Investigating the trajectory of post-COVID impairments: a longitudinal study in Sweden.

Introduction: Individuals recovering from COVID-19 often experience a range of post-recovery symptoms. However, the literature on post-COVID-19 symptoms reveals conflicting results, necessitating a heightened focus on longitudinal studies to comprehend the trajectory of impairments over time. Our study aimed to investigate changes in long-term impairments among individuals infected with COVID-19 and explore potential predictors influencing these changes. Methods: We conducted a web-survey targeting individuals that had been infected with COVID-19 at four time-points: T0 (baseline), T1 (three months), T2 (six months), and T3 (twelve months). The survey included contextual factors, factors related to body functions and structures, and post-COVID impairments. The longitudinal sample included 213 individuals (with a mean age of 48.92 years). Linear mixed models were employed to analyze changes in post-COVID impairments over time and identify impacting factors. Results: Findings revealed a general decline in post-COVID impairments over time, with each symptom exhibiting a dynamic pattern of fluctuations. Factors such as initial infection severity, education level, and work status were significantly associated with the levels of impairments. Discussion: The study emphasizes that post-COVID impairments are not static but exhibit variations over time. Personalized care, especially for vulnerable populations, is crucial. The results underscore the need for long-term monitoring and multidisciplinary treatment approaches. Targeted support and interventions are highlighted for individuals with severe initial infections and those in socioeconomically disadvantaged groups.

Microextraction techniques for occupational biological monitoring: Basic principles, current applications and future perspectives.

The application of green microextraction techniques (METs) is constantly being developed in different areas including pharmaceutical, forensic, food and environmental analysis. However, they are less used in biological monitoring of workers in occupational settings. Developing valid extraction methods and analytical techniques for the determination of occupational indicators plays a critical role in the management of workers' exposure to chemicals in workplaces. Microextraction techniques have become increasingly important because they are inexpensive, robust and environmentally friendly. This study aimed to provide a comprehensive review and interpret the applications of METs and novel sorbents and liquids in biological monitoring. Future perspectives and occupational indicators that METs have not yet been developed for are also discussed.

Trajectories of mental health outcomes following COVID-19 infection: a prospective longitudinal study.

BACKGROUND: The COVID-19 pandemic has triggered a global mental health crisis. Yet, we know little about the lasting effects of COVID-19 infection on mental health. This prospective longitudinal study aimed to investigate the trajectories of mental health changes in individuals infected with COVID-19 and to identify potential predictors that may influence these changes. METHODS: A web-survey that targeted individuals that had been infected with COVID-19 was used at three time-points: T0 (baseline), T1 (six months), and T2 (twelve months). The survey included demographics, questions related to COVID-19 status, previous psychiatric diagnosis, post-COVID impairments, fatigue, and standardized measures of depression, anxiety, insomnia. Linear mixed models were used to examine changes in depression, anxiety, and insomnia over time and identify factors that impacted trajectories of mental health outcomes. RESULTS: A total of 236 individuals completed assessments and was included in the longitudinal sample. The participants' age ranged between 19 and 81 years old (M = 48.71, SD = 10.74). The results revealed notable changes in mental health outcomes over time. The trajectory of depression showed significant improvement over time while the trends in anxiety and insomnia did not exhibit significant changes over time. Younger participants and individuals who experienced severe COVID-19 infection in the acute phase were identified as high-risk groups with worst mental ill-health. The main predictors of the changes in the mental health outcomes were fatigue and post-COVID impairments. CONCLUSIONS: The findings of our study suggest that mental health outcomes following COVID-19 infection exhibit a dynamic pattern over time. The study provides valuable insights into the mental health trajectory following COVID-19 infection, emphasizing the need for ongoing assessment, support, and interventions tailored to the evolving mental health needs of this population.

Low-level occupational exposure to BTEX and dyschromatopsia: a systematic review and meta-analysis.

Objectives. The present study aimed to assess whether occupational exposure to low concentrations of benzene, toluene, ethylbenzene and xylene (BTEX) is associated with color vision impairment. Methods. We queried PubMed, Scopus, Embase, Web of Science and ProQuest as the main databases, as well as gray literature such as Google Scholar. A random-effects model was used to assess relative risk. A funnel plot was created to assess publication bias. Meta-regression analysis was applied to identify variables that explain the between-study variation in the reported risk estimate. Results. An overall standardized mean difference of 0.529 (95% confidence interval [0.269, 0.788]; p < 0.0001) was obtained in the random-effects model, which corresponded to a medium-size effect. Duration and the levels of exposure to benzene, toluene and xylene were the significant predictors of the magnitude of the combined risk estimate. Chronic exposure to low levels of BTEX was associated with dyschromatopsia determined by the color confusion index. Conclusions. The impairments can occur even at exposures lower than the occupational exposure limits of BTEX. However, there are several flaws in the determination of workers' exposure, which did not allow to establish how low a level of these chemicals can cause color vision impairment.

Pulmonary function and respiratory symptoms in workers exposed to respirable silica dust: A historical cohort study.

Background: The adverse health effects of silica are still a major concern in some industries. The purpose of this study was to evaluate pulmonary function in a group of sub-radiological silicotic workers after 11 years of silica dust exposure. Methods: The study sample consisted of 381 exposed and 254 non-exposed workers. The history of pulmonary function parameters was obtained from workers' medical records. The data were collected through interviews with employees and completing questionnaires on demographic variables, detailed occupational and medical history, and respiratory symptoms. Workers' exposure to silica dust was also determined. Results: The mean frequency of workers' exposure to silica dust was 6.3 times greater than its exposure limit. All pulmonary function parameters were significantly lower in the silica-exposed workers, and the difference between the two groups was still statistically significant after adjusting the potential confounding variables. FEV1 showed the greatest reduction, and FVC and FEV1 showed a significant decreasing trend. Also the prevalence of respiratory symptoms was significantly higher in smokers than in nonsmokers among silica-exposed workers. Conclusions: Even in the absence of radiographic evidence of silicosis, exposure to high levels of silica dust is associated with reductions in pulmonary function. In the absence of radiological evidence of silicosis, progressive deterioration of FEV1 over time most likely indicates sub-radiological silicosis. The effects were associated with the severity and duration of exposure. Exposure to sub-TLV levels of silica dust may not affect pulmonary function. Smoking appears to have a synergistic effect in relatively high silica exposures.

A review of extraction methods and analytical techniques for styrene and its metabolites in biological matrices.

We reviewed the toxicokinetics of styrene to introduce reliable surrogates for the biological monitoring of styrene workers. We have also discussed the extraction techniques and analytical methods of styrene and its metabolites. Sample preparation is the main bottleneck of the analytical techniques for styrene and its metabolites. Although some microextraction methods have been developed to overcome such disadvantages, some still have limitations such as long extraction time, fiber swelling and breakage, and the cost and the limited lifetime of the fiber. Among all, microextraction by packed sorbents, coupled with HPLC with ultraviolet detection (MEPS-HPLC-UV), can be the method of choice for determining styrene metabolites. Few studies investigated unchanged styrene in breath samples. Chemical determination of styrene in exhaled breath provides new insights into organ toxicity in workers with inhalation exposures and can be considered a fascinating tool in risk assessment strategies. Taking blood samples is invasive and less accepted by workers. In contrast, breath analysis is the most attractive method for workers because breath samples are easy to collect and non-invasive, and sample collection does not require the transfer of workers to health facilities. Therefore, developing selective and sensitive methods for determining styrene in breath samples is recommended for future studies.

Deep learning for electronic health records: A comparative review of multiple deep neural architectures.

Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast paper-to-paper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the field's ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the world's largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such data-hungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the "goodness" of deep-learning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one. METHODS AND FINDINGS: We used longitudinal data from linked electronic health records of 4.6 million patients aged 18-100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances. CONCLUSIONS: The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.

Elevated blood pressure and risk of aortic valve disease: a cohort analysis of 5.4 million UK adults.

Aims: To test two related hypotheses that elevated blood pressure (BP) is a risk factor for aortic valve stenosis (AS) or regurgitation (AR). Methods and results: In this cohort study of 5.4 million UK patients with no known cardiovascular disease or aortic valve disease at baseline, we investigated the relationship between BP and risk of incident AS and AR using multivariable-adjusted Cox regression models. Over a median follow-up of 9.2 years, 20 680 patients (0.38%) were diagnosed with AS and 6440 (0.12%) patients with AR. Systolic BP (SBP) was continuously related to the risk of AS and AR with no evidence of a nadir down to 115 mmHg. Each 20 mmHg increment in SBP was associated with a 41% higher risk of AS (hazard ratio 1.41, 95% confidence interval 1.38-1.45) and a 38% higher risk of AR (1.38, 1.31-1.45). Associations were stronger in younger patients but with no strong evidence for interaction by gender or body mass index. Each 10 mmHg increment in diastolic BP was associated with a 24% higher risk of AS (1.24, 1.19-1.29) but not AR (1.04, 0.97-1.11). Each 15 mmHg increment in pulse pressure was associated with a 46% greater risk of AS (1.46, 1.42-1.50) and a 53% higher risk of AR (1.53, 1.45-1.62). Conclusion: Long-term exposure to elevated BP across its whole spectrum was associated with increased risk of AS and AR. The possible causal nature of the observed associations warrants further investigation.

Patterns and temporal trends of comorbidity among adult patients with incident cardiovascular disease in the UK between 2000 and 2014: A population-based cohort study.

BACKGROUND: Multimorbidity in people with cardiovascular disease (CVD) is common, but large-scale contemporary reports of patterns and trends in patients with incident CVD are limited. We investigated the burden of comorbidities in patients with incident CVD, how it changed between 2000 and 2014, and how it varied by age, sex, and socioeconomic status (SES). METHODS AND FINDINGS: We used the UK Clinical Practice Research Datalink with linkage to Hospital Episode Statistics, a population-based dataset from 674 UK general practices covering approximately 7% of the current UK population. We estimated crude and age/sex-standardised (to the 2013 European Standard Population) prevalence and 95% confidence intervals for 56 major comorbidities in individuals with incident non-fatal CVD. We further assessed temporal trends and patterns by age, sex, and SES groups, between 2000 and 2014. Among a total of 4,198,039 people aged 16 to 113 years, 229,205 incident cases of non-fatal CVD, defined as first diagnosis of ischaemic heart disease, stroke, or transient ischaemic attack, were identified. Although the age/sex-standardised incidence of CVD decreased by 34% between 2000 to 2014, the proportion of CVD patients with higher numbers of comorbidities increased. The prevalence of having 5 or more comorbidities increased 4-fold, rising from 6.3% (95% CI 5.6%-17.0%) in 2000 to 24.3% (22.1%-34.8%) in 2014 in age/sex-standardised models. The most common comorbidities in age/sex-standardised models were hypertension (28.9% [95% CI 27.7%-31.4%]), depression (23.0% [21.3%-26.0%]), arthritis (20.9% [19.5%-23.5%]), asthma (17.7% [15.8%-20.8%]), and anxiety (15.0% [13.7%-17.6%]). Cardiometabolic conditions and arthritis were highly prevalent among patients aged over 40 years, and mental illnesses were highly prevalent in patients aged 30-59 years. The age-standardised prevalence of having 5 or more comorbidities was 19.1% (95% CI 17.2%-22.7%) in women and 12.5% (12.0%-13.9%) in men, and women had twice the age-standardised prevalence of depression (31.1% [28.3%-35.5%] versus 15.0% [14.3%-16.5%]) and anxiety (19.6% [17.6%-23.3%] versus 10.4% [9.8%-11.8%]). The prevalence of depression was 46% higher in the most deprived fifth of SES compared with the least deprived fifth (age/sex-standardised prevalence of 38.4% [31.2%-62.0%] versus 26.3% [23.1%-34.5%], respectively). This is a descriptive study of routine electronic health records in the UK, which might underestimate the true prevalence of diseases. CONCLUSIONS: The burden of multimorbidity and comorbidity in patients with incident non-fatal CVD increased between 2000 and 2014. On average, older patients, women, and socioeconomically deprived groups had higher numbers of comorbidities, but the type of comorbidities varied by age and sex. Cardiometabolic conditions contributed substantially to the burden, but 4 out of the 10 top comorbidities were non-cardiometabolic. The current single-disease paradigm in CVD management needs to broaden and incorporate the large and increasing burden of comorbidities.