Construction and assessment of a Nomogram model for lung cancer patients with concurrent deep vein thrombosis

FANG Qingqing; TANG Ming; CHANG Xindong; HE Mingfei; LIU Ying; YIN Shiwu

doi:10.3969/j.issn.1674-3806.2024.09.13

Abstract：Objective To construct and assess a Nomogram model for lung cancer patients with concurrent deep vein thrombosis(DVT).Methods The clinical data of 330 cases of lung cancer who were admitted to the Second People's Hospital of Hefei Affiliated to Bengbu Medical University from January 2022 to January 2023 were retrospectively analyzed,and the patients were divided into DVT group(33 cases)and non-DVT group(297 cases)according to the occurrence of DVT at admission.LASSO regression analysis and multivariate logistic regression analysis were used to screen the risk factors of the lung cancer patients with concurrent DVT to construct a Nomogram model,and the model was internally validated to assess its accuracy,consistency and clinical utility.Results Compared with the non-DVT group,the DVT group was older and had a greater proportion of smokers and users of antiangiogenic drugs,and had higher levels of leukocytes,neutrophils,red blood cell distribution width(RDW),neutrophil-lymphocyte ratio(NLR),lactate dehy-drogenase(LDH),brain natriuretic peptide(BNP),D-dimer,prothrombin time(PT),C-reactive protein(CRP)and neuron-specific enolase(NSE),and lower levels of hemoglobin and albumin,and the differences were statistically sig-nificant(P＜0.05).With the occurrence of DVT as the dependent variable,five risk factors of lung cancer complicated with DVT,including hypertension,smoking history,antiangiogenic drugs,D-dimer and albumin were screened by using LASSO regression and multivariate logistic regression and a Nomogram model was constructed based on these screened risk factors.The results of receiver operating characteristic(ROC)curve analysis indicated that the model had good discriminative ability[AUC(95％CI)=0.880(0.811-0.950)].The calibration curves showed that the predicted results of the model had a good fit with the actual observed values.The results of Hosmer-Lemeshow goodness-of-fit test(x2=6.469,P=0.595)further proved that the model had good consistency.The results of decision curve analysis(DC A)showed that the model had a relatively large potential for clinical application when the threshold probability was in the range of 0.07-0.95.The results of clinical impact curve(CIC)analysis showed that within the range of threshold proba-bility,the number of the patients who were predicted to have DVT was always greater than the number of the patients who actually had DVT,suggesting that the Nomogram model could effectively identify the high-risk patients with DVT.Conclusion The Nomogram model of lung cancer complicated with DVT constructed in this study has good predictive ability and clinical utility,which can help clinicians better identify high-risk patients and take appropriate intervention measures in time to improve the patients'prognosis.