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Development and Validation of a Nomogram Incorporating Platelet Distribution Width and the Triglyceride-Glucose Index
Liyong Shi1, Lianshun Lin1, Tanwei Liu1
1Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, The Second Clinical College of Fujian Medical University, Quanzhou, Fujian, 362000, People's Republic of China.
Purpose:
Obstructive sleep apnea (OSA) is a prevalent systemic disorder linked to cardiovascular and metabolic consequences. However, polysomnography (PSG), the diagnostic gold standard, is resource-intensive and not always readily accessible. This study aimed to develop an exploratory nomogram incorporating platelet distribution width (PDW) and the triglyceride-glucose (TyG) index for individualized OSA risk stratification among adults referred for PSG evaluation.
Patients And Methods:
A retrospective analysis of 1328 consecutive adults who underwent PSG, with participants randomly divided into a training cohort (70%) for model development and a validation cohort (30%) for internal validation. Candidate predictors were evaluated using multivariable logistic regression, and LASSO logistic regression was additionally performed as a sensitivity analysis for variable selection. A predictive nomogram was constructed based on clinically relevant and statistically supported predictors, and its performance was evaluated using the area under the curve (AUC), calibration, and decision curve analysis.
Results:
Univariate logistic regression showed that age, sex, BMI, hypertension, PDW, and TyG index were associated with OSA. In multivariable analysis, older age (OR = 1.035, 95% CI: 1.016-1.054, P < 0.001), male sex (OR = 2.990, 95% CI: 1.723-5.189, P < 0.001), higher BMI (OR = 1.240, 95% CI: 1.149-1.339, P < 0.001), elevated PDW (OR = 1.152, 95% CI: 1.025-1.294, P = 0.017), and higher TyG index (OR = 1.710, 95% CI: 1.107-2.642, P = 0.016) were included as predictors in the final nomogram. LASSO logistic regression supported the relevance of PDW and the TyG index, as both were retained under the lambda.min and lambda.1se criteria. The nomogram incorporating age, sex, BMI, PDW, and TyG index achieved an AUC of 0.806 in the training cohort and 0.751 in the validation cohort. Calibration curves showed consistency between predicted and observed OSA probabilities, and decision curve analysis indicated a higher net benefit than the treat-all and treat-none strategies across the evaluated threshold probability range.
Conclusion:
This exploratory nomogram, incorporating readily available clinical variables together with PDW and the TyG index, may help stratify OSA risk among adults referred for PSG evaluation. Further external validation is required before its use can be recommended in broader screening settings.
