RESUMEN
BACKGROUND: Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge. OBJECTIVE: To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19. DESIGN: Multicenter retrospective observational cohort study, 2020-2021, with 30-day follow-up. PARTICIPANTS: Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system. MAIN MEASURES: Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN). KEY RESULTS: The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71-0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set. CONCLUSIONS: This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve.
RESUMEN
The construction of tissue microarrays (TMAs) with cores from a large number of paraffin-embedded tissues (donors) into a single paraffin block (recipient) is an effective method of analyzing samples from many patient specimens simultaneously. For the TMA to be successful, the cores within it must capture the correct histologic areas from the donor blocks (technical accuracy) and maintain concordance with the tissue of origin (analytical accuracy). This can be particularly challenging for tissues with small histological features such as small islands of carcinoma in situ (CIS), thin layers of normal urothelial lining of the bladder, or cancers that exhibit intratumor heterogeneity. In an effort to create a comprehensive TMA of a bladder cancer patient cohort that accurately represents the tumor heterogeneity and captures the small features of normal and CIS, we determined how core size (0.6 vs 1.0 mm) impacted the technical and analytical accuracy of the TMA. The larger 1.0 mm core exhibited better technical accuracy for all tissue types at 80.9% (normal), 94.2% (tumor), and 71.4% (CIS) compared with 58.6%, 85.9%, and 63.8% for 0.6 mm cores. Although the 1.0 mm core provided better tissue capture, increasing the number of replicates from two to three allowed with the 0.6 mm core compensated for this reduced technical accuracy. However, quantitative image analysis of proliferation using both Ki67+ immunofluorescence counts and manual mitotic counts demonstrated that the 1.0 mm core size also exhibited significantly greater analytical accuracy (P=0.004 and 0.035, respectively, r2=0.979 and 0.669, respectively). Ultimately, our findings demonstrate that capturing two or more 1.0 mm cores for TMA construction provides superior technical and analytical accuracy over the smaller 0.6 mm cores, especially for tissues harboring small histological features or substantial heterogeneity.