Is systemic inflammation a missing link between periodontitis and hypertension? Results from two large population-based surveys.
Recent evidence suggests hypertension and periodontitis are closely linked but limited data is available on the nature of the association. We aimed to investigate the relationship between periodontitis and mean arterial blood pressure in a sample of otherwise systemically healthy individuals. A case-control study including 250 cases (participants with periodontitis) and 250 controls (without periodontitis) was designed from a register of clinical trials conducted between 2000 and 2018 in a university setting. Cases were age, sex, and body mass index balanced with controls. Linear, logistic regression, and mediation models were planned to test the association between various periodontal measures and arterial blood pressure. We further investigated the role of systemic inflammation assessed by hs-CRP (high-sensitivity C-reactive protein) and white cell counts. Cases presented with 3.36 mm Hg (95% CI, 0.91–5.82, P =0.007) higher mean systolic blood pressure and 2.16 mm Hg (95% CI, 0.24–4.08, P =0.027) higher diastolic blood pressure than controls. Diagnosis of periodontitis was associated with mean systolic blood pressure (β=3.46±1.25, P =0.005) and greater odds of systolic blood pressure ≥140 mm Hg (odds ratio, 2.3 [95% CI, 1.15–4.60], P =0.018) independent of common cardiovascular risk factors. Similar findings were observed when continuous measures of periodontal status were modeled against systolic blood pressure. Measures of systemic inflammation although elevated in periodontitis were not found to be mediators of the association between periodontitis and arterial blood pressure values. Periodontitis is linked to higher systolic blood pressure in otherwise healthy individuals. Promotion of periodontal and systemic health strategies in the dental and medical setting could help reduce the burden of hypertension and its complications.
Background Chest x-rays are the most commonly used type of x-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret. Several studies have reported discrepancies in chest x-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of x-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest x-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis system where a reading of the inserted chest x-ray is performed, and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses. Objective The overall objective of the study is to perform validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity, and specificity of the algorithm. Methods A prospective validation study will be carried out to compare the diagnosis of the reference radiologists for the users attending the primary care center in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. Anonymized chest x-ray images will be acquired and fed into the AI algorithm interface, which will return an automatic report. A radiologist will evaluate the same chest x-ray, and both assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the AI algorithm. Results will be represented globally and individually for each pathology using a confusion matrix and the One-vs-All methodology. Results Patient recruitment was conducted from February 7, 2022, and it is expected that data can be obtained in 5 to 6 months. In June 2022, more than 450 x-rays have been collected, so it is expected that 600 samples will be gathered in July 2022. We hope to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest x-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests. Conclusions If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety, and agility in the primary care system, while reducing the cost of unnecessary tests. International Registered Report Identifier (IRRID) PRR1-10.2196/39536
Nursing homes have accounted for a significant part of SARS-CoV-2 mortality, causing great social alarm. Using data collected from electronic medical records of 1,319,839 institutionalised and non-institutionalised persons ≥ 65 years, the present study investigated the epidemiology and differential characteristics between these two population groups. Our results showed that the form of presentation of the epidemic outbreak, as well as some risk factors, are different among the elderly institutionalised population with respect to those who are not. In addition to a twenty-fold increase in the rate of adjusted mortality among institutionalised individuals, the peak incidence was delayed by approximately three weeks. Having dementia was shown to be a risk factor for death, and, unlike the non-institutionalised group, neither obesity nor age were shown to be significantly associated with the risk of death among the institutionalised. These differential characteristics should be able to guide the actions to be taken by the health administration in the event of a similar infectious situation among institutionalised elderly people.
The percentage of older people is increasing worldwide. Loneliness and anxious–depressive states are emerging health conditions in this population group, and these conditions give rise to higher morbidity and mortality. Physical activity (PA) and social relationships have been linked to physical and mental health. The objective of this study was to evaluate whether a 4-month programme of moderate PA in a group would improve the emotional state, levels of social support, and quality of life in a sample of individuals > 64 years of age. A multicentre randomised clinical trial was designed in primary care. Ninety (90) participants were selected. After the intervention, there were positive differences between the groups, with significant improvements in the intervention group (IG) in depression, anxiety, health status perception, and social support. Walking in a group two days per week for 4 months reduced clinical depression and anxiety by 59% and 45%, respectively. The level of satisfaction was very high, and adherence was high. In conclusion, the moderate group PA programme improved clinical anxiety, depression, social support, and perceptions of health status in the patients studied.
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