Aging During the Pandemic: Untangling the Complexities of COVID-19 and Geriatric Care

Global burden of disease for musculoskeletal disorders in all age groups, from 2024 to 2050, and a bibliometric-based survey of the status of research in geriatrics, geriatric orthopedics, and geriatric orthopedic diseases

BACKGROUND

Orthopaedic diseases in the elderly pose a significant disease burden, and the number of research papers on the subject is increasing every year.

METHODS

The GBD database was used to analyze the global disease burden of musculoskeletal disorders in all age groups from 2024 to 2050. The source for bibliometrics was the WoSCC SCI-E database.

RESULTS

Global disability-adjusted life years (DALYs) for musculoskeletal disorders by age were dominated by those aged 70 years and older, but the rate of change in DALYs between 2024 and 2050 was approximately ± 1%. We performed a descriptive analysis of 164,521 geriatrics articles, and 7155 geriatric orthopedics and geriatric orthopedic articles, and performed clustering, co-citation, collaborative network, and burst citation analyses based on Citespace and VOSviewer. Seven clustering tags containing hot content and 26 burst citation keywords containing hot content were finally targeted.

CONCLUSION

DALY in older adults over 70 years of age accounts for a significant portion of the disease burden of musculoskeletal disorders. Possible future research hotspots in geriatric orthopedics and geriatric bone diseases include three directions: (1) novel clinical procedures and postoperative management (2) various comorbidities caused by SARS-CoV-2 infections and other pathogens; and (3) effectiveness of Stem Cell Therapy in Clinical Applications and Biological Mechanisms of Stem Cell Therapy.

COVID-19 and social distancing: pandemic has altered social relationships and contacts in older adults over 4 years

Introduction

Social isolation is a main risk factor for loneliness, health issues and psychological diseases. With its restriction measures, the coronavirus pandemic has led to an objective reduction in meaningful interactions, communication, and social contacts in general (social isolation). However, it has been shown that older adults cope differently with social isolation. Therefore, the aim of the present study was to investigate the changes of social contacts of older adults over the pandemic period of 4 years.

Methods

For this purpose, N = 175 older adults (M age = 72.60, SD age = 6.12 years, Mdn age = 72, Range: 60-87 years) were asked at 3 time points (2019, 2021, 2023) with how many people they had contact in the reference month (May, November). In addition to the number of contacts, participants were also asked about the type of the relationship (e.g., family, friends, neighbors), the type of contact (e.g., telephone, video conference and/or by written messages) and the emotional closeness (close, medium, low). We used an ego-centered "social network" circle to measure social contacts of older adults before, during and after the pandemic. The data collection was limited by the changing corona restrictions.

Results

Results indicate that behavior in social contacts essentially depends on age, gender, and level of depression. We found a clear temporal drop in social contacts independently of age and gender during the pandemic. After the pandemic close contacts did not recover to prepandemic level. Especially, Young-Old (<72 years) recovered less in terms of the number of social contacts than the Old-Old (≥72 years).

Discussion

Our study, thus, provides longitudinal insights into the course of social contacts and suggests that social isolation may have more negative and long-term impact on close contacts, which need further clarification and temporal extension.

AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19

BACKGROUND

The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises.

METHODS

AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January-September 2020).

RESULTS

For discharge outcomes, Light Gradient Boosted models achieved an F1 score of 0.9644, while for mortality 0.7545 was reached. A Generalized Linear Model blender achieved an F1 score of 0.9884 for "acute or emergency" cases, and an average blender reached 0.923 for COVID-19 cases. Older age, specific hospitals, and oncology wards were less associated with improved recovery rates, while mortality was linked to abnormal lab results and cardiovascular/respiratory diseases. Patients admitted without referral, or patients in hospitals in the central region and the capital region of Romania were more likely to be acute cases. Finally, counties such as Argeş (South-Muntenia) and Brașov (Center) showed higher COVID-19 infection rates regardless of age.

CONCLUSIONS

AutoML provided valuable insights into patient outcomes, highlighting variations in care and the need for targeted health strategies for both COVID-19 and other health challenges.

Lung Ultrasound Score as a Predictor of Failure to Wean COVID-19 Elderly Patients off Mechanical Ventilation: A Prospective Observational Study

Background

The lung ultrasound score was developed for rapidly assessing the extent of lung ventilation, and it can predict failure to wean various types of patients off mechanical ventilation. Whether it is also effective for COVID-19 patients is unclear.

Methods

This single-center, prospective, observational study was conducted to assess the ability of the 12-region lung ultrasound score to predict failure to wean COVID-19 patients off ventilation. In parallel, we assessed whether right hemidiaphragmatic excursion or previously published predictors of weaning failure can apply to these patients. Predictive ability was assessed in terms of the area under the receiver operating characteristic curve (AUC).

Results

The mean age of the 35 patients in the study was (75 ± 9) years and 12 patients (37%) could not be weaned off mechanical ventilation. The lung ultrasound score predicted these failures with an AUC of 0.885 (95% CI 0.770-0.999, p < 0.001), and a threshold score of 10 provided specificity of 72.7% and sensitivity of 92.3%. AUCs were lower for previously published predictors of weaning failure, and right hemidiaphragmatic excursion did not differ significantly between the two groups.

Conclusion

The lung ultrasound score can accurately predict failure to wean critically ill COVID-19 patients off mechanical ventilation, whereas assessment of right hemidiaphragmatic excursion does not appear helpful in this regard.

Trial Registration

https://clinicaltrials.gov/ct2/show/NCT05706441.