Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study

Risk factors for mortality in hospitalized COVID-19 patients across five waves in Pakistan

This retrospective cohort study aims to describe the clinical characteristics and outcomes and assess risk factors for mortality across the epidemic waves in hospitalized COVID-19 patients in a major tertiary-care center in Pakistan. A total of 5368 patients with COVID-19, hospitalized between March 2020 and April 2022 were included. The median age was 58 years (IQR: 44-69), 41% were females, and the overall mortality was 12%. Comparative analysis of COVID-19 waves showed that the proportion of patients aged ≥ 60 years was highest during the post-wave 4 period (61.4%) and Wave 4 (Delta) (50%) (p < 0.001). Male predominance decreased from 65.2% in Wave 2 to 44.2% in Wave 5 (Omicron) (p < 0.001). Mortality rate was lowest at 9.4% in wave 5 and highest at 21.6% in the post-wave 4 period (p = 0.041). In multivariable analysis for risk factors of mortality, acute respiratory distress syndrome (ARDS) was most strongly associated with mortality (aOR 22.98, 95% CI 15.28-34.55, p < 0.001), followed by need for mechanical ventilation (aOR 6.81, 95% CI 5.13-9.05, p < 0.001). Other significant risk factors included acute kidney injury (aOR 3.05, 95% CI 2.38-3.91, p < 0.001), stroke (aOR 2.40, 95% CI 1.26-4.60, p = 0.008), pulmonary embolism (OR 2.07, 95% CI 1.28-3.35, p = 0.003), and age ≥ 60 years (aOR 2.45, 95% CI 1.95-3.09, p < 0.001). Enoxaparin use was associated with lower mortality odds (aOR 0.45, 95% CI 0.35-0.60, p < 0.001. Patients hospitalized during Wave 4 (aOR 2.22, 95% CI 1.39-3.56, p < 0.001) and the post-wave 4 period (aOR 2.82, 95% CI 1.37-5.80, p = 0.005) had higher mortality odds compared to other waves. The study identifies higher mortality risk in patients admitted in Delta wave and post-wave, aged ≥ 60 years, and with respiratory and renal complications, and lower risk with anticoagulation during COVID-19 waves.

An adaptive ensemble deep learning framework for reliable detection of pandemic patients

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care.