Investigation of house dust mite induced allergy using logistic regression in West Bengal, India

Background: The diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma). Methods: A forward-step logis...

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Main Authors: Priti Mondal, Debarati Dey, Nimai Chandra Saha, Saibal Moitra, Goutam Kumar Saha, Srijit Bhattacharya, Sanjoy Podder
Format: Article
Language:English
Published: Elsevier 2019-12-01
Series:World Allergy Organization Journal
Online Access:http://www.sciencedirect.com/science/article/pii/S193945511931244X
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spelling doaj-e529fcd469c74f6083d7ebe193b2b8852020-11-25T01:56:24ZengElsevierWorld Allergy Organization Journal1939-45512019-12-011212Investigation of house dust mite induced allergy using logistic regression in West Bengal, IndiaPriti Mondal0Debarati Dey1Nimai Chandra Saha2Saibal Moitra3Goutam Kumar Saha4Srijit Bhattacharya5Sanjoy Podder6Allergology and Applied Entomology Research Laboratory, Post Graduate Department of Zoology, Barasat Government College, Kolkata, 700124, West Bengal, IndiaDepartment of Zoology, University of Calcutta, Kolkata, 700019, West Bengal, IndiaVice-Chancellor, University of Burdwan, Burdwan, 713104, West Bengal, IndiaAllergy and Asthma Research Centre, Kolkata, West Bengal, IndiaDepartment of Zoology, University of Calcutta, Kolkata, 700019, West Bengal, IndiaPost Graduate Department of Physics, Barasat Government College, Kolkata, 700124, West Bengal, IndiaAllergology and Applied Entomology Research Laboratory, Post Graduate Department of Zoology, Barasat Government College, Kolkata, 700124, West Bengal, India; Corresponding author. Post Graduate Department of Zoology, Barasat Government College, 10 K.N.C Road, Barasat, Kolkata, West Bengal, PIN-700124, India.Background: The diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma). Methods: A forward-step logistic regression model was developed using a data set of 537 patients of West Bengal, India consisting of clinical variables (SPT based on 6 allergens of house dust and house dust mites, total IgE) and demographic characteristics (age, sex, house conditions). The output probability was estimated from the allergic symptoms shown by the patients. We finally prospectively validated a data set of 600 patients. Results: The gradual inclusion of the variables increased the correlation between observed and predicted probabilities (correlation coefficient (r2) = 0.97). The model development using group-1 showed an accuracy rate of 99%, sensitivity and specificity of 99.7% and 88.6% respectively and the area under the receiver operating characteristics (ROC) curve (AUC) of 99%. The corresponding numbers for the validation of our model with group-2 were 87%, 95.6% and 66% and 86% respectively. The model predicted the probability of symptoms better than SPTs in combination (accuracy rate 0.76–0.80), especially in lower risk cases (probability< 0.8) that are highly difficult to diagnose. Conclusion: This is perhaps the first attempt to model the outcome of HDM allergy in terms of symptoms, which could open up an alternative but highly efficient way for accurate diagnosis of HDM allergy enhancing the efficiency of immunotherapy. Keywords: Asthma, House dust mite allergy, Logistic regression model, ROC, SPThttp://www.sciencedirect.com/science/article/pii/S193945511931244X
collection DOAJ
language English
format Article
sources DOAJ
author Priti Mondal
Debarati Dey
Nimai Chandra Saha
Saibal Moitra
Goutam Kumar Saha
Srijit Bhattacharya
Sanjoy Podder
spellingShingle Priti Mondal
Debarati Dey
Nimai Chandra Saha
Saibal Moitra
Goutam Kumar Saha
Srijit Bhattacharya
Sanjoy Podder
Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
World Allergy Organization Journal
author_facet Priti Mondal
Debarati Dey
Nimai Chandra Saha
Saibal Moitra
Goutam Kumar Saha
Srijit Bhattacharya
Sanjoy Podder
author_sort Priti Mondal
title Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
title_short Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
title_full Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
title_fullStr Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
title_full_unstemmed Investigation of house dust mite induced allergy using logistic regression in West Bengal, India
title_sort investigation of house dust mite induced allergy using logistic regression in west bengal, india
publisher Elsevier
series World Allergy Organization Journal
issn 1939-4551
publishDate 2019-12-01
description Background: The diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma). Methods: A forward-step logistic regression model was developed using a data set of 537 patients of West Bengal, India consisting of clinical variables (SPT based on 6 allergens of house dust and house dust mites, total IgE) and demographic characteristics (age, sex, house conditions). The output probability was estimated from the allergic symptoms shown by the patients. We finally prospectively validated a data set of 600 patients. Results: The gradual inclusion of the variables increased the correlation between observed and predicted probabilities (correlation coefficient (r2) = 0.97). The model development using group-1 showed an accuracy rate of 99%, sensitivity and specificity of 99.7% and 88.6% respectively and the area under the receiver operating characteristics (ROC) curve (AUC) of 99%. The corresponding numbers for the validation of our model with group-2 were 87%, 95.6% and 66% and 86% respectively. The model predicted the probability of symptoms better than SPTs in combination (accuracy rate 0.76–0.80), especially in lower risk cases (probability< 0.8) that are highly difficult to diagnose. Conclusion: This is perhaps the first attempt to model the outcome of HDM allergy in terms of symptoms, which could open up an alternative but highly efficient way for accurate diagnosis of HDM allergy enhancing the efficiency of immunotherapy. Keywords: Asthma, House dust mite allergy, Logistic regression model, ROC, SPT
url http://www.sciencedirect.com/science/article/pii/S193945511931244X
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