中青年人群非酒精性脂肪肝发生风险预测模型的建立

目的建立非酒精性脂肪肝(NAFLD)的发生风险预测模型,为NAFLD的预防及发生提供管理策略。方法选取2015年1月至2018年7月大连医科大学附属第二医院健康管理中心年度体检数据库中18~59岁、至少有2次连续体检记录、基线未发生NAFLD且无重要指标缺失者的数据,观察结局为NAFLD。收集基本信息、体格检查、实验室检查和腹部超声检查资料,将所有研究对象随机分为建模组和验证组。采用SPSS 23.0进行χ²检验、t检验、秩和检验、单因素Cox回归分析。利用建模组资料进行多因素Cox回归分析选取预测指标,用RStudio软件绘制线图,构建NAFLD发生风险预测模型。通过一致性指数(C指数)和校正曲线对建模组和验证组模型的预测效果进行验证。结果本研究共纳入2377名研究对象,其中建模组1585人,验证组792人。本研究共有467人发生NAFLD(累积发病率为19.6%),平均随访时间为(27.06±8.02)个月。其中,建模组NAFLD发病人数为310人(发病率为19.6%),验证组NAFLD发病人数为157人(累积发病率为19.8%)。多因素Cox回归分析结果显示,高密度脂蛋白胆固醇水平(HR=0.334,95%CI:0.209~0.534)为NAFLD发病的独立保护因素,而体质指数(HR=1.220,95%CI:1.172~1.271)、甘油三酯(HR=1.114,95%CI:1.052~1.180)、低密度脂蛋白胆固醇(HR=1.252,95%CI:1.054~1.487)、丙氨酸氨基转移酶(HR=1.013,95%CI:1.005~1.021)、血尿酸(HR=1.003,95%CI:1.001~1.004)为NAFLD发病的独立危险因素(P<0.05,P<0.01)。利用上述影响因素成功构建NAFLD发生风险预测模型。建模组和验证组的C指数分别为0.789(95%CI:0.766~0.812)、0.777(95%CI:0.742~0.812),校正曲线显示模型预测结果与实际观察结果吻合良好。结论本研究构建的NAFLD发生风险预测模型可以准确地预测NAFLD的发生概率,为早期识别NAFLD高危人群提供新思路。     

Queratotomía arcuata transepitelial o intraestromal con láser de femtosegundo para corrección de astigmatismo corneal en el momento de la cirugía de cataratas

PurposeThe aim of this study was to compare the results of intrastromal arcuate incisions (AIs) and transepithelial AIs to treat corneal astigmatism during femtosecond laser-assisted cataract surgery (FLACS).MethodsThis retrospective study included 20 patients with corneal astigmatism between 0.70 and 2.00 diopters (D) who underwent FLACS with concurrent intrastromal AIs in one eye and transepithelial AIs in the fellow eye. The main outcomes measures at 2-3 months of follow-up were the difference between preoperative and postoperative keratometric corneal cylinder (Kcyl), the correction index (CI) and the percentage of overcorrection.ResultsThe mean difference between preoperative and postoperative Kcyl revealed a mean value of 0.36 ± 0.37 D in the transepithelial group and 0.53 ± 0.42 D in the intrastromal group (P < .001). The mean CI was 0.83 ± 0.71 in the transepithelial group and 0.68 ± 0.29 in intrastromal group (P = .17). Five eyes (25%) had an astigmatism overcorrection in the transepithelial group and 1 eye (5%) in the intrastromal group.ConclusionsBoth intrastromal and transepithelial AIs showed potential for mild to moderate astigmatism correction and appeared to be a safe procedure. Despite transepithelial AIs presented a higher CI, the intrastromal AIs results were more predictable.     

Prognostic value of an inflammatory biomarker-based clinical algorithm in septic patients in the emergency department: An observational study

BackgroundTo develop an inflammatory biomarker-based, simple-to-use nomogram for the early identification of septic patients at high risk of mortality in the emergency department (ED).MethodsAll patients diagnosed with sepsis admitted to the intensive care unit (ICU) from the ED were screened from the Medical Information Mart for Intensive Care III database and divided into two cohorts: the primary cohort and the validation cohort. We used bivariate logistic regression analysis to determine independent risk factors and to construct a predictive nomogram and subsequently evaluated the calibration, discrimination and clinical usefulness of the nomogram. The gradient boosting machine (GBM) model was used to more accurately evaluate these variables.ResultsA total of 5663 admissions were enrolled, of which 3964 comprised the primary cohort and 1699 comprised the validation group, with 28-day mortality rates of 21.2% and 20.4%, respectively. Age > 69, neutrophil-to-lymphocyte ratio (NLR) > 9.8, platelet-to-lymphocyte ratio (PLR) > 249.89, lymphocyte-to-monocyte ratio (LMR) ≤ 2.18, and red cell distribution width (RDW) were detected as important determinants of 28-day mortality and included in the nomogram. The calibration plot revealed an adequate fit of the nomogram for predicting the risk of 28-day mortality. Regarding discriminative ability, receiver operating characteristic curve analysis showed that the nomogram had an area under the curve (AUC) of 0.826 (95% CI: 0.811–0.841, P < 0.001) in the primary cohort, which was greater than that of all individual parameters and other scores. Decision curve analysis also indicated that our nomogram was feasible in clinical practice, as the threshold probabilities were 0–0.62 for the primary cohort. The GBM model yielded a significantly greater AUC of up to 0.867.ConclusionsThis proposed simple-to-use nomogram based on age, NLR, PLR, LMR and RDW provides a relatively accurate mortality prediction for septic patients in the ED.     

A novel prognostic nomogram based on microvascular invasion and hematological biomarkers to predict survival outcome for hepatocellular carcinoma patients

PurposeThis study aimed to develop and validate a nomogram for overall survival (OS) prediction in which combine clinical characteristics and hematological biomarkers in patients with hepatocellular carcinoma (HCC).MethodsWe performed a retrospective analysis of 807 HCC patients. All the clinical data of these patients were collected through electronic medical record (EMR). The independent predictive variables were identified by cox regression analysis. We tested the accuracy of the nomograms by discrimination and calibration, and then plotted decision curves to assess the benefits of nomogram-assisted decisions in a clinical context, and compared with the TNM staging systems and microvascular invasion (MVI) on HCC prognosis.ResultsThe primary cohort consisted of 545 patients with clinicopathologically diagnosed with HCC from 2008 to 2013, while 262 patients from 2014 to 2016 in external validation cohort. Variables included in the nomograms were TNM Stage, microvascular invasion (MVI), alpha fetoprotein (AFP), platelet to lymphocyte ratio (PLR) and prothrombin time (PT). The C-index of nomogram was 0.768, which was superior than the C-index of TNM Stage (0.660, P < 0.001) and MVI(0.664, P < 0.001) alone in the primary cohort. In the validation cohort, the models had a C-index of 0.845, and were also statistically higher when compared to C-index values for TNM Stage (0.687, P < 0.001) and MVI(0.684, P < 0.001). Calibration curves showed adequate calibration of predicted and reported OS prediction throughout the range of HCC outcomes. Decision curve analysis demonstrated that the nomogram was clinically useful than the TNM Stage and MVI alone. Moreover, patients were divided into three distinct risk groups for OS by the nomogram: low risk group, middle risk group and a high risk group, respectively.ConclusionThe nomogram presents more accurate and useful prognostic power, which could be used to predict OS for patients with HCC.     

Development and validation of a nomogram for predicting survival of advanced breast cancer patients in China

BackgroundThere is a lack of prognostic models predicting the overall survival (OS) of advanced breast cancer (ABC) patients in China.MethodsData from the China National Cancer Center database that recorded 4039 patients diagnosed with breast cancer between 1987 and 2019 were extracted and a total of 2263 ABC participants were enrolled in this study, which were further randomized 3:1 and divided into training (n = 1706) and validation (n = 557) groups. The nomogram was built based on independent predictors identified by univariate and multivariate cox regression analyses. The discriminatory and predictive capacities of the nomogram were assessed by Harrell’s concordance index (C-index) and calibration plots.ResultsUnivariate and multivariate analyses found that age, Eastern Cooperative Oncology Group (ECOG) score, T-stage, N-stage, tumor subtype, the presence of distant lymph node (DLN)/liver/brain metastasis, local therapy, efficacy of first-line therapy and metastatic-free interval (MFI) were significantly related to OS (all P < 0.05). These variables were incorporated into a nomogram to predict the 2-year and 3-year OS of ABC patients. The C-indexes of the nomogram were 0.700 (95% confidence interval [CI]: 0.683–0.717) for the training set and 0.686 (95% CI: 0.652–0.719) for the validation set. The calibration curves revealed satisfactory consistency between actual survival and nomogram prediction in both the internal and external validations. The nomogram was capable of stratifying patients into different risk cohorts.ConclusionsWe constructed and validated a nomogram that might serve as an efficient tool to provide prognostic prediction for ABC patients and guide the physicians to make personalized treatment decisions.     

Adherence to pelvic lymph node dissection recommendations according to the National Comprehensive Cancer Network pelvic lymph node dissection guideline and the D'Amico lymph node invasion risk stratification

Purpose

To assess adherence rates to pelvic lymph node dissection (PLND) according to National Comprehensive Cancer Network (NCCN) PLND guideline (2% or higher risk) and D’Amico lymph node invasion (LNI) risk stratification (intermediate/high risk) in contemporary North American patients with prostate cancer treated with radical prostatectomy (RP).

Preoperative Nomograms and Artificial Neural Networks (ANNs) for Identification of Surgical Candidates

Selection of prostate cancer risk patients for surgical treatment has traditionally been accomplished by the creation of risk groups, like clinical stage, prostate specific antigen and others. Using these data knowledge-based expert systems were created. Among these the most popular model is the logistic regression model. Ideally, this prediction should be as accurate as possible. Many studies have shown that even expert on its field often are incorrect compared to the validated nomograms and artifical neural networks (ANNs) presented herein. Nomograms are instruments that predict outcomes for the individual patient using algorithms that incorporate multiple variables. Nomograms consist of a set of axes. Each variable is represented by a scale, with each value of that variable corresponding to a specific number of points according to its prognostic impact. In a final pair of axes, the total point value from all he variables is converted to the probability of reaching the end point By using scales, nomograms calculate the continuous probability of a certain outcome, resulting in more accurate predictions than models based on risk grouping. ANNs has gained increasing popularity and are the most popular artificial learning tool in biotechnology. This technique can roughly be described as a universal algebraic function that will distinguish dependency between dependent and independent variables, which is either unknown or very complex. The application of ANNs to complex relationships makes them highly attractive for the study of complexed medical decisions like predicting pathological stage or local recurrence after radical prostatectomy (RPE). Accuracy of nomograms and ANNs for pathological staging and PSA recurrence varies between 72–88.3% versus 77–91%, and 75–81% and 67–83%, respectively.     

抗结核药物所致肝损伤的不良反应分析及风险因素研究

目的探究抗结核药物(ATD)引发药物性肝损伤(DILI)的临床特征和危险因素,建立风险预测模型。方法以60例抗结核药物所致DILI的患者为观察组,以同期行相同方案治疗的结核病患者1100例作为对照组,采用回顾性分析手段对两组患者的一般资料、临床表现、实验室指标等数据,进行对比分析,找出ATD引起DILI的独立危险因素并利用R软件建立列线图风险预测模型。结果60例ATD引起DILI的不良反应报告中以女性为主,占58.33%;平均年龄为46.83±11.24岁,其中40~69岁发生率最高,占56.67%;临床分型中包括40例肝细胞损伤型,13例胆汁淤积型,7例混合型;临床表现以食欲下降、乏力最为显著。风险因素分析中,年龄、基础肝病、糖尿病、胆囊疾病、ALB、TBIL、含吡嗪酰胺、合用其他增加肝毒性药物为TB患者DILI的独立危险因素。R软件建立的列线图预测模型C-index指数高达0.902,说明本模型具有较好的预测能力。结论运用列线图模型评分预测发生DILI的风险,对结核病患者个体化化疗方案制定具有重要指导意义。     

内镜逆行胰胆管造影术后急性胆管炎的危险因素研究及其列线图的构建

目的探讨内镜逆行胰胆管造影术(endoscopic retrograde cholangiopancreatography, ERCP)后急性胆管炎的危险因素及其列线图的构建。方法回顾性分析2014年1月—2019年12月在兰州大学第一医院因胆总管结石接受ERCP的患者临床资料, 纳入术后发生急性胆管炎的患者95例(胆管炎组), 以1∶3比例通过软件随机抽样选择术后未发生急性胆管炎的患者285例(无胆管炎组)。采用Logistic回归模型分析影响ERCP术后急性胆管炎的独立危险因素, 根据多因素分析结果, 建立预测ERCP术后急性胆管炎发生率的列线图模型。结果单因素比较发现 ERCP术后发生胆管炎患者和未发生胆管炎患者在年龄、合并糖尿病、丙氨酸转氨酶、碱性磷酸酶、葡萄糖、胆囊壁粗糙、胆管直径、胆管下端狭窄、行经内镜胆道内支架放置术比例、行经内镜鼻胆管引流术比例方面差异均有统计学意义(P<0.05)。Logistic多因素回归分析显示, 高龄(OR=1.108, 95%CI:1.079～1.138, P<0.001)、合并糖尿病(OR=4.524, 95%CI:1.299～1...     

基于超声内镜下胃小间质瘤特征表现的诊断预测列线图模型建立

目的建立基于超声内镜下特征表现的胃小间质瘤诊断预测列线图模型。方法回顾性收集2015年6月—2021年8月于同济大学附属同济医院消化内科行内镜下切除的长径<2 cm胃黏膜下肿瘤的189例患者临床病理资料。所有病例通过R软件随机函数按2∶1的比例分为建模组(n=126)和验证组(n=63)。在建模组中采用单因素和多因素Logistic回归分析筛选出超声内镜下诊断胃小间质瘤的独立影响因素, 构建列线图模型。在建模组和验证组中绘制受试者工作特征(receiver operator characteristic, ROC)曲线以评价模型的区分度, 采用Hosmer-Lemeshow检验和校准曲线以评价模型的校准度。结果患者年龄>60岁(OR=2.815, 95%CI:1.148～6.900, P=0.024)、病灶位于贲门/胃底(OR=5.210, 95%CI:1.225～22.165, P=0.025)、起源于固有肌层(OR=6.404, 95%CI:2.262～18.135, P<0.001)、呈腔外生长(OR=6.024, 95%CI:1.252～28.971, P=0...