Journal of inflammation research · 2025
Construction of a Nomogram Model Based on the Pan-Immune-Inflammation Value for Prediction of Adverse Clinical Outcomes in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.
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Rebetz Johan, Cederholm Hilma, McGauran David, Moore Ellen, Chi Catherine, Tabak Karlijn, Allhorn Maria, Olsson Martin L, Egesten Arne, Semple John W, Marcoux Genevieve
Аннотация
PURPOSE: This study evaluated the predictive value of the pan-immune-inflammation value (PIV) and developed a nomogram that integrated the PIV to predict adverse clinical outcomes in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).
PATIENTS AND METHODS: In a retrospective, single-center study, 522 patients with AECOPD were randomized 7:3 into the training and validation cohorts. Univariate and multivariate logistic regression were used to determine independent predictors of adverse clinical outcomes. After the optimal cutoff value for the PIV was determined, the training cohort was divided into the high-PIV and low-PIV groups. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were then performed to construct and validate the nomogram.
RESULTS: The regression results identified age, serum albumin (ALB) level, partial pressure of carbon dioxide (PaCO2) level, and the PIV as independent predictors of adverse clinical outcomes, and they were included in the nomogram model. The AUCs of the nomogram model that included these four variables in the training and validation cohorts were 0.720 (95% confidence interval [CI]: 0.658-0.783) and 0.733 (95% CI: 0.626-0.840), respectively. The calibration curves of the two cohorts showed good prediction accuracy (Hosmer-Lemeshow test: both P > 0.05), and the DCA proved that the prediction model has some clinical value.
CONCLUSION: Age, ALB level, PaCO2 level, and the PIV are independent predictors of adverse clinical outcomes in patients with AECOPD and may help healthcare providers identify patients at high risk of adverse outcomes during early admission. Although promising, the nomogram model has only moderate predictive performance. Further studies are required to identify additional significant factors to develop a higher-performing prediction model with which to make more accurate decisions in clinical practice.
PATIENTS AND METHODS: In a retrospective, single-center study, 522 patients with AECOPD were randomized 7:3 into the training and validation cohorts. Univariate and multivariate logistic regression were used to determine independent predictors of adverse clinical outcomes. After the optimal cutoff value for the PIV was determined, the training cohort was divided into the high-PIV and low-PIV groups. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were then performed to construct and validate the nomogram.
RESULTS: The regression results identified age, serum albumin (ALB) level, partial pressure of carbon dioxide (PaCO2) level, and the PIV as independent predictors of adverse clinical outcomes, and they were included in the nomogram model. The AUCs of the nomogram model that included these four variables in the training and validation cohorts were 0.720 (95% confidence interval [CI]: 0.658-0.783) and 0.733 (95% CI: 0.626-0.840), respectively. The calibration curves of the two cohorts showed good prediction accuracy (Hosmer-Lemeshow test: both P > 0.05), and the DCA proved that the prediction model has some clinical value.
CONCLUSION: Age, ALB level, PaCO2 level, and the PIV are independent predictors of adverse clinical outcomes in patients with AECOPD and may help healthcare providers identify patients at high risk of adverse outcomes during early admission. Although promising, the nomogram model has only moderate predictive performance. Further studies are required to identify additional significant factors to develop a higher-performing prediction model with which to make more accurate decisions in clinical practice.