Clinical-epidemiologic variation in patients treated in the first and second wave of COVID-19 in Lambayeque, Peru: A cluster analysis

COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach

OBJECTIVES

Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes.

METHODS

Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles.

RESULTS

The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1: 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient's age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines.

CONCLUSION

Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases.

Association between love breakup and suicidal ideation in Peruvian medical students: a cross-sectional study during the COVID-19 pandemic

Objective

We aimed to determine the association between a major romantic breakup and suicidal ideation in medical students from three universities in Peru.

Methods

A cross-sectional study was conducted during the first pandemic wave in 2021 on medical students from three universities in northern Peru. The outcome was suicidal ideation, measured with question nine of the Patient Health Questionnaire-9 (PHQ-9). Generalized Anxiety Disorder Scale-7 (GAD-7) and Insomnia Severity Index (ISI) were also used to assess mental health symptoms. The exposure was the experience of a major love breakup during the pandemic. In addition, its association with other covariates (age, sex, family members infected with COVID-19, deceased family members with COVID-19, insomnia, and anxiety, among others) was examined.

Results and discussions

Out of 370 students, 19.5% reported a major love breakup during the pandemic (95%CI: 15.5-23.8), and 34.3% had suicidal ideation (95%CI: 29.4-39.4). Having a major love breakup was associated with a higher prevalence of suicidal ideation (PR: 1.49, 95%CI: 1.32-1.67, p < 0.001). Moderate insomnia (PR: 2.56, 95%CI: 1.70-3.87, p < 0.001) and anxiety symptoms (PR: 1.94, 1.10-3.44, p = 0.023) were also associated with suicidal ideation.

Conclusion

Our study provides evidence of a significant association between a major love breakup and suicidal ideation. This finding emphasizes the need for further research to better understand this association and inform the development of effective suicide prevention policies in medical education.

Research on fast marking method for indicator diagram of pumping well based on K-means clustering

Indicator diagram is the key basis for fault diagnosis of pumping wells in oil exploitation. With the rapid development of machine learning, the fault diagnosis of indicator diagram based on deep learning has garnered increasing attention. This kind of methods train neural network models with marked samples, and then inputs images into the trained models and outputs their categories. At present, the preparation of indicator diagram sample set relies on experts' analysis of indicator diagram images one by one. However, it involves extensive manual work and manual marking is prone to errors, so the marked samples are often insufficient in quantity. In order to quickly mark a large number of indicator diagram samples, the oil well data was plotted into standardized indicator diagram, and then three feature extraction methods for indicator diagrams were proposed: feature extraction based on original vector, feature extraction based on three-dimensional pixel tensor, feature extraction based on convolutional neural network. These methods convert the indicator diagram into corresponding feature vectors, which are then clustered using the K-means clustering algorithm, enabling the corresponding indicator diagrams to be classified into different categories based on the clustering results. Using 20,000 randomly selected pieces of data from 100 pumping wells, this study clusters the sample set using the three proposed methods. The results indicated that the time consumption were 0.2, 8.3, and 0.7 h, with accuracy rates of 98%, 92%, and 95%, respectively. For indicator diagrams, the clustering method based on the original vector has outstanding performance in terms of efficiency and accuracy. This provides an automatic tool for the preparation of the pumping well fault diagnosis dataset, and its efficiency can be increased by tens of times compared with manual marking.

Association between Love Breakup and Suicidal Ideation in Peruvian Medical Students: A Cross-Sectional Study during the COVID-19 Pandemic

Objective

We aimed to determine the association between a major romantic breakup and suicidal ideation in medical students from three universities in Peru.

Methods

A cross-sectional study was conducted during the first pandemic wave in 2021 on medical students from three universities in northern Peru. The outcome was suicidal ideation, measured with question nine of the PHQ-9. The exposure was the experience of a major love breakup during the pandemic. In addition, its association with other covariates (age, sex, family members infected with COVID-19, deceased family members with COVID-19, insomnia, and anxiety, among others) was examined.

Results and discussions

Out of 370 students, 19.5% reported a major love breakup during the pandemic (95%CI: 15.5-23.8), and 34.3% had suicidal ideation (95%CI: 29.4-39.4). Having a major love breakup was associated with a higher prevalence of suicidal ideation (PR: 1.49, 95%CI: 1.32-1.67). Moderate insomnia (PR: 2.56, 95%CI: 1.70-3.87) and anxiety symptoms (PR: 1.94, 1.10-3.44) were also associated with suicidal ideation.

Conclusion

Our study provides evidence of a significant association between a major love breakup and suicidal ideation. This finding emphasizes the need for further research to better understand this association and inform the development of effective suicide prevention policies in medical education.

Food insecurity in Piura, Peru, in the context of post-earthquake and the COVID-19 pandemic

Introduction

Piura, located in a seismic zone, faces challenges related to food security. The aim of this study was to analyze aspects related to food insecurity in the region following the 6.1-magnitude earthquake that occurred in 2021, within the context of the COVID-19 pandemic.

Methods

A secondary analysis was performed in 177 people exposed to the earthquake in Piura. Food insecurity was assessed with the Household Food Insecurity Access Scale. Its association with insomnia, resilience, anxiety/depressive symptoms, and general variables were determined using generalized linear models.

Results

Food insecurity was observed in 31.1% (95% CI: 24.3-38.4) of participants. This prevalence was higher in people with mild (PR: 1.73, 95% CI: 1.12-2.70) and moderate (PR: 1.55, 95% CI: 1.09-2.22) anxiety symptoms, severe depressive symptoms (PR: 2.74, 95% CI: 1.24-6.03), and previous exposure to the El Niño Phenomenon (PR: 1.72; 95% CI: 1.06-2.81). An income higher than 5000 Peruvian soles (approximately 1300 US dollars) was associated with a lower prevalence of food insecurity (PR: 0.22, 95% CI: 0.13-0.40).

Conclusions

Overall, three out of 10 individuals experienced food insecurity after the 2021 earthquake in Piura. Food insecurity may be aggravated by mental disorders, previous exposure to the El Niño phenomenon, and the COVID-19 pandemic. Our study contributes to the field by exploring a range of variables related to food insecurity in a unique context: post-earthquake and during the COVID-19 health emergency in Peru. These findings enhance our understanding of food insecurity at the regional level and highlight the need for preventive food security programs in seismic events.

Post-Traumatic Stress Associated with Telework-Related Job Limitation in Latin America

During the pandemic, there has been evidence of work limitations during telework, which are believed to cause mental health problems. Our objective was to assess the association between perceived work limitations during telework and posttraumatic stress during the COVID-19 pandemic in Latin America. A cross-sectional study was conducted in Latin America in 2020. Exposure was measured by self-reporting using a self-perception questionnaire; the SPRINT-E questionnaire was used for outcome measurements. Generalized linear models were applied. Of 1329 participants, 15.2% (n = 202) had posttraumatic stress. In a multivariate analysis, the highest frequency of posttraumatic stress was found among those with moderate depression or more (PR = 1.29; 95% CI: 1.03-1.61), moderate or more anxiety (PR = 2.34; 95% CI: 1.61-3.41), and moderate or more stress (PR = 2.45; 95% CI: 1.46-4.12). In conclusion, there is an association between perceived work limitations during telework and posttraumatic stress in Latin American workers. For this reason, it is recommended that occupational physicians, companies, and institutions assess the frequency of posttraumatic stress and monitor the mental health of workers.

Characteristics of pediatric patients hospitalized with COVID-19 during the third wave (omicron variant) at a referral hospital in Peru

OBJECTIVES.

Motivation for the study. There are few reports on the clinical experience of the population infected with the omicron variant of COVID-19 in Latin America, particularly in pediatric population. Main findings. There was a rapid increase in the number of hospitalizations compared to previous waves, mainly due to respiratory conditions; most patients progressed favorably. Antibiotics and corticosteroids were the most used drugs. Implications. Studying the characteristics of children hospitalized during the third wave of COVID-19 in Peru may increase the knowledge of how the omicron variant affects this population group, which will allow comparisons with possible new waves or diseases.