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.     

成釉细胞瘤预后评估——基于SEER数据库的研究

目的 成釉细胞瘤(AM)是罕见牙源性肿瘤,易复发、浸润生长,本研究旨在探讨成釉细胞瘤预后因素。方法 基于SEER数据库筛选成釉细胞瘤 207例,构建Cox回归模型并绘制诺莫图预测总生存。回顾性分析湖南省肿瘤医院 61例临床资料,用Cox回归分析影响复发的独立因素。结果 基于SEER数据库评估,年龄、肿瘤大小、手术方式、放疗均是影响AM患者总生存的重要因素,构建列线图,一致性指数为0.821,表明预测模型具有中等准确度。受试者工作曲线显示1、3、5、10年的曲线下面积分别为0.852、0.869、0.856、0.879,表明预测模型具有较好的敏感性和特异性。Kaplan-Meier生存分析显示高风险组总生存低于低风险组(P<0.001)。基于回顾性分析,临床症状、手术类型是影响局部复发率的独立因素;面部肿痛,相对于其他症状复发率更低,根治手术相对于姑息手术可降低复发率。结论 年龄、肿瘤大小、手术方式、放疗可能是影响总生存的重要因素;手术方式、临床症状可能是影响复发率的独立因素。     

基于IVIM‐DWI及影像组学的列线图预测宫颈癌同步放化疗后复发

目的 探讨基于体素内不相干运动弥散加权成像（IVIM‐DWI）及MRI影像组学的列线图模型在预测局部晚期宫颈癌（LACC）同步放化疗（CCRT）后复发中的价值。方法 回顾性分析2014年12月至2019年12月于安徽省肿瘤医院接受CCRT并持续随访的111例ⅠB‐ⅣA期宫颈癌患者的临床资料。测量所有患者疗前原发灶的IVIM‐DWI定量参数（ADC、D、D^*、f值）及疗前、疗后T₂WI序列的3D纹理特征,并采用最小绝对收缩和选择算子（LASSO）算法和logistic回归分析筛选纹理特征,计算影像组学评分Rad‐score。采用Cox比例风险模型分析LACC患者CCRT后复发的独立危险因素并构建列线图。结果 外照射剂量、f值、疗前Rad‐score及疗后Rad‐score是宫颈癌CCRT复发的独立预后因素（HR=0.204、3.253、2.544、7.576）并共同组成列线图模型。模型预测1、3、5年无病生存（DFS）期的曲线下面积分别为0.895、0.888和0.916,内部验证一致性指数分别为0.859、0.903和0.867。决策曲线表明,相较于其他模型,列线图具有更高的临床净收益,临床影响曲线进一步直观地展现了模型的预测精度。结论 基于IVIM‐DWI及影像组学的列线图对预测LACC患者CCRT后复发具有较高的临床价值,可为宫颈癌患者的预后评估及个体化治疗提供参考。     

Development and external validation of nomograms predictive of response to radiation therapy and overall survival in nasopharyngeal cancer patients

IntroductionLarge variability in the clinical outcomes has been observed among the nasopharyngeal cancer (NPC) patients with the same stage receiving similar treatment. This suggests that the current Tumour-Node-Metastasis staging systems need to be refined. The nomogram is a useful predictive tool that integrates individual variables into a statistical model to predict outcome of interest. This study was to design predictive nomograms based on the clinical and pathological features of patients with NPC.Materials and methodsClinical data of 270 NPC patients who underwent definitive radiation therapy (RT) alone or concurrent with chemotherapy were collected. Factors predictive of response to RT and overall survival (OS) were determined by univariate and multivariate analyses, and predictive nomograms were created. Nomograms were validated externally by assessing discrimination and calibration using an independent data set (N = 122).ResultsThree variables predictive of response to RT (age, histology classification and N classification) and four predictive of OS (age, performance status, smoking status and N classification), in addition to T classification, were extracted to generate the nomograms. The nomograms were validated externally, which showed perfect correlation with each other.ConclusionThe designed nomograms proved highly predictive of response to RT and OS in individual patients, and could facilitate individualised and personalised patients’ counselling and care.     

急性期脑卒中患者下肢深静脉血栓风险预测模型的构建及应用

目的 构建并应用急性期脑卒中患者下肢深静脉血栓（deep vein thrombosis,DVT）风险列线图预测模型。方法 采用前瞻性研究设计,便利选取2020年1月—2021年4月在南宁市某三级甲等综合医院住院的602例急性期脑卒中患者作为研究对象。其中2020年1月—12月的415例作为建模组,2021年1月—4月的187例作为验证组对模型进行外部验证。采用单因素和多因素Logistic回归分析急性期脑卒中患者下肢DVT危险因素,建立风险预测模型并绘制列线图。采用受试者操作特征曲线（receiver operating characteristic,ROC）和Hosmer-Lemeshow检验验证模型预测效果。结果 建模组415例中有35例发生DVT,发生率为8.4%;验证组187例中有19例发生DVT,发生率为10.2%。建模组中单因素分析结果显示,年龄、诊断、卧床时间、意识状态、偏瘫程度,是否有吸烟史、房颤史、血栓史,是否使用脱水药物、是否留置中心静脉导管、血浆纤维蛋白原、D-二聚体定量是急性期脑卒中患者发生DVT的影响因素。多因素Logistic回归分析结果显示,年龄、意识状态、偏瘫程度、是否使用脱水药物是急性期脑卒中患者发生DVT的独立影响因素（OR值分别为1.901、1.702、1.940、3.231,均P<0.05）,以上述4个因素为自变量构建列线图,模型ROC曲线下面积为0.850,约登指数最大值为0.758时,灵敏度为83%,特异度为82%,最佳临界值为0.071。Hosmer-Lemeshow拟合优度检验 χ²=2.143,P=0.951;外部验证组ROC曲线下面积为0.893,约登指数最大值为0.746时,灵敏度为90%,特异度为85%,最佳临界值为0.084。结论 构建的列线图可个性化预测急性期脑卒中患者DVT发生风险,有助于护理人员制订相应的干预措施。     

颅脑手术患者全麻苏醒期发生低氧血症护理预警模型的建立与验证

目的构建个体化预测颅脑手术全麻苏醒期发生低氧血症的护理预警模型,并对模型的预测效能予以验证。方法分别选取2019年10月-2020年6月和2020年7月-2020年9月于我院行全麻下颅脑外科手术的患者作为训练集(n=119)和验证集(n=38),收集患者的临床资料,使用单因素和Logistic回归多因素分析训练集患者全麻苏醒期发生低氧血症的影响因素,并建立相关列线图模型。结果观察者的警觉性/镇静评估量表(OAA/S)评分BMI≥24、吸烟、高血压、气道不通畅、OAA/S评分≥3分和Riker躁动评分≥5分是颅脑手术全麻苏醒期发生低氧血症的危险因素(P<0.05)。依此建立预测颅脑手术全麻苏醒期发生低氧血症的护理预警模型,模型验证结果显示训练集和验证集的C-index分别为0.861和0.796,校正曲线均趋近于理想曲线,ROC曲线的AUC分别为0.875(95%CI 0.833~0.892)和0.826(95%CI 0.787~0.873),表明该模型具有良好的预测能力。结论颅脑手术全麻苏醒期发生低氧血症危险因素较多,基于风险因素构建的列线图护理模型能有效预警全麻苏醒期发生低氧血症的风险概率,对颅脑手术患者的临床护理具有一定的指导意义。     

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.     

Nomograms as a Tool in Predicting Prostate Cancer Prognosis

Prostate cancer (PCa) is a heterogeneous disease with different disease states. Clinical judgment has significant biases. Nomograms are graphical calculating tools that use several clinical variables to determine a specific clinical outcome. Nomograms as prediction tools use multiple variables and continuous variables continuously, and their characteristics include patient populations, clinical end points, accuracy, and whether internal and/or external validations were performed. Numerous nomograms are available for most PCa stages, and their availability and accuracy make them powerful tools for the improvement of clinical prediction and important aids in personalised medicine for both patients and physicians.     

Development and internal validation of a nomogram predicting the probability of prostate cancer Gleason sum upgrading between biopsy and radical prostatectomy pathology

OBJECTIVE: Previous reports indicate that as many as 43% of men with low grade PCa at biopsy will be diagnosed with high-grade PCa at RP. We explored the rate of upgrading from biopsy to RP specimen in our contemporary cohort, and developed a model capable of predicting the probability of biopsy Gleason sum upgrading. MATERIALS AND METHODS: The study cohort consisted of 2982 men treated with RP, with available clinical stage, serum prostate specific antigen and biopsy Gleason scores. These clinical data were used as predictors in multivariate logistic regression models (LRM) addressing the rate of Gleason sum upgrading between biopsy and RP pathology. LRM regression coefficients were used to develop a nomogram predicting the probability of Gleason sum upgrading and was subjected to 200 bootstrap resamples for internal validation and to reduce overfit bias. RESULTS: Overall, 875 patients were upgraded (29.3%). In multivariate LRMs, all predictors were highly significant (all p values <0.0001). Bootstrap-corrected predictive accuracy of the nomogram predicting the probability of Gleason sum upgrading between biopsy and RP was 0.804. CONCLUSION: We developed a highly accurate clinical aid for treatment decision-making. It may prove useful when the possibility of a more aggressive Gleason variant may change the treatment options.