Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19

Host Response Protein Biomarkers Indicative of Persistent Acute Kidney Injury in Critically Ill COVID-19 Patients

IMPORTANCE

Sepsis-related host-response anomalies contribute to acute kidney injury (AKI) duration. Data on the host-response specific to COVID-19-associated AKI (COVID-AKI) in critically ill patients is limited.

OBJECTIVES

We postulated that persistent COVID-AKI (> 48 hr) differs in host response from transient (< 48 hr) or no COVID-AKI.

DESIGN, SETTING, AND PARTICIPANTS

This prospective biomarker study observed patients with severe acute respiratory syndrome coronavirus 2 infection, without chronic kidney disease, in three ICUs from March 2020 to July 2020. AKI was assessed by hourly urine output and daily plasma creatinine.

MAIN OUTCOMES AND MEASURES

Luminex and enzyme-linked immunosorbent assay were used to analyze 48 plasma protein biomarkers across six pathophysiological domains, which were tested with mixed-effects models.

RESULTS

Of 177 included patients, 106 (59.9%) had AKI within the first 48 hours of admission, of whom 76 (71.7%) had persistent AKI and 30 (28.3%) transient AKI. Those with persistent AKI often had obesity, hypertension, and a higher Sequential Organ Failure Assessment score due to the renal component. Longitudinal analyses revealed that seven proteins were elevated in persistent AKI compared with no AKI. These were related to inflammation (triggering receptor expressed on myeloid cells 1, p < 0.001; tumor necrosis factor receptor 1, p < 0.001; procalcitonin, p = 0.001), complement activation (mannan-binding lectin serine protease-2, p = 0.001), kidney dysfunction (cystatin C, p < 0.001; neutrophil gelatinase-associated lipocalin, p < 0.001), and lung dysfunction (Clara cell secretory protein 16, p < 0.001). AKI (duration) was not associated with differences in the cytokine signaling, endothelial cell activation, or coagulation domains.

CONCLUSIONS AND RELEVANCE

In contrast with sepsis-associated AKI, primarily inflammation-related biomarker levels correlated with COVID-AKI persistence. This study offers insights into COVID-AKI and may guide approaches to mitigate its persistence.

Biomarker Panels for Predicting Progression of Kidney Disease in Acute Kidney Injury Survivors

Key Points

Clinical characteristics and biomarkers after hospital discharge can predict major adverse kidney events among AKI survivors. Clinical impact plots based on parsimonious prediction models illustrate the potential to optimize post-AKI care by identifying high-risk patients.

Background

AKI increases the risk of CKD. We aimed to identify combinations of clinical variables and biomarkers that predict long-term kidney disease risk after AKI.

Methods

We analyzed data from a prospective cohort of 723 hospitalized patients with AKI in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI study. Using machine learning, we investigated 75 candidate predictors including biomarkers measured at 3-month postdischarge follow-up to predict major adverse kidney events (MAKEs) within 3 years, defined as a decline in eGFR ≥40%, development of ESKD, or death.

Results

The mean age of study participants was 64±13 years, 68% were male, and 79% were of White race. Two hundred four patients (28%) developed MAKEs over 3 years of follow-up. Random forest and least absolute shrinkage and selection operator penalized regression models using all 75 predictors yielded area under the receiver-operating characteristic curve (AUC) values of 0.80 (95% confidence interval [CI], 0.69 to 0.91) and 0.79 (95% CI, 0.68 to 0.90), respectively. The most consistently selected predictors were albuminuria, soluble TNF receptor-1, and diuretic use. A parsimonious model using the top eight predictor variables showed similarly strong discrimination for MAKEs (AUC, 0.78; 95% CI, 0.66 to 0.90). Clinical impact utility analyses demonstrated that the eight-predictor model would have 55% higher efficiency of post-AKI care (number needed to screen/follow-up for a MAKE decreased from 3.55 to 1.97). For a kidney-specific outcome of eGFR decline or ESKD, a four-predictor model showed strong discrimination (AUC, 0.82; 95% CI, 0.68 to 0.96).

Conclusions

Combining clinical data and biomarkers can accurately identify patients with high-risk AKI, enabling personalized post-AKI care and improved outcomes.

Role of plasma suPAR, sTNFR1, and sTNFR2 levels in risk stratification and outcome prediction of complicated acute kidney injury in elderly patients with coronavirus disease 2019

OBJECTIVE

The aim of this study is to investigate the early prognostic efficacy of plasma soluble urokinase-type plasminogen activator receptor (suPAR), soluble tumor necrosis factor receptor 1 (sTNFR1), and soluble tumor necrosis factor receptor 2 (sTNFR2) in complicated acute kidney injury (AKI) in patients with coronavirus disease 2019 (COVID-19), and to analyze the relevant factors contributing to complicated AKI in these patients.

METHODS

Patients with COVID-19 hospitalized at the Affiliated Baiyun Hospital of Guizhou Medical University from March 2022 to March 2024 were selected as study participants. A total of 589 patients met the inclusion and exclusion criteria, 68 patients complicated with AKI were classified as AKI group, and the remaining 521 cases were divided into proportion sampling method and randomly selected 200 samples, which were classified as non-AKI group. Additionally, 50 healthy controls were enrolled as the control group. Logistic regression analysis was conducted to identify the relevant factors associated with complicated AKI in patients with COVID-19. Receiver operating characteristic (ROC) curves were plotted to evaluate the prognostic efficacy of plasma suPAR, sTNFR1, and sTNFR2 indicators for complicated AKI in patients with COVID-19.

RESULTS

Among the patients with COVID-19 in the AKI group, 43 were males (63.20 %), with a median age of 79.00 (interquartile range: 75.00, 83.00) years, while the non-AKI group comprised 83 males (41.50 %), with a median age of 73.00 (interquartile range: 60.00, 80.75) years. Comparison of the sex and age between the two groups indicated that males and elderly patients had increased risks of complicated AKI (P < 0.05). Plasma levels of suPAR, sTNFR1, and sTNFR2 in the AKI group were significantly higher than those in the non-AKI group (P < 0.05). Logistic regression analysis indicated that suPAR and sTNFR2 were independent factors influencing complicated AKI in patients with COVID-19 (P < 0.05). The ROC curve for a single indicator showed that suPAR had the highest prognostic efficacy for complicated AKI, with an area under the curve (AUC) of 0.813, a sensitivity of 79.4 %, and a specificity of 74.0 %. The combined use of suPAR and sTNFR2 for risk assessment yielded the highest AUC of 0.838, with a sensitivity of 66.2 % and a specificity of 87.5 %. The combined risk assessment using all three indicators (suPAR, sTNFR1, and sTNFR2) had an AUC of 0.837, with a sensitivity of 64.7 % and a specificity of 89.0 %.

CONCLUSION

Elderly patients had increased risks of complicated AKI. Indicators such as suPAR, sTNFR1, and sTNFR2 can assist in assessing the risk in patients with COVID-19 complicated AKI, with suPAR demonstrating the highest prognostic efficacy as a single indicator. The combined detection of suPAR, sTNFR1, and sTNFR2 offers greater prognostic value than using any single indicator.

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients' clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

Heterogeneity in the definition of major adverse kidney events: a scoping review

Acute kidney injury (AKI) is associated with persistent renal dysfunction, the receipt of dialysis, dialysis dependence, and mortality. Accordingly, the concept of major adverse kidney events (MAKE) has been adopted as an endpoint for assessing the impact of AKI. However, applied criteria or observation periods for operationalizing MAKE appear to vary across studies. To evaluate this heterogeneity for MAKE evaluation, we performed a systematic scoping review of studies that employed MAKE as an AKI endpoint. Four major academic databases were searched, and we identified 122 studies with increasing numbers over time. We found marked heterogeneity in applied criteria and observation periods for MAKE across these studies, with some even lacking a description of criteria. Moreover, 13 different observation periods were employed, with 30 days and 90 days as the most common. Persistent renal dysfunction was evaluated by estimated glomerular filtration rate (34%) or serum creatinine concentration (48%); however, 37 different definitions for this component were employed in terms of parameters, cut-off criteria, and assessment periods. The definition for the dialysis component also showed significant heterogeneity regarding assessment periods and duration of dialysis requirement (chronic vs temporary). Finally, MAKE rates could vary by 7% [interquartile range: 1.7-16.7%] with different observation periods or by 36.4% with different dialysis component definitions. Our findings revealed marked heterogeneity in MAKE definitions, particularly regarding component assessment and observation periods. Dedicated discussion is needed to establish uniform and acceptable standards to operationalize MAKE in terms of selection and applied criteria of components, observation period, and reporting criteria for future trials on AKI and related conditions.

Unraveling Chronic Cardiovascular and Kidney Disorder through the Butterfly Effect

Chronic Cardiovascular and Kidney Disorder (CCKD) represents a growing challenge in healthcare, characterized by the complex interplay between heart and kidney diseases. This manuscript delves into the "butterfly effect" in CCKD, a phenomenon in which acute injuries in one organ lead to progressive dysfunction in the other. Through extensive review, we explore the pathophysiology underlying this effect, emphasizing the roles of acute kidney injury (AKI) and heart failure (HF) in exacerbating each other. We highlight emerging therapies, such as renin-angiotensin-aldosterone system (RAAS) inhibitors, SGLT2 inhibitors, and GLP1 agonists, that show promise in mitigating the progression of CCKD. Additionally, we discuss novel therapeutic targets, including Galectin-3 inhibition and IL33/ST2 pathway modulation, and their potential in altering the course of CCKD. Our comprehensive analysis underscores the importance of recognizing and treating the intertwined nature of cardiac and renal dysfunctions, paving the way for more effective management strategies for this multifaceted syndrome.

Our Experience with SARS-CoV-2 Infection and Acute Kidney Injury: Results from a Single-Center Retrospective Observational Study

BACKGROUND

Renal failure in COVID-19 patients is reportedly related to multiple factors such as a direct SARS-CoV-2 cytopathic effect, cytokine storm, the association of pulmonary and/or cardiovascular lesions, the presence of thrombotic microangiopathy, endothelial damage, or the use of potentially nephrotoxic medications.

METHODS

We retrospectively analyzed 466 cases of SARS-CoV-2 infection, comparing 233 patients with acute kidney injury (AKI) with 233 patients without AKI in terms of their demographic characteristics, comorbidities, clinical background, laboratory investigations, time of AKI onset, therapy, and outcomes after using univariate analysis and a CART decision-tree approach. The latter was constructed in a reverse manner, starting from the top with the root and branching out until the splitting ceased, interconnecting all the predictors to predict the overall outcome (AKI vs. non-AKI).

RESULTS

There was a statistically significant difference between the clinical form distribution in the two groups, with fewer mild (2 vs. 5) and moderate (54 vs. 133) cases in the AKI group than in the non-AKI group and more severe and critical patients in the AKI cohort (116 vs. 92 and 60 vs. 3). There were four deaths (1.71%) in the non-AKI group and 120 deaths in the AKI group (51.5%) (p-value < 0.001). We noted statistically significant differences between the two study groups in relation to different tissue lesions (LDH), particularly at the pulmonary (CT severity score), hepatic (AST, ALT), and muscular levels (Creatine kinase). In addition, an exacerbated procoagulant and inflammatory profile in the study group was observed. The CART algorithm approach yielded decision paths that helped sort the risk of AKI progression into three categories: the low-risk category (0-40%), the medium-risk category (40-80%), and the high-risk category (>80%). It recognized specific inflammatory and renal biomarker profiles with particular cut-off points for procalcitonin, ferritin, LDH, creatinine, initial urea, and creatinine levels as important predictive factors of AKI outcomes (93.3% overall performance).

CONCLUSIONS

Our study revealed the association between particular risk factors and AKI progression in COVID-19 patients. Diabetes, dyspnea on admission, the need for supplemental oxygen, and admission to the intensive care unit all had a crucial role in producing unfavorable outcomes, with a death rate of more than 50%. Necessary imaging studies (CT scan severity score) and changes in specific biomarker levels (ferritin and C-reactive protein levels) were also noted. These factors should be further investigated in conjunction with the pathophysiological mechanisms of AKI progression in COVID-19 patients.