Inflammatory Biomarker-Based Risk Prediction Model for Endovascular Reconstruction in Acute Lower Limb Ischemia: A Multicenter Development and Validation Study

Abstract

Background

Inflammatory markers are associated with poor prognosis of peripheral vascular diseases. We aim to determine the relationship between inflammatory markers and the prognosis of acute lower limb ischemic disease and to construct and verify a prognostic model based on inflammatory indicators.

Methods

We evaluated 295 patients with a diagnosis of acute lower limb ischemia (ALI) from multiple centers between 2020 and 2023. The association between baseline disease characteristics with length of stay and half-year cutoff results were determined using Statistical Product and Service Solutions software and R language, respectively. We identified predictive factors and built a nomogram to predict 30-day amputation rate in patients with ALI after endovascular surgery.

Results

In the training cohort, 34 patients underwent amputation within 30-day after endovascular surgery. Atrial fibrillation, diabetes, Rutherford grade IIb, higher neutrophil-to-lymphocyte ratio (NLR), higher platelet-to-lymphocyte ratio (PLR), lower hemoglobin, higher low-density lipoprotein (LDL), and higher triglyceride (TG were independently associated with 30-day amputation. Preoperative NLR, PLR, and LDL presented a good discriminative ability (NLR: area under the receiver operating characteristic curve [AUC] = 0.927; PLR: AUC = 0.839; LDL: AUC = 0.724). Five independent risk factors, such as diabetes, Rutherford grade, NLR, PLR, and LDL, were screened from the results of the multivariate logistic analysis of the training cohort and included in the nomogram. The calibration curve also proved that the nomogram predicted outcomes were close to the ideal curve and the decision curve analysis curve showed that all patients could benefit with threshold probability within 0–95%.

Conclusion

A nomogram for postoperative endovascular reconstruction of ALI was constructed with good predictive performance, which can be used as an auxiliary diagnosis of the potential risk factors and assist surgeon in making a personalized diagnosis and treatment for patients.