Mortality and associated risk factors of COVID-19 infection in dialysis patients in Qatar: A nationwide cohort study.

CONTEXT: Patients on maintenance dialysis are more susceptible to COVID-19 and its severe form. We studied the mortality and associated risks of COVID-19 infection in dialysis patients in the state of Qatar. METHODS: This was an observational, analytical, retrospective, nationwide study. We included all adult patients on maintenance dialysis therapy who tested positive for COVID-19 (PCR assay of the nasopharyngeal swab) during the period from February 1, 2020, to July 19, 2020. Our primary outcome was to study the mortality of COVID-19 in dialysis patients in Qatar and risk factors associated with it. Our secondary objectives were to study incidence and severity of COVID-19 in dialysis patients and comparing outcomes between hemodialysis and peritoneal dialysis patients. Patient demographics and clinical features were collected from a national electronic medical record. Univariate Cox regression analysis was performed to evaluate potential risk factors for mortality in our cohort. RESULTS: 76 out of 1064 dialysis patients were diagnosed with COVID-19 (age 56±13.6, 56 hemodialysis and 20 peritoneal dialysis, 56 males). During the study period, 7.1% of all dialysis patients contracted COVID-19. Male dialysis patients had double the incidence of COVID-19 than females (9% versus 4.5% respectively; p<0.01). The most common symptoms on presentation were fever (57.9%), cough (56.6%), and shortness of breath (25%). Pneumonia was diagnosed in 72% of dialysis patients with COVID-19. High severity manifested as 25% of patients requiring admission to the intensive care unit, 18.4% had ARDS, 17.1% required mechanical ventilation, and 14.5% required inotropes. The mean length of hospital stay was 19.2 ± -12 days. Mortality due to COVID-19 among our dialysis cohort was 15%. Univariate Cox regression analysis for risk factors associated with COVID-19-related death in dialysis patients showed significant increases in risks with age (OR 1.077, CI 95%(1.018-1.139), p = 0.01), CHF and COPD (both same OR 8.974, CI 95% (1.039-77.5), p = 0.046), history of DVT (OR 5.762, CI 95% (1.227-27.057), p = 0.026), Atrial fibrillation (OR 7.285, CI 95%(2.029-26.150), p = 0.002), hypoxia (OR: 16.6; CI 95%(3.574-77.715), p = <0.001), ICU admission (HR30.8, CI 95% (3.9-241.2), p = 0.001), Mechanical ventilation (HR 50.07 CI 95% (6.4-391.2)), p<0.001) and using inotropes(HR 19.17, CI 95% (11.57-718.5), p<0.001). In a multivariate analysis, only ICU admission was found to be significantly associated with death [OR = 32.8 (3.5-305.4), p = 0.002)]. CONCLUSION: This is the first study to be conducted at a national level in Qatar exploring COVID-19 in a dialysis population. Dialysis patients had a high incidence of COVID-19 infection and related mortality compared to previous reports of the general population in the state of Qatar (7.1% versus 4% and 15% versus 0.15% respectively). We also observed a strong association between death related to COVID-19 infection in dialysis patients and admission to ICU.

The National Early Warning Score and its subcomponents recorded within ±24 h of emergency medical admission are poor predictors of hospital-acquired acute kidney injury.

Hospital-acquired acute kidney injury (H-AKI) is a common cause of avoidable morbidity and mortality. Therefore, in the current study, we investigated whether vital signs data from patients, as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. We analysed all emergency admissions (n=33,608) to York Hospital with NEWS data over a 24-month period. Here, we report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3), and similarly for maximum NEWS (models B0,B1,B2,B3). Of the total emergency admissions, 4.05% (1,361/33,608) had H-AKI. Models using the index NEWS had lower AUCs (0.59-0.68) than models using the maximum NEWS AUCs (0.75-0.77). The maximum NEWS model (B3) was more sensitive than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Based on these results, we suggest that the index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but appears to be unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload, albeit with improved sensitivity.