BIOMAT Database Logo BIOMAT Database Logo

Predictors of late asthmatic response. Logistic regression and classification tree analyses. 2000

P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Cardiovascular Research Institute, Department of Medicine, University of California at San Francisco, San Francisco, California 94143-0111, USA.

To identify predictors of the late asthmatic response (LAR), we reviewed data from 60 asthmatic subjects who had undergone allergen challenge over the past 5 yr (33 females, age 31.4 +/- 6.7 yr [mean +/- SD], FEV(1) 90% +/- 14% predicted). Variables considered likely predictors of LAR included baseline FEV(1), PC(20) methacholine (PC(20)), sputum eosinophil percent, and the decrease in FEV(1) within 20 min of allergen challenge. A LAR (FEV(1) >/= 15% fall between 3 and 7 h after challenge) was documented in 57% of subjects. A variety of logistic regression methods revealed a significant inverse association between LAR and PC(20) (odds ratio [OR] = 0.14 [95% CI = 0.03-0.66]) and a positive association between LAR and the decrease in FEV(1) at 20 min (OR = 1.18 [1.04 -1.33]). Classification tree analysis revealed that a threshold of 0.25 mg/ml for PC(20) was most predictive of LAR; LAR developed in 87% of those with PC(20) </= 0.25 mg/ml (n = 23) and in 38% of those with PC(20) > 0.25 mg/ml (n = 37). Notably, in subjects with PC(20) > 0.25 mg/ml, the incidence of LAR increased from 38% to 57% if the allergen-induced decline in FEV(1) at 20 min was >/= 27%. Surprisingly, baseline FEV(1) and percent eosinophils in induced sputum were not significantly associated with LAR. We conclude that a threshold value of 0.25 mg/ml for PC(20) methacholine is a good predictor of LAR. Measuring the PC(20) methacholine may be useful as a screening method to improve the efficiency of identifying asthmatic subjects with a LAR.

UI MeSH Term Description Entries
D006968 Hypersensitivity, Delayed An increased reactivity to specific antigens mediated not by antibodies but by sensitized T CELLS. Hypersensitivity, Tuberculin-Type,Hypersensitivity, Type IV,Tuberculin-Type Hypersensitivity,Type IV Hypersensitivity,Delayed Hypersensitivity,Delayed Hypersensitivities,Hypersensitivity, Tuberculin Type,Tuberculin Type Hypersensitivity,Tuberculin-Type Hypersensitivities,Type IV Hypersensitivities
D007958 Leukocyte Count The number of WHITE BLOOD CELLS per unit volume in venous BLOOD. A differential leukocyte count measures the relative numbers of the different types of white cells. Blood Cell Count, White,Differential Leukocyte Count,Leukocyte Count, Differential,Leukocyte Number,White Blood Cell Count,Count, Differential Leukocyte,Count, Leukocyte,Counts, Differential Leukocyte,Counts, Leukocyte,Differential Leukocyte Counts,Leukocyte Counts,Leukocyte Counts, Differential,Leukocyte Numbers,Number, Leukocyte,Numbers, Leukocyte
D008297 Male Males
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D012044 Regression Analysis Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable. Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D012130 Respiratory Hypersensitivity A form of hypersensitivity affecting the respiratory tract. It includes ASTHMA and RHINITIS, ALLERGIC, SEASONAL. Airway Hyperresponsiveness,Hypersensitivity, Respiratory,Airway Hyper-Responsiveness,Airway Hyper Responsiveness,Airway Hyperresponsivenesses,Hyper-Responsiveness, Airway,Hyperresponsiveness, Airway,Respiratory Hypersensitivities
D001985 Bronchial Provocation Tests Tests involving inhalation of allergens (nebulized or in dust form), nebulized pharmacologically active solutions (e.g., histamine, methacholine), or control solutions, followed by assessment of respiratory function. These tests are used in the diagnosis of asthma. Allergen Bronchial Provocation Tests,Allergen Challenge, Endobronchial,Antigen Bronchial Provocation Tests,Bronchial Allergen Challenge,Bronchial Challenge Tests,Inhalation Provocation Tests,Provocation Tests, Bronchial,Endobronchial Challenge Tests,Allergen Challenge, Bronchial,Allergen Challenges, Bronchial,Allergen Challenges, Endobronchial,Bronchial Allergen Challenges,Bronchial Challenge Test,Bronchial Provocation Test,Challenge Test, Bronchial,Challenge Test, Endobronchial,Challenge Tests, Bronchial,Challenge Tests, Endobronchial,Challenge, Bronchial Allergen,Challenge, Endobronchial Allergen,Challenges, Bronchial Allergen,Challenges, Endobronchial Allergen,Endobronchial Allergen Challenge,Endobronchial Allergen Challenges,Endobronchial Challenge Test,Inhalation Provocation Test,Provocation Test, Bronchial,Provocation Test, Inhalation,Provocation Tests, Inhalation,Test, Bronchial Challenge,Test, Bronchial Provocation,Test, Endobronchial Challenge,Test, Inhalation Provocation,Tests, Bronchial Challenge,Tests, Bronchial Provocation,Tests, Endobronchial Challenge,Tests, Inhalation Provocation
D004804 Eosinophils Granular leukocytes with a nucleus that usually has two lobes connected by a slender thread of chromatin, and cytoplasm containing coarse, round granules that are uniform in size and stainable by eosin. Eosinophil
D005260 Female Females
D005541 Forced Expiratory Volume Measure of the maximum amount of air that can be expelled in a given number of seconds during a FORCED VITAL CAPACITY determination . It is usually given as FEV followed by a subscript indicating the number of seconds over which the measurement is made, although it is sometimes given as a percentage of forced vital capacity. Forced Vital Capacity, Timed,Timed Vital Capacity,Vital Capacity, Timed,FEVt,Capacities, Timed Vital,Capacity, Timed Vital,Expiratory Volume, Forced,Expiratory Volumes, Forced,Forced Expiratory Volumes,Timed Vital Capacities,Vital Capacities, Timed,Volume, Forced Expiratory,Volumes, Forced Expiratory

Related Publications

P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Using classification tree and logistic regression methods to diagnose myocardial infarction.
January 1998, Studies in health technology and informatics,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Predictors of Sexual Harassment Using Classification and Regression Tree Analyses and Hurdle Models: A Direct Replication.
July 2023, Journal of sex research,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Classification and regression tree analysis in public health: methodological review and comparison with logistic regression.
December 2003, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Predictors of success of external cephalic version and cephalic presentation at birth among 1253 women with non-cephalic presentation using logistic regression and classification tree analyses.
August 2017, Acta obstetricia et gynecologica Scandinavica,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Logistic Regression-Based Trichotomous Classification Tree and Its Application in Medical Diagnosis.
November 2016, Medical decision making : an international journal of the Society for Medical Decision Making,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Identification of insulin resistance in Asian Indian adolescents: classification and regression tree (CART) and logistic regression based classification rules.
May 2009, Clinical endocrinology,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Increased efficiency of analyses: cumulative logistic regression vs ordinary logistic regression.
February 1998, Community dentistry and oral epidemiology,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Understanding linear and logistic regression analyses.
March 2007, CJEM,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.
November 2015, Haemophilia : the official journal of the World Federation of Hemophilia,
P C Avila, and M R Segal, and H H Wong, and H A Boushey, and J V Fahy
Tree-based model checking for logistic regression.
May 2007, Statistics in medicine,
Copied contents to your clipboard!