Older adults are frequently exposed to medicines with systemic anticholinergic properties, which are linked to increased risk of negative health outcomes. The association between systemic anticholinergics and lung function has not been reported. The aim of this study was to investigate if exposure to systemic anticholinergics influences lung function in older adults. Participants of the southernmost centres of the Swedish National study on Aging and Care (SNAC) were followed from 2001 to 2021. In total, 2936 subjects (2253 from Good Aging in Skåne and 683 from SNAC-B) were included. An extensive medical examination including spirometry assessments was performed during the study visits. The systemic anticholinergic burden was described using the anticholinergic cognitive burden scale. The effect of new use of systemic anticholinergics on the annual change in forced expiratory volume (FEV1s) was estimated using mixed models. During follow-up, 802 (27.3%) participants were exposed to at least one systemic anticholinergic medicine. On average, the FEV1s of participants without systemic anticholinergic exposure decreased 37.2 ml/year (95% CI [33.8; 40.6]) while participants with low and high exposure lose 47.2 ml/year (95% CI [42.4; 52.0]) and 43.7 ml/year (95% CI [25.4; 62.0]). A novel association between new use of medicines with systemic anticholinergic properties and accelerated decrease in lung function in older adults was found. The accelerated decrease is comparable to that observed in smokers. Studies are needed to further explore this potential side effect of systemic anticholinergics. © The Author(s) 2024.

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia.

Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency.

Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535.

Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

Background

Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.

Objective

The proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset.

Methods

Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures.

Results

The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order.

Conclusion

This study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis.

This contribution corrects cost data from our previously published version in which the 6-month cost data was not censored at baseline and 6-month survey dates. Consequently, the average costs for persons with mild cognitive impairment (PwMCI) and their informal caregivers include costs that occurred outside the initial 6-month period for both intervention and control groups. In this erratum, we have repeated the analysis after appropriately censoring the costs. The results led to numerical differences. However, as both the intervention and control groups have been treated exactly the same, the differences between groups remain insignificant and the general interpretation of the results stand as presented in the original publication. The interpretation of the results in terms of cost-effectiveness has changed for informal caregivers, shifting from "dominant" to "not cost-effective", and for dyads, shifting from "less costly and less effective" to "more costly and less effective". Consequently, ICER and CEAC curves have also changed (see Corrected Fig. 1). However, these changes did not affect the conclusion of the article. 

Background: Previous phase 3 studies showed that the AS01E-adjuvanted respiratory syncytial virus (RSV) prefusion F protein-based vaccine for older adults (RSVPreF3 OA) is well tolerated and efficacious in preventing RSV-associated lower respiratory tract disease in adults ≥ 60 years of age. This study evaluated lot-to-lot immunogenicity consistency, reactogenicity, and safety of three RSVPreF3 OA lots. Methods: This phase 3, multicenter, double-blind study randomized (1:1:1) participants ≥ 60 years of age to receive one of three RSVPreF3 OA lots. Serum RSVPreF3-binding immunoglobulin G (IgG) concentration was assessed at baseline and 30 days post-vaccination. Lot-to-lot consistency was demonstrated if the two-sided 95 % confidence intervals (CIs) of the RSVPreF3-binding IgG geometric mean concentration (GMC) ratios between each lot pair at 30 days post-vaccination were within 0.67 and 1.50. Solicited adverse events (AEs) within four days, unsolicited AEs within 30 days, and serious AEs (SAEs) and potential immune-mediated diseases within six months post-vaccination were recorded. Results: A total of 757 participants received RSVPreF3 OA, of whom 708 were included in the per-protocol set (234, 237, and 237 participants for each lot). Lot-to-lot consistency was demonstrated: GMC ratios were 1.06 (95 % CI: 0.94–1.21), 0.92 (0.81–1.04), and 0.87 (0.77–0.99) between the lot pairs (lot 1/2; 1/3; 2/3). For the three lots, the RSVPreF3-binding IgG concentration increased 11.84-, 11.29-, and 12.46-fold post-vaccination compared to baseline. The reporting rates of solicited and unsolicited AEs, SAEs, and potential immune-mediated diseases were balanced between lots. Twenty-one participants reported SAEs; one of these–a case of atrial fibrillation–was considered by the investigator as vaccine-related. SAEs with a fatal outcome were reported for four participants, none of which were considered by the investigator as vaccine-related. Conclusion: This study demonstrated lot-to-lot immunogenicity consistency of three RSVPreF3 OA vaccine lots and indicated that the vaccine had an acceptable safety profile. ClinicalTrials.gov: NCT05059301. © 2024 GSK

Inadequate sleep in older adults is linked to health issues such as frailty, cognitive impairment, and cardiovascular disorders. Maintaining regular sleep patterns is important for healthy aging, making effective sleep monitoring essential. While polysomnography (PSG) is the gold standard for diagnosing sleep disorders, its regular use in home settings is limited. Alternative objective monitoring methods in the home can offer insights into natural sleep patterns and factors affecting them without the limitations of PSG.

This scoping review aims to examine current technologies, sensors, and sleep parameters used for home-based sleep monitoring in older adults. It also aims to explore various predictors and outcomes associated with sleep to understand the factors of sleep monitoring at home. 

We identified 54 relevant articles using PubMed, Scopus, Web of Science, and an AI tool (Research Rabbit), with 48 studies using wearable technologies and eight studies using non-wearable technologies. Further, six types of sensors were utilized. The most common technology employed was actigraphy wearables, while ballistocardiography and electroencephalography were less common. The most frequent objective parameters of sleep measured were Total Sleep Time (TST), Wakeup After Sleep Onset (WASO), and Sleep Efficiency (SE), with only six studies evaluating sleep architecture in terms of sleep stages. Additionally, six categories of predictors and outcomes associated with sleep were analyzed, including Health-related, Environmental, Interventional, Behavioral, Time and Place, and Social associations. These associations correlate with TST, WASO, and SE and include in-bed behaviors, exterior housing conditions, aerobic exercise, living place, relationship status, and seasonal thermal environments.

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.

Objective: This study investigated oral health status in 60-year-old individuals over 12 years.

Materials and Methods: Data were obtained from The Swedish National Study on Aging and Care (SNAC). One hundred nineteen 60-year-old individuals (48% females) underwent a clinical and radiographic baseline examination (2001–2003) and follow-up examination in 2013–2015. For statistical analyses, paired t-tests and McNemar's test were performed. Statistical significance was determined at p < 0.05.

Results: At the 12-year follow-up, the mean number of teeth and the proportion of individuals having ≥ 20 teeth decreased (p < 0.001). The mean number of teeth with buccal/lingual and approximal caries lesions increased (p < 0.029 and p < 0.031). Individuals with a distance from the cement-enamel junction to the bone of ≥ 5 mm increased in total (p < 0.002) and in males (p < 0.006). The prevalence of gingivitis increased in total (p < 0.001). The prevalence of periodontitis showed a significant increase in total (p < 0.043) and in females (p < 0.039).

Conclusion: The present study indicates that oral health status in 60-year-old individuals deteriorates over 12 years. However, the deteriorations were minor in terms of tooth loss, caries lesions, and changes in periodontal status. © 2024 The Author(s). International Journal of Dental Hygiene published by John Wiley & Sons Ltd.

Background: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.

Methods: This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care — Blekinge (). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.

Results: Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.

Conclusion: There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.

Objective: This study examined the prevalence and long-term survival of atrial fibrillation (AF) in the older population.

Methods: Data was recruited from the longitudinal SNAC study from baseline (2001-2004) for up to 10 years.

Results: The population comprised 6,904 persons (59% women) (mean age 73.9 years). The prevalence of AF was 4.9% and increased with age. The hazard ratio (HR) for death in those with AF at baseline was 1.29 during the 10-year observation period. Cox regression analysis in persons with AF (n = 341) showed that men had a higher HR for death (1.57). CHA2DS2-VASc scores were significantly associated with death within 10 years (HR 1.29/score). Any form of anticoagulant use was reported in 146 (42.8%) and was significantly associated with survival (p = .031).

Conclusions: The prevalence of AF in the general population was almost 5%, and it shortened life expectancy by nearly 2.4 years over a 10-year period. Despite the proven efficacy of OAC therapies, our results demonstrate that AF continues to be associated with increased mortality, especially among men, and that many older people are at high risk of developing a stroke because they do not receive appropriate anticoagulant therapy. These results emphasize the need for improved preventive and therapeutic modalities.