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Establishment Of An Early Predictive Model For The Efficacy Of The Combination Of Traditional Chinese And Western Medicine In The Treatment Of Stage ?B-?B Non-small Cell Lung Cancer

Posted on:2020-12-04Degree:MasterType:Thesis
Country:ChinaCandidate:Y F FuFull Text:PDF
GTID:2404330602460976Subject:Integrative Medicine
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BackgroundAccording to the latest statistics,lung cancer,morbidity and mortality account for the first place in malignant tumors,while non-small cell lung cancer accounts for about 85%of lung cancer.Many patients are advanced at the time of diagnosis and have lost the opportunity to undergo radical surgery.Although platinum-based dual-drug chemotherapy is still the first-line treatment for advanced(IIIB-IVB)non-small cell lung cancer patients,it is targeted therapy and immunization.In the era of treatment,the recommended first-line treatments have changed a lot.The forecasting model builds relevant models through mathematical equations through the analysis of existing data,and quickly finds the results in the form of nomograms.It can calculate with reference to multiple variables at the same time,which has important value in clinical practice.The predictive model has been used in the clinical diagnosis and treatment of Western medicine to predict the long-term prognosis,lymph node metastasis,and risk of brain metastasis.It is still in its infancy in the field of traditional Chinese medicine,and the predictive model for the treatment of non-small cell lung cancer with integrated traditional Chinese and Western medicine.No one is established.Through the study of the data in the cohort,it is of great significance to establish a nomogram of the predictive model to guide the clinical diagnosis and treatment of integrated Chinese and Western medicine.ObjectiveA two-way cohort for the treatment of stage IIIB-IVB non-small cell lung cancer with integrated Chinese and Western medicine was initially established.The efficacy prediction model(nomogram)was established by retrospective cohort data,and the model was externally validated using prospective cohort data to explore a combination of Chinese and Western medicine treatment for IIIB-IV Bnon-small cell lung cancer.MethodsA two-way cohort study design was conducted to observe patients who had undergone at least one(inclusive)assessment of stage IIIB-IVB non-small cell lung cancer hospitalized at the Cancer Center of Guangzhou University of Traditional Chinese Medicine from time to time.The retrospective cohort was clearly diagnosed as NSCLC from 2016 to 2018 and later,and the prospective cohort was clearly diagnosed as NSCLC on a daily basis.The patient's baseline information,previous Chinese and Western medical treatment process,tumor indicators,imaging results,follow-up data,and outcomes were entered into statistical software,and variables were selected,predictive models were generated,and the stability of the model was verified using prospective cohort data.Results1)The sample size of the ?B-?B non-small cell lung cancer cohort in our center has a large gender difference,but the median OS is at 12 months,and the survival status is in line with the existing clinical treatment level.The average age of the patients was 61 years old,and the patients with stage IV accounted for 85%.Therefore,the patients in our center are mainly elderly patients.2)In the pathological type distribution,the non-small cell lung cancer was 89.14%of the total number,which was basically consistent with the general clinical incidence.3)From the preliminary single factor analysis,it can be seen that although the surgery has certain protective effect on advanced patients,the statistical significance is P>0.05.Through multiple regression analysis,it can be known that after adjusting chemotherapy,targeted therapy and radiotherapy,Chinese medicine is accepted.Treatment still reduced the risk of death by 26%and was statistically significant(P=0.0187).4)Among the TCM syndromes of single factor analysis,the heat-blocking lung increased the risk of death by 23%compared with lung spleen qi deficiency,but it was not statistically significant after adjustment.5)Patients with EGFR mutations had a HR value of 0.69 after regression analysis,which reduced the risk of death by 31%,with statistical difference(P<0.05),but the risk of death was reduced by 11%in patients with ALK mutations.Statistical difference(P?0.73).6)Among patients with adenocarcinoma,the median OS is 14 months,and the combination of traditional Chinese and Western medicine has a certain effect.7)The adjusted(Stepwise)AUC curve in the above table can predict the survival time of 17.27 months,but the area under the curve is only 0.654,and the prediction accuracy is low.8)According to the data,the risk of death in women is lower than that in men.The higher the TCM symptom score,the higher the risk of death.For every 1 point increase in TCM symptom score,the risk of death increases by 2.8%,while for patients receiving TCM treatment,it can be reduced by 20%of death risk(P=0.07),the risk of death in patients with targeted therapy was reduced by 31%(P<0.05).ConclusionThrough the analysis of the basic data of this data,we can see that the cohort construction of lung cancer patients in our center has taken shape,and the effect of integrated Chinese and Western medicine treatment has shown certain advantages in our center.After the analysis,traditional Chinese medicine treatment can play a protective role,with statistical differences.The data from the real world further proves the effectiveness of traditional Chinese medicine,indicating that Chinese medicine combined with Western medicine treatment can bring better survival benefits to patients.Although the prediction of the nomogram produced by the data analysis is low,it has no clinical application value and can be used as a reference for clinicians.However,through the continuous expansion and improvement of the data,the prediction model can be gradually improved by a specific algorithm.
Keywords/Search Tags:Non-small cell lung cancer, integrated Chinese and Western medicine treatment, cohort study, predictive model
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