Interannual Variation and Statistical Prediction of Summer Dry and Hot Days in South China from 1970 to 2018

The frequent occurrence of dry and hot(DH)days in South China in summer has a negative impact on social development and human health.This study explored the variation characteristics of DH days and the possible reasons for this knotty problem.The findings revealed a notable increase in the number of DH days across most stations,indicating a significant upward trend.Additionally,DH events were observed to occur frequently.The number of DH days increased during 1970-1990,decreased from 1991 to 1997,and stayed stable after 1997.The key climate factors affecting the interannual variability of the number of DH days were the Indian Ocean Basin warming(IOBW)in spring and the East Asian Summer Monsoon(EASM).Compared with the negative phase of IOBW,in the positive phase of IOBW,500 hPa and 850 hPa geopotential height enhanced,the West Pacific subtropical high strengthened and extended abnormally to the west,more solar radiation reached the surface,surface outgoing longwave radiation increased,and there was an anomalous anticyclone in eastern South China.The atmospheric circulation characteristics of the positive and negative phases of ESAM were opposite to those of IOBW,and the abnormal circulation of the positive(negative)phases of ESAM was unfavorable(favorable)for the increase in the number of DH days.A long-term prediction model for the number of summer DH days was established using multiple linear regression,incorporating the key climate factors.The correlation coefficient between the observed and predicted number of DH days was 0.65,and the root-mean-square error was 2.8.In addition,independent forecasts for 2019 showed a deviation of just 1 day.The results of the independent recovery test confirmed the stability of the model,providing evidence that climatic factors did have an impact on DH days in South China.

Statistical Analysis and Prediction of the Concentration of Harmful Algae in Bohai Bay

Based on the data of phytoplankton concentration and environmental factors in Bohai Bay from May to September in 2003. the relationship between environmental factors and phytoplankton biomass was analyzed. By analysis of variance, the weather condition was found to have no direct relation with phytoplankton biomass. Correlation coefficients showed that temperature, pH value,the concentrations of silicate and nitrate exhibited linear relationship with phytoplankton biomass.With principal component analysis, pollution types which affected the abundance of phytoplankton included point sources such as municipal and industrial effluents, agricultural runoff and earth＇s surface water. Using multivariate stepwise regression method and taking the correlation analysis results into consideration, a multi-step regression equation was developed to predict the concentration of phytoplankton in September 2003. Combined results show that temperature, pH value, the concentrations of silicate and nitrate are the critical ecological factors affecting the phytoplankton biomass in Bohai Bay.

Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin,China

Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.

Corrected Statistical Energy Analysis Model in a Non-Reverberant Acoustic Space

Statistical Energy Analysis(SEA)is a well-known method to analyze the flow of acoustic and vibration energy in a complex structure.This study investigates the application of the corrected SEA model in a non-reverberant acoustic space where the direct field component from the sound source dominates the total sound field rather than a diffuse field in a reverberant space which the classical SEA model assumption is based on.A corrected SEA model is proposed where the direct field component in the energy is removed and the power injected in the subsystem considers only the remaining power after the loss at first reflection.Measurement was conducted in a box divided into two rooms separated by a partition with an opening where the condition of reverberant and non-reverberant can conveniently be controlled.In the case of a non-reverberant space where acoustic material was installed inside the wall of the experimental box,the signals are corrected by eliminating the direct field component in the measured impulse response.Using the corrected SEA model,comparison of the coupling loss factor(CLF)and damping loss factor(DLF)with the theory shows good agreement.

Korean Morphological Analysis for Korean-Vietnamese Statistical Machine Translation

This paper describes the experiments with Korean-to-Vietnamese statistical machine translation(SMT). The fact that Korean is a morphologically complex language that does not have clear optimal word boundaries causes a major problem of translating into or from Korean. To solve this problem, we present a method to conduct a Korean morphological analysis by using a pre-analyzed partial word-phrase dictionary(PWD).Besides, we build a Korean-Vietnamese parallel corpus for training SMT models by collecting text from multilingual magazines. Then, we apply such a morphology analysis to Korean sentences that are included in the collected parallel corpus as a preprocessing step. The experiment results demonstrate a remarkable improvement of Korean-to-Vietnamese translation quality in term of bi-lingual evaluation understudy(BLEU).

Understanding groundwater table using a statistical model

In this study, a statistical model was established to estimate the groundwater table using precipitation, evaporation, the river stage of the Liangduo River, and the tide level of the Yellow Sea, as well as to predict the groundwater table with easily measurable climate data in a coastal plain in eastern China. To achieve these objectives, groundwater table data from twelve wells in a farmland covering an area of 50 m ~ 150 m were measured over a 12-month period in 2013 in Dongtai City, Jiangsu Province. Trend analysis and correlation analysis were conducted to study the patterns of changes in the groundwater table. In addition, a linear regression model was established and regression analysis was conducted to understand the relationships between precipitation, evaporation, river stage, tide level, and groundwater table. The results are as follows： （1） The groundwater table was strongly affected by climate factors （e.g., precipitation and evaporation）, and river stage was also a significant factor affecting the groundwater table in the study area （p 〈 0.01, where p is the probability value）. （2） The groundwater table was especially sensitive to precipitation. The significance of the factors of the groundwater table were ranked in the following descending order： precipitation, evaporation, and river stage. （3） A triple linear regression model of the groundwater table, precipitation, evaporation, and river stage was established. The linear relationship between the groundwater table and the main factors was satisfied by the actual values versus the simulated values of the groundwater table （R^2 = 0.841, where R^2 is the coefficient of determination）.

Potential hydraulic connectivity of coal mine aquifers based on statistical analysis of hydrogeochemistry

Mining activities interfere with the natural groundwater chemical environment,which may lead to hydrogeochemical changes of aquifers and mine water inrush disasters.This study analyzed the hydrochemical compositions of 80 water samples in three aquifers and developed a water source identification model to explore the control factors and potential hydraulic connection of groundwater chemistry in a coal mine.The results showed that the hydrochemical types of the three aquifers were different.The main hydrochemical compositions of the loose-layer,coal-bearing,and limestone aquifers were HCO_(3)·Cl-Na,SO_(4)·HCO_(3)-Na,and SO_(4)-Na·Ca,respectively.The correlation,Unmix,and factor an-alyses showed that the hydrochemical composition of groundwater was controlled by the dissolution of soluble minerals(such as calcite,dolomite,gypsum,and halite)and the weathering of silicate minerals.The factor score plot combined with Q-mode cluster analysis demon-strated no remarkable hydraulic connection among the three aquifers in the study area.The water source identification model effectively identified the source of inrush water.Moreover,the mixing ratio model rationally quantified the contributions of the three aquifers to inrush water.

Foxtail Millet Planting Willingness of Farmers in North China Based on Analysis of SPSS Statistical Data

Based on the field survey of the foxtail millet planting,processing and sales in foxtail millet production areas,it found out basic demands of foxtail millet farmers and processing enterprises for policies. It made statistical analysis with the aid of SPSS statistical software. It found that current situation of China's foxtail millet industry is not optimistic. Finally,it came up with policy recommendations including enhancing actual effect of policy,establishing special fund,increasing scientific and technological support,and encouraging mechanized planting.

Deterministic tools to predict gas assisted gravity drainage recovery factor

Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.

RESIDUAL INTERACTION IN THE STATISTICAL SPECTROSCOPIC ANALYSIS——THE CHARGE FORM FACTOR OF ^(20)Ne

By using the statistical spectroscopic theory, we have discussed the form factors of some s-d shell nuclei under the mean field approximation. Without introducing any free parameters, the experimental data are well reproduced in the

Factors associated with heterochronic gastric cancer development post-endoscopic mucosal dissection in early gastric cancer patients

BACKGROUND Endoscopic mucosal resection is an innovative method for treating early gastric cancer and has been widely used in clinical practice.AIM To analyze the factors associated with the development of heterochronic gastric cancer in patients with early gastric cancer who had undergone endoscopic mucosal dissection(EMD).METHODS A cohort of patients with early gastric cancer treated using EMD was retrospectively analyzed,and patients who developed heterochronic gastric cancer after the surgery were compared with those who did not.The effects of patient age,sex,tumor size,pathological type,and surgical technique on the development of heterochronic gastric cancer were assessed using statistical analysis.RESULTS Of the 300 patients with early gastric cancer,150 patients developed heterochronic gastric cancer after EMD.Statistical analysis revealed that patient age(P value=XX),sex(P value=XX),tumor size(P value=XX),pathological type(P value=XX),and surgical technique(P value=XX)were significantly associated with the occurrence of heterochronic gastric cancer.CONCLUSION Age,sex,tumor size,pathological type,and surgical technique are key factors influencing the occurrence of heterochronic gastric cancer after EMD in patients with early gastric cancer.To address these factors,postoperative follow-up and management should be strengthened to improve the prognosis and survival rate of patients.

Factor analysis for the statistical modeling of earthquake-induced landslides

Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes.In most existing studies,the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records.Unlike rainfall-induced landslides,earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems,and the development of the models for these landslides should instead depend on early earthquake warnings and estimations.Hence,in this study,factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes.Factors such as the slope gradient,lithology(geology),aspect,and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.

Adaptive Statistical Spatial Downscaling of Precipitation Supported by High-Resolution Atmospheric Simulation Data for Mountainous Areas of Nepal

In complex terrain regions, it is very challenging to obtain high accuracy and resolution precipitation data that are required in land hydrological studies. In this study, an adaptive precipitation downscaling method is proposed based on the statistical downscaling model MicroMet. A key input parameter in the MicroMet is the precipitation adjustment factor(PAF) that shows the elevation dependence of precipitation. Its value is estimated conventionally based on station observations and suffers sparse stations in high altitudes. This study proposes to estimate the PAF value and its spatial variability with precipitation data from high-resolution atmospheric simulations and tests the idea in Nepal of South Himalayas, where rainfall stations are relatively dense. The result shows that MicroMet performs the best with the PAF value estimated from the simulation data at the scale of approximately 1.5 degrees. Not only the value at this scale is qualitatively consistent with early knowledge obtained from intensive observations, but also the downscaling performance with this value is better than or comparable to that with the PAF estimated from dense station data. Finally, it is shown that the PAF estimation, although critical, cannot replace the importance of increasing input station density for downscaling.

Analysis of the Factors Influencing the Participation of University Students in Rural Education for Poverty Alleviation

The principle of“poverty alleviation first helps the poor”is fundamental to poverty alleviation through education in rural areas.It serves as an important foundation for improving the soft power of rural culture and promoting the development of rural cultural construction.However,college students,being one of the main participants in educational poverty alleviation,have not been equipped with a well-established institutionalized participation mechanism and a sufficient awareness of participation.To enhance college students’awareness and participation in rural education and poverty alleviation and to improve the institutionalization,this research focuses on college students as a group,delves into the current situation and willingness of college students to participate in rural education and poverty alleviation,and analyzes the influencing factors affecting college students’participation in rural education and poverty alleviation by means of a questionnaire and a computerized statistical algorithm.Lastly,based on mathematical and statistical analysis,the research puts forward corresponding optimization countermeasures and suggestions from the perspectives of the government,colleges and universities,and villages,so as to provide decision-making guidelines for solving the problems of rural education development and talent constraints.

2017—2021年云南省肺结核与糖尿病共病患者抗结核治疗效果及影响因素分析

目的:分析云南省利福平敏感肺结核与糖尿病共病患者(简称“共病患者”)抗结核治疗效果及影响治疗效果的危险因素,为改善共病患者抗结核治疗效果提供依据。方法:通过“中国疾病预防控制信息系统”子系统“监测报告管理系统”获取2017年1月1日至2021年12月31日云南省登记的共病患者病案信息,包括社会人口学信息、结核病诊断信息、抗结核治疗信息和抗结核治疗结局判定信息等,最终纳入3984例。分析共病患者抗结核治疗效果,并运用Cox回归模型分析影响共病患者治疗结局的危险因素。结果:3984例共病患者抗结核成功治疗率为89.36%(3560/3984),不良治疗结局发生率为10.64%(424/3984),死亡占4.27%(170/3984),治疗过程中丢失占2.46%(98/3984),失败占2.31%(92/3984),因药物不良反应中断治疗占1.61%(64/3984)。44.22%(179/424)的不良结局发生在治疗的前2个月。Cox回归模型分析显示,≥65岁者出现不良治疗结局的风险较<35岁者高(HR=2.612,95%CI:1.483~4.601),复治患者出现不良治疗结局的风险是初治患者的1.405倍(95%CI:1.072~1.843),病原学阳性者出现不良治疗结局的风险是阴性或未查患者的1.473倍(95%CI:1.183~1.834)。结论:云南省共病患者抗结核治疗效果不佳,应重视共病患者,尤其是≥65岁老年人、复治、病原学阳性患者,抗结核治疗过程中治疗效果监测及健康管理,及时给予干预。

互联网医院平台对结核病患者就诊体验的影响因素分析

目的:调查在互联网医院平台就诊的结核病患者的就诊体验并分析其影响因素,为改善结核病患者对“互联网+”医疗服务的就诊体验、提升互联网医院平台的服务质量提供参考。方法:以随机数字表法抽取2022年1—5月在首都医科大学附属北京胸科医院互联网医院平台线上就诊并且符合纳入标准的175例结核病患者进行电话调查,自主设计问卷完成患者基本情况、就诊情况、满意度等方面的数据收集,并采用“互联网+”患者就诊体验量表对患者体验进行评价,收回有效问卷150份,有效率为85.7%(150/175),分别采用t检验、方差分析和多元线性回归分析对患者就诊体验进行单因素和多因素分析。结果:患者就诊体验总体得分为(59.44±7.06)分,其中安全性维度得分最高[(4.41±0.62)分],获得性维度得分最低[(3.97±0.66)分]。单因素分析结果显示,患者的年龄(F=8.477,P<0.01)、居住地(F=3.285,P<0.05)、职业(F=3.158,P<0.01)对患者体验的差异具有统计学意义;就诊等待时间(t=-3.099,P<0.01)、问题咨询时间(t=3.725,P<0.01)、医生的专业度(F=4.887,P<0.01)对患者体验的差异具有统计学意义;患者对整体就诊(F=16.134,P<0.01)、就诊流程(F=21.862,P<0.01)、就医资源获取(F=24.165,P<0.01)、就诊医生(F=20.458,P<0.01)及医院整体(F=17.176,P<0.01)的满意度对患者体验的差异均具有统计学意义。在多元线性回归分析中,相较于北京郊区,天津或河北等环京地区患者的就诊体验更好(t=2.386,P<0.05);相较于离退休人员,政府事业单位、公司职员、个体经营者、无业人员的就诊体验更好(t=2.585,P<0.05;t=2.626,P<0.05;t=2.839,P<0.01;t=2.424,P<0.05);就诊等待时间≥30 min、有足够的问题咨询时间、对就医资源获取便捷性和对就诊医生的满意度高的患者就诊体验更好(t=3.402,P<0.01;t=-2.775,P<0.01;t=2.329,P<0.05;t=2.064,P<0.05)。结论:结核病患者在互联网医院平台就诊的总体体验较好,影响患者就诊体验的主要因素有年龄、居住地、职业、就诊等待时间、问题咨询时间,以及对资源获取和看诊医生的满意度,对以上问题进行改善并加强对重点人群的关注,有助于提升互联网医院平台的就医体验和服务质量。

福建省县级结核病定点医疗机构抗结核固定剂量复合制剂使用情况及其影响因素分析

目的:分析福建省县级结核病定点医疗机构抗结核固定剂量复合制剂(fixed-dose combination,FDC)使用情况及其影响因素,为进一步推广抗结核FDC和规范全省抗结核治疗方案提供理论依据。方法:采用典型抽样法从福建省9个设区市和平潭综合实验区共抽取18家结核病定点医院作为研究现场,调查各研究现场2020年1月1日至2021年12月31日登记并治疗且符合入组标准的980例利福平敏感肺结核患者FDC使用及替换情况,采用两水平logistic模型分析影响FDC使用的因素。结果:980例患者初始治疗方案的FDC使用率为80.41%(788/980)。两水平logistic模型分析结果显示,年龄为60~90岁和有并发症/合并症均为未使用FDC的危险因素(OR=1.499,95%CI:1.000~2.247,P=0.049;OR=1.844,95%CI:1.231~2.761,P=0.003),而体质量为38~54kg、55~70kg和71~90kg的患者未使用FDC的风险均降低(OR=0.297,95%CI:0.123~0.721,P=0.007;OR=0.334,95%CI:0.136~0.823,P=0.017;OR=0.217,95%CI:0.065~0.722,P=0.013)。有16.50%(130/788)使用FDC治疗的患者在后续的治疗中使用“散药”替换FDC。FDC方案替换率在不同地区[2.96%(4/135)~32.00%(24/75)]、60~90岁[20.45%(54/264)]与13~59岁[14.50%(76/524)]患者间的差异均有统计学意义(χ^(2)=50.752,P<0.001;χ^(2)=4.513,P=0.034)。结论:福建省抗结核FDC使用率约为80%,不同地区FDC使用率存在较大差异。患者年龄、体质量及有并发症/合并症均是使用FDC的影响因素。

基于多指标成分定量联合多元统计分析评价不同产地鹅不食草药材质量

基于多指标成分定量联合多元统计分析综合评价不同产地鹅不食草质量差异,提高鹅不食草药材的整体质量控制水平。以Waters XTerra MS C18柱为色谱柱,乙腈-0.2%磷酸为流动相,采用HPLC法同时测定鹅不食草中绿原酸、异绿原酸A、异绿原酸C、槲皮素、山柰酚、3-甲氧基槲皮素、山金车内酯D、山金车内酯C、小堆心菊素C、短叶老鹳草素A、羽扇豆醇、β-谷甾醇和蒲公英甾醇含量。对16批鹅不食草多指标成分定量检测结果进行聚类分析(cluster analysis,CA)、主成分分析(principal component analysis,PCA)和因子分析(factor analysis,FA),对不同产地鹅不食草药材进行分组和综合质量评价。利用正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)分析挖掘影响产品质量的差异性标志物。方法学验证符合中华人民共和国药典要求,13种成分在各自范围内线性关系良好(R^(2)>0.999),平均加样回收率(n=9)在96.88%~100.1%之间(RSD<2.0%)。CA结果显示16批鹅不食草聚为3类。PCA结果显示前2个主成分特征值分别为10.187和2.059,方差贡献率分别为78.361%和15.839%,表明前2个主成分代表鹅不食草94.200%信息量,对鹅不食草质量评价具有很好的代表性。FA结果显示16批鹅不食草样品主成分综合得分在-1.451~1.344,其中浙江和江苏产鹅不食草综合得分较高,排名居于前5位,湖北、湖南和广东产鹅不食草综合得分排名居中,贵州和四川产鹅不食草综合得分排名靠后。OPLS-DA结果显示短叶老鹳草素A、异绿原酸A、小堆心菊素C、槲皮素、山金车内酯C是影响鹅不食草产品质量的差异性标志物。所建立的HPLC法操作便捷,结果准确,可用于鹅不食草多指标成分定量控制;多元统计分析可用于不同产地鹅不食草的整体质量评价。

User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.

老年髋部骨折手术延迟的影响因素分析及风险预测模型构建

目的:探讨老年髋部骨折手术延迟的影响因素,构建老年髋部骨折手术延迟风险预测模型。方法:选取2019年11月至2022年11月采用手术治疗的老年髋部骨折患者的病例资料进行研究,将纳入研究的患者按照2∶1的比例随机分为训练集(用于模型构建)和验证集(用于模型验证)。从病历系统中提取纳入患者的信息,包括年龄、性别、体质量指数、骨折类型、美国麻醉医师协会(American Society of Anesthesiologists, ASA)分级、伤前日常活动能力(activities of daily living, ADL)、是否服用影响凝血功能的药物、入院至手术时间、手术方式,是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、肝功能不全、肾功能不全、电解质紊乱、尿酮体异常、下肢静脉血栓、凝血功能异常,以及入院后血清肿瘤坏死因子-α、C反应蛋白水平等。将训练集中的患者根据入院至手术时间分为早期手术组(入院至手术时间<48 h)和延迟手术组(入院至手术时间≥48 h)。先对2组患者的相关信息进行单因素对比分析,再对单因素分析中组间差异有统计学意义的因素进行多因素Logistic回归分析及多重共线性诊断;采用R软件基于贝叶斯网络模型构建老年髋部骨折手术延迟风险预测模型,并采用Netica软件进行贝叶斯网络模型推理。采用受试者操作特征(receiver operating characteristic, ROC)曲线评价老年髋部骨折手术延迟风险预测模型的区分度,采用校准曲线评价老年髋部骨折手术延迟风险预测模型的校准度。结果:(1)分组结果。共纳入老年髋部骨折患者318例,训练集212例、验证集106例。根据入院至手术时间,训练集中早期手术组78例、延迟手术组134例。(2)老年髋部骨折手术延迟影响因素的单因素分析结果。2组患者ASA分级、是否服用影响凝血功能的药物及是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常的比较,组间差异均有统计学意义(χ~2=3.862,P=0.049;χ~2=26.806,P=0.000;χ~2=29.852,P=0.000;χ~2=21.743,P=0.000;χ~2=25.226,P=0.000;χ~2=5.415,P=0.020;χ~2=11.683,P=0.001;χ~2=14.686,P=0.000;χ~2=6.057,P=0.014)。(3)老年髋部骨折手术延迟影响因素的多因素分析及多重共线性诊断结果。多因素Logistic回归分析结果显示,服用影响凝血功能的药物及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均是老年髋部骨折手术延迟的影响因素[β=0.328,P=0.000,OR=5.112,95%CI(2.686,9.728);β=0.322,P=0.000,OR=5.425,95%CI(2.884,10.203);β=0.302,P=0.000,OR=3.956,95%CI(2.189,7.148);β=0.312,P=0.000,OR=4.560,95%CI(2.476,8.398);β=0.291,P=0.021,OR=1.962,95%CI(1.108,3.474);β=0.296,P=0.001,OR=2.713,95%CI(1.520,4.844);β=0.303,P=0.000,OR=3.133,95%CI(1.729,5.679);β=0.296,P=0.015,OR=2.061,95%CI(1.154,3.680)];多重共线性诊断结果显示,上述影响因素均不存在共线性(VIF=1.134,VIF=1.266,VIF=1.465,VIF=1.389,VIF=1.342,VIF=1.183,VIF=1.346,VIF=1.259)。(4)基于贝叶斯网络模型的老年髋部骨折手术延迟风险预测模型的构建与推理结果。基于贝叶斯网络模型构建的老年髋部骨折手术延迟风险预测模型包括8个节点、8条有向边。模型显示,服用影响凝血功能的药物及合并精神障碍、呼吸系统疾病、电解质紊乱、凝血功能异常直接影响手术延迟的发生,合并心功能不全、高血压、糖尿病间接影响手术延迟的发生;推理结果显示,患者合并心功能不全、凝血功能异常及精神障碍时,手术延迟发生率为64.1%。(5)老年髋部骨折手术延迟风险预测模型的评价结果。采用训练集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.861[P=0.000,95%CI(0.810,0.912)],灵敏度为91.29%,特异度为93.35%;校准曲线显示其一致性指数为0.866[P=0.000,95%CI(0.702,0.943)];采用验证集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.848[P=0.000,95%CI(0.795,0.901)],灵敏度为91.62%,特异度为92.46%;校准曲线显示其一致性指数为0.879[P=0.000,95%CI(0.723,0.981)]。结论:服用影响凝血功能的药物以及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均为老年髋部骨折手术延迟的影响因素,基于上述因素构建的老年髋部骨折手术延迟风险预测模型具有较高的应用价值。