ObjectiveTo analyze the risk factors and develop a nomagram predictive model for early recurrence after curative resection for hepatocellular carcinoma (HCC). MethodsThe clinicopathologic data of the patients with HCC who underwent radical hepatectomy at the First Affiliated Hospital of Xinjiang Medical University from August 2017 to August 2021 were retrospectively collected. The univariate and multivariate logistic regression analysis were used to screen for the risk factors of early recurrence for HCC after radical hepatectomy, and a nomogram predictive model was established based on the risk factors. The receiver operating characteristic (ROC) curve and calibration curve were used to validate the predictive performance of the model, and the decision curve analysis (DCA) curve was used to evaluate its clinical practicality. ResultsA total of 302 patients were included based on the inclusion and exclusion criteria, and 145 (48.01%) of whom experienced early recurrence. The results of multivariate logistic regression model analysis showed that the preoperative neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), γ-glutamate transferase (GGT), alpha fetoprotein (AFP), tumor size, and microvascular invasion (MVI) were the influencing factors of early recurrence for HCC after radical resection (P<0.05). The nomogram was established based on the risk factors. The area under the ROC curve of the nomogram was 0.858 [95%CI (0.816, 0.899)], and the Brier index of the calibration curve of the nomogram was 0.152. The predicted result of the nomogram was relatively close to the true result (Hosmer-Lemeshow test, P=0.913). The DCA result showed that the clinical net benefit of intervention based on the predicted probability of the model was higher than that of non-intervening in all HCC patients and intervening in all HCC patients when the threshold probability was in the range of 0.1 to 0.8. ConclusionsThe results of this study suggest that for the patients with the risk factors such as preoperative NLR greater than 2.13, PLR greater than 108.15, GGT greater than 46.0 U/L, AFP higher than 18.96 μg/L, tumor size greater than 4.9 cm, and presence of preoperative MVI need to closely pay attention to the postoperative early recurrence. The nomogram predictive model constructed based on these risk factors in this study has a good discrimination and accuracy, and it could obtain clinical net benefit when the threshold probability is 0.1 to 0.8.
ObjectiveTo explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model for these risk factors. MethodsA case-control study was conducted. The patients with T2DM accompanied with ASO and those with T2DM alone, admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, were retrospectively collected according to the inclusion and exclusion criteria. The basic characteristics and relevant laboratory test indicators of two groups were compared. The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM, and then a nomogram predictive model was developed. ResultsThe study included 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO. The significant differences were observed between the two groups in terms of smoking history, white blood cell count, neutrophil count, lymphocyte count, platelet count, systemic immune-inflammation index, systemic inflammatory response index (SIRI), high-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein α (Apoα), serum cystatin C, free-triiodothyronine (FT3), total triiodothyronine, free triiodothyronine / total triiodothyronine ratio, fibrinogen (Fib), fibrinogen degradation products, and plasma D-dimer (P<0.05). Further the results of the multivariate logistic regression analysis revealed that the history of smoking, increased levels of Fib and SIRI value increased the probability of occurrence of ASO in the patients with T2DM [OR (95%CI) = 2.921 (1.023, 4.227), P=0.003; OR (95%CI) =2.641 (1.810, 4.327), P<0.001; OR (95%CI) = 1.020 (1.004, 1.044), P=0.018], whereas higher levels of ApoA1 and FT3 were associated with a reduced probability of occurrence of ASO in the patients with T2DM [OR (95%CI) = 0.231 (0.054, 0.782), P=0.018; OR (95% CI) = 0.503 (0.352, 0.809), P=0.003]. The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO in the T2DM patients [area under the receiver operating characteristic curve (95%CI) =0.788 (0.730, 0.846)]. The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test, χ2= 5.952, P= 0.653). The decision curve analysis showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of 0.18 to 0.80 compared to no intervention or universal intervention. ConclusionThe analysis indicates that T2DM patients with a smoking history, elevated Fib level, and reduced levels of ApoA1 and FT3 should be closely monitored for ASO risk. The nomogram predictive model based on these features has a good discriminatory power for predicting ASO in T2DM patients, though its value warrants further investigation.