A nomogram was developed to enhance the use of multinomial logistic regression modeling in diagnostic research

Abstract

Objectives: We developed a nomogram to facilitate the interpretation and presentation of results from multinomial logistic regression models.

Study design and setting: We analyzed data from 376 frail elderly with complaints of dyspnea. Potential underlying disease categories were heart failure (HF), chronic obstructive pulmonary disease (COPD), the combination of both (HF and COPD), and any other outcome (other). A nomogram for multinomial model was developed to depict the relative importance of each predictor and to calculate the probability for each disease category for a given patient. Additionally, model performance of the multinomial regression model was assessed.

Results: Prevalence of HF and COPD was 14% (n = 54), HF 24% (n = 90), COPD 20% (n = 75), and Other 42% (n = 157). The relative importance of the individual predictors varied across these disease categories or was even reversed. The pairwise C statistics ranged from 0.75 (between HF and Other) to 0.96 (between HF and COPD and Other). The nomogram can be used to rank the disease categories from most to least likely within each patient or to calculate the predicted probabilities.

Conclusions: Our new nomogram is a useful tool to present and understand the results of a multinomial regression model and could enhance the applicability of such models in daily practice.

Keywords: Chronic obstructive Pulmonary disease; Diagnostic research; Heart failure; Multinomial; Nomogram; Regression analyses.