SARS-CoV-2 neutralizing antibody response after three doses of mRNA1273 vaccine and COVID-19 in hemodialysis patients.

Background: In hemodialysis patients, a third vaccination is frequently administered to augment protection against coronavirus disease 2019 (COVID-19). However, the newly emerged B.1.1.159 (Omicron) variant may evade vaccinal protection more easily than previous strains. It is of clinical interest to better understand the neutralizing activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants after booster vaccine or COVID-19 infection in these mostly immunocompromised patients. Methods: Hemodialysis patients from four dialysis centers were recruited between June 2021 and February 2022. Each patient provided a median of six serum samples. SARS-CoV-2 neutralizing antibodies (nAbs) against wild type (WT) or Omicron were measured using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit. Results: Forty-two patients had three doses of mRNA1273. Compared to levels prior to the third dose, nAb-WT increased 18-fold (peak at day 23) and nAb-Omicron increased 23-fold (peak at day 24) after the third dose. Peak nAb-WT exceeded peak nAb-Omicron 27-fold. Twenty-one patients had COVID-19 between December 24, 2021, and February 2, 2022. Following COVID-19, nAb-WT and nAb-Omicron increased 12- and 40-fold, respectively. While levels of vaccinal and post-COVID nAb-WT were comparable, post-COVID nAb-Omicron levels were 3.2 higher than the respective peak vaccinal nAb-Omicron. Four immunocompromised patients having reasons other than end-stage kidney disease have very low to no nAb after the third dose or COVID-19. Conclusions: Our results suggest that most hemodialysis patients have a strong humoral response to the third dose of vaccination and an even stronger post-COVID-19 humoral response. Nevertheless, nAb levels clearly decay over time. These findings may inform ongoing discussions regarding a fourth vaccination in hemodialysis patients.

SARS-COV-2 Neutralizing Antibody Response to Booster Vaccination in Patients on Hemodialysis

BACKGROUND AND AIMS SARS-CoV-2 antibody titers after two doses of vaccination decrease over time. Hemodialysis patients are especially vulnerable to COVID-19 as they are immunocompromised, putting them at higher risk of infection and poorer response to vaccines. Therefore, administrating the third dose (‘booster’) in these patients is key to reduce COVID-19 infections and prevent severe illness. Dialysis patients were among the first group of patients who received booster vaccinations. To study the humoral response to the third injection in this group, we collected serum from 33 patients on hemodialysis and measured neutralizing antibody titers against SARS-CoV-2 before and after their booster doses. METHOD Patients were recruited from a dialysis center in New York City, NY from June to September 2021. Data on COVID-19 vaccination and demographics were collected upon enrollment. Blood samples were taken after enrollment. SARS-CoV-2 neutralization antibodies were assayed using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (Cat#L00847-A). Corresponding neutralizing antibody titers are presented as Unit/mL (U/mL). RESULTS A total of 33 in-center hemodialysis patients who had received three doses of vaccination were studied. Patients had a mean age of 61 years, 23 (70%) were male. Out of these, 31 (94%) patients received three doses of mRNA-1273 (Moderna), and two patients received the BNT162b2 (Pfizer BioNTech) vaccine. A total of 138 serum samples were analyzed (ranging from 156 days before to 85 days after the booster). Figure 1 shows the antibody titer distribution of all samples in these 33 patients. Each color indicates an individual patient. Each patient has up to 12 data points before and after the booster. The mean neutralizing antibody titers of all 48 data points pre-booster is 29.291 U/mL (range: 228–188.600). Seven days post-booster, the mean neutralizing antibody titer is 73.088 U/mL (range: 12.401–254.504). Mean titer is 169.826 U/mL (range: 17.830–375.046) at 14–28 days post-booster. After the peak time, we observe a decline of the titers. At 72–85 days, the mean titer is 72.179 (range: 33.702–204.382). We fitted a nonparametric mixed effects model with an adaptive spline and a random intercept for each subject to neutralizing antibody titers on the log10 scale. The estimate of the mean trajectory and its 95% confidence interval are shown in Fig. 2. The estimated peak time is 18.2 days with a 95% confidence interval (0–27.7). CONCLUSION Our results suggest that hemodialysis patients have a strong humoral response to booster vaccination. Neutralizing antibody titers peak at 18 days post-booster and wane to an average of 42% of peak value after 10–12 weeks.FIGURE 1: Time-course of neutralizing antibody titers before and after booster vaccination. The colors identify individual hemodialysis patients.FIGURE 2: A nonparametric mixed effects model with an adaptive spline and a random intercept for each subject to neutralizing antibody titers. The red line indicates the average titer, and the gray area indicates the 95% confidence interval. The circles are means across all data points.

Transmission of SARS-CoV-2 considering shared chairs in outpatient dialysis: a real-world case-control study.

BACKGROUND: SARS-CoV-2 can remain transiently viable on surfaces. We examined if use of shared chairs in outpatient hemodialysis associates with a risk for indirect patient-to-patient transmission of SARS-CoV-2. METHODS: We used data from adults treated at 2,600 hemodialysis facilities in United States between February 1st and June 8th, 2020. We performed a retrospective case-control study matching each SARS-CoV-2 positive patient (case) to a non-SARS-CoV-2 patient (control) treated in the same dialysis shift. Cases and controls were matched on age, sex, race, facility, shift date, and treatment count. For each case-control pair, we traced backward 14 days to assess possible prior exposure from a 'shedding' SARS-CoV-2 positive patient who sat in the same chair immediately before the case or control. Conditional logistic regression models tested whether chair exposure after a shedding SARS-CoV-2 positive patient conferred a higher risk of SARS-CoV-2 infection to the immediate subsequent patient. RESULTS: Among 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. CONCLUSIONS: The risk of indirect patient-to-patient transmission of SARS-CoV-2 infection from dialysis chairs appears to be low.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival.

INTRODUCTION: The 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. METHODS: We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 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 nonsurvivor 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. RESULTS: There were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8895 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, neutrophils, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patients. For COVID-19 positive group, we observed significantly different clinical trends (p < 0.05) in pre-HD pulse rate, lymphocytes, neutrophils, and albumin between survivors and nonsurvivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days. CONCLUSION: We 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.

Short Communication - Characteristics of healthcare workers who died during the fight against COVID-19 in China

ABSTRACT Coronavirus disease 2019(COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non-healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals.

Arterial oxygen saturation and hypoxemia in hemodialysis patients with COVID-19.

BACKGROUND: Maintenance hemodialysis (MHD) patients are particularly vulnerable to coronavirus disease 2019 (COVID-19), a viral disease that may cause interstitial pneumonia, impaired alveolar gas exchange and hypoxemia. We ascertained the time course of intradialytic arterial oxygen saturation (SaO2) in MHD patients between 4 weeks pre-diagnosis and the week post-diagnosis of COVID-19. METHODS: We conducted a quality improvement project in confirmed COVID-19 in-center MHD patients from 11 dialysis facilities. In patients with an arterio-venous access, SaO2 was measured 1×/min during dialysis using the Crit-Line monitor (Fresenius Medical Care, Waltham, MA, USA). We extracted demographic, clinical, treatment and laboratory data, and COVID-19-related symptoms from the patients' electronic health records. RESULTS: Intradialytic SaO2 was available in 52 patients (29 males; mean ± standard deviation age 66.5 ± 15.7 years) contributing 338 HD treatments. Mean time between onset of symptoms indicative of COVID-19 and diagnosis was 1.1 days (median 0; range 0-9). Prior to COVID-19 diagnosis the rate of HD treatments with hypoxemia, defined as treatment-level average SaO2 <90%, increased from 2.8% (2-4 weeks pre-diagnosis) to 12.2% (1 week) and 20.7% (3 days pre-diagnosis). Intradialytic O2 supplementation increased sharply post-diagnosis. Eleven patients died from COVID-19 within 5 weeks. Compared with patients who recovered from COVID-19, demised patients showed a more pronounced decline in SaO2 prior to COVID-19 diagnosis. CONCLUSIONS: In HD patients, hypoxemia may precede the onset of clinical symptoms and the diagnosis of COVID-19. A steep decline of SaO2 is associated with poor patient outcomes. Measurements of SaO2 may aid the pre-symptomatic identification of patients with COVID-19.

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.

Characteristics of healthcare workers who died during the fight against COVID-19 in China.

Coronavirus disease 2019 (COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non- healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals.