Development and validation of an integrated nomogram to predict personalized new baseline functional outcomes after partial nephrectomy

BackgroundThe prediction of new baseline renal function after partial nephrectomy (PN) has important clinical implications. This study aimed to establish a precise personalized nomogram integrating pre-, intra- and post-operative variables to predict new baseline function after PN.MethodsThis nomogram was constructed based on 213 consecutive PN cases at a large-volume institution from 2014 to 2017 and externally validated by a prospective cohort from January to December 2018 at the same institution. Multivariate cox regression and logistic least absolute shrinkage and selection operator (LASSO) regression were used to select predictors. The performance of the nomogram was assessed by the concordance index (C-index), calibration plot, decision curve analysis and Kaplan-Meier plot.ResultsThe average drop percent of the estimated glomerular filtration rate (eGFR) was −8.6% (−12.3%, −7.2%). Multivariate Cox regression analysis and LASSO regression revealed that age, baseline eGFR, RENAL nephrometry score, ischemia time, and AKI were independent predictive factors. These five factors were subsequently incorporated to establish an integrated nomogram, with a C-index of 0.910, excellent calibration plot and net clinical benefit. An external validation of 67 patients showed a C-index of 0.801, excellent calibration and clinical net benefit.ConclusionsOur proposed nomogram based on pre-, intra- and post-operative outcomes accurately predicts personalized new baseline eGFR after PN. The successful personalized prediction of at-risk individuals at an early stage can provide multi-professional consideration and timely management.     

Establishment and validation of a novel predictive model to quantify the risk of bone metastasis in patients with prostate cancer

BackgroundPatients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa.MethodsClinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram’s discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis.ResultsA total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival.ConclusionsNovel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.     

Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study

ObjectivesRisk of death is high for hemodialysis (HD) patients but it varies considerably among individuals. There is few clinical tool to predict long-term survival rates for HD patients yet. The aim of this study was to develop and validate a easy-to-use nomogram for prediction of 1-, 5-, and 10-year survival among HD patients.MethodsThis study retrospectively enrolled 643 adult HD patients who was randomly assigned to two cohorts: the training cohort (n = 438) and validation cohort (n = 205), univariate survival analyses were performed using Kaplan–Meier’s curve with log-rank test and multivariate Cox regression analyses were performed to identify predictive factors, and a easy-to-use nomogram was established. The performance was assessed using the area under the curve (AUC), calibration plots, and decision curve analysis.ResultsThe score included seven commonly available predictors: age, diabetes, use of arteriovenous fistula (AVF), history of emergency temporary dialysis catheter placement, cardiovascular disease (CVD), hemoglobin (Hgl), and no caregiver. The score revealed good discrimination in the training and validation cohort (AUC 0.779 and 0.758, respectively) and the calibration plots showed well calibration, indicating suitable performance of the nomogram model. Decision curve analysis showed that the nomogram added more net benefit compared with the treat-all strategy or treat-none strategy with a threshold probability of 10% or greater.ConclusionsThis easy-to-use nomogram can accurately predict 1-, 5-, and 10-year survival for HD patients, which could be used in clinical decision-making and clinical care.Abbreviations:     

A novel nomogram combined PIRADS v2 and neutrophil-to-lymphocyte ratio to predict the risk of clinically significant prostate cancer in men with PSA < 10 ng/ml at first biopsy

ObjectiveTo determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy.MethodsUnivariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis.ResultsThis retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP.ConclusionPIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.     

A high neutrophil-lymphocyte ratio and platelet-lymphocyte ratio are associated with a worse outcome in inflammatory breast cancer

IntroductionInflammatory breast cancer (IBC) is an uncommon, but aggressive form of breast cancer that accounts for a disproportionally high fraction of breast cancer related mortality. The aim of this study was to explore the peripheral immune response and the prognostic value of blood-based biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), in a large IBC cohort.Patients & methodsWe retrospectively identified 127 IBC patients and collected lab results from in-hospital medical records. The differential count of leukocytes was determined at the moment of diagnosis, before any therapeutic intervention. A cohort of early stage (n = 108), locally advanced (n = 74) and metastatic breast cancer patients (n = 41) served as a control population.ResultsThe NLR was significantly higher in IBC compared to an early stage breast cancer cohort, but no difference between IBC patients and locally advanced breast cancer patients was noted. In the metastatic setting, there was also no significant difference between IBC and nIBC. However, a high NLR (>4.0) remained a significant predictor of worse outcome in IBC patients (HR: 0.49; 95% CI: 0.24–1.00; P = .05) and a lower platelet-lymphocyte ratio (PLR) (≤210) correlated with a better disease-free survival (DFS) (HR: 0.51; 95% CI: 0.28–0.93; P = .03).ConclusionPatients with a high NLR (>4.0) have a worse overall prognosis in IBC, while the PLR correlated with relapse free survival (RFS). Since NLR and PLR were not specifically associated with IBC disease, they can be seen as markers of more extensive disease.     

术前炎性指标构建的列线图模型预测结直肠癌患者术后生存分析

目的探讨基于术前炎性指标构建的列线图模型预测结直肠癌患者术后生存的价值。方法采用队列研究设计,选取2011年1月至2014年6月空军第986医院行结直肠癌根治术的233例结直肠癌患者,根据5年随访结果,将患者分成生存组(99例)和死亡组(134例)。比较两组患者术前1 d炎性指标水平,单因素和Cox回归分析结直肠癌患者术后5年生存的影响因素,应用R软件建立列线图术后存活预测模型。结果两组患者术前淋巴细胞计数、中性粒细胞计数、血小板计数、C反应蛋白、血小板/淋巴细胞比值(PLR)、中性粒细胞/淋巴细胞(NLR)和C反应蛋白/白蛋白比值(CAR)等指标比较,差异有统计学意义(P<0.05),而白细胞计数和白蛋白比较,差异无统计学意义;肿瘤大小(OR=1.379,95%CI:1.094~1.737)、浸润深度(OR=2.020,95%CI:1.126~3.622)、NLR(OR=1.496,95%CI:1.009~2.219)、PLR(OR=1.927,95%CI:1.060~3.504)和CAR(OR=2.326,95%CI:1.479~3.657)是结直肠癌患者术后生存的独立影响因素(P<0.05)。列线图预测术后生存模型的C-index为0.831(95%CI:0.781~0.911),校准预测曲线和理想曲线拟合良好。结论术前NLR、PLR和CAR与结直肠癌术后生存呈负相关,且列线图具有预测结直肠癌患者术后生存情况的潜在价值。     

A nomogram based on PI-RADS v2.1 and clinical indicators for predicting clinically significant prostate cancer in the transition zone

BackgroundThis study attempted to develop a nomogram for predicting clinically significant prostate cancer (cs-PCa) in the transition zone (TZ) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators.MethodsWe retrospectively reviewed 383 patients with suspicious prostate lesions in the TZ as a training cohort and 128 patients as the validation cohort from January 2015 to March 2020. Multivariable logistic regression analysis was performed to determine independent predictors for building a nomogram, and the performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve.ResultsThe PI-RADS v2.1 score and prostate-specific antigen density (PSAD) were independent predictors of TZ cs-PCa. The prediction model had a significantly higher AUC (0.936) than the individual predictors (0.914 for PI-RADS v2.1 score, P=0.045, 0.842 for PSAD, P<0.001). The nomogram showed good discrimination (AUC of 0.936 in the training cohort and 0.963 in the validation cohort) and favorable calibration. When the PI-RADS v2.1 score was combined with PSAD, the diagnostic sensitivity and specificity were 80.7% and 93.8%, respectively, which were better than those of the PI-RADS v2.1 score (sensitivity, 74.2%; specificity, 92.5%) and PSAD (sensitivity, 66.1%; specificity, 88.2%).ConclusionsThe newly constructed nomogram exhibits satisfactory predictive accuracy and consistency for TZ cs-PCa. PI-RADS v2.1 based on bp-MRI is a strong predictor in the detection of TZ cs-PCa. Adding PSAD to PI-RADS v2.1 could improve its diagnostic performance, thereby avoiding unnecessary biopsies.     

Population-based outcome of muscle-invasive bladder cancer following radical cystectomy: who can benefit from adjuvant chemotherapy?

BackgroundThe benefit of adjuvant chemotherapy remains controversial in muscle-invasive bladder cancer (MIBC) after radical cystectomy. The present study’s primary objective was to construct a predictive tool for the reasonable application of adjuvant chemotherapy.MethodsAll of the patients analyzed in the present study were recruited from the Surveillance Epidemiology and End Results program between 2004 and 2015. Propensity score matching (PSM) was used to reduce inherent selection bias. Cox proportional hazards models were applied to identify the independent prognostic factors of overall survival (OS) and cancer-specific survival (CSS), which were further used to construct prognostic nomogram and risk stratification systems to predict survival outcomes. The prognostic nomogram’s performance was assessed by concordance index (C-index), receiver-operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the prognostic nomogram.ResultsA total of 6,384 patients with or without adjuvant chemotherapy were included after PSM. Several independent predictors for OS and CSS were identified and further applied to establish a nomogram for 3-, 5- and 10-year, respectively. The nomogram showed favorable discriminative ability for the prediction of OS and CSS, with a C-index of 0.709 [95% confidence interval (CI): 0.699–0.719] for OS and 0.728 (95% CI: 0.718–0.738) for CSS. ROC and calibration curves showed satisfactory consistency. The DCA revealed high clinical positive net benefits of the prognostic nomogram. The different risk stratification systems showed that adjuvant chemotherapy resulted in better OS (P<0.001) and CSS (P<0.001) than without adjuvant chemotherapy for high-risk patients; while the OS (P=0.350) and CSS (P=0.260) for low-risk patients were comparable.ConclusionsWe have constructed a predictive model and different risk stratifications for selecting a population that could benefit from postoperative adjuvant chemotherapy. Adjuvant chemotherapy was found to be beneficial for high-risk patients, while low-risk patients should be carefully monitored.     

NLR和PLR在腹膜透析相关性感染中的诊断价值探讨

目的探讨外周血中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)、血小板与淋巴细胞比值(platelet-to-lymphocyte ratio,PLR)对腹膜透析相关性感染的诊断及预测价值。方法回顾性分析71例腹膜透析并发相关感染的患者及102例同期入院行腹膜功能及透析充分性评估的维持性腹膜透析患者的临床资料,分别按照是否发生腹膜透析相关性感染、NLR及PLR的最佳截断值进行分组,分析NLR、PLR等指标与腹膜透析相关性感染的关系。应用受试者工作特征曲线(receiver operating characteristic curve,ROC)评价NLR、PLR、NLR联合PLR对腹膜透析相关性感染的诊断及预测价值。结果相关性分析提示腹膜透析相关性感染与NLR、PLR、腹透液白细胞计数及hs-CRP呈正相关(均P<0.01);而与血清白蛋白、血镁、血磷呈负相关(均P<0.01)。NLR与腹膜透析相关性感染、PLR、腹透液白细胞计数及hs-CRP呈正相关(均P<0.01);与血镁及血清白蛋白呈负相关(均P<0.01);与血磷无相关性(P>0.05)。PLR与腹膜透析相关性感染、NLR、腹透液白细胞计数及hs-CRP呈正相关(均P<0.01);与血镁、血磷及血清白蛋白呈负相关(均P<0.05)。单因素Logistic回归显示低血清白蛋白(OR=0.808,95%CI 0.748~0.874,P<0.01)、低血镁(OR=0.001,95%CI 0.000~0.015,P<0.01)、低血磷(OR=0.324,95%CI 0.165~0.635,P=0.01)、高hs-CRP(OR=1.246,95%CI 1.149~1.351,P<0.01)、高NLR(OR=1.570,95%CI 1.315~1.815,P<0.01)、高PLR(OR=1.010,95%CI 1.006~1.014,P<0.01)是腹膜透析相关性感染的危险因素;多因素分析显示低血清白蛋白(OR=0.837,95%CI 0.704~0.995,P=0.043)、高hs-CRP(OR=1.296,95%CI 1.149~1.461,P<0.01)及高NLR(OR=1.522,95%CI 1.055~2.195,P=0.025)是腹膜透析相关性感染的危险因素。从ROC曲线可以看出,NLR、PLR、NLR联合PLR及hs-CRP诊断腹膜透析相关性感染的敏感度分别为64.8%、53.5%、94.4%、93.0%,特异度分别为87.3%、87.3%、98.0%、90.2%。结论与腹膜透析未发生相关性感染的患者相比,腹膜透析相关感染人群的NLR、PLR、腹透液白细胞计数及hs-CRP水平明显升高,而白蛋白、血镁、血磷明显降低。且高NLR、高hs-CRP、低血清白蛋白是腹膜透析相关性感染危险因素。此外,NLR联合PLR对腹膜透析相关性感染的临床诊断敏感性及特异性均优于hs-CRP。     

Development and validation of a novel nomogram to predict overall survival of patients with moderate to severe chronic kidney disease

IntroductionThe risk of death significantly increased from stage 3 chronic kidney disease (CKD) onward. We aimed to construct a novel nomogram to predict the overall survival (OS) of patients afflicted with CKD from stage 3–5.MethodsA total of 882 patients with stage 3–5 CKD were enrolled from the NHANES 2001–2004 survey. Data sets from the 2003–2004 survey population were used to develop a nomogram that would predict the risk of OS. The 2001–2002 survey population was used to validate the nomogram. Least absolute shrinkage and selection operator (Lasso) regression was conducted to screen the significant predictors relative to all-cause death. The multivariate Cox regression based on the screened factors was applied to effectively construct the nomogram. The performance of the nomogram was evaluated according to the C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve with 1000 bootstraps resample. Kaplan–Meier’s curves were used for testing the discrimination of the prediction model.ResultsFive variables (age, urinary albumin-to-creatinine ratio (UACR), potassium, cystatin C (Cys C), and homocysteine) were screened by the Lasso regression. The nomogram was constructed using these factors, as well as the CKD stage. The included factors (age, CKD stage, UACR, potassium, Cys C, and homocysteine) were all significantly related to the death of CKD patients, according to the multivariate Cox regression analysis. The internal validation showed that this nomogram demonstrates good discrimination and calibration (adjusted C-index: 0.70; AUC of 3-, 5-, and 10-year OS were 0.75, 0.78, and 0.77, respectively). External validation also demonstrated exceedingly similar results (C-index: 0.72, 95% CI: 0.69–0.76; AUC of 3-, 5-, and 10-year OS were 0.76, 0.79, and 0.80, respectively).ConclusionsThis study effectively constructed a novel nomogram that incorporates CKD stage, age, UACR, potassium, Cys C, and homocysteine, which can be conveniently used to facilitate the individualized prediction of survival probability in patients with stage 3–5 CKD. It displays valuable potential for clinical application.     

Significance of preoperative hematologic scoring in predicting death among patients with non-metastatic renal cell carcinoma undergoing nephrectomy

PurposeNeutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) are hematologic scoring and indicators of the systemic inflammatory response. The increasing use of NLR and PLR have been associated with poor outcome in various types of malignancy. We evaluated the effect of NLR and PLR on survival outcomes of nonmetastatic renal cell carcinoma (RCC).Materials and methodsWe retrospectively review 150 patients who had undergone nephrectomy for nonmetastatic RCC between 2006 and 2016. Cancer specific survival (CSS) was assessed using Kaplan–Meier method and compared using log-rank test. We applied univariate and multivariate Cox regression model to analyze the association of NLP and PLR with clinical outcome.ResultsAt median follow up of 33 months, 45 patients had died. High PLR (>100) was an independent prognostic hematologic marker for CSS (hazard ratio [HR] 2.61, 95% confidence interval [CI],1.08–6.31; P = 0.034). Univariate analysis identified elevated NLR (p = 0.005), and anemia (p = 0.023) were significantly associated with CSS.ConclusionElevated PLR is a strong hematologic prognosis factor in term of survival for patients with nonmetastatic RCC undergoing nephrectomy with curative intent. The PLR is an easily obtained biomarker which is useful for preoperative risk stratification.     

Correlation of preoperative neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with metabolic parameters in patients undergoing sleeve gastrectomy

BackgroundObesity and its related diseases, type 2 diabetes (T2D) and overall metabolic syndrome, often show a low-grade of chronic inflammation due to loss of balance between pro- and anti-inflammatory signals. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are considered cost-effective markers for the detection of this subclinical inflammation.ObjectivesTo evaluate the potential prognostic factor of NLR and PLR as inflammatory biomarkers on weight loss and T2D remission after sleeve gastrectomy (SG).SettingUniversity Medical Institutions.MethodsPatients who underwent SG as primary treatment for severe obesity were included. Anthropometric and blood parameters were measured at baseline and postoperatively (1, 2, and 5 yr after surgery). The prognostic ability of NLR and PLR was evaluated by a receiver operator characteristic curve and a cutoff point was calculated. A value of P < .05 was considered significant.ResultsA total of 182 patients were analyzed. Preoperative NLR showed an inverse correlation with excess weight loss (Spearman −.525; P = .033) and units of body mass index lost (Spearman −.502; P = .039) 5 years after surgery. Preoperative NLR also showed a direct correlation with fasting glucose (Spearman .685; P = .002) and Homeostasis Model Assessment of Insulin Resistance (Spearman .764; P < .001). Lower preoperative NLR is also associated with a complete remission of T2D at 5 years. Preoperative PLR did not show any correlation with the variables studied.ConclusionThe preoperative NLR is a potential prognostic factor of long-term weight loss and T2D remission in patients undergoing SG. PLR does not correlate with metabolic parameters in these patients.     

评估应用术前PLR、NLR及AGR预测乳腺癌患者预后的价值

目的探究术前血小板淋巴细胞比值（PLR）、中性粒细胞淋巴细胞比值（NLR）及白蛋白球蛋白比值（AGR）在评估乳腺癌患者预后中的价值。 方法选取2013年1月至2017年12月收治的1184例浸润性乳腺癌女性患者为浸润性乳腺癌组,随机选取仅患乳腺纤维腺瘤的患者279例为乳腺纤维腺瘤组。收集患者一般资料、术后病理资料、血型、术前外周血血小板、中性粒细胞、淋巴细胞数量以及血清白蛋白和球蛋白水平,并计算得出PLR、NLR及AGR。应用受试者功能特征曲线下面积来评估三者预测乳腺癌患者预后的能力。本研究使用SPSS 20.0及MedCalc软件进行统计学分析和绘图,P<0.05代表差异具有统计学意义。 结果浸润性乳腺癌患者的术前PLR及NLR均值显著高于乳腺纤维腺瘤患者（P<0.05）,而AGR低于乳腺纤维腺瘤患者（P<0.05）。Cox比例回归风险分析显示,患者的诊断年龄、PLR、NLR、AGR、肿瘤直径、组织学分级、阳性淋巴结个数和分子分型均为乳腺癌的预后危险因素（P<0.05）。ROC曲线分析结果得出,PLR、NLR及AGR的最佳诊断临界值分别为147.4、2.9及1.7。应用术前PLR（AUC=0.796,P<0.001）、NLR（AUC=0.716,P<0.001）及AGR（AUC=0.748,P<0.001）预测乳腺癌患者预后均有价值,且PLR价值更高。 结论术前PLR、NLR及AGR对乳腺癌患者预后的判断均具价值,三者相比,PLR价值更高,有望成为判断乳腺癌患者预后的补充指标。     

A nomogram incorporating PD-L1, NLR,and clinicopathologic features to predict inguinal lymph node metastasis in penile squamous cell carcinoma

BackgroundAccurate preoperative prediction of inguinal lymph node metastasis (LNM) aids in clinical decision making, especially for patients with penile cancer with clinically negative lymph nodes. We aim to develop a nomogram to predict the preoperative risk of LNM by incorporating clinicopathologic features and tumor biomarkers.MethodsEighty-four patients with penile cancer with clinically negative lymph nodes were enrolled. The programmed death ligand 1 (PD-L1) expression profile was detected by immunohistochemistry. The neutrophil-to-lymphocyte ratio (NLR) was calculated based on parameters of a routine blood examination. Multivariate logistic regression analysis was utilized to construct predictive nomograms for LNM based on data of 64 patients. The nomogram performance was assessed for calibration, discrimination, and clinical use.ResultsTumor grade, lymphovascular invasion, PD-L1, and NLR were independent predictors of LNM. Then, 4 prediction models were constructed. Clinical model included tumor grade and lymphovascular invasion. NLR model was built by adding the NLR to clinical model. PD-L1 model was built by adding the PD-L1 to clinical model. Finally, a combined model was built by adding both PD-L1 and NLR to clinical model. Combined model showed the best performance compared with other models. It showed good discrimination with a C-index of 0.89, and good calibration. In addition, decision curve analysis suggested that model 4 was clinically useful.ConclusionsWe developed a nomogram that incorporated tumor grade, lymphovascular invasion, PD-L1, and NLR that could be conveniently used to predict the preoperative individualized risk of inguinal LNM in patients with penile cancer.     

Identification and validation of a five apoptosis-related genes signature for prediction of disease-free survival for testicular germ cell tumors

BackgroundMore and more studies have paid attention to the role of apoptosis in tumorigenesis. A variety of apoptosis-related genes (ARGs) are related to tumor progression and resistance to chemotherapy drugs. Therefore, this study aims to establish a prognostic marker for ARG-based testicular germ cell tumors (TCGT).MethodsTCGT sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database. The sequencing data of normal tissues came from the GTEx database. Through univariate COX, LASSO, and multiple COX regression analyses, we screened out key ARGs related to prognosis and constructed a risk signature and a prognostic nomogram. Finally, we performed internal and external verification to verify the signature we have established.ResultsFive ARGs, including CHGA, LPCAT1, PPP1CA, PSMB5, UBR2 were selected out and utilized to establish a novel signature. Based on this signature, TCGT patients were divided into high-risk groups and low-risk groups. The results showed that the disease-free survival (DFS) of patients in the high-risk group was lower than that in the low-risk group (P=0.02268). The subsequent univariate and multivariate Cox regression analysis further proved that the features we established are valuable independent prognostic factors (P<0.05). Also, a prognostic nomogram was created to visualize the relationship between various prognostic-related factors and the 1-, 3-, and 5-year DFS of TCGT in the TCGA cohort.ConclusionsWe constructed a new nomogram based on ARGs to predict the risk of testicular tumor recurrence. It can help clinicians better and more intuitively predict the survival of patients.     

Development and validation of prognostic nomograms and a web-based survival rate calculator for sarcomatoid renal cell carcinoma in pre- and post-treatment patients

BackgroundTo develop a clinical prediction model and web-based survival rate calculator to predict the overall survival (OS) and cancer-specific survival (CSS) of sarcomatoid renal cell carcinoma (SRCC) for clinical diagnosis and treatment.MethodsSRCC patient data were retrieved from Surveillance, Epidemiology, and End Results (SEER) database. Factors independently associated with survival were identified by a Cox regression analysis. Nomograms of the prediction model were constructed using a SEER training cohort and validated with a SEER validation cohort. At the same time, the decision analysis curve, receiver operating characteristic curve, and calibration curve were also used to examine and evaluate the model. A web-based survival rate calculator was constructed to help assist in the assessment of the disease condition and clinical prognosis.ResultsThe records of 2,742 SRCC cases were retrieved from SEER, while 1,921 cases with a median OS of 14 and CSS of 32 months were used as the training cohort. The developed nomograms were more accurate than that of the American Joint Committee on Cancer staging (C-indexes of 0.767 versus 0.725 for OS and 0.775 versus 0.715 for CSS), with better discrimination than that of the American Joint Committee on Cancer (AJCC) stage model and the calibration was validated in the SEER validation cohort. The model’s 3- and 5-year OS and CSS were superior to AJCC and T staging on the analysis decision curve. The prognosis prediction of SRCC established by the prediction model could be evaluated through the web-based survival rate calculator, which plays a guiding role in clinical treatment.ConclusionsNomograms and a web-based survival rate calculator predicting the OS and CSS of SRCC patients with better discrimination and calibration were developed.     

男性原发骨质疏松症骨密度与新型炎症指标的相关性

目的 了解系统性炎症相关指标及其他炎症指标水平与男性原发性骨质疏松症患者骨密度(bone mineral density, BMD)值的相关性。方法 筛选出2021年1月至2022年5月在广州中医药大学第一附属医院符合纳入标准与排除标准的男性患者，共311例。根据诊断标准将患者分为骨质疏松组、骨量减少组以及骨量正常组并进行回顾性研究。结果 骨质疏松组中SII、NLR、MLR以及ESR明显高于骨量正常组，差异有统计学意义(P<0.05)。二元Logistics回归分析发现PLR(P<0.05)与ESR(P<0.05)为男性发生原发性骨质疏松的危险因素。受试者工作特征(receiver operating characteristic, ROC)曲线显示PLR曲线下面积为0.658,临界值为189.17;ESR的曲线下面积为0.639,临界值为18.50;多指标联合曲线下面积为0.713。在男性原发性骨质疏松症患者中对不同部位骨密度与炎症指标进行相关性分析，结果表明除MLR与L1～L4 BMD无明显相关性，SII、NLR、PLR、CRP、ESR与L1～L4 BMD呈负相关...     

在尿路结石患者中预测草酸钙结石列线图的建立与应用

目的建立和应用个性化的列线图模型,探讨列线图预测尿路结石患者中草酸钙结石的准确性及可行性。方法回顾性分析2017年1月1日至2018年12月31日在中山大学附属第五医院接受手术治疗的298例泌尿系结石患者资料,以7∶3的比例随机分为建模组和验证组,基于建模组采用最小绝对值收敛和选择算子回归(Lasso)模型及多变量Logistic回归分析选择草酸钙结石的最佳预测特征,根据最佳预测特征以列线图的形式构建预测模型。通过C指数、校准曲线和决策曲线分别评估列线图的辨别力、校准和临床实用性,并基于验证组对外部验证进行评估。结果在LASSO模型中选择的最佳预测特征包括结石位置、甘油三酯(TG)和尿比重(SG)。将以上最佳预测特征和性别、年龄一起建立列线图模型后,建模组和验证组的C指数分别为0.706、 0.603,表明模型具有良好的辨别能力。校准曲线中标准曲线与预测校准曲线贴合良好,提示校正效果良好。决策曲线分析表明,在草酸钙结石可能性阈值为31%时使用该列线图可以在临床上获益。结论本研究建立的列线图预测模型可有效预测草酸钙结石,有助于筛选和早期识别草酸钙尿路结石的高危患者,对泌尿科医师进行临床治...     

Establishment and validation of a nomogram for predicting overall survival of node-negative perihilar cholangiocarcinoma

AimThere lacks a predictive model for overall survival (OS) of node-negative perihilar cholangiocarcinoma (PHC). This study aimed at developing and validating a prognostic nomogram to predict OS of node-negative PHC after resection.MethodsWe established a nomogram via multivariate regression analysis by using the design cohort (n = 410, obtained from Surveillance, Epidemiology, and End Results database), and its external verification was done in the validation cohort (n = 100, the First Affiliated Hospital of Sun Yat-sen University). Predictive accuracy of the nomogram was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA). Performance of the nomogram was compared with the American Joint Committee on Cancer (AJCC) staging system.ResultsMultivariate regression analysis revealed that age, tumor grade, and the count of examined lymph nodes were independent prognostic factors for OS of node-negative PHC. The nomogram had a C-index of 0.603 and 0.626 in design cohort and validation cohort, respectively, which was better than that of AJCC staging system (both p < 0.05). The calibration curves showed good consistency between actual and nomogram-predicted OS probabilities. DCA showed that nomogram had better clinical usefulness. Furthermore, the nomogram-predicted scores could stratify the patients into three risk groups, and patients in higher risk group had worse prognosis than those in lower risk group (all p < 0.05).ConclusionThe proposed nomogram had a better prognostic accuracy than the AJCC staging system in predicting postoperative OS of node-negative PHC. It was helpful to guide the adjuvant therapeutic strategies for node-negative PHC.     

Development and validation of a nomogram for predicting acute kidney injury after orthotopic liver transplantation

BackgroundWe aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT).MethodsA total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD).ResultsOverall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients’ BMI (OR 1.10, 95% CI 1.04–1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22–4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95–0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03–1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05).ConclusionsWith excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT.