Millions of SARS-CoV-2 Infected Individuals Are Not Aware That They May Have Undiagnosed Acute Kidney Injury

0
55

SARS-CoV-2 And Acute Kidney Injury(AKI) : A new study led by researchers from the University of Queensland-Australia along with scientists from the University of Oxford-UK, Monash University-Australia and University of Southern California-USA has found that globally, millions of SARS-CoV-2 infected individuals are unaware that they might be having undiagnosed acute kidney injury.

The study findings were published in the peer reviewed journal: PLOS Medicine. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003969


Acute kidney injury (AKI), previously called acute renal failure (ARF), denotes a sudden and often reversible reduction in kidney function, as measured by glomerular filtration rate (GFR).[1][2][3] Although, immediately after a renal insult, blood urea nitrogen (BUN) or creatinine levels may be within the normal range. The only sign of acute kidney injury may be a decline in urine output. AKI can lead to the accumulation of water, sodium, and other metabolic products.

It can also result in several electrolyte disturbances. It is a very common condition, especially among hospitalized patients. It can be seen in up to 7% of hospital admissions and 30% of ICU admissions. There is no clear definition of AKI; however, several different criteria have been used in research studies such as RIFLE, AKIN (Acute Kidney Injury Network), and KDIGO (Kidney Disease: Improving Global Outcomes) criteria. Among these, KDIGO is the most recent and most commonly used tool. According to KDIGO, AKI is the presence of any of the following:

  1. Increase in serum creatinine by 0.3 mg/dL or more (26.5 micromoles/L or more) within 48 hours
  2. Increase in serum creatinine to 1.5 times or more baseline within the prior seven days
  3. Urine volume less than 0.5 mL/kg/h for at least 6 hours

Etiology

The impetus for glomerular filtration is the difference in the pressures between the glomerulus and the Bowman space. This pressure gradient is affected by the renal blood flow and is under the direct control of the combined resistances of afferent and efferent vascular pathways. Nevertheless, whatever the cause of AKI, renal blood flow reduction is a common pathologic pathway for declining glomerular filtration rate. Pathophysiology of AKI has always been traditionally divided into three categories: prerenal, renal, and post-renal. Each of these categories has several different causes associated with it.[4][5]

The prerenal form of AKI is because of any cause of reduced blood flow to the kidney. This may be part of systemic hypoperfusion resulting from hypovolemia or hypotension, or maybe due to selective hypoperfusion to the kidneys, such as those resulting from renal artery stenosis and aortic dissection. However, tubular and glomerular function tends to stay normal. Few examples with the mechanism of prerenal AKI are listed below:

  1. Hypovolemia: hemorrhage, severe burns, and gastrointestinal fluid losses such as diarrhea, vomiting, high ostomy output.
  2. Hypotension from the decreased cardiac output: cardiogenic shock, massive pulmonary embolism, acute coronary syndrome
  3. Hypotension from systemic vasodilation: septic shock, anaphylaxis, anesthesia administration, hepatorenal syndrome
  4. Renal vasoconstriction: NSAIDs, iodinated contrast, amphotericin B, calcineurin inhibitors, hepatorenal syndrome
  5. Glomerular efferent arteriolar vasodilation: ACE inhibitors, angiotensin receptor blockers

Intrinsic renal causes include conditions that affect the glomerulus or tubule, such as acute tubular necrosis and acute interstitial nephritis. This underlying glomerular or tubular injury is associated with the release of vasoconstrictors from the renal afferent pathways. Prolonged renal ischemia, sepsis, and nephrotoxins being the most common ones. It is worth mentioning that prerenal injury can convert into a renal injury if the offending factor’s exposure is prolonged enough to cause cellular damage. Few examples of this mechanism are listed below:

  1. Acute tubular necrosis: ischemia from prolonged prerenal injury, drugs such as aminoglycosides, vancomycin, amphotericin B, pentamidine; rhabdomyolysis, intravascular hemolysis
  2. Acute interstitial nephritis: Drugs such as beta-lactam antibiotics, penicillins, NSAIDs, proton pump inhibitors (PPIs), 5-ASA; infections, autoimmune conditions (SLE, IgG related disease)
  3. Glomerulonephritis: anti-glomerular basement membrane disease, immune complex-mediated diseases such as SLE, post-infectious glomerulonephritis, cryoglobulinemia, IgA nephropathy, Henoch-Schonlein purpura.
  4. Intratubular obstruction: monoclonal gammopathy seen in multiple myeloma, tumor lysis syndrome, toxins such as ethylene glycol. 

Post-renal causes mainly include obstructive causes, which lead to congestion of the filtration system leading to a shift in the filtration driving forces. The most common ones being renal/ureteral calculi, tumors, blood clots, or any urethral obstruction. Another noteworthy fact is that a unilateral obstruction may not always present as AKI, especially if the obstruction is gradual such as a tumor, because a normal working contralateral kidney may compensate for the function of the affected kidney. Therefore, the most common etiology of post-renal AKI is bladder outlet obstruction.

reference link :https://www.ncbi.nlm.nih.gov/books/NBK441896/


In the largest, multinational cohort of hospitalised patients with COVID-19, it was found that an extended KDIGO criteria for the diagnosis of AKI, which includes a fall in sCr during admission, identified almost twice as many cases of AKI than the traditional KDIGO definition.

The majority of these additional cases were stage 1 AKI, occurring early in the admission, supporting the hypothesis that they may represent recovering CA-AKI. This group had comparatively worse outcomes than patients without AKI, making their identification and exploration in future studies enormously important.

The estimated incidence of KDIGO AKI, 16.8%, is consistent with that reported in the first systematic review of AKI in COVID-19 patients [20], while the incidence of eKDIGO AKI fits those studies from the larger New York City cohorts, which had similar rates of ICU admission [2,21].

The mortality rate of 50% among KDIGO-diagnosed AKI patients falls within the range (34% to 50%) reported in previous studies using the same AKI definition [1,2,21–23]. While the inability to exclude readmitted patients may have introduced a degree of survival bias, the fact that readmission rates of less than 3% are seen in other large studies suggests that the effect of this bias is likely to be relatively small [2,21,23].

In line with what is known regarding AKI susceptibility and sequelae, patients identified in the present study as having AKI—by either definition—were more likely to have CKD, hypertension, and type 2 diabetes mellitus, be on an ACEi or ARB, and generally have more medical complications during their admission than patients who did not develop AKI.

The admission eGFR, sCr, and blood urea nitrogen levels of the eKDIGO AKI population, and specifically those in the deKDIGO group, demonstrated significant impairment early in the admission. This is suggestive of CA-AKI, which would otherwise have gone unrecognised.

While these patients had comparatively milder AKI and disease severity than patients in the KDIGO group, they nonetheless incurred significantly more morbidity and mortality than patients without AKI, even after adjusting for confounding factors. With regard to the increased prevalence of stage 1 AKI using the eKDIGO definition, there is growing evidence that even mild episodes of AKI may contribute to the development of CKD [24–26].

This raises the important question of whether this new group of COVID-19 AKI patients would benefit from early management strategies to improve long-term outcomes. Such measures are typically simple – management of fluid balance and removal of nephrotoxic medication for example – and readily implementable, even in resource-poor environments. A follow-up study, similar to the 0by25 feasibility study [8], may be warranted to explore such questions.

The earlier timing of peak AKI in the hospital stay and large proportion of stage 1 cases in the eKDIGO group suggests several possible etiologies. It may point to a prerenal pattern of injury occurring in the setting of dehydration from gastrointestinal fluid losses, fever and anorexia—a finding supported by the identification of acute tubular injury in autopsy studies of patients with COVID-19 [27].

However, it is also possible that a proportion of these additional, milder, cases of AKI, captured by down trending sCr, are a consequence of early rehydration of patients with either previously normal kidney function or CKD. While the reduced admission eGFR of this group (median 54 ml/min/1.73 m2) makes the former less likely, preadmission sCr measurements would be required to reliably identify the latter. It is reassuring that the proportion of reported CKD in the KDIGO and eKDIGO groups is very similar (19% and 18%, respectively).

It is interesting to consider to what extent the large number of additional cases captured by the extended KDIGO definition are a COVID-specific consequence. While meta-analysis suggests that global estimates of AKI incidence in adult hospitalised patients range between 3% and 18% [28], there are no current estimates of global AKI incidence according to the eKDIGO definition.

Moving forward, evaluation of the eKDIGO definition for the diagnosis of AKI in various hospital and community settings will be needed to shed light on whether our findings are particular to a COVID-affected population. In this context, it should be noted that approximately 20% of the analysis cohort had a diagnosis of COVID-19 made on clinical grounds, most likely due to testing shortages and high resource demands during surge phases of the pandemic.

While this may have resulted in the potential inclusion of patients with other respiratory illnesses, given that other common respiratory illnesses were notably less prevalent during the pandemic [29,30], it is plausible that a significant proportion of these clinically diagnosed patients did in fact have COVID-19.

This study has some key limitations. The exclusion of patients without 2 sCr measurements may have introduced a degree of selection bias. This could be responsible for the absence of expected geographical differences found between the eKDIGO and no AKI groups and may also have resulted in an underestimation of AKI cases by both definitions [7].

The lack of a time-standardised collection of sCr across all sites also represents a limitation of the study. Patients having more frequent sCr collections may represent a population with more severe illness in whom AKI would be more readily detected, therefore affecting the overall AKI incidence rates and potentially generating a negative survival bias. Nevertheless, it is reassuring that the number of AKI cases are a small proportion of the total sCr collected on any given day (<18%) (S2 Fig), suggesting that the bias introduced by ad hoc sampling was low.

The lack of standardisation in sCr collection may have also affected the reporting of time to peak AKI, the magnitude of peak AKI reached in each individual patient and, in those experiencing both a rise and fall in sCr during their admission, whether AKI was captured during the former phase (KDIGO) or the latter (eKDIGO).

With regard to the distinction between community and hospital acquired AKI, often, a 48-hour threshold is used to identify CA-AKI [31]. Such a definition would preclude many patients in this study who were identified as having AKI on day 3 of admission. It is worth noting that these patients would be identified as CA-AKI (or transient hospital–associated AKI) by the definition proposed by Warnock and colleagues, which integrates sCr trajectories and does not adhere to the somewhat arbitrary 48-hour cutoff [32]. Whether or not the additional cases of AKI captured by eKDIGO are truly reflective of CA-AKI will ultimately require studies that assess this population in a variety of community settings.

To our knowledge, this is the first study to systematically examine an extended KDIGO definition for the identification of AKI against the traditional KDIGO criteria in hospitalised COVID-19 patients. Our population is, as far as we know, the largest and only multinational cohort of patients with COVID-19 from all income country levels. The use of an extended KDIGO definition to diagnose AKI in this population resulted in a significantly higher incidence rate compared to traditional KDIGO criteria. These additional cases of AKI appear to be occurring in the community or early in the hospital admission and are associated with significantly worse outcomes, highlighting the importance of examining their role and long-term impact in future studies.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Questo sito usa Akismet per ridurre lo spam. Scopri come i tuoi dati vengono elaborati.