Predictors for detecting chronic respiratory diseases in community surveys: A pilot cross-sectional survey in four South and South East Asian low- and middle-income countries

Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review

BACKGROUND

Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined.

OBJECTIVE

Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice.

METHODS

The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor.

RESULTS

In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent.

CONCLUSIONS

Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.

Global Burden of Asthma, and Its Impact on Specific Subgroups: Nasal Polyps, Allergic Rhinitis, Severe Asthma, Eosinophilic Asthma

Background

The complex nature of asthma has resulted in a poor understanding of its epidemiology, particularly in low-and middle-income countries (LMIC). Clinical subgroups, such as patients with severe asthma, eosinophilic asthma, allergic rhinitis, or nasal polyps, experience additional barriers to care.

Methods

Prevalence estimates for asthma and key clinical subgroups were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 and from a targeted literature review conducted through PubMed in October of 2021. National estimates were calculated and the roles of potential explanatory factors were explored through qualitative analysis.

Results

In total, 162 publications from 69 countries were included. Across continents, asthma prevalence values ranged from 3.44% (Asia), 3.67% (Africa), 4.90% (South America), 5.69% (Europe), 8.29% (North America), to 8.33% (Oceania). Globally, of those with asthma, 26.70% had severe asthma, 30.99% had eosinophilic asthma, 48.95% had allergic rhinitis, and 7.0% to 25.40% had nasal polyps. Countries with higher air quality, income status, and healthcare access and quality reported a higher asthma prevalence.

Conclusion

Asthma prevalence values were low in LMICs, potentially indicating health system deficiencies resulting in low diagnosis and reporting. The prevalence of eosinophilic asthma and severe asthma phenotypes was high in many countries, although the prevalence estimates of all asthma subgroups were quite variable.