidence interval: 1.013, 1.065), patients with heart failure (odds ratio = 4.42; 95% confidence interval: 2.06, 9.49), coronary artery disease (odds ratio = 3.27; 95% confidence interval: 1.69, 6.30), multimorbidities (odds ratio = 1.593; 95% confidence interval: 1.247, 2.036), fever (odds ratio = 6.66; 95% confidence interval: 1.94, 27.81), CRP above 100 mg/L (odds ratio = 4.76; 95% confidence interval: 1.48, 15.30), and pneumonia (odds ratio = 19.18; 95% confidence interval: 6.47, 56.83). Conclusions This national study identified older age, coronary artery disease, heart failure, multimorbidities, fever and pneumonia as risk factors for death in patients with COVID-19 on chronic hemodialysis. The death rate was comparable to other countries and estimated at 23.8%.

Purpose To validate healthcare claim‐based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. Methods Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims‐based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. Results PPVs were 94% (95% CI, 83%–99%) for ASD, 88% (76%–95%) for ADHD, 98% (89%–100%) for learning disability, 98% (89%–100%) for speech/language disorder, 82% (69%–91%) for intellectual disability, and 92% (81%–98%) for behavioral disorder. A total of 19 of the 50 algorithm‐based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%–53%). Among the 31 false‐positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual‐motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. Conclusions PPVs were generally high (range: 82%–98%), suggesting that claims‐based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.