CKD and the Long-COVID Syndrome

Approximately 221 million people worldwide have been diagnosed with infection from SARS-CoV-2, the virus that causes COVID-19.1 In the United States alone there have been over 40 million cases and nearly 650 thousand deaths (as of September 2021).1 The Centers for Disease Control and Prevention (CDC) uses the term “post-COVID conditions” to describe health issues that persist >30 days after a person is first infected with SARS-CoV-2.2 These CDC post-infection categories include long COVID, multiorgan effects of COVID, and longer-term effects of COVID-19 treatment or hospitalization.

Long COVID is a syndrome of lingering health effects of COVID. It is characterized by varied symptoms, including fatigue, sleep disorder, difficulty concentrating (brain fog), headache, loss of smell or taste, palpitations, chest pain, cough and shortness of breath, joint or muscle pain, and mental health issues such as depression and anxiety. The causes of long COVID are still unclear, although there are several hypotheses, including long-term effects of endothelial cell damage, ongoing infection, and autoimmune effects. Estimates from the UK’s Office of National Statistics (ONS), point to a prevalence of long COVID of about 21% at 5 weeks and 10% at 12 weeks from onset of COVID-19.3

While kidney involvement is a well-recognized complication of acute COVID-19, kidney sequelae as a component of the long COVID syndrome has not been previously reported. A recent publication by Benjamin Bowie and colleagues in the Journal of the American Society of Nephrology (JASN)4 has skillfully documented a significant burden of kidney disease among survivors after 30 days of infection with the SARS-CoV-2 virus. This study should set off alarm bells for both clinicians and policy workers.

Alarm bells for clinicians because there could be a staggering clinical burden for nephrologists in the wake of the COVID pandemic and for policy makers because resources will need to be rapidly identified to expand kidney services and funding at a health system, and, more broadly, at a country level if millions need care for complications of kidney disease, including dialysis and transplantation.

The paper by Bowie and colleagues assembled a large cohort of subjects (89,216 30-day COVID-19 survivors and 1,637,467 non-infected controls). They examined the risks of acute kidney injury (AKI), decline in estimated glomerular filtration rate (eGFR), end-stage kidney disease (ESKD), and major adverse kidney events (MAKE), defined as eGFR decline ≥50%, ESKD, or all-cause mortality in the 30 days post-acute COVID infection (the long COVID phase). They defined infection as testing positive for COVID. Sophisticated methods were used to reduce the influence of confounders and to characterize intra-individual eGFR trajectory. They also looked at outcomes based on whether patients were non-hospitalized, hospitalized, and admitted to intensive care. They excluded patients who had a prior history of ESKD or developed ESKD in the acute phase of COVID-19 (30 days immediately following the positive test for COVID-19). They report a markedly increased risk of AKI, eGFR decline, ESKD, and the composite endpoint of eGFR decline ≥50%, ESKD, or all-cause mortality.

The data were especially concerning for the increased risk of AKI (adjusted hazard ratio [aHR], 1.94, 95% confidence interval [CI], 1.86-2.04), and of ESKD (aHR, 2.96, 95% CI, 2.49-3.51). The rate of eGFR loss correlated with the severity of COVID infection, and whether patients were hospitalized (worse among those who hospitalized and developed AKI as part of their acute COVID infection (—8.41 [95% CI, —9.72 to —7.10] ml/min/1.73m2 per year in those hospitalized with an AKI).

The Bowie study has several limitations, including generalizability—only men in the VA system and in the United States were evaluated, and no individual data, such as urine data, were examined in evaluating AKI. Additionally, while the authors used established and valid statistical methods to reduce the potential effect of confounding, they could not exclude it completely. All that said, the study was a remarkably well-conducted analysis and that has three implications.

First, if the study result is generalized, then the sheer number of individuals who might have some form of kidney involvement is staggering. This will need to be factored into the developing of future healthcare infrastructure for taking care of patients with kidney disease. The authors suggest integrated multidisciplinary post-COVID clinics. This has already been implemented in many parts of the world: Ireland5, Egypt6, and in the United States in the form of post-COVID care centers (PCCC).7,8 This conventional approach is time-consuming and potentially expensive. Novel alternatives have been proposed, including using artificial intelligence deep learning algorithms (DLA) to screen patients for CKD. Data to support this approach with retinal photographs were published by Sabanayagam and colleagues in Lancet Digital Health.9 Data mining electronic medical records could be the future.

Second, research into understanding the mechanistic factors associated with this increased risk of kidney involvement needs to be pursued with great urgency. So far, the cause(s) for long COVID have proven elusive. Important questions need answers: Is persistent subclinical SARS-CoV-2 infection the cause of kidney involvement in long COVID? What risk factors increase the likelihood of kidney disease? What is the role of social determinants in increasing this risk?

Third, if the number of patients requiring dialysis dramatically increases because of the long-term effects of COVID-19, the cost of taking care of additional patients will need to be planned. Stage 4 and 5 CKD costs more than USD 45,000 each year and the cost increases as patients transition onto dialysis (in the United States more than USD 90,000 per year).10

In summary, the Bowie study was well done and should ring alarm bells, because of its potential impact on patient care and planning for healthcare resources and funding.

References

  1. https://www.worldometers.info/coronavirus/ Accessed September 6, 2021
  2. The Long Haul: Forging a Path through the Lingering Effects of COVID-19: Testimony: House Energy and Commerce Subcommittee on Health. https://www.cdc.gov/washington/testimony/2021/t20210428.htm (accessed Sep 7, 2021).
  3. Office for National Statistics. 2020. Prevalence of long COVID symptoms and COVID-19 complications.https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandlifeexpectancies/datasets/prevalenceoflongcovidsymptomsandcovid19complications (accessed Sep 7, 2021).
  4. Bowe B, Xie Y, Xu E, Al-Aly Z. Kidney outcomes in long COVID. JASN Se 2021, ASN.2021060734; DOI 10.1681.2021060734. https://jasn.asnjournals.org/content/early/2021/08/25/ASN.2021060734 (accessed Sep 7, 2021).
  5. O’Brien H, Tracey MJ, Ottewill C, et al. An integrated multidisciplinary model of COVID-19 recovery care. Ir J Med Sci. 2021;190(2):461-468. doi:10.1007/s11845-020-02354-9.
  6. Aiash H, Khodor M, Shah J, et al. Integrated multidisciplinary post-COVID-19 care in Egypt. Lancet Glob Health. 2021 Jul;9(7):e908-e909. doi: 10.1016/S2214-109X(21)00206-0. Epub 2021 May 18. Erratum in: Lancet Glob Health. 2021 Jul;9(7):e915. PMID: 34019842; PMCID: PMC8131059.
  7. https://www.nbcnews.com/health/health-news/inside-post-covid-clinics-how-specialized-centers-are-trying-treat-n1258879 (accessed Sep 7, 2021).
  8. https://www.survivorcorps.com/pccc-ma (accessed Sep 7, 2021).
  9. Sabanayagam C, Xu D, Ting DSW, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020 Jun;2(6):e295-e302. doi: 10.1016/S2589-7500(20)30063-7. Epub 2020 May 12. PMID: 33328123.
  10. Golestaneh L, Alvarez PJ, Reaven NL, et al. All-cause costs increase exponentially with increased chronic kidney disease stage. Am J Manag Care. 2017 Jun;23(10 Suppl):S163-S172. PMID: 28978205.