Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 6;23(1):491.
doi: 10.1186/s12890-023-02773-1.

Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country

Dhanusha Harshinie Punyadasa  1 , Vindya Kumarapeli  2 , Wijith Senaratne  3
Affiliations

Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country

Dhanusha Harshinie Punyadasa et al. BMC Pulm Med. .

Abstract

Background: Asthma patients experience higher rates of hospitalizations due to exacerbations leaving a considerable clinical and economic burden on the healthcare system. The use of a simple, risk prediction tool offers a low-cost mechanism to identify these high-risk asthma patients for specialized care. The study aimed to develop and validate a risk prediction model to identify high-risk asthma patients for hospitalization due to exacerbations.

Methods: Hospital-based, case-control study was carried out among 466 asthma patients aged ≥ 20 years recruited from four tertiary care hospitals in a district of Sri Lanka to identify risk factors for asthma-related hospitalizations. Patients (n = 116) hospitalized due to an exacerbation with respiratory rate > 30/min, pulse rate > 120 bpm, O2 saturation (on air) < 90% on admission, selected consecutively from medical wards; controls (n = 350;1:3 ratio) randomly selected from asthma/medical clinics. Data was collected via a pre-tested Interviewer-Administered Questionnaire (IAQ). Logistic Regression (LR) analyses were performed to develop the model with consensus from an expert panel. A second case-control study was carried out to assess the criterion validity of the new model recruiting 158 cases and 101 controls from the same hospitals. Data was collected using an IAQ based on the newly developed risk prediction model.

Results: The developed model consisted of ten predictors with an Area Under the Curve (AUC) of 0.83 (95% CI: 0.78 to 0.88, P < 0.001), sensitivity 69.0%, specificity 86.1%, positive predictive value (PPV) 88.6%, negative predictive value (NPV) 63.9%. Positive and negative likelihood ratios were 4.9 and 0.3, respectively.

Conclusions: The newly developed model was proven valid to identify adult asthma patients who are at risk of hospitalization due to exacerbations. It is recommended as a simple, low-cost tool for identifying and prioritizing high-risk asthma patients for specialized care.

Keywords: Asthma; Exacerbation; Hospitalization; Lower middle-income country; Risk prediction model; Sri Lanka; Validation.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
ROC curve for summary risk scores against the presence or absence of asthma hospitalization among study participants
See this image and copyright information in PMC

References

    1. Global Asthma Network . The Global asthma report 2018. Auckland: Global Asthma Network; 2018.
    1. Dharmage SC, Perret JL, Custovic A. Epidemiology of asthma in children and adults. Front Pediatr. 2019;7:246. doi: 10.3389/fped.2019.00246. - DOI - PMC - PubMed
    1. Medical Statistics Unit. Annual health bulletin 2019. Colombo; 2019. https://www.health.gov.lk/moh_final/english/public/elfinder/files/public.... Accessed 23 Apr 2023.
    1. Song HJ, Blake KV, Wilson DL, et al. Medical costs and productivity loss due to mild, moderate, and severe asthma in the United States. J Asthma Allergy. 2020;13:545. doi: 10.2147/JAA.S272681. - DOI - PMC - PubMed
    1. Lachman BS, Pengetnze Y. Improved asthma outcomes from use of predictive modeling as part of a system of care. J Allergy Clin Immunol. 2017;139(2):AB191. doi: 10.1016/j.jaci.2016.12.621. - DOI