Trajectories of Clinical and Laboratory Characteristics Associated with COVID-19 in Hemodialysis Patients by Survival

IntroductionThe clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients. MethodsWe used data from adult HD patients treated at an integrated kidney disease company who received a RT-PCR test to investigate suspicion of a SARS-CoV-2 infection between 01 May and 01 Sep 2020. Nonparametric smoothing splines were used to fit data for individual trajectories and estimate the mean change over time in patients testing positive or negative for SARS-CoV-2 and those who survived or died within 30 days of first suspicion or positive test date. For each clinical parameter of interest, the difference in average daily changes between COVID-19 positive versus negative group and COVID-19 survivor versus non-survivor group was estimated by fitting a linear mixed effects model based on measurements in the 14 days before (i.e., day-14 to day 0) day 0. ResultsThere were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8,895 SARS-CoV-2 positive). We observed significantly different trends (p<0.05) in pre-HD systolic blood pressure (SBP), pre-HD pulse rate, body temperature, ferritin, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patient. For COVID-19 positive group, we observed significantly different clinical trends (p<0.05) in pre-HD pulse rate, lymphocytes, albumin and neutrophil-lymphocyte ratio (NLR) between survivors and non-survivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days. ConclusionWe observed unique temporal trends in various clinical and laboratory parameters among HD patients who tested positive versus negative for SARS-CoV-2 infection and those who survived the infection versus those who died. These trends can help to define the physiological disturbances that characterize the onset and course of COVID-19 in HD patients

Transmission of SARS-CoV-2 Considering Shared Chairs in Outpatient Dialysis: A Real-World Case-Control Study

BackgroundSARS-CoV-2 is primarily transmitted through aerosolized droplets; however, the virus can remain transiently viable on surfaces. ObjectiveWe examined transmission within hemodialysis facilities, with a specific focus on the possibility of indirect patient-to-patient transmission through shared dialysis chairs. DesignWe used real-world data from hemodialysis patients treated between February 1st and June 8th, 2020 to perform a case-control study matching each SARS-CoV-2 positive patient (case) to a non-SARS-CoV-2 patient (control) in the same dialysis shift and traced back 14 days to capture possible exposure from chairs sat in by SARS-CoV-2 patients. Cases and controls were matched on age, sex, race, facility, shift date, and treatment count. Setting2,600 hemodialysis facilities in the United States. PatientsAdult (age [≥]18 years) hemodialysis patients. MeasurementsConditional logistic regression models tested whether chair exposure after a positive patient conferred a higher risk of SARS-CoV-2 infection to the immediate subsequent patient. ResultsAmong 170,234 hemodialysis patients, 4,782 (2.8%) tested positive for SARS-CoV-2 (mean age 64 years, 44% female). Most facilities (68.5%) had 0 to 1 positive SARS-CoV-2 patient. We matched 2,379 SARS-CoV-2 positive cases to 2,379 non-SARS-CoV-2 controls; 1.30% (95%CI 0.90%, 1.87%) of cases and 1.39% (95%CI 0.97%, 1.97%) of controls were exposed to a chair previously sat in by a shedding SARS-CoV-2 patient. Transmission risk among cases was not significantly different from controls (OR=0.94; 95%CI 0.57 to 1.54; p=0.80). Results remained consistent in adjusted and sensitivity analyses. LimitationAnalysis used real-world data that could contain errors and only considered vertical transmission associated with shared use of dialysis chairs by symptomatic patients. ConclusionsThe risk of indirect patient-to-patient transmission of SARS-CoV-2 infection from dialysis chairs appears to be low. Primary Funding SourceFresenius Medical Care North America; National Institute of Diabetes and Digestive and Kidney Diseases (R01DK130067)

Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease?

BackgroundWe developed two unique machine learning (ML) models that predict risk of: 1) a major COVID-19 outbreak in the service county of a local HD population within following week, and 2) a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after following 3 or more days. MethodsWe used county-level data from United States population (March 2020) and HD patient data from a network of clinics (February-May 2020) to develop two ML models. First was a county-level model that used data from general and HD populations (21 variables); outcome of a COVID-19 outbreak in a dialysis service area was defined as a clinic being located in one of the national counties with the highest growth in COVID-19 positive cases (number and people per million (ppm)) in general population during 22-28 Mar 2020. Second was a patient-level model that used HD patient data (82 variables) to predict an individual having an undetected SARS-CoV-2 infection that is identified in subsequent [≥]3 days. ResultsAmong 1682 counties with dialysis clinics, 82 (4.9%) had a COVID-19 outbreak during 22-28 Mar 2020. Area under the receiver operating characteristic curve (AUROC) for the county-level model was 0.86 in testing dataset. Top predictor of a county experiencing an outbreak was the COVID-19 positive ppm in the general population in the prior week. In a select group (n=11,664) used to build the patient-level model, 28% of patients had COVID-19; prevalence was by design 10% in the testing dataset. AUROC for the patient-level model was 0.71 in the testing dataset. Top predictor of an HD patient having a SARS-CoV-2 infection was mean pre-HD body temperature in the prior week. ConclusionsDeveloped ML models appear suitable for predicting counties at risk of a COVID-19 outbreak and HD patients at risk of having an undetected SARS-CoV-2 infection.