Development and validation of a clinical factor-based nomogram for predicting imaging-defined cardiopulmonary abnormality risk in an asymptomatic screening population

Table 4 Univariate and multivariate binary logistic regression analysis of clinical characteristics

Variable	Univariate analysis			Multivariate (enter) regression
	OR	95%CI	P value	OR	95%CI	P value
Sex	1.483	1.078-2.039	0.015	0.427	0.276-0.660	< 0.001
Age	1.041	1.020-1.063	< 0.001	1.028	1.005-1.052	0.018
BMI	1.187	1.121-1.256	< 0.001	1.150	1.079-1.226	< 0.001
Metabolic status	4.015	2.877-5.602	< 0.001	3.266	2.259-4.723	< 0.001
Smoking history	1.819	1.587-2.085	< 0.001	1.968	1.665-2.325	< 0.001
Work style	1.199	1.006-1.429	0.043	1.020	0.829-1.255	0.851
Alcohol consumption history	1.058	0.927-1.208	0.403
High-oil high-salt diet	1.326	0.852-2.062	0.211
Sleep duration	0.938	0.718-1.227	0.641
NYHA class	1.363	0.752-2.470	0.307
Family history of heart disease	0.775	0.565-1.063	0.113
Occupational exposure history	0.802	0.395-1.631	0.543
Family history of lung cancer	1.227	0.615-2.449	0.562

The multivariate analysis employed the forced entry (enter) method, incorporating all six variables identified as significant in univariate screening. Work style did not achieve statistical significance in the multivariate model (P = 0.851). The final nomogram incorporated five independently associated factors: Gender, age, body mass index, metabolic status, and smoking history. NYHA: New York Heart Association; BMI: Body mass index; OR: Odds ratio; CI: Confidence interval.