Application of Renewal Gray GM (1,1) Model to Prediction of Landslide Deformation

The gray renewal GM (1,1) landslide prediction model was established by improving the gray model. Based on the established model, the author has made prediction of landslide deformation to the Xiangjiapo landslide and the Lianziya dangerous rock body. The results show that the gray renewal GM (1,1) model can supplement the new information in time and remove the old information which reduces the meaning of the information because of time lapse. Therefore, the model is closer to reality.

Application of Gray Metabolic GM (1,1) Model in Prediction of Annual Total Yields of Chinese Aquatic Products

To predict the annual total yields of Chinese aquatic products in future five years ( 2011-2015) ,based on the theory and method of gray system,this paper firstly establishes a conventional GM ( 1,1) model and a gray metabolic GM ( 1,1) model respectively to predict the annual total yields of Chinese aquatic products in 2006-2009 and compare the prediction accuracy between these two models. Then,it selects the model with higher accuracy to predict the annual total yields of Chinese aquatic products in future five years. The comparison indicates that gray metabolic GM ( 1,1) model has higher prediction accuracy and smaller error,thus it is more suitable for prediction of annual total yields of aquatic products. Therefore,it adopts the gray metabolic GM ( 1,1) model to predict annual total yields of Chinese aquatic products in 2011-2015. The prediction results of annual total yields are 55. 32,57. 46,59. 72,62. 02 and 64. 43 million tons respectively in future five years with annual average increase rate of about 3. 7% ,much higher than the objective of 2. 2% specified in the Twelfth Five-Year Plan of the National Fishery Development ( 2011 to 2015) . The results of this research show that the gray metabolic GM ( 1,1) model is suitable for prediction of yields of aquatic products and the total yields of Chinese aquatic products in 2011-2015 will totally be able to realize the objective of the Twelfth Five-Year Plan.

Study on the Prediction of Rice Blast Based on the Unbiased GM (1,1) Model

To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.

Prediction of the amount of urban waste solids by applying a gray theoretical model

Urban waste solids are now becoming one of the most crucial environmental problems. There are several different kinds of technologies normally used for waste solids disposal, among which landfill is more favorable in China than others, especially for urban waste solids. Most of the design works up to now are based on a roughly estimation of the amount of urban waste solids without any theoretical support, which lead to a series problems. To meet the basic information requirements for the design work, the amount of the urban waste solids was predicted in this research by applying the gray theoretical model GM (1,1) through non linear differential equation simulation. The model parameters were estimated with the least square method (LSM) by running a certain MATALAB program, and the hypothesis test results show that the residual between the prediction value and the actual value approximately comply with the normal distribution N (0,0 21 2), and the probability of the residual within the range (-0 17, 0 19) is more than 95%, which indicate obviously that the model can be well used for the prediction of the amount of waste solids and those had been already testified by the latest two years data about the urban waste solids from Loudi City of China. With this model, the predicted amount of the waste solids produced in Loudi City in the next 30 years is 8049000 ton in total.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

Prediction of efficient outputs based on GM(1,N) model and weak DEA efficiency

This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.

Evaluation and prediction of earth pressure balance shield performance in complex rock strata:A case study in Dalian,China

This research explores the potential for the evaluation and prediction of earth pressure balance shield performance based on a gray system model.The research focuses on a shield tunnel excavated for Metro Line 2 in Dalian,China.Due to the large error between the initial geological exploration data and real strata,the project construction is extremely difficult.In view of the current situation regarding the project,a quantitative method for evaluating the tunneling efficiency was proposed using cutterhead rotation(R),advance speed(S),total thrust(F)and torque(T).A total of 80 datasets with three input parameters and one output variable(F or T)were collected from this project,and a prediction framework based gray system model was established.Based on the prediction model,five prediction schemes were set up.Through error analysis,the optimal prediction scheme was obtained from the five schemes.The parametric investigation performed indicates that the relationships between F and the three input variables in the gray system model harmonize with the theoretical explanation.The case shows that the shield tunneling performance and efficiency are improved by the tunneling parameter prediction model based on the gray system model.

Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.

血清核因子-κB、肿瘤坏死因子-α、单核细胞趋化因子-1与老年冠状动脉粥样硬化性心脏病合并衰弱综合征的关系及预测模型构建

目的分析血清核因子-κB(NF-κB)、肿瘤坏死因子-α(TNF-α)、单核细胞趋化因子-1(MCP-1)与老年冠状动脉粥样硬化性心脏病(CHD)合并衰弱综合征的关系并构建列线图预测模型。方法回顾性分析上海市第一人民医院嘉定分院2021年3月至2023年3月收治的50例老年CHD合并衰弱综合征患者的病例资料,将其设为观察组,采用埃德蒙顿衰弱等级量表(EFS)进行衰弱程度评估,分为轻度、中度、重度三组。另选取同期上海市第一人民医院嘉定分院收治的50例单纯老年CHD患者设为对照组,以及同期进行健康体检的50例老年人设为健康组。比较三组血清NF-κB、TNF-α、MCP-1水平及EFS评分,以及不同衰弱程度组血清NF-κB、TNF-α、MCP-1水平,Pearson法分析血清NF-κB、TNF-α、MCP-1与EFS评分的相关性。采用单因素及多因素logistic回归分析老年CHD合并衰弱综合征的危险因素,通过R软件构建列线图预测模型,以Bootstrap法进行内部验证,Hosmer-Lemeshow拟合优度检验,受试者工作特征曲线(ROC)对预测模型进行评价。结果观察组患者血清NF-κB、TNF-α、MCP-1水平及EFS评分均高于对照组和健康组(P<0.05)。重度组患者血清NF-κB、TNF-α、MCP-1水平均高于中度组、轻度组(P<0.05)。血清NF-κB、TNF-α、MCP-1水平与EFS评分均呈正相关关系(r=0.409、0.392、0.411,P<0.05)。多因素Logistic回归分析显示,年龄、抑郁症状、焦虑症状、营养不良、NYHA分级、身体活动量是老年CHD合并衰弱综合征的危险因素(P<0.05)。据此建立的列线图模型预测老年CHD合并衰弱综合征的区分能力较好(C-index=0.912),Hosmer-Lemeshow检验显示其拟合优度良好(χ^(2)=3.068,P=0.412);该预测模型预测老年CHD合并衰弱综合征的曲线下面积为0.915(95%CI 0.823~0.956)。结论老年CHD合并衰弱综合征患者血清NF-κB、TNF-α、MCP-1水平明显增高,且与衰弱严重程度密切相关,三者联合检测可提高对衰弱综合征的诊断效能。衰弱综合征的发生与多种因素有关,临床应针对相应的危险因素及早给予对症处理,以降低衰弱综合征发生风险。

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.

晚期非小细胞肺癌患者经PD-1抑制剂治疗后生存预后的列线图预测模型构建

目的探索中性粒细胞与淋巴细胞比值(NLR)等相关指标对经程序性死亡受体1(PD-1)抑制剂治疗的晚期非小细胞肺癌患者预后的影响并构建列线图预测模型。方法选取2020年2月至2022年4月于该院接受PD-1抑制剂治疗的198例晚期非小细胞肺癌患者,随访至2022年8月,根据临床结局分为死亡组(46例)和存活组(152例)。记录死亡组和存活组患者的临床资料,分析影响经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的相关因素;采用受试者工作特征(ROC)曲线分析NLR、血小板与淋巴细胞比值(PLR)、淋巴细胞与单核细胞比值(LMR)对患者预后的预测价值;采用多因素Logistic回归模型分析影响患者预后的独立危险因素;采用R语言软件4.0“rms”包构建患者预后的列线图预测模型,校正曲线对列线图预测模型进行内部验证。结果与存活组比较,死亡组吸烟、TNM分期Ⅳ期、ECOG评分2分比例及NLR、PLR、LMR较高(P<0.05)。NLR、PLR、LMR的曲线下面积分别为0.707、0.793、0.819,最佳临界值分别为4.72%、179.21%、3.44%。吸烟、TNM分期、美国东部肿瘤协作组评分、NLR、PLR、LMR是影响PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的独立危险因素(P<0.05)。内部验证结果显示,列线图模型预测经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的C-index为0.847(95%CI 0.769~0.902),校准曲线趋于理想曲线。列线图模型预测经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的阈值>0.22,列线图预测模型提供临床净收益,并且临床净收益均高于吸烟、TNM分期、ECOG评分、NLR、PLR、LMR。结论该研究基于吸烟、TNM分期、ECOG评分、NLR、PLR、LMR构建了经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的列线图预测模型,具有重要的临床应用价值。

多序列影像组学结合临床及影像学特征预测脑胶质瘤IDH1基因突变

目的探讨脑胶质瘤的多序列影像组学特征及临床相关参数预测脑胶质瘤异柠檬酸脱氢酶1 (isocitrate dehydrogenase, IDH1)基因突变的价值。材料与方法 回顾性分析81例经组织病理学证实并有IDH1基因突变状态信息的脑胶质瘤患者。应用T2WI、T1WI、扩散加权成像(diffusion weighted imaging, DWI)、表观扩散系数(apparent diffusion coefficient, ADC)和对比增强MRI(contrast enhancement MRI, CE-MRI)五种图像进行影像组学特征提取,每个序列可提取107个影像组学特征,以上特征经单因素秩和检验、相关性分析及最小绝对收缩和选择算子(least absolute shrinkage selection operator, LASSO)降维筛选后,剩余特征采用多因素logistic回归分别建立各序列模型及多序列融合模型,包括T2WI模型、T1WI模型、DWI模型、ADC模型、CE-MRI模型和多序列影像组学模型。最后将多序列影像组学模型输出的组学分数与临床多因素模型结合建立联合模型。上述模型采用受试者工作特征(receiver operating characteristic, ROC)曲线分析各模型的预测效能,并采用DeLong非参数检验比较曲线下面积(area under the curve, AUC)的差异。此外,采用决策曲线分析(decision curve analysis, DCA)评估多序列影像组学模型和联合模型鉴别IDH1基因突变状态的临床收益。结果 联合模型在胶质瘤IDH1基因突变预测中表现出最佳的效能(AUC为0.928)。多序列影像组学模型的AUC值均高于T2WI、DWI和ADC模型(分别为0.865 vs. 0.752、0.656、0.631,P值均<0.05);联合模型的AUC值高于T2WI、T1WI、T1增强和多序列影像组学模型(分别为0.928 vs. 0.752、0.827、0.829、0.865,P值均<0.05);但联合模型和临床模型之间的AUC值差异无统计学意义(分别为0.928和0.880,P>0.05)。决策曲线分析表明,联合模型较多序列影像组学模型鉴别IDH1基因突变的临床收益高。结论 多序列影像组学特征、临床及MRI影像学特征的结合对术前鉴别脑胶质瘤IDH1基因突变有重要价值。

基于TDOGM(1,N)灰色模型的高校科研能力预测与滞后效应分析

针对高校人才、经费、平台等科研资源投入与科研产出存在的不平衡现象,提出利用带时滞效应的TDOGM(1,N)灰色模型,对在一定的资源投入下,高校科研能力未来发展趋势进行预测,为高校科学配置投入资源、有效评价科研投入效率提供理论基础。通过研究发现,利用带时滞效应的TDOGM(1,N)灰色模型进行投入与产出关系分析、预测,其精度比传统的OGM(1,N)灰色模型高。同时,预测模型还能充分反映其核心要素存在的滞后效应。

Prediction of Planing Craft Motion Based on Grey System Theory

In order to minimize the harm caused by the instability of a planing craft, a motion prediction model is essential. This paper analyzed the feasibility of using an MGM（1,N） model in grey system theory to predict planing craft motion and carried out the numerical simulation experiment. According to the characteristics of planing craft motion, a recurrence formula was proposed of the parameter matrix of an MGMfl,N） model. Using this formula, data can be updated in real-time without increasing computational complexity significantly. The results of numerical simulation show that using an MGM（1,N） model to predict planing motion is feasible and useful for prediction. So the method proposed in this study can reflect the planing craft motion mechanism successfully, and has rational and effective functions of forecasting and analyzing trends.

The softening prediction of HSLA steel heat-affected zone based on the grey system

The high-strength low-alloy（ HSLA ） steel heat-affected zone （HAZ）softening was predicted using a grey model. HSLA steel DILLIMAX690E, NK-HITEN61OU2 and BHW35 were taken as examples in the research on ultra-narrow gap automatic welding technology. Test results turned out to be that the errors between the values calculated by the Grey Model （GM） （ 1,1 ） model and their actual value were less than 2%, indicating that the grey prediction method could accurately reflect the actual situation of the high-strength low-alloy steel heat-affected zone softening. This method will play a crucial role in guiding the applications of HSLA steel welded structures in the future.

Intelligent predictive model of ventilating capacity of imperial smelt furnace

In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

Proposal and evaluation of a new norm index-based QSAR model to predict pEC50 and pCC50 activities of HEPT derivatives

The search and development of anti-HIV drugs is currently one of the most urgent tasks of pharmacological studies. In this work, a quantitative structure-activity relationship （QSAR） model based on some new norm indexes, was obtained to a series of more than 150 HEPT derivatives （1-[（2-hydroxyethoxy）methyl]-6-（phenylthio）thymine） to find their pEC50 （the required effective concentration to achieve 50% protection of MT-4 cells against the cytopathic effect of virus） and pCC50 （the required cytotoxic concentration to reduce visibility of 50% mock infected cell） activities. The model efficiencies were then validated using the leave-one-out cross validation （LOO-CV） and y- randomization test. Results indicated that this new model was efficient and could provide satisfactory results for prediction of pECso and pCC50 with the higher R2 train and the higher Rt2est. By using the leverage approach, the applicability domain of this model was further investigated and no response outlier was detected for HEFT derivatives involved in this work. Comparison results with reference methods demonstrated that this new method could result in significant improvements for predicting pEC50 and pCC50 of anti-HIV HEPT derivatives. Moreover, results shown in this present study suggested that these two absolutely different activities pECso and pCC50 of anti-HIV HEPT derivatives could be predicted well with a totally similar QSAR model, which indicated that this model mizht have the potential to be further utilized for other biological activities of HEFT derivatives.

甲状腺乳头状癌患者术前血清TK1表达水平联合甲状腺超声特征构建中央区淋巴结转移预测模型及验证

目的探讨血清胸苷激酶1(serum thymidine kinase 1,sTK1)联合甲状腺超声特征预测甲状腺乳头状癌(papillary thyroid carcinoma,PTC)患者中央区淋巴结转移(central lymph node metastasis,CLNM)的风险。方法选取2020年10月~2021年12月陕西省人民医院确诊的CLNM的PTC患者110例为观察组,同期无CLNM的PTC患者104例为对照组。所有患者行甲状腺超声检查,检测sTK1等指标水平,采用t检验比较两组间sTK1等指标的水平差异,卡方检验分析颈部超声结果的差异,Logistic回归分析CLNM的独立危险因素,构建列线图预测模型,并选取2022年1~5月陕西省人民医院确诊的80例PTC患者对模型的预测准确度进行外部验证。结果观察组sTK1水平高于对照组(2.06±0.75pmol/L vs 1.59±0.66pmol/L),差异有统计学意义(t=4.75,P<0.001),而血清TSH,TG,TGAb水平与是否发生CLNM无关,差异无统计学意义(t=0.74,0.75,0.61,均P>0.05)。sTK1预测PTC患者CLNM的曲线下面积(AUC)为0.678,截断值为1.50 pmol/L,灵敏度和特异度分别为79.1%,61.0%。单因素分析结果显示,s TK1>1.5pmol/L,边界不清、结节微钙化、肿瘤数目、肿瘤直径>1cm和淋巴结明显血流信号是PTC患者CLNM的独立风险因素(χ^(2)=5.24~26.72,均P<0.05),而性别、年龄、低回声、纵横比>1、肿瘤位置与CLNM无关(χ^(2)=0.27~7.16,均P>0.05)。基于上述危险因素构建预测模型并进行准确度验证,采用建模原始数据进行内部验证AUC为0.826,验证队列进行外部验证AUC为0.809,表明该模型具有一定的预测准确度。结论PTC患者术前sTK1表达水平联合甲状腺超声特征构建CLNM预测模型具有一定的临床应用价值,当sTK1>1.5 pmol/L,边界不清、结节微钙化、肿瘤多灶、肿瘤直径>1cm,淋巴结有明显血流信号时,发生CLNM的概率较高,建议行预防性中央区淋巴结清扫术。

超声多模态评分及sFlt-1、D-Dimer对原因不明复发性流产妊娠结局的预测

目的探讨超声多模态评分及可溶性血管内皮生长因子受体-1(Soluble vascular endothelial growth factor receptor-1,sFlt-1)、D-二聚体(D-dimer,D-D)对原因不明复发性流产(Unexplained recurrent spontaneous abortion,URSA)妊娠结局的预测价值,并构建URSA不良妊娠结局的风险预测模型。方法选取2020年4月至2022年3月东莞市妇幼保健院收治的有URSA史的早孕患者136例,根据随访妊娠结局分为流产组和未流产组,比较两组超声多模态评分及sFlt-1、D-D水平,采用多因素Logistic回归分析法分析影响URSA妊娠再次流产的危险因素,建立风险预测模型并进行模型验证。结果136例孕妇保胎治疗后流产率为38.24%。流产组年龄≥35岁、饮酒史、URSA家族史、自然流产≥4次患者占比及血清sFlt-1、D-D水平均高于未流产组,孕酮及超声多模态评分均低于未流产组(P<0.05)。二元Logistic回归分析显示,自然流产≥4次、sFlt-1高水平、D-D高水平、超声多模态评分低是影响URSA妊娠再流产的独立危险因素(P<0.05)。预测模型方程:Logit(P)=-1.635+0.605×自然流产≥4次+1.332×sFlt-1+0.841×D-D+1.042×超声多模态评分;内部验证结果显示,预测模型的校正曲线与理想曲线拟合良好(Hosmer-Lemeshowχ^(2)=0.322,P=0.113)。受试者工作特征(Receiver operating characteristic,ROC)曲线结果显示,Logistic预测模型的AUC为0.933(95%CI:0.877~0.969),优于sFlt-1、D-D、超声多模态评分单项预测(P<0.05)。结论血清sFlt-1、D-D水平升高、超声多模态评分降低、自然流产≥4次是URSA妊娠再流产的高危因素,构建的风险预测模型区分度、拟合度好,能够直观准确预测URSA不良妊娠结局发生风险。

中医证素参与的晚期非小细胞肺癌PD-1抑制剂近期疗效预测模型构建与验证

目的评估PD-1抑制剂治疗非小细胞肺癌(NSCLC)近期疗效的预测因素,构建预测模型。方法前瞻性纳入2019年10月—2021年11月间,符合入组标准、应用PD-1抑制剂的晚期NSCLC患者221例,2021年5月1日前入组的为建模组(n=149例),之后的为验证组(n=72例)。采集患者的一般临床资料及中医四诊信息,并进行中医证素辨别。使用R软件4.0.4版本构建客观缓解率的列线图临床预测模型,通过受试者工作特征曲线及校准曲线来评价该模型的预测能力和区分度,并通过验证组进行外部验证。结果221例患者经PD-1抑制剂治疗2~4个周期后,总的客观缓解率为44.80%。建模组多因素Logistic回归分析发现,TPS评分(OR=0.261,P=0.001)、治疗线数(OR=3.749,P=0.002)、治疗模式(OR=2.796,P=0.019)、气虚病性证素(OR=2.296,P=0.043)、阴虚病性证素(OR=3.228,P=0.005)是PD-1抑制剂近期疗效的独立预测因素。基于以上5个独立预测因子构建PD-1抑制剂近期疗效的列线图预测模型,建模组和验证组的AUC值分别为0.8317和0.7535,两组校准曲线在预测值与真实值之间符合的平均绝对误差分别为0.053和0.039,显示出较高吻合度,表明该模型的预测性能良好。结论基于中医气虚病性证素、阴虚病性证素以及TPS评分、治疗线数和治疗模式构建的列线图模型是预测晚期非小细胞肺癌PD-1抑制剂近期疗效的稳定有效工具。