Asthma: Algorithm to Identify Primary Care Patients at Risk of Crisis

Dawn O'Shea

October 19, 2021

A research team led by the University of East Anglia has used routinely collected primary care data to identify patients at high risk of asthma-related crisis events, defined as A&E attendance, hospitalisation or death.

In a study reported in British Journal of General Practice, the team applied multivariable logistic regression to a data set of 61,861 people with asthma across England and Scotland using the Clinical Practice Research Datalink.

External validation was performed using the Secure Anonymised Information Linkage Databank of 174,240 patients from Wales.

For the development of the algorithm, the outcome was defined as one or more hospitalisation(s) within 12 months. For the validation of the algorithm, it was defined as a crisis event that comprised an asthma-related hospitalisation, A&E attendance or death within a 12-month period.

Univariate logistic regression models were used to identify baseline measures of disease severity, patient demographics and comorbidities predictive of one or more future outcome event. Variables showing an association (P<.05) with an asthma exacerbation resulting in hospital admission in univariable analyses were entered into a multivariable model that was reduced to produce a final list of predictors of hospital admission. Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking and blood eosinophilia.

The final model was used to create at-risk scores, indicating the risk of an asthma-related crisis event for each patient in the data set.

The algorithm demonstrated receiver operating characteristic of 0.72 in the derivation and 0.71 in the validation cohorts.

Using the top 7% of the score as a cut-off, the algorithm correctly identified 28.5% of the asthma population most at risk and 93.3% of those not at risk.

Based on the findings, the authors deduced that a practice can expect a crisis event to occur in 6% of the group that is at risk compared with 1.1% of the rest of the population with asthma. Eighteen people would need to be followed to identify one admission.

The algorithm can identify people who are at a five-fold increased risk (absolute difference of 5%) of an asthma-related crisis event compared with those not at risk.

Although risk factors for asthma attacks are known and there are other algorithms, they frequently require knowledge about patient characteristics such as adherence to medicine or asthma symptoms. This is time consuming and has to be done individually, but the algorithm detailed in this study can identify patients at risk automatically using routinely collected data and does not require patients to attend a consultation.

The authors say the tool has the potential to save clinicians’ time and provide accurate real-time assessments of patients’ risk and bypasses the dangers of inverse care associated with poor attendance at appointments.

They say it could be used to generate alerts or prompts to identify patients at high risk of asthma crisis events so care can be targeted appropriately.

The algorithm is currently being used in a study to validate the role of at-risk asthma registers in primary care (ARRISA-UK).

This article originally appeared on Univadis, part of the Medscape Professional Network.

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