乙型肝炎肝硬化患者行脾切除术后门静脉血栓形成的列线图预测模型的建立与验证

目的 建立并验证乙肝肝硬化患者行脾切除术后门静脉血栓形成的列线图预测模型,评价其临床应用价值。方法 回顾性收集重庆医科大学附属第二医院2014年1月~2020年1月期间收治的共180例乙肝肝硬化并行脾切除术患者的临床资料,本研究中患者行脾切除术后均常规予以低分子肝素或口服抗凝剂抗凝治疗,必要时加用双嘧达莫抗血小板治疗。通过随机数字表法按 7:3比例分为建模人群（120例）和验证人群（60例）,并比较两人群的临床资料有无可比性。在建模人群中根据术后1月内有无门静脉血栓形成分为血栓组（49例）与无血栓组（71例）,通过单因素及多因素Logistic回归分析筛选出乙肝肝硬化患者行脾切除术后门静脉血栓形成的独立危险因素,将这些因素纳入并建立列线图预测模型。分别在建模人群及验证人群中对预测模型进行内部及外部验证：采用AUROC（C指数）验证模型区分度;GiViTI校准带及Hosmer-Lemeshow检验验证模型校准度;DCA曲线验证模型临床有效性。结果 建模人群与验证人群间临床资料差异均无统计学意义（P>0.05）,具有可比性。单因素及多因素Logistic回归分析显示：血栓组与无血栓组在消化道出血史、门静脉直径、脾静脉直径、脾脏体积、静脉曲张程度、术后D-二聚体、血小板变化值等方面差异有显著统计学意义（P<0.05）,其中门静脉直径、脾静脉直径、术后血小板变化值是乙肝肝硬化脾切除术后门静脉血栓形成的独立危险因素（P<0.05）,将上述独立危险因素纳入并成功建立个体化列线图预测模型。分别在建模人群及验证人群中对列线图预测模型进行内部及外部验证：AUROC（C指数）分别为.880（95%CI：0.818~0.942）和0.873（95%CI：0.785~0.960）,预测模型区分度良好;GiViTI校准曲线带的80%及95%CI区域均未穿过45°角平分线,P值分别为0.965和0.632,Hosmer-Lemeshow检验中P值分别为0.624和0.911,预测模型的校准度较高;DCA曲线中阈概率值设定为30.5%,两人群临床净获益分别为30%和34%,表明预测模型具有临床有效性。结论 通过建立乙肝肝硬化脾切除术后门静脉血栓形成的列线图预测模型,有助于临床早期筛选并识别乙肝肝硬化脾切除术后门静脉血栓形成的高危患者。     

Development and validation of a novel vancomycin dosing nomogram for achieving high-target trough levels at 2 canadian teaching hospitals

Background:

Recent guidelines recommend a vancomycin trough (predose) level between 15 and 20 mg/L in the treatment of invasive gram-positive infections, but most initial dosing nomograms are designed to achieve lower targets (5–15 mg/L). Clinicians need guidance about appropriate initial dosing to achieve the higher target.

Objective:

To develop and validate a high-target vancomycin dosing nomogram to achieve trough levels of 15–20 mg/L.

Methods:

A retrospective study was conducted at 2 teaching hospitals, St Paul’s Hospital and Vancouver General Hospital in Vancouver, British Columbia. Patients who were treated with vancomycin between January 2008 and June 2010 and who had achieved a trough level of 14.5–20.5 mg/L were identified. Demographic and clinical data were collected. Multiple linear regression was used to develop a vancomycin dosing nomogram for each hospital site. An integrated nomogram was constructed by merging the data from the 2 hospitals. A unique set of patients at each institution was used for validating their respective nomograms and a pooled group of patients for validating the integrated nomogram. Predictive success was evaluated, and a nomogram was deemed significantly different from another nomogram if p < 0.05 via “χ² testing.

Results:

Data from 78 patients at one hospital and 91 patients at the other were used in developing the respective institutional nomograms. For each hospital’s data set, both age and initial serum creatinine were significantly associated with the predicted dosing interval (p < 0.001). Validation in a total of 105 test patients showed that the integrated nomogram had a predictive success rate of 56%.

Conclusions:

A novel vancomycin dosing nomogram was developed and validated at 2 Canadian teaching hospitals. This integrated nomogram is a tool that clinicians can use in selecting appropriate initial vancomycin regimens on the basis of age and serum creatinine, to achieve high-target levels of 15–20 mg/L. The nomogram should not replace clinical judgment for patients with unstable and/or reduced renal function.     

建立MRI影像组学列线图术前预测子宫内膜癌浸润深肌层

目的 构建MRI影像组学列线图,评价其预测子宫内膜癌(EC)深肌层浸润(DMI)的价值.方法 分析133例EC患者术前临床及影像学资料,根据术后病理所见肌层浸润情况将其分为DMI组(n=62)和非DMI组(n=71).获取并筛选其影像组学特征,计算影像组学得分(Radscore),利用多因素logistic回归筛选常规...     

采用列线图对乳腺癌进行新辅助治疗后病理完全缓解及无复发生存的预测

1 文献类型 治疗。 2 证据水平 2b。     

Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis

We aimed to construct and validate nomogram models that predict the incidence of lung metastasis (LM) in patients with renal cell carcinoma (RCC) and evaluate overall survival (OS) and cancer-specific survival (CSS) among RCC patients with LM.The Surveillance, Epidemiology, and End Results database was analyzed for RCC patients diagnosed between 2010 and 2015. The X-tile program was used to determine the best cutoff values for age at initial diagnosis and tumor size. Logistic regression analysis was performed to explore independent risk factors for LM, and COX regression analysis was used to identify prognostic indicators for OS and CSS in lung metastatic RCC patients. Subsequently, 3 nomograms were established, and receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were utilized to validate their accuracy.We randomly assigned 10,929 patients with RCC to 2 groups with 1:1 allocation. Multivariate logistic analyses revealed that pathology, tumor (T) stage, nodes (N) stage, race, grade, surgery, metastatic sites, and tumor size were independent risk factors for LM. Multivariate Cox analyses showed that pathology, T stage, N stage, age, surgery, metastatic sites, and residence were independent prognostic factors for OS and CSS in patients with LM. Then, nomograms were developed based on the multivariate logistic and Cox regression analyses results. The ROC and DCA curves confirmed that these nomograms achieved satisfactory discriminative power.Three effective nomograms were constructed and validated that can be used to assist clinicians in predicting the incidence of LM and evaluating the prognosis of lung metastatic RCC.