Statistical models for respiratory disease diagnosis and prognosis

Prevalence and risk factors associated with allergic rhinitis in Mexican school children: Global Asthma Network Phase I

Background

The International Study of Asthma and Allergies in Childhood (ISAAC) showed a wide variability in prevalence and severity of allergic rhinitis (AR) and rhinoconjunctivitis (ARC), in addition to other atopic diseases (Asher et al, 2006).¹ The Global Asthma Network (GAN) has continued to study these conditions.

Objective

To estimate the prevalence of AR and ARC in children and adolescents in Mexico and to assess their association with different risk factors.

Methods

GAN Phase I is a cross-sectional, multicentre survey carried out in 15 centres corresponding to 14 Mexican cities throughout 2016–2019 using the validated Spanish language version of the GAN Phase I questionnaires. The questionnaires were completed by 35 780 parents of 6–7 year old primary school pupils (children) and by 41 399 adolescents, 13–14 years old.

Results

The current and cumulative prevalence of AR was higher in the adolescents (26.2–37.5%, respectively) in comparison to the children (17.9–24.9%, respectively), especially in female participants. This tendency was also observed in the current prevalence of ARC, where 15.1% of female adolescents reported nasal symptoms accompanied with itchy-watery eyes in the past year. The most important risk factors for AR and ARC were the presence of wheezing in the past 12 months, wheezing in the first year of life, the previous diagnosis of asthma and eczema symptoms. Furthermore, allergic symptoms had a negative tendency concerning altitude.

Conclusion

This is the largest AR epidemiological study ever conducted in Mexico. It shows an increase in AR prevalence, as well as significant associations with modifiable risk factors, which could help to establish recommendations to reduce the burden of this condition.

A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study

BACKGROUND

Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers.

METHODS

We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model.

RESULTS

Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women.

CONCLUSIONS

Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

Development and validation of models to predict respiratory function in persons with long-term spinal cord injury

STUDY DESIGN

Multicenter, cross-sectional study.

OBJECTIVES

To validate previously developed respiratory function prediction models for persons with long-term spinal cord injury (SCI) and if necessary develop and validate new models.

SETTING

Ten SCI rehabilitation centers.

METHODS

Five respiratory function parameters were measured in adults with chronic, traumatic, motor complete SCI (C4-T12). First, the models published in 2012 were validated using Bland-Altman plots. Then, new models were calculated using 80% of the dataset by multiple regression analysis with the candidate predictors gender, age, height, weight, time post injury (TPI), lesion level, and smoking. In a third step, the new models were validated using the other 20% of the dataset by Bland-Altman plots.

RESULTS

In total 613 participants were included. For persons with long-term SCI, the 2012 models were poorly predictive, especially for respiratory muscle strength (R2 = 0.4). Significant predictors for all respiratory function parameters in the new models (R2 = 0.7-0.8) were lesion level, gender and weight. Small effects on single outcome parameters were observed for TPI and age whereas smoking had no effect. For the new models the mean differences between measured and predicted values for respiratory muscle strength were 4.0 ± 36.0 cm H₂O and for lung function parameters -0.5 ± 1.2 L (FVC), -0.3 ± 0.9 L (FEV1) and -0.5 ± 2.0 L/s (PEF).

CONCLUSION

We did not find better models for lung function in long-term SCI but those for respiratory muscle strength showed better accuracy.

SPONSORSHIP

The content of this publication was developed under grant from Wings for Life, grant number WFL-CH-017/14.