乳腺癌术后化疗患者营养不良风险的列线图模型构建

目的 分析乳腺癌术后化疗患者营养不良风险的影响因素,并根据分析结果构建列线图模型.方法 选取2019年7月至2021年7月扬州大学附属医院收治的231例乳腺癌术后化疗患者的相关资料,以体质指数(BMI)<18.5 kg/m2及营养风险筛查2002(NRS 2002)评分≥3分作为判断营养不良的标准,将其分为营养不良组(...     

Validation over time of a nomogram including HER2 status to predict the sentinel node positivity in early breast carcinoma

Background

The molecular subtypes of breast cancer have different axillary status. A nomogram including the interaction covariate between estrogen receptor (ER) and HER2 has been recently published (Reyal et al. PLOS One, May 2011) and allows to identify the patients with a high risk of positive sentinel lymph node (SLN). The purpose of our study was to validate this model on an independent population.

Methods

We studied 755 consecutive patients treated at Institut Curie for operable breast cancer with sentinel node biopsies in 2009. The multivariate model, including age, tumor size, lymphovascular invasion and interaction covariate between ER and HER2 status, was used to calculate the theoretical risk of positive sentinel lymph node (SLN) for all patients. The performance of the model on our population was then evaluated in terms of discrimination (area under the curve AUC) and of calibration (Hosmer–Lemeshow HL test).

Results

our population was significantly different from the training population for the following variables: median tumor size in mm, lymphovascular invasion, positive ER and age. The nomogram showed similar results in our population than in the training population in terms of discrimination (AUC = 0.72 [0.68–0.76] versus 0.73 [0.7–0.75] and calibration (HL p = 0.4 versus p = 0.35).

Conclusions

Despite significant differences between the two populations concerning variables which are part of the nomogram, the model was validated in our population. This nomogram is robust over time to predict the likelihood of positive SLN according to molecular subtypes defined by surrogate markers ER and HER2 determined by immunohistochemistry in clinical practice.     

Development of a nomogram for predicting a positive repeat prostate biopsy

AIM: To fi nd risk factors of cancer in patients who had a repeat biopsy and to develop the nomogram using our cohort. METHODS: Among 3500 patients who had a prostate biopsy over 11 years between 2000 and 2010 at our hospital, we studied a total of 807 repeat biopsy sessions in 459 patients who had at least 1 initial negative biopsy. At each biopsy session, we recorded patient age, number of previous biopsy sessions, number of biopsy cores, number of previously negative biopsy cores, months from the initial biopsy, months from the previous biopsy, serum PSA, PSA slope, digital rectal examination fi ndings, hypoechoic lesions suspicious for a cancer on transrectal ultrasonography, total prostate volume, transitional zone(TZ) volume, PSA density, PSA TZ density and history of high grade prostatic intraepithelial neoplasia(HGPIN) or atypical small acinar proliferation(ASAP). Clinical and pathological variables were correlated with the outcome of repeat biopsies. A nomogram was developed based on logistic regression analyses and calibration was performed.RESULTS: Overall, 17% of repeat biopsies had a cancer. With receiver operating characteristics analyses, the highest area under the curve(AUC) was obtained based on all available 13 variables, which were age, PSA, digital rectal examination, PSA density, prostate volume, TZ volume, PSA TZ density, cumulative number of biopsy cores, HGPIN, ASAP, months from previous negative biopsy, initial negative biopsy and number of biopsy cores. Based on multivariable logistic regression analysis, a nomogram was constructed with an AUC of 0.74, which was greater than that of any single risk factor. The calibration plot seemed to be good.CONCLUSION: Our nomogram for predicting a positive repeat biopsy can provide probabilities for cancer and may help clinical judgment on whether to do a repeat prostate biopsy.     

External validation of a nomogram predicting overall survival of patients diagnosed with endometrial cancer

Objectives

Nomograms are predictive models that provide the overall probability of a specific outcome. Nomograms have shown better individual discrimination than currently used staging systems in numerous tumor entities. Recently, a nomogram for predicting overall survival (OS) in women with endometrial cancer was introduced by Memorial Sloan-Kettering Cancer Center (MSKCC). The aim of this study was to test the validity of the MSKCC endometrial cancer nomogram using an independent, external patient cohort.

Methods

The MSKCC nomogram is based on five readily available clinical characteristics. A multi-institutional endometrial cancer database was used to test the nomogram's validity. All consecutive patients treated for endometrial cancer between December 1995 and May 2011 and who had all nomogram variables documented were identified for analysis.

Results

Seven hundred sixty-five eligible patients were identified and used for external validation analysis. In the Austrian patient cohort, median OS was 134 months, and 3-year and 5-year OS rates were 83.8% (95% CI, 80.6-86.5%) and 77.2% (95% CI, 43.5-80.5%), respectively. The nomogram concordance index was 0.71 (SE = 0.017; 95% CI, 0.68-0.74). The correspondence between the actual OS and the nomogram predictions suggests a good calibration of the nomogram in the validation cohort.

Conclusion

The MSKCC endometrial cancer nomogram was externally validated and was shown to be generalizable to a new and independent patient population. The nomogram provides a more individualized and accurate estimation of OS for patients diagnosed with endometrial cancer following primary therapy. The nomogram can be used for counseling patients more accurately and for better stratifying patients for clinical trials.     

预测心脏术后引流时间延长风险列线图的建立

【摘要】目的建立可预测心脏术后患者引流时间延长的列线图,便于进行更好的临床管理。方法对中山大学附属孙逸仙纪念医院2014年1月至2016年1月期间152例行开胸心脏手术的病人进行回顾性分析,收集患者的一般资料、既往病史、围手术期相关情况和术后引流时间等资料。通过Logistic回归法分析并筛选术后引流时间的显著影响因素,建立预测术后引流时间延长的列线图。结果单因素分析显示性别、体外循环转机时间、升主动脉阻断时间、吸烟、疾病类型、凝血酶原国际标准比值（PT-INR）、白细胞计数、谷草转氨酶（AST）、谷丙转氨酶（ALT）、术前肌酐、N端脑那肽前体（N-proBNP）、左房内径、左室收缩功能与心脏术后引流时间具有相关性。多因素Logistic回归分析中吸烟史、术前肌酐、白细胞计数、体外循环转机时间、凝血酶原国际比值是独立预后因素,并用于绘制了便于临床使用的列线图。列线图初始的一致性指数（C-idex）为0.78。经过1000次的模型内部验证,并进行矫正,C-idex为0.76。列线图模型的敏感度为80.0%（95%置信区间69.2%~88.4%）,其ROC分析的曲线下面积为0.78（95%置信区间0.74~0.82）。阳性比值比（PLR）为2.43,阴性比值比（NLR）为0.30。结论心脏术后引流时间与多种因素相关,基于相关影响因素建立的预测模型能较为准确预测术后引流时间延长的风险。     

慢性阻塞性肺疾病病人呼吸机相关性肺炎 91例的病原谱及其列线图预测模型

目的分析慢性阻塞性肺疾病( COPD)病人呼吸机相关性肺炎( VAP)病原菌感染特点,构建列线图预测模型。方法选择 2019年 1月至 2021年 12月在南充市第二人民医院接受机械通气治疗的 193例慢性阻塞性肺疾病急性加重期( AECOPD)病人,根据有无 VAP将病人分为两组,在单因素分析基础上行多因素 logistic回归分析,构建列线图预测模型,以 ROC曲线分析模型预测价值,并以计算机模拟充分采样( bootstrap)法进行内部验证。结果该研究的 193例中共 91例( 47.15%)病人出现 VAP,91例病人中共分离出病原菌 108株,其中革兰阴性菌占 72.22%,革兰阳性菌占 15.74%,真菌占 12.04%,单一感染 62例,混合感染 29例。单因素分析基础上行多因素分析结果显示:年龄 ≥60岁、气道干预方式为气道切开、合并糖尿病、机械通气时间 ≥ 4d、使用抗菌药物联合用药、使用抑酸剂、有吸烟史及 APACHEⅡ评分≥15分为 AECOPD病人 VAP发生的危险因素(P<0.05)。根据上述因素以 R语言建立列线图预测模型,受试者操作特征(ROC)曲线下面积 0.84,95%CI为(0.78,0.90)Bootstrap法对列线图进行内部验证,平均绝对误差为 0.02,预测曲线与标准曲线基本拟合。结论 AECOPD病人 VAP发生率较高,主,要因感染革兰阴性菌所致, VAP的发生率受病人年龄、气道干预方式、合并糖尿病情况、机械通气时间、糖皮质激素使用情况、抗菌药物联合用药、抑酸剂使用情况、吸烟史及 APACHEⅡ评分的影响,以上述因素构建的列线图模型具有较高的区分度与准确度。     

经腹腔镜逆行胆囊切除术治疗复杂胆囊结石中转开腹的预测列线图的构建与验证

目的 探讨经腹腔镜逆行胆囊切除术（laparoscopic retrograde cholecystectomy,LRC）治疗复杂胆囊结石（complicated gallbladder stones,CGS）中转开腹的相关危险因素,构建预测列线图并进行验证。方法 选取2016年9月至2019年3月青海红十字医院行LRC治疗CGS的病人380例作为训练集,2019年4月至2021年9月该院行LRC治疗CGS的病人350例作为验证集,训练集依据术中是否中转开腹分为中转组（34例）和非中转组（346例）。单因素分析两组病人的临床病理特征,logistic多元回归模型分析经LRC治疗CGS中转开腹的危险因素,基于该多因素logistic回归模型,构建中转开腹的列线图,并对其进行外部验证以及绘制校正曲线。结果 训练集与验证集两组一般资料比较,各临床因素组间均差异无统计学意义（P>0.05）;单因素分析显示训练集中中转组与非中转组在身体质量指数（body mass index,BMI）、上腹部手术史、糖尿病、胆囊颈结石、胆囊增大、胆囊炎发作时间、胆囊壁增厚、结石数量、白蛋白、白细胞计数（w...     

Clinicopathologic features,treatment, survival,and prognostic factors of combined hepatocellular and cholangiocarcinoma: A nomogram development based on SEER database and validation in multicenter study

PurposeThe aim of the study was to comprehensively understand the combined hepatocellular and cholangiocarcinoma (CHC) and develop a nomogram for prognostic prediction of CHC.MethodsData were collected from the Surveillance, Epidemiology and End Results (SEER) database (year 2004–2014). Propensity-score matching (PSM) was used to match the demographic characteristic of the CHC versus hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). A nomogram model was established to predict the prognosis in terms of cancer specific survival (CSS). The established nomogram was externally validated by a multicenter cohort.ResultsA total of 71,756 patients enrolled in our study including 62,877 HCC patients, 566 CHC patients, and 8303 ICC patients. The CHC, HCC, and ICC are not exactly similar in clinical characteristic. After PSM, the CSS of CHC was better than HCC but comparable to ICC. Tumor size, M stage, surgery, chemotherapy, and surgery were independently prognostic factors of CHC and were included in the establishment of novel nomogram.The c-index of the novel nomogram in SEER training set and multicenter validation was 0.779 and 0.780, respectively, which indicated that the model was with better discrimination power. In addition, decision curve analyses proved the favorable potential clinical effect of the predictive model. Lastly, a risk classification based on nomogram also verified the reliability of the model.ConclusionCHC had better survival than HCC but was comparable to ICC. The nomogram was established based on tumor size, M stage, chemotherapy, surgery, and radiotherapy and well validated by external multicenter cohort.     

A predictive model for blood transfusion during liver resection

BackgroundA predictive model that can identify patients who are at increased risk of intraoperative blood transfusion could guide preoperative transfusion risk counseling, optimize health care resources, and reduce medical costs. Although previous studies have identified some predictors for particular populations, there is currently no existing model that uses preoperative variables to accurately predict blood transfusion during surgery, which could help anesthesiologists optimize intraoperative anesthetic management.MethodsWe collected data from 582 patients who underwent elective liver resection at a university-affiliated tertiary hospital between January 1, 2018, and December 31, 2020. The data set was then randomly divided into a training set (n = 410) and a validation set (n = 172) at a 7:3 ratio. The least absolute shrinkage and selection operating regression model was used to select the optimal feature, and multivariate logistic regression analysis was applied to construct the transfusion risk model. The concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the discrimination ability, and the calibration ability was assessed with calibration curves. In addition, we used decision curve analysis (DCA) to estimate the clinical application value. For external validation, the test set data were employed.ResultsThe final model had 8 predictor variables for intraoperative blood transfusion, which included the following: preoperative hemoglobin level, preoperative prothrombin time >14 s, preoperative total bilirubin >21 μmol/L, respiratory diseases, cirrhosis, maximum lesion diameter >5 cm, macrovascular invasion, and previous abdominal surgery. The model showed a C-index of 0.834 (95% confidence interval, 0.789–0.879) for the training set and 0.831 (95% confidence interval, 0.766–0.896) for the validation set. The AUCs were 0.834 and 0.831 for the training and validation sets, respectively. The calibration curve showed that our model had good consistency between the predictions and observations. The DCA demonstrated that the transfusion nomogram was reliable for clinical applications when an intervention was decided at the possible threshold across 1%–99% for the training set.ConclusionWe developed a predictive model with excellent accuracy and discrimination ability that can help identify those patients at higher odds of intraoperative blood transfusion. This tool may help guide preoperative counseling regarding transfusion risk, optimize health care resources, reduce medical costs, and optimize anesthetic management during surgery.     

IVc期下咽癌特征分析及列线图预后模型构建

目的 探讨IVc期下咽癌患者临床特征,筛选预后影响因素,构建列线图预后模型。 方法 在监测、流行病学及预后数据库中收集IVc期下咽癌患者的临床资料,应用χ2检验分析远处转移的相关特征,单因素及多因素Cox回归分析筛选预后影响因素,并构建列线图预后模型。 结果 肺(53.7%)是最常见的转移部位。位于梨状窝的肿瘤(P=0.029)和高级别肿瘤(P=0.010)更易发生肺转移。手术、化疗、骨和肝转移是独立预后因素。基于独立预后因素的列线图预后模型C-index为0.686(95%CI 0.649-0.723)。 结论 下咽癌最易向肺转移。基于手术、化疗、有无骨转移和肝转移的列线图预后模型预测能力较好。