Development and Assessment of Prediction Models for the Development of COPD in a Typical Rural Area in Northwest China

Figures & data

Table 1 Univariate Logistic Regression Analyses of COPD Influencing Factors

Variables	OR	95% CI	P value
Age, year
<40	1.000
40–49	0.938	0.626–1.404	0.754
50–59	1.835	1.231–2.736	0.003
60–69	3.150	2.073–4.787	<0.001
70–79	6.000	2.602–13.835	<0.001
Sex
Male	1.000
Female	1.545	1.244–1.917	<0.001
BMI (kg/m2)	1.073	1.044–1.103	<0.001
Waist (cm)	1.009	1.000–1.018	0.051
Education level
Illiterate	1.000
Primary or Higher	0.672	0.508–0.890	0.006
Marital status
Married	1.000
Divorce and so on	0.949	0.706–1.276	0.706
Gross annual income (yuan)
<10,000	1.000
≥10,000	0.737	0.594–0.914	0.005
Type of medical insurance
Rural cooperative medical care	1.000
Self-funded and so on	1.001	0.474–2.111	0.999
Alcohol intake status
Yes	1.000
No	1.345	0.847–2.136	0.209
Tea consumption
Yes	1.000
No	1.090	0.858–1.385	0.479
Fresh fruits consumption
Never or rarely	1.000
1–3times/month	0.858	0.301–2.448	0.775
1–3 times/week	0.644	0.255–1.627	0.352
4–6 times/week	1.092	0.826–1.443	0.538
Everyday	0.891	0.691–1.148	0.372
Barbecue
Yes	1.000
No	0.743	0.563–0.981	0.036
Active smoking
No	1.000
Yes	1.470	1.117–1.935	0.006
Passive smoking
No	1.000
Yes	1.551	1.101–2.186	0.012
Main source of energy
Biomass or coal	1.000
Electricity or gas	0.356	0.165–0.767	0.008
Ventilation system
No	1.000
Yes	0.569	0.410–0.789	0.001
Stove
Yes	1.000
No	0.605	0.382–0.957	0.032
Tuberculosis
No	1.000
Yes	2.077	1.235–3.493	0.006
Physical exercise
No	1.000
Yes	1.239	0.812–1.890	0.319
Insomnia
No	1.000
Yes	0.599	0.276–1.300	0.195

Abbreviations: OR, odds ratio; CI, confidence interval; BMI, body mass index.

Figure 1 Screening predictors using LASSO binary logistic regression model.

Notes: LASSO coefficient profiles of the 18 features. Find the optimal value using the minimum standard and the minimum standard I-Standard Error method and draw a vertical dotted line.

Abbreviation: LASSO, least absolute shrinkage and selection operator.

Figure 2 A nomogram to predict the development of COPD.

Notes: The medication nonadherence nomogram was developed, with age, sex, barbeque, smoking, passive smoking, type of energy, ventilation systems, and Post-Bronchodilator FEV1.

Figure 3 Calibration curve to predict the development of COPD.

Notes: The x-axis represents the predicted COPD risk. The y-axis represents the actual diagnosed COPD. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram. The closer the solid line is to the diagonal, the more accurate the prediction.

Figure 4 ROC curve to predict the development of COPD.

Abbreviations: ROC, receiver operator characteristics; AUC, area under the curve.

Figure 5 Decision curve of prediction models for the development of COPD.