Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PURPOSE

To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients.

MATERIALS AND METHODS

Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed.

RESULTS

Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]).

CONCLUSIONS

An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

Klotho-derived peptide KP1 ameliorates SARS-CoV-2-associated acute kidney injury

Introduction: The severe cases of COVID-19, a disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), often present with acute kidney injury (AKI). Although old age and preexisting medical conditions have been identified as principal risk factors for COVID-19-associated AKI, the molecular basis behind such a connection remains unknown. In this study, we investigated the pathogenic role of Klotho deficiency in COVID-19-associated AKI and explored the therapeutic potential of Klotho-derived peptide 1 (KP1). Methods: We assessed the susceptibility of Klotho deficient Kl/Kl mice to developing AKI after expression of SARS-CoV-2 N protein. The role of KP1 in ameliorating tubular injury was investigated by using cultured proximal tubular cells (HK-2) in vitro and mouse model of ischemia-reperfusion injury (IRI) in vivo. Results: Renal Klotho expression was markedly downregulated in various chronic kidney disease (CKD) models and in aged mice. Compared to wild-type counterparts, mutant KL/KL mice were susceptible to overexpression of SARS-CoV-2 N protein and developed kidney lesions resembling AKI. In vitro, expression of N protein alone induced HK-2 cells to express markers of tubular injury, cellular senescence, apoptosis and epithelial-mesenchymal transition, whereas both KP1 and Klotho abolished these lesions. Furthermore, KP1 mitigated kidney dysfunction, alleviated tubular injury and inhibited apoptosis in AKI model induced by IRI and N protein. Conclusion: These findings suggest that Klotho deficiency is a key determinant of developing COVID-19-associated AKI. As such, KP1, a small peptide recapitulating Klotho function, could be an effective therapeutic for alleviating AKI in COVID-19 patients.

Temporal validation of the MMCD score to predict kidney replacement therapy and in-hospital mortality in COVID-19 patients

BACKGROUND

Acute kidney injury has been described as a common complication in patients hospitalized with COVID-19, which may lead to the need for kidney replacement therapy (KRT) in its most severe forms. Our group developed and validated the MMCD score in Brazilian COVID-19 patients to predict KRT, which showed excellent performance using data from 2020. This study aimed to validate the MMCD score in a large cohort of patients hospitalized with COVID-19 in a different pandemic phase and assess its performance to predict in-hospital mortality.

METHODS

This study is part of the "Brazilian COVID-19 Registry", a retrospective observational cohort of consecutive patients hospitalized for laboratory-confirmed COVID-19 in 25 Brazilian hospitals between March 2021 and August 2022. The primary outcome was KRT during hospitalization and the secondary was in-hospital mortality. We also searched literature for other prediction models for KRT, to assess the results in our database. Performance was assessed using area under the receiving operator characteristic curve (AUROC) and the Brier score.

RESULTS

A total of 9422 patients were included, 53.8% were men, with a median age of 59 (IQR 48-70) years old. The incidence of KRT was 8.8% and in-hospital mortality was 18.1%. The MMCD score had excellent discrimination and overall performance to predict KRT (AUROC: 0.916 [95% CI 0.909-0.924]; Brier score = 0.057). Despite the excellent discrimination and overall performance (AUROC: 0.922 [95% CI 0.914-0.929]; Brier score = 0.100), the calibration was not satisfactory concerning in-hospital mortality. A random forest model was applied in the database, with inferior performance to predict KRT requirement (AUROC: 0.71 [95% CI 0.69-0.73]).

CONCLUSION

The MMCD score is not appropriate for in-hospital mortality but demonstrates an excellent predictive ability to predict KRT in COVID-19 patients. The instrument is low cost, objective, fast and accurate, and can contribute to supporting clinical decisions in the efficient allocation of assistance resources in patients with COVID-19.