Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island.

BACKGROUND: In order for healthcare systems to prepare for future waves of COVID-19, an in-depth understanding of clinical predictors is essential for efficient triage of hospitalized patients. METHODS: We performed a retrospective cohort study of 259 patients admitted to our hospitals in Rhode Island to examine differences in baseline characteristics (demographics and comorbidities) as well as presenting symptoms, signs, labs, and imaging findings that predicted disease progression and in-hospital mortality. RESULTS: Patients with severe COVID-19 were more likely to be older (p = 0.02), Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or have COPD (15.4% vs. 6.6%, p = 0.02). In multivariate regression, Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1-3.9) and diabetes (aOR 2.2, 95%CI: 1.3-3.9) were independent predictors of severe disease, while older age (aOR 1.04, 95% CI: 1.01-1.07), admission from a nursing facility (aOR 2.7, 95% CI 1.1-6.7), and hematological co-morbidities predicted mortality (aOR 3.4, 95% CI 1.1-10.0). In the first 24 hours, respiratory symptoms (aOR 7.0, 95% CI: 1.4-34.1), hypoxia (aOR 19.9, 95% CI: 2.6-152.5), and hypotension (aOR 2.7, 95% CI) predicted progression to severe disease, while tachypnea (aOR 8.7, 95% CI: 1.1-71.7) and hypotension (aOR 9.0, 95% CI: 3.1-26.1) were associated with increased in-hospital mortality. CONCLUSIONS: Certain patient characteristics and clinical features can help clinicians with early identification and triage of high-risk patients during subsequent waves of COVID-19.

Coronavirus Disease 2019 in Kidney Transplant Recipients: Single-Center Experience and Case-Control Study.

BACKGROUND: Kidney transplant recipients (KTR) are considered high-risk for morbidity and mortality from coronavirus disease 2019 (COVID-19). However, some studies did not show worse outcomes compared to non-transplant patients and there is little data about immunosuppressant drug levels and secondary infections in KTR with COVID-19. Herein, we describe our single-center experience with COVID-19 in KTR. METHODS: We captured KTR diagnosed with COVID-19 between March 1, 2020 and May 18, 2020. After exclusion of KTR on hemodialysis and off immunosuppression, we compared the clinical course of COVID-19 between hospitalized KTR and non-transplant patients, matched by age and sex (controls). RESULTS: Eleven KTR were hospitalized and matched with 44 controls. One KTR and 4 controls died (case fatality rate: 9.1%). There were no significant differences in length of stay or clinical outcomes between KTR and controls. Tacrolimus or sirolimus levels were >10 ng/mL in 6 out of 9 KTR (67%). Bacterial infections were more frequent in KTR (36.3%), compared with controls (6.8%, P = .02). CONCLUSIONS: In our small case series, unlike earlier reports from the pandemic epicenters, the clinical outcomes of KTR with COVID-19 were comparable to those of non-transplant patients. Calcineurin or mammalian target of rapamycin inhibitor (mTOR) levels were high. Bacterial infections were more common in KTR, compared with controls.

Validation of pneumonia prognostic scores in a statewide cohort of hospitalised patients with COVID-19.

OBJECTIVE: We aimed to externally validate the predictive performance of two recently developed COVID-19-specific prognostic tools, the COVID-GRAM and CALL scores, and prior prognostic scores for community-acquired pneumonia (CURB-65), viral pneumonia (MuBLSTA) and H1N1 influenza pneumonia (Influenza risk score) in a contemporary US cohort. METHODS: We included 257 hospitalised patients with laboratory-confirmed COVID-19 pneumonia from three teaching hospitals in Rhode Island. We extracted data from within the first 24 hours of admission. Variables were excluded if values were missing in >20% of cases, otherwise, missing values were imputed. One hundred and fifteen patients with complete data after imputation were used for the primary analysis. Sensitivity analysis was performed after the exclusion of one variable (LDH) in the complete dataset (n = 257). Primary and secondary outcomes were in-hospital mortality and critical illness (mechanical ventilation or death), respectively. RESULTS: Only the areas under the receiver-operating characteristic curves (RO-AUC) of COVID-GRAM (RO-AUC = 0.775, 95% CI 0.525-0.915) for in-hospital death, and CURB65 for in-hospital death (RO-AUC = 0.842, 95% CI 0.674-0.932) or critical illness (RO-AUC = 0.766, 95% CI 0.584-0.884) were significantly better than random. Sensitivity analysis yielded similar trends. Calibration plots showed better agreement between the estimated and observed probability of in-hospital death for CURB65, compared with COVID-GRAM. The negative predictive value (NPV) of CURB65 ≥2 was 97.2% for in-hospital death and 88.1% for critical illness. CONCLUSIONS: The COVID-GRAM score demonstrated acceptable predictive performance for in-hospital death. The CURB65 score had better prognostic utility for in-hospital death and critical illness. The high NPV of CURB65 values ≥2 may be useful in triaging and allocation of resources.