Published in:
Open Access 01-12-2023 | Research
Lifestyle-based nomogram for identifying the Chaoshan inhabitants of China at high risk of Helicobacter pylori infection
Published in: BMC Gastroenterology | Issue 1/2023
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Background
Helicobacter pylori (HP) infection is associated with various diseases. Early detection can prevent the onset of illness. We constructed a nomogram to predict groups at high risk of HP infection.
Methods
Patients who underwent regular medical check-ups at hospital in Chaoshan, China from March to September 2022 were randomly allocated to the training and validation cohorts. Risk factors including basic characteristics and lifestyle habits associated with HP infection were analyzed by logistic regression analyses. The independent varieties were calculated and plotted into a nomogram. The nomogram was internally validated by receiver operating characteristic curve, calibration, and decision curve analyses (DCAs).
Results
Of the 945 patients, 680 were included in the training cohort and 265 in the validation cohort. 356 patients in training cohort with positive 13 C-UBT results served as the infected group, and 324 without infection were the control group. The multivariate regression analyses showed that the risk factors for HP infection included alcohol consumption (OR = 1.29, 95%CI = 0.78–2.13, P = 0.03), family history of gastric disease (OR = 4.35, 95%CI = 1.47–12.84, P = 0.01), living with an HP-positive individual (OR = 18.09, 95%CI = 10.29–31.82, P < 0.0001), drinking hot tea (OR = 1.58, 95%CI = 1.05–2.48, P = 0.04), and infection status of co-drinkers unknown (OR = 2.29, 95%CI = 1.04–5.06, P = 0.04). However, drinking tea > 3 times per day (OR = 0.56, 95%CI = 0.33–0.95, P = 0.03), using serving chopsticks (OR = 0.30, 95%CI = 0.12–0.49, P < 0.0001) were protective factors for HP infection. The nomogram had an area under the curve (AUC) of 0.85 in the training cohort. The DCA was above the reference line within a large threshold range, indicating that the model was better. The calibration analyses showed the actual occurrence rate was basically consistent with the predicted occurrence rate. The model was validated in the validation cohort, and had a good AUC (0.80), DCA and calibration curve results.
Conclusions
This nomogram, which incorporates basic characteristics and lifestyle habits, is an efficient model for predicting those at high risk of HP infection in the Chaoshan region.