ABSTRACT
Background: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI and assess performance over time. Method(s): Patients with positive SARS CoV-2 PCR hospitalized between 3/1/2020 to 1/14/2022 at 19 Texas hospitals were included. Those with AKI present on admission were excluded. Comorbidities, demographics, baseline laboratory data, and inflammatory biomarkers were obtained from the EHR and used to build nested models for AKI in an inception cohort. Models were validated in four out-of-time cohorts. Model discrimination and calibration measures were compared to assess performance. Result(s): Of 13,468 patients, 5,676 were in the Inception Cohort and 7,792 in subsequent validation cohorts grouped based on predominance of COVID variants, with cohorts 1 and 3 containing a mix of variants, cohort 2 corresponding to Delta predominance, and cohort 4 to Omicron. Prevalence of AKI was 13.7% in inception and 12.6%, 12.4%, 13.3%, and 14.4% in the validation cohorts. Proportion of AKI stages 2 or 3 vs. 1 was lower in the Omicron-dominant cohort 4 compared to the inception cohort (28/139 vs. 257/776, P=0.008), but was no different for cohorts 1-3. The final model containing demographics, comorbidities and baseline WBC, hemoglobin, hsCRP, ferritin, and D-dimer, had an AUC=0.781 (95% CI, 0.763, 0.799). Compared to the inception cohort, discrimination by AUC (validation 1: 0.785 [0.760, 0.810], P=0.14, validation 2: 0.754 [0.716, 0.795], P=0.14, validation 3: 0.778 [0.751, 0.806], P=0.14, and validation 4: 0.743 [0.695, 0.789], P=0.14) and calibration by ECI (validation 1: 0.116 [0.041, 0.281], P=1.0, validation 2: 0.081 [0.045, 0.295], P=0.64, validation 3: 0.055 [0.030, 0.162], P=1.0, and validation 4: 0.120 [0.043, 0.472], P=0.50) showed stable performance over time. Conclusion(s): Using demographics, comorbidities, admission laboratory values, and inflammatory biomarkers, we developed and externally validated a model to accurately predict AKI in hospitalized patients with COVID-19. A lower proportion of patients hospitalized during the Omicron-dominant period of the pandemic experienced severe AKI, but our predictive model withstood changes in practice patterns and virus variants.
ABSTRACT
The COVID-19 pandemic raises important questions about immunosuppression management and outcomes in kidney transplant recipients. Kidney transplant recipients with positive SARS-CoV2 PCR seen in outpatient clinics or hospitalized at University and Parkland Hospitals from 3/1-10/1/20 were followed for 90 days. Univariate and multivariate backward selection logistic regression was used to identify risk factors for a composite event of AKI, ICU admission, or death. Non-parametric methods compared biomarkers based on changes in immunosuppressive drugs. Of 59 patients, mean age (SD) was 51 (14) years, 35 (59%) were male, 13 (22%) black and 36 (61%) Hispanic. 29 (50%) had a baseline eGFR <60 mL/min/1.73 m2, 52 (88%) had hypertension and 33 (56%) diabetes. 55 (93%) were on calcineurin inhibitors (CNI) and 49 (83%) on an antimetabolite at baseline. 6 (10%) were treated for acute rejection in the 12 months prior. Initial ferritin level was higher in those who had CNI dose decreased or discontinued vs. those with CNI unchanged, median (IQR) 1271 (839-1932) vs 283 (124-569) ng/mL, p=0.0002. Patients who stopped CNI showed significantly higher peak hsCRP values than those maintained on the same dose, median (IQR) 344 (145-374) vs 41 (22-116) vs mg/L, p=0.03. There were 31 composite events, 43 hospitalizations, 13 ICU admissions, and 12 deaths. Of 52 patients with creatinine values, 29 (56%) had AKI, of which 10 (35%) required RRT. 13 (46%) had recovery of AKI at 90 days, defined as serum creatinine within 10% of baseline. Factors associated with the composite are shown (table). eGFR< 60 and peak hsCRP remained in the multivariable model associated with the composite, with area under the curve =0.89. 1OR per 1 unit increase X 109/L1 Over half of kidney transplant patients with COVID-19 had AKI and 73% required hospitalization. Elevated markers of inflammation were associated with changes in CNI regimen. An eGFR<60 and higher peak hsCRP were associated with increased risk of death, ICU admission, or AKI.
ABSTRACT
Background: AKI in hospitalized patients with COVID-19 is a common adverse complication. Our aim was to investigate risk factors associated with AKI and whether AKI in this setting is independently associated with in-hospital mortality at 30 days. Methods: All adult patients admitted with a positive SARS-CoV-2 PCR between 3 /2021 to 1/2021 to nineteen hospitals who had a COVID-associated billing diagnosis and no history of ESKD or kidney transplant were included. AKI was defined according to the Kidney Disease Improving Global Outcomes guidelines. Risk factors associated with AKI were evaluated with univariable and multivariable logistic regression, and mortality was evaluated using Kaplan-Meier and Cox Proportional Hazards models. Results: The study cohort included 9,681 patients, of which 3,666 (38%) met criteria for AKI. Compared with patients without AKI, patients with AKI were older [mean (SD) age 67 (16) vs. 60 (18) years], more likely to be male (58% vs. 47%), and more likely to be black (21% vs. 15%). Patients with AKI were also more likely to have diabetes mellitus (52% vs. 32%), hypertension (78% vs. 57%), CKD (55% vs. 17%), and coronary artery disease (20% vs. 11%). Patients with AKI were also more likely to be on ACEi/ ARB on admission (51% vs. 37%), require mechanical ventilation (21% vs. 3.2%) or have higher WBC, hs-CRP, ferritin, D-dimer, and cardiac troponin). P-values were <0.001 for all of the aforementioned comparisons. Risk factors significantly associated with AKI in the multivariable model included age, sex, race, hypertension, CKD, diabetes, ACEi or ARB on admission, mechanical ventilation, WBC on admission, hs-CRP, ferritin, d dimer and troponin. Death occurred more frequently in patients with AKI (22.1%;n=811) than in those without (3%;n=178). Patient with AKI had higher mortality risk as compared to those without AKI, hazard ratio (HR) 3.08 (95% CI 2.56-3.71), that remained significant even after controlling for all variables associated with AKI, such as age, sex, race, comorbidities, inflammatory biomarkers, elevated troponin, and COVID-related treatments, HR 1.64 (95% CI 1.34-2.01). Conclusions: Patients with COVID-19 who develop AKI have a higher mortality. We found risk factors associated with AKI in the setting of COVID, and that the increased mortality risk associated with AKI in COVID-19 is independent of these factors.
ABSTRACT
Background: AKI is a complication in patients hospitalized with COVID-19 and is associated with poor outcomes. We aimed to develop predictive models for AKI development and recovery in patients hospitalized with COVID-19. Methods: Patients with a positive SARS-CoV2 PCR admitted to 19 Texas hospitals from 3/13/2020-1/1/2021 were included. AKI presence and stages were determined using KDIGO guidelines. Individuals with AKI present on admission (POA) were excluded for predictive models. Patients were followed for 90 days to evaluate for renal recovery (serum creatinine ≤1.1 times baseline). Nested models for AKI were built using logistic regression: Model 1 included age, sex, race, smoking status, presence of hypertension (HTN), diabetes (DM), chronic kidney disease (CKD), coronary artery disease (CAD), and chronic heart failure (CHF), and use of ACEI/ARB;Model 2, added admission WBC, hs-CRP, and hemoglobin;Model 3, added ferritin and D-Dimer to Model 2 to assess for accuracy improvements. 10-fold stratified cross validation was done to evaluate model performance. Results: Of 8392 patients, 2702 (32%) had AKI, of which 2281 (84%) recovered by 90 days: 92% of stage 1, 75% of stage 2, and 40% of stage 3 AKI, p for trend <0.001. After excluding AKI present on admission, 776 of 5671 developed AKI during the hospitalization. Percentages of AKI stages 1, 2 and 3 were 67%, 8%, and 25%. Overall, 152 (20%) of 776 required RRT. Patients with AKI were older, more likely to be male, black, and have hypertension, diabetes, coronary artery disease, congestive heart failure, and CKD. The interval improvement of each AKI predictive model was statistically significant, with last model AUC of 78.1 (95% CI 76.3%-79.9%) and all p<0.001. The final model had improvement in all metrics when compared to Models 1 and 2, with a sensitivity of 69%, specificity 76%, positive predictive value 32%, negative predictive value 94%, positive likelihood ratio 3.02, and negative likelihood ratio 0.40. Conclusions: AKI is common among patients hospitalized with COVID-19, but a large proportion recover renal function by 90 days. Recovery rate is lower based on stepwise higher stages of AKI. Addition of inflammatory biomarkers to demographics and medical comorbidities can improve prediction of AKI in this patient population.