Elderly and aged asthma have different characteristics: results of a multicenter study.

Background/aim: Characteristics of asthma in the elderly population is not well-known. The aim of the present study was to evaluate asthma in the elderly population, to compare disease characteristics between patients diagnosed <60 (aged asthma) and ≥60 (elderly asthma) years of age. Materials and methods: The study was a prospective, multicenter, cross-sectional type. A questionnaire was filled out to patients 60 years of age and over, that have been followed for asthma for at least 3 months. Asthma Control Test (ACT), eight-item Morisky Medication Adherence Scale (MMAS-8) was filled out, inhaler device technique was assessed. Results: A total of 399 patients were included from 17 tertiary care centers across the country. Mean age was 67.11 years and 331 (83%) were female. The age at asthma diagnosis was ≥60 in 146 (36.6%) patients. Patients diagnosed ≥60 years were older (p < 0.001), had higher education level (p < 0.001), more commonly had first-degree relative with asthma (p = 0.038), asthma related comorbidities (p = 0.009) and accompanying rhinitis/rhinosinusitis (p = 0.005), had better asthma control (p = 0.001), were using less controller medications (p = 0.014). Inhaler technique was correct in 37% of the patients with no difference in between the groups. Treatment compliance was better in elderly asthma patients (p < 0.001). In the multivariate logistic regression analysis, having well-controlled asthma (odds ratio = 1.61, CI = 1.04-2.51), and high medication adherence rate (odds ratio = 2.43, CI = 1.48-4.0) were associated with being in the elderly asthma group. Conclusion: The characteristics of asthma are different among patients aged 60 years and over which seems to be related to onset age of asthma. In our cohort, the elderly asthma patients had higher education level, and treatment adherence and asthma control was better. Patients diagnosed ≥60 years of age did not have more severe disease.

Development and validation of a simple risk scoring system for a COVID-19 diagnostic prediction mode.

Introduction: In a resource-constrained situation, a clinical risk stratification system can assist in identifying individuals who are at higher risk and should be tested for COVID-19. This study aims to find a predictive scoring model to estimate the COVID-19 diagnosis." Materials: Patients who applied to the emergency pandemic clinic between April 2020 and March 2021 were enrolled in this retrospective study. At admission, demographic characteristics, symptoms, comorbid diseases, chest computed tomography (CT), and laboratory findings were all recorded. Development and validation datasets were created. The scoring system was performed using the coefficients of the odds ratios obtained from the multivariable logistic regression analysis." Result: Among 1187 patients admitted to the hospital, the median age was 58 years old (22-96), and 52.7% were male. In a multivariable analysis, typical radiological findings (OR= 8.47, CI= 5.48-13.10, p< 0.001) and dyspnea (OR= 2.85, CI= 1.71-4.74, p< 0.001) were found to be the two important risk actors for COVID-19 diagnosis, followed by myalgia (OR= 1.80, CI= 1.08- 2.99, p= 0.023), cough (OR= 1.65, CI= 1.16-2.26, p= 0.006) and fatigue symptoms (OR= 1.57, CI= 1.06-2.30, p= 0.023). In our scoring system, dyspnea was scored as 2 points, cough as 1 point, fatigue as 1 point, myalgia as 1 point, and typical radiological findings were scored as 5 points. This scoring system had a sensitivity of 71% and a specificity of 76.3% for a cut-off value of >2, with a total score of 10 (p< 0.001). Conclusions: The predictive scoring system could accurately predict the diagnosis of COVID-19 infection, which gave clinicians a theoretical basis for devising immediate treatment options. An evaluation of the predictive efficacy of the scoring system necessitates a multi-center investigation.