Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis

Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods

Introduction

The aim of the present study was to use cluster analysis and ensemble methods to evaluate the association between quality of life, socio-demographic factors to predict nutritional risk in community-dwelling Brazilians aged 80 and over.

Methods

This cross-sectional study included 104 individuals, both sexes, from different community locations. Firstly, the participants answered the sociodemographic questionnaire, and were sampled for anthropometric data. Subsequently, the Mini-Mental State Examination (MMSE) was applied, and Mini Nutritional Assessment Questionnaire (MAN) was used to evaluate their nutritional status. Finally, quality of life (QoL) was assessed by a brief version of World Health Organizations' Quality of Life (WHOQOL-BREF) questionnaire and its older adults' version (WHOQOL-OLD).

Results

The K-means algorithm was used to identify clusters of individuals regarding quality-of-life characteristics. In addition, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms were used to predict nutritional risk. Four major clusters were derived. Although there was a higher proportion of individuals aged 80 and over with nutritional risk in cluster 2 and a lower proportion in cluster 3, there was no statistically significant association. Cluster 1 showed the highest scores for psychological, social, and environmental domains, while cluster 4 exhibited the worst scores for the social and environmental domains of WHOQOL-BREF and for autonomy, past, present, and future activities, and intimacy of WHOQOL-OLD.

Conclusion

Handgrip, household income, and MMSE were the most important predictors of nutritional. On the other hand, sex, self-reported health, and number of teeth showed the lowest levels of influence in the construction of models to evaluate nutritional risk. Taken together, there was no association between clusters based on quality-of-life domains and nutritional risk, however, predictive models can be used as a complementary tool to evaluate nutritional risk in individuals aged 80 and over.

Risk Factors for the Impairment of Ambulation in Older People Hospitalized with COVID-19: A Retrospective Cohort Study

(1) Background: Some older people hospitalized with COVID-19 have experienced reduced ambulation capacity. However, the prevalence of the impairment of ambulation capacity still needs to be established. Objective: To estimate the prevalence of, and identify the risk factors associated with, the impairment of ambulation capacity at the point of hospital discharge for older people with COVID-19. (2) Methods: A retrospective cohort study. Included are those with an age > 60 years, of either sex, hospitalized due to COVID-19. Clinical data was collected from patients' medical records. Ambulation capacity prior to COVID-19 infection was assessed through the patients' reports from their relatives. Multiple logistic regressions were performed to identify the risk factors associated with the impairment of ambulation at hospital discharge. (3) Results: Data for 429 older people hospitalized with COVID-19 were randomly collected from the medical records. Among the 56.4% who were discharged, 57.9% had reduced ambulation capacity. Factors associated with reduced ambulation capacity at discharge were a hospital stay longer than 20 days (Odds Ratio (OR): 3.5) and dependent ambulation capacity prior to COVID-19 (Odds Ratio (OR): 11.3). (4) Conclusion: More than half of the older people who survived following hospitalization due to COVID-19 had reduced ambulation capacity at hospital discharge. Impaired ambulation prior to the infection and a longer hospital stay were risks factors for reduced ambulation capacity.

Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms

The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (p < 0.05). The random forest performed well (r = 0.98, R² = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.

Ambulation capacity, age, immunosuppression, and mechanical ventilation are risk factors of in-hospital death in severe COVID-19: a cohort study

IMPORTANCE

Despite ambulation capacity being associated with a decreased level of physical activity and survival may be influenced by the functional capacity, studies have not addressed the association between ambulation capacity and death in patients hospitalized by COVID-19.

OBJECTIVE

To verify the functional, clinical, and sociodemographic risk factors associated with in-hospital death in individuals with severe COVID-19.

METHODS

It is a cohort retrospective study performed at a large tertiary hospital. Patients 18 years of age or more, of both sexes, hospitalized due to severe COVID-19 were included. Cases with dubious medical records and/or missing essential data were excluded. Patients were classified according to their ambulation capacity before the COVID-19 infection. Information regarding sociodemographic characteristics, in-hospital death, total hospital stays, Intensive Care Unit (ICU) stays, and the necessity of Mechanical Ventilation (MV) were collected from medical records and registered in a RedCap database. Multiple logistic regression analysis was used to identify possible factors associated with the in-hospital death rate.

RESULTS

Data from 1110 participants were included in the statistical analysis. The median age of the patients was 57 (46‒66) years, 58.42% (n = 590) were male, and 61.73% (n = 602) were brown or black. The case fatality rate during hospitalization was 36.0% (n = 363). In-hospital death was associated with ambulation capacity; dependent ambulators (OR = 2.3; CI 95% = 1.2-4.4) and non-functional ambulation (OR = 1.9; CI 95% = 1.1-3.3), age [older adults (OR = 3.0; CI 95% = 1.9‒4.), ICU stays (OR = 1.4; CI 95% = 1.2‒1.4), immunosuppression (OR = 5.5 CI 95% = 2.3‒13.5) and mechanical ventilation (OR = 27.5; CI 95% = 12.0-62.9).

CONCLUSION AND RELEVANCE

Decreased ambulation capacity, age, length of ICU stay, immunosuppression, and mechanical ventilation was associated with a high risk of in-hospital death due to COVID-19.