Lower Urinary Tract Symptoms in a prospective cohort of COVID-19 survivors

ABSTRACT Purpose: To analyze the prevalence of lower urinary tract symptoms (LUTS) in patients who survived moderate and severe forms of COVID-19 and the risk factors for LUTS six months after hospitalization. Materials and Methods: In this prospective cohort study, patients were evaluated six months after hospitalization due to COVID-19. LUTS were assessed using the International Prostate Symptom Score. General health was assessed through the Hospital Anxiety and Depression Scale and the EQ5D-L5 scale, which evaluates mobility, ability to perform daily activities, pain and discomfort and completed a self-perception health evaluation. Results: Of 255 participants, 54.1% were men and the median age was 57.3 [44.3 - 66.6] years. Pre-existing comorbidities included diabetes (35.7%), hypertension (54.5%), obesity (30.2%) and physical inactivity (65.5%). One hundred and twenty-four patients (48.6%) had a hospital stay >15 days, 181 (71.0%) were admitted to an ICU and 124 (48.6%) needed mechanical ventilation. Median IPSS was 6 [3-11] and did not differ between genders. Moderate to severe LUTS affected 108 (42.4%) patients (40.6% men and 44.4% women; p=0.610). Nocturia (58.4%) and frequency (45.9%) were the most prevalent symptoms and urgency was the only symptom that affected men (29.0%) and women (44.4%) differently (p=0.013). LUTS impacted the quality of life of 60 (23.5%) patients with women more severely affected (p=0.004). Diabetes, hypertension, and self-perception of worse general health were associated with LUTS. Conclusions: LUTS are highly prevalent and bothersome six months after hospitalization due to COVID-19. Assessment of LUTS may help ensure appropriate diagnosis and treatment in these patients.

Lower Urinary Tract Symptoms in a prospective cohort of COVID-19 survivors.

PURPOSE: To analyze the prevalence of lower urinary tract symptoms (LUTS) in patients who survived moderate and severe forms of COVID-19 and the risk factors for LUTS six months after hospitalization. MATERIALS AND METHODS: In this prospective cohort study, patients were evaluated six months after hospitalization due to COVID-19. LUTS were assessed using the International Prostate Symptom Score. General health was assessed through the Hospital Anxiety and Depression Scale and the EQ5D-L5 scale, which evaluates mobility, ability to perform daily activities, pain and discomfort and completed a self-perception health evaluation. RESULTS: Of 255 participants, 54.1% were men and the median age was 57.3 [44.3 - 66.6] years. Pre-existing comorbidities included diabetes (35.7%), hypertension (54.5%), obesity (30.2%) and physical inactivity (65.5%). One hundred and twenty-four patients (48.6%) had a hospital stay >15 days, 181 (71.0%) were admitted to an ICU and 124 (48.6%) needed mechanical ventilation. Median IPSS was 6 [3-11] and did not differ between genders. Moderate to severe LUTS affected 108 (42.4%) patients (40.6% men and 44.4% women; p=0.610). Nocturia (58.4%) and frequency (45.9%) were the most prevalent symptoms and urgency was the only symptom that affected men (29.0%) and women (44.4%) differently (p=0.013). LUTS impacted the quality of life of 60 (23.5%) patients with women more severely affected (p=0.004). Diabetes, hypertension, and self-perception of worse general health were associated with LUTS. CONCLUSIONS: LUTS are highly prevalent and bothersome six months after hospitalization due to COVID-19. Assessment of LUTS may help ensure appropriate diagnosis and treatment in these patients.

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data.

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals' daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.