Introduction: Our study aimed to analyze whether renal parameters can predict mortality from COVID-19 disease in hospitalized patients. Methods: This retrospective cohort includes all adult patients with confirmed COVID-19 disease who were consecutively admitted to the tertiary hospital during the four-month period (1.9. - 31.12.2020). We analyzed their basic laboratory values, urinalysis, comorbidities, length of hospitalization, and survival. The RIFLE and KDIGO criteria were used for AKI and CKD grading, respectively. To display renal function evolution and the severity of renal damage, we subdivided patients further into 6 groups as follows: group 1 (normal renal function), group 2 (CKD grade 2+3a), group 3 (AKI-DROP defined as whose s-Cr dropped by >33.3% during the hospitalization), group 4 (CKD 3b), group 5 (CKD 4+5) and group 6 (AKI-RISE defined as whose s-Cr was elevated by ≥ 50% within 7 days or by ≥26.5 μmol/L within 48-hours during hospitalization). Then, we used eGFR on admission independently of renal damage to check whether it can predict mortality. Only 4 groups were used: Group I – normal renal function (eGFR>1.5 ml/s), group II mild renal involvement (eGFR 0.75-1.5), group III - moderate (eGFR 0.5-0.75) and group IV – severe (GFR<0.5). Results: 680 patients were included in our cohort. 244 patients displayed normal renal function, 207 patients fulfilled AKI, and 229 patients suffered from CKD. In total, a significantly higher mortality rate was found in the AKI and the CKD groups vs. normal renal function - 37.2% and 32.3% vs. 9.4%, respectively (P<0.001). In addition, the groups 1-6 divided by severity of renal damage reported mortality as 9.4%, 21.2%, 24.1%, 48.7%, 62.8% and 55.1%, respectively (P<0.001). The mean hospitalization duration of alive patients with normal renal findings was 9.5 days, while 12.1 days in patients with any renal damage (P<0.001). When all patients were compared according to eGFR on admission, the mortality was as follows: Group I (normal) 9.8%, Group II (mild) 22.1%, group III (moderate) 40.9% and group IV (severe) 50.5%, respectively (P<0.001). It was a significantly better mortality predictor than CRP on admission (AUC 0.7053 vs. 0.6053). Conclusions: Mortality in patients with abnormal renal function was 3 times higher compared to patients with normal renal function. Also, patients with renal damage had a worse and longer hospitalization course. Lastly, eGFR on admission, independently of any renal damage, was an excellent tool for predicting mortality. Further, the change in s-Cr levels during hospitalization reflected the mortality prognosis.
Introduction: The main objective of this study was to identify the best combination of admission day parameters for predicting COVID-19 mortality in hospitalized patients. Furthermore, we sought to compare the predictive capacity of pulmonary parameters to that of renal parameters for mortality from COVID-19. Methods: In this retrospective study, all patients admitted to a tertiary hospital between September 1st, 2020 and December 31st, 2020, who were clinically symptomatic and tested positive for COVID-19, were included. We gathered extensive data on patient admissions, including laboratory results, comorbidities, chest X-rays (CXR) images, and SpO2 levels, to determine their role in predicting mortality. Experienced radiologists evaluated the CXR images and assigned a score from 0 to 18 based on the severity of COVID-19 pneumonia. Further, we categorized patients into two independent groups based on their renal function using the RIFLE and KDIGO criteria to define the AKI and CKD groups. The first group (“AKI&CKD”) was subdivided into six sub-groups: normal renal function (A); CKD Grade 2+3a (B); AKI-DROP (C); CKD Grade 3b (D); AKI-RISE (E); and Grade 4+5 CKD (F). The second group was based only on eGFR at the admission and thus it was divided into four grades: Grade 1, Grade 2+3a, Grade 3b, and Grade 4+5. Results: The cohort comprised 619 patients. Patients who died during hospitalization had a significantly higher mean radiological score (8.6 ± 1.5) compared to those who survived (7.1 ± 1.2), with a P-value < 0.01. Moreover, we observed that the risk for mortality was significantly increased as renal function deteriorated, as evidenced by the AKI&CKD and eGFR groups (P < 0.001 for each group). Regarding mortality prediction, the area under the curve (AUC) for renal parameters (AKI&CKD group, eGFR group, and age) was found to be superior to that of pulmonary parameters (age, radiological score, SpO2, CRP, and D-dimer) with an AUC of 0.8068 versus 0.7667. However, when renal and pulmonary parameters were combined, the AUC increased to 0.8813. Optimal parameter combinations for predicting mortality from COVID-19 were identified for three medical settings: Emergency Medical Service (EMS), the emergency department, and the Internal medicine floor. The AUC for these settings was 0.7874, 0.8614, and 0.8813, respectively. Conclusions: Our study demonstrated that selected renal parameters are superior to pulmonary parameters in predicting COVID-19 mortality for patients requiring hospitalization. When combining both renal and pulmonary factors, the predictive ability of mortality significantly improved. Additionally, we identified the optimal combination of factors for mortality prediction in three distinct settings: EMS, Emergency Department, and Internal Medicine Floor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.