NPJ Primary Care Respiratory Medicine最新文献

Obstructive sleep apnea (OSA) is a well-established risk factor for hypertension, with sleep duration being a modifiable factor influencing this risk. However, sleep misperception among OSA patients makes it unclear how subjective and objective sleep duration are associated with the prevalence and incidence of hypertension in this population. This study aims to examine these associations using data from the Sleep Heart Health Study cohort. Participants with OSA (apnea-hypopnea index ≥ 15 events/hour) were categorized based on objective sleep duration from polysomnography (PSGTST) and subjective sleep duration (morning-reported sleep time, AMTST; habitual sleep time, HABTST). Hypertension prevalence was assessed at baseline, while hypertension incidence was evaluated during a five-year follow-up. Multivariable logistic regression and Poisson log-link models were employed to explore the association between sleep duration and hypertension risk, with restricted cubic splines used to assess nonlinear trends. Among 2574 participants with OSA, 1263 had hypertension at baseline. Over 5.25 years, 376 of 1001 patients without baseline hypertension developed hypertension. Shorter PSGTST was linearly associated with higher hypertension prevalence (p = 0.009) and incidence (p = 0.024). HABTST showed a U-shaped relationship with hypertension prevalence, while AMTST was not significantly associated with either outcome. In patients with OSA, objective sleep duration is linearly and inversely associated with both the prevalence and incidence of hypertension, showing stronger and more consistent associations than subjective sleep duration measures. These findings highlight the value of incorporating objective sleep assessment in evaluating hypertension risk in this population.

Chronic respiratory diseases (CRD) are major contributors to mortality. The "obesity paradox" suggests that higher body mass index (BMI) may confer survival benefits in CRD patients. This study investigates the association between BMI and mortality risk in CRD patients, focusing on the mediating role of the triglyceride-glucose (TyG) index. A cross-sectional analysis of 7689 participants with CRD was conducted. Participants were categorized by BMI into <25.0, 25.0-29.9, 30.0-34.9, 35.0-39.9, and ≥40 kg/m². Outcomes included all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality. Cox regression models assessed associations, and mediation analysis evaluated the role of the TyG index. Among 7689 CRD patients, higher BMI was associated with lower all-cause mortality (HR for BMI 25.0-29.9: 0.81, 95% CI 0.70-0.94; HR for BMI 30.0-34.9: 0.72, 95% CI 0.61-0.85; HR for BMI 35.0-39.9: 0.72, 95% CI 0.59-0.88; HR for BMI ≥ 40: 0.82, 95% CI 0.66-1.02) and non-CVD mortality (HR for BMI 25.0-29.9: 0.77, 95% CI 0.65-0.91; HR for BMI 30.0-34.9: 0.65, 95% CI 0.54-0.79; HR for BMI 35.0-39.9: 0.66, 95% CI 0.52-0.83; HR for BMI ≥ 40: 0.69, 95% CI 0.53-0.89), but not CVD mortality. The TyG index mediated a significant proportion of the association between BMI and mortality (mediation effects: -22.39 to -18.49%). Kaplan-Meier survival curves and restricted cubic spline regression further illustrated the significant associations between BMI and all-cause mortality and non-CVD mortality, while no significant association was observed for CVD mortality. Higher BMI is associated with lower mortality risk in CRD patients, particularly for non-CVD causes, mediated by the TyG index. This highlights the potential role of insulin resistance in the "obesity paradox" and suggests that metabolic health interventions may improve outcomes in CRD.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-management, and support clinical decision making. Longitudinal Scottish primary care data for 21,250 asthma patients were used to predict the risk of asthma attacks in the following year. A selection of machine learning algorithms (i.e., Naïve Bayes Classifier, Logistic Regression, Random Forests, and Extreme Gradient Boosting), hyperparameters, training data enrichment methods were explored, and validated in a random unseen data partition. Our final Logistic Regression model achieved the best performance when no training data enrichment was applied. Around 1 in 3 (36.2%) predicted high-risk patients had an attack within one year of consultation, compared to approximately 1 in 16 in the predicted low-risk group (6.7%). The model was well calibrated, with a calibration slope of 1.02 and an intercept of 0.004, and the Area under the Curve was 0.75. This model has the potential to increase the efficiency of routine asthma care by creating new personalized care pathways mapped to predicted risk of asthma attacks, such as priority ranking patients for scheduled consultations and interventions. Furthermore, it could be used to educate patients about their individual risk and risk factors, and promote healthier lifestyle changes, use of self-management plans, and early emergency care seeking following rapid symptom deterioration.

Patients seen in general practices can achieve improved asthma control with better identification of factors that contribute to uncontrolled asthma. Information is lacking on the proportion of patients with uncontrolled asthma, associated patient characteristics, and opportunities to improve management. The objectives of this study were to determine the proportion of general practice patients with uncontrolled asthma, as assessed during a regular consultation with the AsthmaOptimiser digital tool, identify the opportunities for improved management, and to evaluate the usability of this tool which is based on treatment recommendations from GINA. The CAPTURE study was a non-interventional, prospective, observational study of the AsthmaOptimiser in general practice settings in the Netherlands. Patients were at least 18 years of age with an asthma diagnosis. A total of 34 Dutch general practitioners or nurse practitioners participated in the study and planned to use the AsthmaOptimiser with 5 to 10 adult patients per practice. Interviews were conducted to gather information from practitioners about the tool's usability, its content, and areas for improvement. Of the 220 patients enrolled, 60% had uncontrolled asthma, of whom 64% had opportunities for management improvement that could be initiated during a primary care visit. Specialist referrals were advisable according to the AsthmaOptimiser in 45 patients with uncontrolled asthma. Practitioners reported that the AsthmaOptimiser was an added value and had suggestions on how to improve the tool. In Dutch general practices, the AsthmaOptimiser helped general practitioners identify opportunities for improved disease management by addressing poor disease control. Overall, the general practitioners found the AsthmaOptimiser easy to use and a good addition to asthma consultations.

We investigated healthcare avoidance during the first COVID-19 wave in a Dutch region with high infection rates. A mixed-method, multiphase study used (1) primary care electronic health records to identify patients, (2) questionnaires to capture patients with unreported COVID-19 symptoms, and (3) interviews om care avoidance. Additionally, a natural language model estimated COVID-19 incidence from routine care data. Of 2361 respondents (39% response rate), 535 (23%) reported COVID-19 symptoms; 180 sought help, mainly from GPs. Care-seeking rates did not differ significantly between those with or without relatives who experienced severe illness or death before their own illness (p = 0.270). Interviews showed the main barriers were feeling not ill enough and concerns about an overstressed healthcare system, especially GPs. Only a third of participants with symptoms sought help, mostly from GPs. Serious illness or death of loved ones had no significant impact. Findings highlight the need for clear communication and accessible healthcare, including telemedicine, for future pandemics.

Past epidemiological studies demonstrated mixed findings on the association between diabetes and lung cancer. Given the possible links between diabetes, smoking, and respiratory diseases, this study aims to examine the interaction patterns among factors associated with the risk of lung cancer among diabetes patients. A territory-wide retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. Conditional inference survival tree was applied to examine the interaction patterns among factors associated with the risk of lung cancer. A total of 385,521 patients were included. During a median follow-up of 6.2 years, 3395 developed lung cancer. Age emerged as primary factor in differentiating the risk of lung cancer. Conditional on age ( ≤ 64 vs >64 years), smoking appeared as subsequent dominant risk factor within each subpopulation. Among old smokers aged >64 years characterized by long duration of diabetes (median: 6-8 years), chronic obstructive pulmonary disease (COPD) emerged as key risk factor. Six distinct subgroups of diabetes patients with different risk levels of lung cancer according to age, smoking, metformin use, and COPD status were identified. Findings of the study suggest the interaction patterns among age, smoking, and COPD on the risk of lung cancer among diabetes patients, providing targets for public health interventions.

An infection of SARS-CoV-1, the causative agent of Severe Acute Respiratory Syndrome (SARS), may be followed by long-term clinical sequala. We hypothesized a greater 20-year multimorbidity incidence in people hospitalized for SARS-CoV-1 infection than those for influenza during similar periods. We conducted a retrospective cohort study using a territory-wide public healthcare database in Hong Kong. All patients aged ≥15 hospitalized for SARS in 2003 or influenza in 2002 or 2004 with no more than one of 30 listed chronic disease were included. Demographics, clinical history, and medication use were adjusted for in the inverse-probability-of-treatment-weighted Poisson regression analyses. We identified 1255 hospitalizations for SARS-CoV-1 infection and 687 hospitalizations for influenza. Overall crude multimorbidity incident rates were 1.5 per 100 person-years among SARS patients and 5.6 among influenza patients. Adjusted multimorbidity incidence rate ratio (IRR) was estimated at 0.78 [95% confidence interval (CI), 0.70-0.86) for SARS patients compared with influenza patients. Analysis by follow-up period shows a potentially greater risk among SARS patients in the first year of follow-up (IRR 1.33, 95% CI 0.97-1.84), with the risk in influenza patients increasing in subsequent years. Subgroup analyses by age and sex showed consistent results with the main analysis that SARS-CoV-1 infection was not followed by a higher incidence of multimorbidity than influenza. Notable differences in the patterns of multimorbidity were identified between the two arms. To conclude, we found no evidence of a higher multimorbidity incidence after hospitalization for SARS than for influenza over the long-term.

Underdiagnosis and overdiagnosis commonly occur in Chronic Obstructive Pulmonary Disease (COPD) patients. We assessed diagnostic accuracy, clinical characteristics, healthcare utilization, and care plan registration for patients undergoing primary care. We conducted a cross-sectional, population-based study using a health record registry from four primary healthcare centers in Catalonia (Spain) for patients aged ≥15 years. The variables included sociodemographic characteristics, dyspnea, comorbidities, spirometry results, treatments, and healthcare use. Logistic regression models were used to analyze differences between patients with and without airflow limitation, and ordinal logistic regression models were used to examine the association between disease severity and healthcare use. Among the 2610 patients, 54% had spirometry data, 29.5% had confirmed airflow obstruction, and 24% were overdiagnosed according to the GOLD criteria. Patients without airflow obstruction were younger (OR: 0.98, 95% CI: 0.96-0.99) and more likely to be current smokers (OR: 1.44, 95% CI: 1.13-1.84). Airflow obstruction severity was significantly associated with increased use of emergency home ambulance use (OR: 1.7, 95% CI: 1.23-2.35), emergency department visits (OR: 1.48, 95% CI: 1.11-1.99), and hospital admission (OR: 1.8, 95% CI: 1.32-2.47), but not primary care visits and follow-up frequency. COPD is frequently overdiagnosed in primary healthcare settings. The severity of airflow obstruction is associated with increased healthcare utilization, including hospital admissions. Improved diagnostic accuracy and management may enhance COPD care and reduce healthcare costs.