Comparative outcomes of SARS-CoV-2 primary and reinfection in older adult patients.

Background: The outcomes of older adult people acquiring SARS-CoV-2 reinfection was unclear. This study aimed to compare the outcomes of older adult patients with COVID-19 reinfection and those with primary infection. Methods: This retrospective cohort study used electronic medical records from the TriNetX Research Network. Older adult patients (aged ≥65 years) with COVID-19 between January 1, 2022, and December 31, 2022, were included in the study. The patients were subsequently categorized into reinfection or primary infection groups, according to whether they manifested two distinct COVID-19 episodes with an intervening period of more than 90 days. Propensity score matching was performed for covariate adjustment between the reinfection and primary infection groups. The primary outcome was a composite outcome, including emergency department visits, hospitalization, intensive care unit admission, mechanical ventilation use, and mortality, following primary infection and reinfection. Results: After matching, 31,899 patients were identified in both the reinfection and primary infection groups. The risk of primary composite outcomes was 7.15% (n = 2,281) in the reinfection group and 7.53% (n = 2,403) in the primary infection group. No significant difference in the primary outcome was observed between groups (HR, 0.96; 95% CI, 0.91 to 1.02, p = 0.17). In addition, there was no significant differences between the reinfection and primary infection groups in terms of emergency department visit (HR, 1.03; 95% CI, 0.95 to 1.11, p = 0.49), all-cause hospitalization (HR, 0.94; 95% CI, 0.86 to 1.02, p = 0.14), intensive care unit admission (HR, 0.92; 95% CI, 0.67 to 1.28, p = 0.62), mechanical ventilation use (HR,1.35 95% CI, 0.69 to 2.64 p = 0.38), and all-cause mortality (HR, 0.94; 95% CI, 0.74 to 1.20, p = 0.62). Conclusion: There were no significant differences in clinical outcomes between older adult patients with COVID-19 reinfection and those with primary infection.

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN).

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.