The arrival of COVID-19 in Brazil and the impact on coronary artery bypass surgery

The Impact of the COVID-19 Pandemic on Surgical Activities: A Single-Center Experience and Literature Review

Aim The aim of this article is to investigate the effect of the coronavirus disease 2019 (COVID-19) pandemic on our surgical department, which is situated in Athens, Greece, as well as to review published literature on the COVID-19 pandemic's impact on surgical activities in our department.  Material and methods We retrospectively reviewed the surgical procedures that were performed in the surgical department of a tertiary University hospital in Athens, Greece, before and during the pandemic. Furthermore, we performed a literature review evaluating articles on surgical activity and COVID-19 published from the beginning of the pandemic up until the January of 2022 on the PubMed database.  Results In total, 894 patients were included in the study. Of those, 264 (29.5%) underwent surgery during the control period and 630 (70.5%) in the pandemic period. Overall, we performed 20.5% fewer surgeries in the post-sanitary period. In particular, elective surgeries decreased on average by 23.9%, emergency procedures decreased by 8.9%, and oncology surgeries increased by an average of 6.4% after the year 2020. Concerning the review of literature, 51 studies were selected for this review. According to them, the main effect of the pandemic on the surgical sector was reflected in the reduction of total surgeries, mainly due to the postponement of elective surgical procedures, which showed a median reduction of 54% compared to the pre-COVID-19 period. A smaller decrease was observed in the number of emergency and oncological surgeries. Conclusions Reduced surgical activity during the pandemic, due to the health measures imposed, requires courageous corrective interventions to avoid its adverse effects, such as disease progression, increased treatment costs, reduced quality of life, and ultimately the survival of the patients.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Introduction

The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients.

Methods

In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified.

Results

The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795).

Conclusions

Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.