Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Navigating breast cancer brain metastasis:Risk factors,prognostic indicators,and treatment perspectives

In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.

Predicting lymph node metastasis in colorectal cancer:An analysis of influencing factors to develop a risk model

BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

Serum CA125, HE4 and ROMA index in elderly patients with ovarian cancer

Objective:To study the value of serum tumor markers, carbohydrate antigen 125 (CA125), human epididymis secretory protein 4 (HE4) and ovarian cancer risk factor (ROMA) index in elderly patients with ovarian cancer, so as to provide a choice for clinical diagnosis.Methods:A total of 110 cases of ovarian cancer treated in our hospital in December 2017-December 2015 were selected as malignant group. In addition, 120 cases of benign ovarian tumors in the same period were selected as the benign group, and 92 healthy women who came to the hospital for health examination were selected as the control group. Serum HE4, CA125 levels and positive rates were detected by microparticle enzyme immunochemiluminescence assay, and ROMA index values were combined to assess the risk of ovarian cancer.Results:Malignant group serum CA125, HE4 level and ROMA index were significantly higher than those in the benign group and the control group, the level of CA125 in positive group was higher than control group, but the difference in level of HE4 and ROMA index between benign group and control group was not statistically significant. The positive rates of serum CA125, HE4 and ROMA index in malignant group were 76.4%, 92.7%, 96.4%, which were significantly higher than those in benign group (28.3%, 18.3%, 15%). The negative predictive value, positive predictive value, specificity and sensitivity of CA125 were all lower than those of HE4. The negative predictive value, positive predictive value, specificity and sensitivity of the combined ROMA index were higher than those of single diagnosis.Conclusions: Serum CA125, HE4 and ROMA index in elderly patients with ovarian cancer are significantly higher than those in elderly patients with benign ovarian tumors and healthy women. The combined diagnosis is the highest, with Gao Min's high sensitivity and specificity, which can be popularized in clinical practice.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Ultrasound Features Improve Diagnostic Performance of Ovarian Cancer Predictors in Distinguishing Benign and Malignant Ovarian Tumors

To determine whether ultrasound features can improve the diagnostic performance of tumor markers in distinguishing ovarian tumors,we enrolled 719 patients diagnosed as having ovarian tumors at Nanfang Hospital from September 2014 to November 2016.Age,menopausal status,histopathology,the International Federation of Gynecology and Obstetrics(FIGO)stages,tumor biomarker levels,and detailed ultrasound reports of patients were collected.The area under the curve(AUC),sensitivity,and specificity of the bellow-mentioned predictors were analyzed using the receiver operating characteristic curve.Of the 719 patients,531 had benign lesions,119 had epithelial ovarian cancers(EOC),44 had borderline ovarian tumors(BOT),and 25 had non-EOC.AUCs and the sensitivity of cancer antigen 125(CAI25),human epididymis-specific protein 4(HE4),Risk of Ovarian Malignancy Algorithm(ROMA),Risk of Malignancy Index(RMI1),HE4 model,and Rajavithi-Ovarian Cancer Predictive Score(R-OPS)in the overall population were 0.792,0.854,0.856,0.872,0.893,0.852,and 70.2%,56.9%,69.1%,60.6%,77.1%,71.3%,respectively.For distinguishing EOC from benign tumors,the AUCs and sensitivity of the above mentioned predictors were 0.888,0.946,0.947,0.949,0.967,0.966,and 84.0%,79.8%,87.4%,84.9%,90.8%,89.1%,respectively.Their specificity in predicting benign diseases was 72.9%,94.4%,87.6%,95.9%,86.3%,90.8%,respectively.Therefore,we consider biomarkers in combination with ultrasound features may improve the diagnostic performance in distinguishing malignant from benign ovarian tumors.

Clinical Performance of ADNEX (The Assessment of Different NEoplasias in the adneXa) Model in Early Diagnosis and Staging of Benign and Malignant Ovarian Tumors

Objective: To investigate the clinical value of ADNEX model in early diagnosis and staging of benign and malignant ovarian tumors. Method: 136 cases of ovarian cancer patients treated in our hospital were retrospectively analyzed using the ADNEX risk model and MRI data. The accuracy of the two diagnostic methods was compared with the results of pathological examination as gold standard. Results: For qualitative assessment, the accuracy and sensitivity of the ADNEX model were 78.70% and 93%, while the accuracy and sensitivity of MRI examination were 80.1%, and 90.7%, respectively. The diagnostic values of the two methods were not statistically different (P > 0.05). For ovarian tumor staging, the ADNEX model was significantly less accurate and specific for staging borderline tumor than MRI examination, although it had significantly higher sensitivity (P 0.05). Conclusion: ADNEX risk model has certain diagnostic and predictive value to distinguish benign from malignant ovarian tumors. It is useful to detect and exclude ovarian tumor. However, for early diagnosis, it is not accurate enough and further study is needed to validate this usefulness.

Breast and Ovarian Cancer in Young Women of the Arabian Gulf Region: Relationship to Age

It is widely known that cancer is a disease of “old-age”. However available data show that this is not the case for many types of cancers. Incidences of breast and ovarian cancers have varying rates of change with age. Breast cancer data of Arabian-gulf women, show that the incidence rates increase with age and reach a maximum at 39 year. It then declines linearly with age to about 55 years. The rate of increase and its changes with age are similar to those of many other countries. In the premenopausal phase the relationship between incidence and age could be adequately modeled using a linear model for the logarithmic transformations of age and incidence. Similar observations are made for the ovarian cancer incidences. Results: It is shown that the rate of increase in breast and ovarian cancer incidence with respect to age is increasing in the premenopausal ages. Moreover, the burden of the disease with respect to mortality and “Disability Adjusted Life Years” or DALY, varied considerably among the six gulf countries. Conclusions: We conclude, based on the age incidence relationship that the number of cancer cases may double in the next period that follows our study period (1998-2009). Moreover, if the six countries have identical relationship between age and the two types of cancer, there should be an integrated and unified effort to have a common strategy for prevention and control.

Effect of low anterior resection syndrome on quality of life in colorectal cancer patients:A retrospective observational study

BACKGROUND Low anterior resection syndrome(LARS)is a common complication of anuspreserving surgery in patients with colorectal cancer,which significantly affects patients'quality of life.AIM To determine the relationship between the incidence of LARS and patient quality of life after colorectal cancer surgery and to establish a LARS prediction model to allow perioperative precision nursing.METHODS We reviewed the data from patients who underwent elective radical resection for colorectal cancer at our institution from April 2013 to June 2020 and completed the LARS score questionnaire and the European Organization for Research and Treatment of Cancer Core Quality of Life and Colorectal Cancer Module questionnaires.According to the LARS score results,the patients were divided into no LARS,mild LARS,and severe LARS groups.The incidence of LARS and the effects of this condition on patient quality of life were determined.Univariate and multivariate analyses were performed to identify independent risk factors for the occurrence of LARS.Based on these factors,we established a risk prediction model for LARS and evaluated its performance.RESULTS Among the 223 patients included,51 did not develop LARS and 171 had mild or severe LARS.The following quality of life indicators showed significant differences between patients without LARS and those with mild or severe LARS:Physical,role,emotional,and cognitive function,total health status,fatigue,pain,shortness of breath,insomnia,constipation,and diarrhea.Tumor size,partial/total mesorectal excision,colostomy,preoperative radiotherapy,and neoadjuvant chemotherapy were identified to be independent risk factors for LARS.A LARS prediction model was successfully established,which demonstrated an accuracy of 0.808 for predicting the occurrence of LARS.CONCLUSION The quality of life of patients with LARS after colorectal cancer surgery is significantly reduced.

胃癌术后辅助化疗期间恶心呕吐风险预测模型的建立及验证

目的探讨胃癌病人手术后化疗期间恶心呕吐的风险因素,并构建相应的风险预测模型。方法2020年2月~2021年2月收治的胃癌病人作为建模集组,用于探讨胃癌病人手术后化疗期间恶心呕吐的风险因素,并构建相应的风险预测模型,将2021年3月~2022年2月(第1年)、2022年3月~2023年2月(第2年)、2023年3月~2024年2月(第3年)作为验证集组用于验证建模集组构建的风险预测模型。统计建模集组病人化疗期间呕吐发生情况。采用单因素和多因素Logistic回归分析胃癌病人手术后化疗期间恶心呕吐风险因素,并构建相应的风险预测模型。采用受试者工作特征曲线(receiver operating characteristic curve,ROC)以2021年3月~2024年2月3年的验证集验证列线图预测模型的准确性。结果建模集组共纳入112例,其中75例未发生化疗相关性恶心呕吐,纳入对照组,37例病人发生了化疗相关性恶心呕吐,纳入观察组。单因素分析显示,年龄、性别、饮酒史、晕动病史、化疗次数、既往化疗相关性恶心呕吐史、妊吐史、匹茨堡睡眠质量指数(PSQI)、心理预期发生化疗后恶心呕吐等与病人发生化疗相关性恶心呕吐有关(P<0.05)。将单因素分析得到具有统计学意义的因素进行Logistic回归分析,结果显示,年龄、性别、晕动病史、化疗次数、妊吐史、PSQI、心理预期发生化疗后恶心呕吐是胃癌病人术后化疗期间恶心呕吐的危险因素(P<0.05)。根据Logistic回归得到具有统计学意义的因素构建风险预测模型。使用Bootstrap法对模型进行内部验证,第1年验证集组ROC曲线下面积(AUC)为0.71(95%CI:0.71~1.00),第2年验证集组0.69(95%CI:0.58~0.96),第3年验证集组0.66(95%CI:0.54~0.95)。结论年龄、性别、晕动病史、化疗次数、妊吐史、PSQI、心理预期发生化疗后恶心呕吐是胃癌病人术后化疗期间恶心呕吐的危险因素,上述因素构建的胃癌病人化疗期间恶心呕吐的风险预测模型具有较好的预测效能。

早期胃癌患者内镜黏膜下剥离术后疲劳预测模型的构建

目的探究早期胃癌患者胃内镜黏膜下剥离术后疲劳(POF)的危险因素,建立并验证早期胃癌POF风险预测模型。方法采用便利抽样法选取2020年10月-2022年12月于我院治疗的232例早期胃癌患者为研究对象,使用一般资料表、中文版多维度疲乏症状量表、阿姆斯特丹术前焦虑与信息需要量表、匹兹堡睡眠质量量表、艾森克人格问卷简式量表收集相关数据,通过单因素分析和二分类logistic回归分析发生术后疲劳的危险因素,并建立列线图预测模型,选取2023年1-6月在我院治疗的72例患者对模型进行验证。结果经检验,年龄、性别、睡眠障碍、病灶切除面积、气质类型、术后疼痛和胃肠功能紊乱是早期胃癌患者POF发生的独立危险因素,并以此建立早期胃癌患者POF的列线图模型,经验证该模型具有较好的准确度(H-L检验:χ^(2)=2.822,P=0.945)和区分度(AUC=0.0.909,95%CI:0.937~0.980,P<0.001),最大约登指数为0.648,灵敏度为0.832,特异度为0.813,截断值0.319,校准曲线为斜率近似于1的直线。结论该模型预测效果良好,可为医护人员对早期胃癌POF高风险人群筛查提供参考,及时采取预防性措施。

结直肠癌患者术前营养风险指数对术后腹腔感染的预测价值

目的 探讨结直肠癌患者术前营养风险指数(NRI)对其术后腹腔感染的预测价值。方法 前瞻性纳入2019年4月至2022年4月漯河医学高等专科学校第三附属医院收治的125例接受腹腔镜根治术治疗的结直肠癌患者为研究对象,进行相关检查,计算患者术前NRI。术后进行14 d随访,根据患者随访期间术后腹腔感染发生情况分为发生组和未发生组。经logistic回归分析检验结直肠癌患者术前NRI与术后腹腔感染的关系,并探究其对患者术后腹腔感染的预测价值。结果 125例患者中31例(24.80%)出现术后并发腹腔感染,94例(75.20%)未发生术后腹腔感染。两组患者术前NRI、手术时间、肿瘤TNM分期比较,差异有统计学意义(P<0.05);组间其他一般资料比较,差异无统计学意义(P>0.05)。logistic回归分析结果显示,肿瘤TNM分期Ⅲ期是结直肠癌患者术后腹腔感染的危险因素(OR>1,P<0.05),术前NRI高是结直肠癌患者发生术后腹腔感染的保护因素(OR<1,P<0.05)。绘制受试者工作特征曲线,结果显示,术前NRI预测结直肠癌患者发生术后腹腔感染的AUC≥0.7,具有一定预测价值,且当其取最佳阈值90.770时,可获得最佳预测价值。结论 术前NRI低会增加结直肠癌患者术后腹腔感染发生风险,检测术前NRI预测患者术后腹腔感染发生风险。

肺癌根治术后肺部感染病原菌分布及其早期风险预测模型的构建

目的研究肺癌根治术后肺部感染病原菌分布及其早期风险预测模型的构建。方法选取72例肺癌患者作为研究对象。所有患者均接受肺癌根治术,根据患者术后肺部感染分为感染组(n=18)和未感染组(n=54)。分析感染组患者肺部感染病原菌分布。对比2组患者临床资料,并运用多因素Logistics回归模型筛选出患者术后感染的独立危险因素,基于独立危险因素创建列线图预测模型,并对列线图的预测性和准确度进行验证。结果72例患者出现了18例肺部感染,感染率为25.00%。18例感染总共分离出35株病原菌,其中革兰阴性菌22株(62.85%),以铜绿假单胞菌、肺炎克雷伯菌和大肠埃希菌为主;革兰阳性菌9株(25.71%),以金色葡萄球菌和溶血性葡萄球菌为主;真菌4株(11.42%),以白色念珠菌为主。与未感染组患者相比,感染组患者年龄≥60岁、有吸烟史、术前FEV1≤80%、手术时间≥150 min、多叶切除以及术中出血量≥200 mL等占比更高(P<0.05);年龄、吸烟史、术前FEV1、手术时间、切除范围、术中出血量均是术后肺部感染的独立影响因素(P<0.05)。列线图结果提示,年龄≥60岁为52分、有吸烟史为65分、术前FEV1≤80%为68分、手术时间≥150 min为49分、多叶切除为78分、术中出血量≥200 mL为70分,经验证,其模型预测风险的精准性及区分度较高。结论肺癌根治术后肺部感染病原菌以革兰阴性菌为主。年龄、吸烟史、术前FEV1、手术时间、切除范围以及术中出血量均是患者术后肺部感染的影响因素,基于此创建的预测模型,区分度和准确度较高。

三角肌结节指数联合术前因素构建老年肱骨近端骨折锁定钢板内固定失效的风险预测模型

背景:老年肱骨近端骨折是三大骨质疏松性骨折之一,解剖锁定钢板内固定是国内大部分医生治疗难以复位和复杂骨折类型的首选,但术后发生复位失效的概率较高,严重影响患者生活质量。目的:探讨三角肌结节指数与老年肱骨近端骨折术后复位失效的相关性,分析筛选出老年肱骨近端骨折术后复位失效的术前独立风险因素,并构建和验证临床预测模型的有效性。方法:收集2012年6月至2021年6月佛山市中医院符合标准的接受切开复位锁定钢板治疗的153例老年肱骨近端骨折患者的临床资料,根据其是否发生术后复位失效分为复位失效亚组和复位维持亚组。采用先单因素后多因素Logistic回归分析筛选独立风险因素,通过R语言构建列线图,内部验证采用Bootstrap法重抽样1000次后,通过Hosmer-Lemeshow拟合优度关联检验、绘制受试者工作特征曲线、校准曲线、临床决策和影响曲线评价其拟合优度、区分度、校准能力和临床应用价值。选择2013年6月至2021年8月收治的55例老年肱骨近端骨折患者作为模型外部验证组,评价预测模型的稳定性和准确度。结果与结论:①训练组153例患者中,44例患者出现钢板内固定术后复位失效,失效率为28.8%;多因素Logistic回归分析结果显示,三角肌结节指数[OR=9.782,95%CI(3.798,25.194)]、骨折端内翻成角移位[OR=4.209,95%CI(1.472,12.031)]、肱骨内侧柱粉碎[OR=4.278,95%CI(1.670,10.959)]是老年肱骨近端骨折术后复位失效的独立风险因素(P<0.05);②基于独立风险因素构建预测模型并绘制列线图,训练组Hosmer-Lemeshow检验结果显示,χ^(2)=0.812(P=0.976),曲线下面积=0.830[95%CI(0.762,0.898)];校准图结果表明模型预测风险和实际发生风险有较好的一致性;决策曲线和临床影响曲线结果表明列线图具有较好的临床适用性;③预测模型在验证组预测术后复位失效总正确率86%,曲线下面积=0.902[95%CI(0.819,0.985)];④提示三角肌结节指数<1.44、肱骨内侧柱粉碎、骨折端内翻成角移位是老年肱骨近端骨折术后复位失效的独立风险因素;⑤此次研究构建的风险预测模型内、外部验证表明该模型区分度、准确度和临床适用度较高,可用于个性化预测和筛选老年肱骨近端骨折术后复位失效的高危人群,模型的阈值风险概率高于65%时的预测高风险人数和实际发生人数高度匹配,临床医生应采用针对性治疗。

基于生物信息学构建口腔鳞状细胞癌免疫基因的预后模型

目的旨在构建免疫相关基因(IRGs)的风险预测模型,以预测口腔鳞状细胞癌(OSCC)患者的预后。方法应用生物信息学技术分析OSCC的转录组测序数据,鉴定出差异表达的IRGs(DEIRGs)。通过Cox回归分析构建DEIRGs的风险预测模型,并对其预测能力进行评估。分析该模型与临床病理和免疫细胞浸润的相关性。结果通过比较OSCC和正常样本共鉴定出3634个差异表达基因,其中包括330个DEIRGs(FDR<0.05,|logFC|>1)。单因素Cox回归分析筛选出与预后相关的20个DEIRGs(P<0.05),多因素Cox回归分析筛选出其中15个DEIRGs用于构建风险预测模型。该模型可作为OSCC患者的独立预后因素(P<0.001),预测患者预后的能力具有较高的准确性(AUC=0.732),并与临床分期(t=-3.484,P<0.001)、B细胞(Cor=-0.180,P=0.002)和CD4^(+)T细胞(Cor=-0.127,P=0.026)密切相关。结论基于15个预后相关DEIRGs构建的风险预测模型能够有效地预测OSCC患者的预后,可帮助临床医生为不同风险的OSCC患者选择个性化的治疗策略。

血清人附睾蛋白4、糖类抗原15-3、ROMA指数对上皮性卵巢癌复发的预测价值

目的分析血清人附睾蛋白4(HE4)、糖类抗原15-3(CA15-3)联合卵巢癌风险评估模型(ROMA)指数对上皮性卵巢癌(EOC)复发的预测价值。方法选取2018年1月1日至2022年1月1日于该院妇产科就诊的135例EOC患者作为研究对象。分析HE4、CA15-3水平及ROMA指数在不同肿瘤类型、淋巴结转移情况、国际妇产联盟分期(FIGO)分期及复发情况的EOC患者上的差异。绘制受试者工作特征(ROC)曲线分析HE4、CA15-3及ROMA指数单独及3项指标联合检测对EOC患者3年复发的预测价值。结果浆液性囊腺癌患者的ROMA指数、HE4、CA15-3水平均高于黏液性囊腺癌、子宫内膜样腺癌、交界性癌、透明细胞癌、其他类型癌患者,差异均有统计学意义(P<0.05);子宫内膜样腺癌患者HE4水平明显高于黏液性囊腺癌、交界性癌、透明细胞癌及其他类型癌患者,且透明细胞癌患者HE4水平明显高于黏液性囊腺癌、交界性癌及其他类型癌患者,差异均有统计学意义(P<0.05);透明细胞癌患者CA15-3水平明显高于黏液性囊腺癌、子宫内膜样腺癌、交界性癌及其他类型癌患者,且子宫内膜样腺癌患者CA15-3水平明显高于黏液性囊腺癌、交界性癌及其他类型癌患者,差异均有统计学意义(P<0.05);子宫内膜样腺癌患者ROMA指数明显高于黏液性囊腺癌、交界性癌、透明细胞癌及其他类型癌患者,且透明细胞癌患者ROMA指数明显高于黏液性囊腺癌、交界性癌及其他类型癌患者,差异均有统计学意义(P<0.05)。FIGO分期Ⅲ期患者ROMA指数、HE4、CA15-3水平均明显高于FIGO分期Ⅰ期及FIGO分期Ⅱ期患者,且FIGOⅡ期患者明显高于FIGOⅠ期患者,差异均有统计学意义(P<0.05)。有淋巴结转移的患者ROMA指数、HE4、CA15-3水平均明显高于无淋巴结转移的患者,差异均有统计学意义(P<0.05)。在规定的时间内,共计有28例患者出现复发,107例患者未出现复发。EOC复发患者的ROMA指数、HE4、CA15-3水平均明显高于未复发EOC患者,差异均有统计学意义(P<0.05)。ROC曲线分析结果显示,HE4、CA15-3、ROMA指数单独及3项指标联合预测ECO患者3年内复发的曲线下面积(AUC)分别为0.670、0.716、0.669及0.798。结论EOC患者血清HE4、CA15-3水平及ROMA指数与患者的术后复发密切相关,术前联合检测血清HE4、CA15-3水平及ROMA指数有助于进一步提高对EOC患者术后复发的预测价值。

老年脓毒症患者住院期间死亡风险预测模型的建立与验证

目的建立并验证可预测老年脓毒症患者住院死亡风险的模型。方法回顾性纳入2019年1月—2022年12月哈尔滨医科大学附属第一医院重症医学科住院治疗的238例脓毒症患者,以住院期间转归情况为主要结局指标,分为死亡组68例(28.57%)和生存组170例(71.43%)。采用多因素Logistic回归法筛选脓毒症患者住院死亡的独立危险因素,并根据影响因素构建预测脓毒症患者住院死亡风险的模型。采用受试者工作特征(ROC)曲线对预测模型的性能进行评定,结果以曲线下面积(AUC)表示;基于2016年1月—2018年12月的176例脓毒症患者的临床资料进行外部验证。结果单因素分析显示,与生存组比较,死亡组年龄>70岁的比率、急性肾损伤(AKI)Ⅲ期比率及红细胞分布宽度(RDW)、纤维蛋白原、乳酸、血肌酐、英国早期预警评分(NEWS)、快速序贯器官衰竭评分(qSOFA)升高,氧合指数、白蛋白降低,差异均有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,年龄>70岁(OR=1.426,95%CI:1.055~1.928)、乳酸>6 mmol/L(OR=1.436,95%CI:1.105~1.867)、RDW>16%(OR=1.354,95%CI:1.080~1.698)、AKIⅢ期(OR=1.982,95%CI:1.407~2.791)、qSOFA>2分(OR=1.853,95%CI:1.255~2.738)是脓毒症患者住院期间死亡的独立危险因素(P<0.05)。根据回归分析结果,建立脓毒症患者死亡风险方程,一致性指数(Cindex)=-1.694+0.355×年龄+0.303×RDW+0.362×乳酸+0.684×AKIⅢ期+0.617×qSOFA。ROC曲线显示,内部验证时Cindex预测脓毒症患者住院期间死亡的AUC为0.882(95%CI:0.834~0.929),灵敏度为83.82%,特异度为77.06%;外部验证时Cindex预测脓毒症患者住院期间死亡的AUC为0.823(95%CI:0.757~0.889),灵敏度为74.13%,特异度为81.36%。结论年龄、乳酸、RDW、AKI分期、qSOFA与老年脓毒症死亡风险具有相关性,基于这些参数构建的模型可能有助于预测老年脓毒症住院期间全因死亡风险。

口腔癌游离皮瓣移植术后谵妄的相关因素及影响研究

目的研究口腔癌(oral cavity cancer,OCC)患者游离皮瓣移植术后谵妄(postoperative delirium,POD)的发病率和危险因素,并探讨预后营养指数(prognostic nutritional index,PNI)、系统免疫炎症指数(system immune inflammation index,SII)与OCC患者POD的相关性。方法回顾性收集徐州市中心医院2016年1月—2023年5月行游离皮瓣移植术且资料完整的OCC患者138例,其中男89例,女49例;年龄27~88岁,平均(60.04±10.89)岁。用SPSS 26.0软件包分析POD相关危险因素,Logistic回归筛选独立危险因素并建立预测模型,以受试者的工作曲线(ROC)下面积(AUC)及最佳临界值时的敏感度和特异度检验模型效果。结果POD发病率为10.9%(15/138)。单因素分析:年龄≥60岁、PNI、SII、总蛋白、手术时间、气管切开、术中输血、术后睡眠障碍、ICU监护时间、视觉模拟(visual analogue scale,VAS)疼痛评分、术后并发症具有统计学意义(P<0.05);多因素Logistic回归分析:年龄≥60岁、PNI、SII、输血、睡眠障碍、疼痛是POD的独立危险因素。PNI<50.075,SII>754.308时POD风险升高;PNI&SII-预测模型AUC=0.919,约登指数为0.689,灵敏度=0.933,特异度=0.756。结论PNI和SII是OCC皮瓣患者POD的独立危险因素,可作为预测POD发生的指标。PNI&SII-预测模型对OCC皮瓣患者POD具有较好的预测价值,可用于指导临床早期干预与治疗。

胸腔镜肺癌切除术后合并心肺并发症的危险因素分析及列线图预测模型构建

目的:探讨肺癌患者经胸腔镜切除术后合并心肺并发症的危险因素并构建列线图预测模型。方法:选取2019年10月至2022年9月在我院进行胸腔镜切除术的228例肺癌患者为研究对象,将术后出现心肺并发症的52例患者纳入发生组,未出现心肺并发症的176例患者纳入未发生组。记录两组患者的临床资料,独立样本t检验或χ^(2)检验行单因素分析,探索胸腔镜切除术后合并心肺并发症的相关因素;MedCalc软件对计量指标行ROC曲线分析,探索其对胸腔镜切除术后合并心肺并发症的预测价值;Logistic逐步回归分析探讨胸腔镜切除术后合并心肺并发症的独立危险因素;R语言软件4.0“rms”包构建肺癌患者经胸腔镜切除术后合并心肺并发症的列线图预测模型,校正曲线对列线图预测模型进行内部验证,计算一致性指数(concordance index,C-index),决策曲线对列线图预测模型进行临床预测效能评估。结果:发生组与未发生组患者在年龄、吸烟史、糖尿病史、冠心病史、手术时间、术中出血量、FEV1方面的差异具有统计学意义。年龄、手术时间、术中出血量的AUC分别为0.739、0.785、0.736,最佳截断值分别为55岁、178 min、96 mL。年龄、吸烟史、冠心病史、手术时间、FEV1是肺癌患者经胸腔镜切除术后合并心肺并发症的独立危险因素。内部验证结果指出,列线图预测模型的校正曲线与原始曲线及理想曲线接近,C-index为0.883(95%CI:0.833~0.933),模型拟合度高;列线图预测模型的阈值>0.23,可提供临床净收益,且临床净收益均高于年龄、吸烟史、冠心病史、手术时间、FEV1。结论:本研究基于胸腔镜肺癌切除术后合并心肺并发症的独立危险因素即年龄、吸烟史、冠心病史、手术时间、FEV1构建了列线图预测模型,对心肺并发症的发生具有较好的预测价值,有助于胸腔镜肺癌切除术患者心肺并发症的临床监测,以期降低心肺并发症的发病率,减少不良事件的发生。