Trajectories of clinical and laboratory characteristics associated with <scp>COVID</scp>‐19 in hemodialysis patients by survival

Chaudhuri, Sheetal; Lasky, Rachel; Jiao, Yanmei; Larkin, John; Monaghan, Caitlin K.; Winter, Anke; Neri, Luca; Kotanko, Peter; Hymes, Jeffrey; Lee, Sang-Ho; Wang, Yuedong; Kooman, Jeroen P.; Maddux, Franklin W.; Usvyat, Len

doi:10.1111/hdi.12977

Cited by 8 publications

(8 citation statements)

References 17 publications

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“…In contrast, we found that the CRP values increased from baseline data in the acute phase of infection and decreased after the infection. Likewise, other authors have found similar results in albumin and CRP (Chaudhuri et al, 2022). These biochemical modifications could also help to understand the physiological changes caused by SARS-CoV-2 in HD patients.…”

Section: Discussionsupporting

confidence: 77%

Vaccination and Nutritional Outcomes of Hemodialysis Patients Infected With SARS-CoV-2

Villaverde-Núñez,

Ramírez López,

Rodríguez

et al. 2023

Nursing Research

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Background Patients on hemodialysis are particularly vulnerable to COVID-19 and may have a reduced response to vaccination due to a decreased immune response. The nutritional status prior or during the infection could also impact on the clinical effectiveness of vaccination. Objectives We aim to describe the evolution of clinical and nutritional biomarkers of hemodialysis patients infected with SARS-CoV-2 and to assess their association with vaccination status. Methods An observational, analytic, longitudinal, retrospective multicenter study was carried out in 82 patients on hemodialysis with SARS-CoV-2 infection. Nutritional status was assessed using the Geriatric Nutritional Risk Index (GNRI), anthropometry and biochemical parameters. The association of the vaccine doses with clinical and nutritional related variables were also evaluated. Results The percentage of vaccinated patients was similar to non-vaccinated patients. Prior to infection, the majority of the patients were malnourished. They presented lower albumin, creatinine and urea levels than the well-nourished patients. Significant deterioration of nutritional status after infection was evidenced considering GNRI score, dry weight and Body Mass Index. Albumin and creatinine also decreased significantly post-infection whereas C-reactive protein increased in the acute phase. Significant inverse correlation was found between the variation of post-pre GNRI scores and basal albumin and C-reactive protein at 7 days. Additionally, we observed the opposite trend between albumin at 30 days and basal cholesterol. A negative value in the GNRI variation was associated with bilateral pneumonia, need for hospitalization and nutritional support. Vaccinated patients presented substantially less bilateral pneumonia and hospitalization. No significant effects were observed between vaccine doses and the variation in nutritional status, although a positive correlation was detected with the albumin at 7 days and C-reactive protein prior to infection and the number of vaccine doses received. Discussion COVID-19 is associated with affectations in the nutritional status and biomarkers in hemodialysis patients. In this study vaccines have demonstrated a protective effect against the clinical consequences of COVID. However, they have shown limitations in preventing the deterioration of nutritional status after infection. The results highlight the importance of promoting the vaccination in these patients as well as incorporating nutritional assessment before, during and after the infection.

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Section: Discussionsupporting

confidence: 77%

Vaccination and Nutritional Outcomes of Hemodialysis Patients Infected With SARS-CoV-2

Villaverde-Núñez,

Ramírez López,

Rodríguez

et al. 2023

Nursing Research

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“…Mortality among those with COVID-19 on haemodialysis has been widely reported on, with age, frailty and co-morbidity burden featuring as strong predictors. 14 16 17 However, mortality is commonly reported as a death occurring within 28 or 30 days of a positive PCR test 77–81 and could therefore include mortalities not strictly attributable to COVID-19, particularly given the characteristics of those most at risk. Furthermore, risk factors for mortality may not be COVID-19 specific; this can be challenging to differentiate between and therefore act on from a public health perspective.…”

Section: Discussionmentioning

confidence: 99%

“…Most studies that have investigated COVID-19 among those receiving haemodialysis examined risk of mortality, mostly reporting age, frailty and comorbidity burden as important predictors. 14–19 Severe disease represents a different but important clinical outcome, conferring significant mental and physical morbidity, and may have different risk factors that associate with it. 2 20–23 Associations with this outcome have not been investigated in detail among patients receiving haemodialysis.…”

Section: Introductionmentioning

confidence: 99%

Risk factors associated with COVID-19 severity among patients on maintenance haemodialysis: a retrospective multicentre cross-sectional study in the UK

et al. 2022

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ObjectivesTo assess the applicability of risk factors for severe COVID-19 defined in the general population for patients on haemodialysis.SettingA retrospective cross-sectional study performed across thirty four haemodialysis units in midlands of the UK.ParticipantsAll 274 patients on maintenance haemodialysis who tested positive for SARS-CoV-2 on PCR testing between March and August 2020, in participating haemodialysis centres.ExposureThe utility of obesity, diabetes status, ethnicity, Charlson Comorbidity Index (CCI) and socioeconomic deprivation scores were investigated as risk factors for severe COVID-19.Main outcomes and measuresSevere COVID-19, defined as requiring supplemental oxygen or respiratory support, or a C reactive protein of ≥75 mg/dL (RECOVERY trial definitions), and its association with obesity, diabetes status, ethnicity, CCI, and socioeconomic deprivation.Results63.5% (174/274 patients) developed severe disease. Socioeconomic deprivation associated with severity, being most pronounced between the most and least deprived quartiles (OR 2.81, 95% CI 1.22 to 6.47, p=0.015), after adjusting for age, sex and ethnicity. There was no association between obesity, diabetes status, ethnicity or CCI with COVID-19 severity. We found no evidence of temporal evolution of cases (p=0.209) or clustering that would impact our findings.ConclusionThe incidence of severe COVID-19 is high among patients on haemodialysis; this cohort should be considered high risk. There was strong evidence of an association between socioeconomic deprivation and COVID-19 severity. Other risk factors that apply to the general population may not apply to this cohort.

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“…As the COVID-19 pandemic evolves, it is unclear whether or not previously identified predictors [e.g., residing in a nursing home in (14, 15), clinical and laboratory parameters in (15)(16)(17)] are still predictive and previously built machine learning models [e.g., XGBoost in (13)] are still effective for the early detection of COVID-19 cases. Not only has the original virus undergone mutations that have resulted in multiple variants with different clinical presentations (18-20), but also the social environment has significantly changed.…”

Section: Introductionmentioning

confidence: 99%

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data

Duan

et al. 2023

Front. Nephrol.

Self Cite

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BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.MethodsWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.ResultFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.ConclusionAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

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

Cited by 8 publications

References 17 publications

Vaccination and Nutritional Outcomes of Hemodialysis Patients Infected With SARS-CoV-2

Vaccination and Nutritional Outcomes of Hemodialysis Patients Infected With SARS-CoV-2

Risk factors associated with COVID-19 severity among patients on maintenance haemodialysis: a retrospective multicentre cross-sectional study in the UK

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data

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