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

  1. Sheetal Chaudhuri
  2. Rachel Lasky
  3. Yue Jiao
  4. John Larkin
  5. Caitlin Monaghan
  6. Anke Winter
  7. Luca Neri
  8. Peter Kotanko
  9. Jeffrey Hymes
  10. Sangho Lee
  11. Yuedong Wang
  12. Jeroen P. Kooman
  13. Franklin Maddux
  14. Len Usvyat

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Abstract

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.

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  1. Version published to 10.1111/hdi.12977
  2. ScreenIT

    SciScore for 10.1101/2021.02.28.21252383: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This analysis was performed under a protocol reviewed by New England Institutional Review Board (Needham Heights, MA, United States; Version 1.0 NEIRB# 17-1376378-1) who determined this analysis of existing patient data that was de-identified by the investigator was exempt and did not require informed consent.
    Consent: This analysis was performed under a protocol reviewed by New England Institutional Review Board (Needham Heights, MA, United States; Version 1.0 NEIRB# 17-1376378-1) who determined this analysis of existing patient data that was de-identified by the investigator was exempt and did not require informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Although most patients were likely tested for clinical reasons, which is known for most of the COVID-19 positive patients, this is a limitation of the analysis. In general, absolute differences in trends, although significant, were small and we therefore suggest using multiple markers in combination for risk prediction, as shown in our previous paper regarding a machine learning prediction model developed for early detection of patients with COVID-19 11. Moreover, it is important to realize that the trends do not show the mean of individual trajectories, but an aggregate of the cohort groups. We cannot rule out that there might be some minimal temporal bias secondary to the definition of the reference date/day 0 for suspicion/testing that may have an impact on the trajectories. Also, there is a possibility that the incubation period of the virus was prolonged due to low immunity in dialysis patients. Nonetheless, the comparisons in trends in daily changes during the weeks prior suspicion/testing are anticipated to have reasonably captured signals. Further investigations should consider inclusion of these insights to improve the precision of COVID-19 risk scores and prediction models being developed and used in care paradigms.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    About SciScore

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  3. Version published to 10.1101/2021.02.28.21252383v1 on medRxiv