key: cord-0815356-kgisbhbb
authors: Almeida, Catarina; Reis, Marina Sofia Rodrigues; Alferes, Daniela; Ribeiro, Catarina Isabel; Rodrigues, Sara Daniela; Moreira, Carla; Carmo, Rute; Lopes, Daniela; Santos, Maria Clara; Pereira, Susana; Gomes, Ana Marta; Sousa, Sónia; Ventura, Ana; Almeida, Clara; Fernandes, João Carlos
title: MO824 PREDICTORS OF MORTALITY OF COVID-19 IN CHRONIC HEMODIALYSIS PATIENTS
date: 2021-05-29
journal: Nephrol Dial Transplant
DOI: 10.1093/ndt/gfab098.0016
sha: d509de51933297878a94aedfc295f827a2717a26
doc_id: 815356
cord_uid: kgisbhbb

BACKGROUND AND AIMS: Coronavirus disease 2019 (COVID-19) has affected the care of patients on chronic hemodialysis (HD). It has been reported that older adults and those with comorbidities, such as diabetes mellitus, hypertension, cardiovascular disease and chronic kidney disease are prone to develop severe disease and poorer outcomes. By virtue of their average old age, multiple comorbidities, immunosuppression and frequent contact with other patients in dialysis facilities, chronic HD patients are at particular risk for severe COVID-19 infection. The aim of this study was to compare clinical presentation, laboratory and radiologic data and outcomes between HD and non-HD COVID-19 patients and find possible risk factors for mortality on HD patients. METHOD: A single center retrospective cohort study including patients on HD hospitalized with a laboratory confirmed COVID-19 infection, from March 1st to December 31st of 2020 and matched them to non-dialysis patients (non-HD) (1:1). Data regarding patient baseline characteristics, symptoms, laboratory and radiologic results at presentation were collected, as well as their outcomes. Categorical variables are presented as frequencies and percentages, and continuous variables as means or medians for variables with skewed distributions. A paired Student’s t-test was performed on parametric continuous values or Mann-Whitney for non-parametric continuous variables. Chi-squared test was performed for comparing categorical variables. Logistic regression was used to identify risk factors for mortality on HD patients. A p-value of less than 0,05 indicated statistical significance. RESULTS: A total of 34 patients HD patients were included, 70,6% male, mean age of 76,5 years, median time of dialysis of 3,0 years. Among them 85,3% were hypertensive, 47,1% diabetic, 47,1% had cardiovascular disease, 30,6% pulmonary chronic disease and 23,5% cancer. The most frequent symptoms were fever (67,6%), shortness of breath (61,8%) and cough (52,9%). At admission, 55,9% of patients needed oxygen supply, one required mechanic ventilation and was admitted to intensive care unit. Regarding laboratory data, the most common features were lymphopenia in 58,9% (median- 795/uL), elevated LDH in 64,7% (median- 255 U/L), raised C-reactive protein in 97,1% (median-6,3 mg/dlL, raised D-dimer in 95,8% (median 1,7 ng/mL), and all patients presented high ferritin (median 1658 ng/mL) and elevated Troponin T (median 130ng/mL). The majority presented with radiologic changes, particularly bilateral infiltrates in 29,4%. Concerning clinical outcomes, the median hospitalization time was 11 days and 13 patients (38,2%) developed bacterial superinfection. Mortality rate was 32,4%. When matched to 34 non-HD patients there was no statistical significant differences in sex, age and comorbidities. The HD group had a tendency to more ventilator support need (p=0,051), higher ferritin and troponin levels (p=<0,001 for both), whereas the non-HD group presented with greater levels of transaminases (p= 0,017). There was o significant difference in hospitalization time (median of 11 vs 7 days, p=0,222) neither in mortality (median of 32,4 vs 35,3%, p=0,798). When the logistic regression was performed, only bacterial superinfection was a predictor for mortality on hemodialysis patients (p=0,004). CONCLUSION: Our study compared outcomes for COVID-19 patients on chronic HD to non-dialysis patients and showed no difference in hospitalization time nor in death rate. In spite of these results, the mortality in patients on chronic HD is still not negligible, with up to 32% of in-hospital mortality. Bacterial superinfection is a predictive risk factor for mortality. Hence the importance of interventions to mitigate the burden of COVID-19 in these patients, by preventing its spread, particularly in hemodialysis centers.

Centro Hospitalar Lisboa Ocidental -Hospital de Santa Cruz, Nefrologia, Carnaxide, Portugal BACKGROUND AND AIMS: Chronic kidney disease (CKD) is known to have significant morbi-mortality worldwide. Patients with CKD and in particular those with ESRD normally carry a large burden of comorbidities and the beginning of hemodialysis leads to a higher risk of decompensation. In fact, annual mortality rates among hemodialysis patients is 10 to 30 times higher than those of the general population. Various studies have demonstrated that incident patients experience the higher mortality rate within the first 3 to 4 months of dialysis. Predicting early mortality is important to help the decision of initiating hemodialysis versus conservative care. Therefore we conducted a case control study to evaluate early mortality predictors in incident hemodialysis patients in our hemodialysis center. METHOD: This is a retrospective case-control study, which to evaluate early mortality predictors in incident hemodialysis patients from January 2013 to December 2018. Descriptive statistics were calculated and expressed as mean (6standard deviation [SD]) or median (intraquartile range [IQR]) for parametric and non-parametric continuous variables and count (%) for categorical variables, respectively. We compared variables between survivors and non-survivors at 3 months after initiation of hemodialysis by using Student's t-test, Mann-Whitney U test, or Fisher's exact test where appropriate. Multivariate logistic regression was used to calculate the adjusted odds ratio (OR) with 95% confidence intervals (CI) for the variables associated with early mortality. RESULTS: From a total of 559 incident hemodialysis patients, 43 cases were identified (7.7%), and three controls were obtained for each case. From the 172 pts in the study mean (SD) age was 72.4 years (614), 58.1% were male, and the most common etiologies of CKD were unknown etiology (22.1%, n=38) and diabetic nephropathy (16.9%, n=29). 34.4% (n=59) were dependent of assistance in daily living activities, median (IQR) Charlson Comorbidity Index was 8 (6.10). The non survivors compared to the survivors were older (78.8 6 9.2 vs 70.3 6 14.7, p < 0,001), had more AKI or acute-on-chronic CKD (18 (41.9%) vs 18 (14%), p <0,001), emergency start of hemodialysis (29 (67.4%) vs 48 (37.2%), p= 0.001), more catheter use as vascular access (38 (88.4%) vs 92 (71.3%), p=0.024), congestive heart failure (30 (69.8%) vs 32 (24.8%), p < 0.001), ischemic cardiomyopathy (20 (46.5%) vs 30 (23.3%), p=0.004), COPD (13 (30.2%) vs 11 (8.5%), p<0.001), peripheral vascular disease (14 (32.6%) vs 20 (15.5%), p=0.015), Charlson comorbidity index (10 (8-11) vs 7 (6-9), p<0.001), dependence of assistance in daily living activities (22 (51.2%) vs 37 (28.7%), presence of nephrology appointments for >3 months before ESRD (23 (53.5%) vs 102 (79.1%), p=0.01), eGFR (12.3 (6.1) vs 9.1 (4.2), p<0.001), serum albumin (3.1 (2.9-3.5) vs 3.5 (3-3.8), p=0.002). A multivariable analysis was performed and the most suitable model to predict early mortality was age (p=0.003, OR 1.07, 95% C.I. 1.023-1.121), emergency start of hemodialysis (p<0.001, OR 8.35, 95% CI 3.385-20.606), congestive heart failure (p=0.004, OR 3.65, 95% CI 1.519-8.776), peripheral vascular disease (p=0.035, OR 2.97, 95% CI 1.081-8.134). Hosmer-Lemeshow goodness-of-fit performed well (X2 6.67 DF 8; p =0.57), Nagelkerke R2 0.46; AUROC (95% CI) 0.86 (0.80-0.92). CONCLUSION: The percentage of early mortality in our population (7.7%) was compatible with national and European rates. Our model identifies as independent mortality predictors age, emergency start of hemodialysis, congestive heart failure and peripheral vascular disease with an AUROC 0,86. This could help identify patients that could benefit from a more conservative care. chronic HD patients are at particular risk for severe COVID-19 infection. The aim of this study was to compare clinical presentation, laboratory and radiologic data and outcomes between HD and non-HD COVID-19 patients and find possible risk factors for mortality on HD patients. METHOD: A single center retrospective cohort study including patients on HD hospitalized with a laboratory confirmed COVID-19 infection, from March 1st to December 31st of 2020 and matched them to non-dialysis patients (non-HD) (1:1). Data regarding patient baseline characteristics, symptoms, laboratory and radiologic results at presentation were collected, as well as their outcomes. Categorical variables are presented as frequencies and percentages, and continuous variables as means or medians for variables with skewed distributions. A paired Student's t-test was performed on parametric continuous values or Mann-Whitney for non-parametric continuous variables. Chi-squared test was performed for comparing categorical variables. Logistic regression was used to identify risk factors for mortality on HD patients. A p-value of less than 0,05 indicated statistical significance. RESULTS: A total of 34 patients HD patients were included, 70,6% male, mean age of 76,5 years, median time of dialysis of 3,0 years. Among them 85,3% were hypertensive, 47,1% diabetic, 47,1% had cardiovascular disease, 30,6% pulmonary chronic disease and 23,5% cancer. The most frequent symptoms were fever (67,6%), shortness of breath (61,8%) and cough (52,9%). At admission, 55,9% of patients needed oxygen supply, one required mechanic ventilation and was admitted to intensive care unit. Regarding laboratory data, the most common features were lymphopenia in 58,9% (median-795/uL), elevated LDH in 64,7% (median-255 U/L), raised C-reactive protein in 97,1% (median-6,3 mg/dlL, raised D-dimer in 95,8% (median 1,7 ng/mL), and all patients presented high ferritin (median 1658 ng/mL) and elevated Troponin T (median 130ng/mL). The majority presented with radiologic changes, particularly bilateral infiltrates in 29,4%. Concerning clinical outcomes, the median hospitalization time was 11 days and 13 patients (38,2%) developed bacterial superinfection. Mortality rate was 32,4%. When matched to 34 non-HD patients there was no statistical significant differences in sex, age and comorbidities. The HD group had a tendency to more ventilator support need (p=0,051), higher ferritin and troponin levels (p=<0,001 for both), whereas the non-HD group presented with greater levels of transaminases (p= 0,017). There was o significant difference in hospitalization time (median of 11 vs 7 days, p=0,222) neither in mortality (median of 32,4 vs 35,3%, p=0,798). When the logistic regression was performed, only bacterial superinfection was a predictor for mortality on hemodialysis patients (p=0,004). CONCLUSION: Our study compared outcomes for COVID-19 patients on chronic HD to non-dialysis patients and showed no difference in hospitalization time nor in death rate. In spite of these results, the mortality in patients on chronic HD is still not negligible, with up to 32% of in-hospital mortality. Bacterial superinfection is a predictive risk factor for mortality. Hence the importance of interventions to mitigate the burden of COVID-19 in these patients, by preventing its spread, particularly in hemodialysis centers. reduced overall mortality compared to hemodialysis (HD) in patients with end-stage kidney disease (ESKD). It remains, however, difficult to translate these average results into clinical practice as absolute treatment effects may substantially differ between individuals. The aim of this study was to develop and validate a treatment effect prediction model to determine which patients would benefit the most from HDF or HD in terms of all-cause mortality. METHOD: We used an IPD meta-analysis based on four RCTs comparing HDF with HD on mortality endpoints to derive a Royston-Parmar model for prediction of absolute treatment effect of HDF based on pre-specified patient and disease characteristics. Validation of the model with regard to model discrimination, calibration and net benefit was performed using internal-external cross validation. RESULTS: The median predicted gain in median survival was 44 (Q1-Q3: 44-46) days for every year of treatment with HDF compared to HD. The overall gain in median survival with HDF ranged from 2 to 48 months (Figure) . Patients who benefited most from HDF were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate calibration and discrimination. Decision curve analysis indicated that prediction-based treatment allocation improved the net clinical benefit compared to treating all with patients HDF or treating all with HD. CONCLUSION: Although overall mortality is reduced by HDF compared to HD in ESKD patients, the absolute survival gain can vary greatly between individuals. Our results indicate that the effects of HDF on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making.

MO825 Figure 1 : Histograms for the distribution of (A) predicted gain in median survival for hemodiafiltration (HDF) versus hemodialysis (HD) in months, (B) predicted gain in median survival per year for HDF versus HD in days, (C) predicted gain in median survival for HDF with a convection volume of 23L per 1.73m2 (body surface area-adjusted), i.e. high-volume HDF, in months, and (D) predicted gain in median survival per year for high-volume HDF in days, in the pooled data. 

UK (n=52), and Australia (n=30). Respondents' level of agreement was assessed using a 7-point scale, from 1 (do not agree at all) to 7 (strongly agree). PRF data were also captured for 1435 HD patients with CKD-aP from all countries. All nephrologists who completed the interviews and surveys were currently treating >5 HD patients with CKD-aP. RESULTS: Most nephrologists (75%) agreed that CKD-aP is under-diagnosed in HD patients, which is mainly driven by the lack of systematic screening by nephrologists and under-reporting of the condition by patients. The main barriers to screening for CKD