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1.
IntroductionMuch controversy exists over whether to perform lateral neck dissection (LND) on patients with papillary thyroid carcinoma (PTC). This study aimed to build predictive nomograms that could individually estimate lateral neck metastasis (LNM) risk and help determine follow up intensity.Patients and methodsUnifocal PTC patients who underwent LND between April 2012 and August 2014 were identified. Clinical and pathological variables were retrospectively evaluated using univariate and stepwise multivariate logistic regression analysis. Variables that had statistical significance in final multivariate logistic models were chosen to build nomograms, which were further corrected using the bootstrap resampling method.ResultsIn all, 505 PTC patients were eligible for analysis. Among these, 178 patients (35.2%) had lateral neck metastasis. Two nomograms were generated: nomogram (c) and nomogram (c + p). Nomogram (c) incorporated four clinical variables: age, tumor size, tumor site, and extrathyroidal extension (ETE). It had a good discriminative ability, with a C-index of 0.79 (bootstrap-corrected, 0.78). Nomogram (c + p) incorporated two clinical variables and two pathological variables: tumor size, tumor site, extranodal extension (ENE), and number of positive nodes in the central compartment. Nomogram (c + p) showed an excellent discriminative ability, with a C-index of 0.86 (bootstrap-corrected, 0.85).ConclusionTwo predictive nomograms were generated. Nomogram (c) is a clinical model, whereas nomogram (c + p) is a clinicopathological model. Each nomogram incorporates only four variables and can give an accurate estimate of LNM risk in unifocal PTC patients, which may assist clinicians in patient counseling and decision making regarding LND.  相似文献   
2.

Background

Available models for predicting lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP) might not be applicable to men diagnosed via magnetic resonance imaging (MRI)-targeted biopsies.

Objective

To assess the accuracy of available tools to predict LNI and to develop a novel model for men diagnosed via MRI-targeted biopsies.

Design, setting, and participants

A total of 497 patients diagnosed via MRI-targeted biopsies and treated with RP and extended pelvic lymph node dissection (ePLND) at five institutions were retrospectively identified.

Outcome measurements and statistical analyses

Three available models predicting LNI were evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses. A nomogram predicting LNI was developed and internally validated.

Results and limitations

Overall, 62 patients (12.5%) had LNI. The median number of nodes removed was 15. The AUC for the Briganti 2012, Briganti 2017, and MSKCC nomograms was 82%, 82%, and 81%, respectively, and their calibration characteristics were suboptimal. A model including PSA, clinical stage and maximum diameter of the index lesion on multiparametric MRI (mpMRI), grade group on targeted biopsy, and the presence of clinically significant PCa on concomitant systematic biopsy had an AUC of 86% and represented the basis for a coefficient-based nomogram. This tool exhibited a higher AUC and higher net benefit compared to available models developed using standard biopsies. Using a cutoff of 7%, 244 ePLNDs (57%) would be spared and a lower number of LNIs would be missed compared to available nomograms (1.6% vs 4.6% vs 4.5% vs 4.2% for the new nomogram vs Briganti 2012 vs Briganti 2017 vs MSKCC).

Conclusions

Available models predicting LNI are characterized by suboptimal accuracy and clinical net benefit for patients diagnosed via MRI-targeted biopsies. A novel nomogram including mpMRI and MRI-targeted biopsy data should be used to identify candidates for ePLND in this setting.

Patient summary

We developed the first nomogram to predict lymph node invasion (LNI) in prostate cancer patients diagnosed via magnetic resonance imaging-targeted biopsy undergoing radical prostatectomy. Adoption of this model to identify candidates for extended pelvic lymph node dissection could avoid up to 60% of these procedures at the cost of missing only 1.6% patients with LNI.  相似文献   
3.
Summary The apparent efficiency of sub-maximum exercise tends to be lower in subjects with a large aerobic power. This is probably an artefact arising from neglect of the oxygen debt in the calculation of mechanical efficiency. Changes in the extent of oxygen debt can obscure an increased skill of performance with training. Efficiency is improved by repetition of a given mode of exercise, but not by other forms of training. Habituation is greater during work than at rest, but even during work the change in pulse rate of young men does not exceed 2–5 beats/min over 5 experimental days. Habituation is lost if the test procedure is not repeated during training; this can complicate assessments of training from the response to sub-maximum exercise.  相似文献   
4.
目的 探讨中学生出现抑郁症状的影响因素,建立风险预测的列线图模型,为防控中学生抑郁提供理论依据。方法 采用分层整群抽样的方法,抽取大连市城市和农村中学生共3 470名,使用学生健康行为调查表及抑郁量表进行问卷调查;采用多因素logistic回归分析抑郁症状的影响因素,并建立列线图,预测中学生出现抑郁症状的风险。 结果 中学生的抑郁症状检出率为24.12%,肯定有抑郁症状的检出率为16.22%。遭受校园欺凌(OR = 2.748,95%CI:2.257~3.346)、被家长打骂(OR = 2.025,95%CI:1.679~2.433)、学段(职高:OR = 1.883,95%CI:1.286~2.758;高中:OR = 1.242,95%CI:1.001~1.541)、上网时间越长(≥3 h:OR = 1.773,95%CI:1.366~2.302;2~<3 h:OR = 1.525,95%CI:1.190~1.954)、女生(OR = 1.352,95%CI:1.141~1.603)、农村地区(OR = 1.351,95%CI:1.126~1.622)均是中学生抑郁症状检出的危险因素;体育课时数多(2 节:OR = 0.685,95%CI:0.504~0.931;≥3 节:OR = 0.583,95%CI:0.425~0.799)、睡眠时间充足(OR = 0.676,95%CI:0.562~0.812)都是中学生抑郁症状检出的保护因素;基于以上影响因素建立的列线图模型具有较好的区分度(一致性指数C - index = 0.700,95%CI:0.680~0.721)和准确度(Hosmer - Lemeshow检验χ2 = 2.885,P = 0.941)。 结论 遭受校园欺凌、被家长打骂、职高或高中、上网时间越长、女生、来自农村地区、体育课时数少、睡眠时间不足的中学生更容易出现抑郁症状,可以利用列线图直观、有效地预测中学生出现抑郁症状的风险,从而有针对性地对高危群体及时采取干预措施。  相似文献   
5.
目的构建列线图模型以预测新型冠状病毒病2019(COVID-19)的死亡风险,以早期筛选死亡高危患者。 方法收集2020年1月至2020年4月武汉大学人民医院(东院)和2022年4月至2022年5月上海市第九人民医院(北院)收治COVID-19患者的临床资料。以武汉大学人民医院患者(166例)作为训练集,上海市第九人民医院患者(52例)作为验证集。采用先单因素后多因素Logistic回归分析确定死亡的独立危险因素,应用R语言构建列线图模型。采用受试者工作特征曲线(ROC)、C指数及校准曲线评估列线图模型的预测准确性及判断能力,决策曲线分析评估模型的临床应用价值。通过验证集对模型进行外部验证。 结果本研究共纳入重型/危重型COVID-19患者218例,其中67例(30.73%)死亡,多因素Logistic回归分析显示,≥3种基础疾病、APACHE Ⅱ评分(5~40分)、中性粒细胞/淋巴细胞(0~90)、乳酸(0~16mmol/L)均是死亡的独立危险因素。ROC曲线分析显示,训练集的曲线下面积(AUC)为0.869(95%CI:0.811~0.927),验证集AUC为0.797(95%CI:0.671~0.924),训练集与验证集校准曲线经Hosmer-Lemeshow拟合优度检验(P=0.473,P=0.421)。临床决策曲线分析表明,该列线图预测模型的临床应用价值高。 结论本研究构建COVID-19患者死亡风险列线图模型预测效能良好,可个体化、可视化、图形化预测,有助于医师早期做出合适临床决策及诊疗。  相似文献   
6.
BackgroundChildhood obesity places a major burden on global public health. We aimed to identify and characterize potential factors, both individually and jointly, in association with overweight and obesity in Chinese preschool-aged children.MethodsWe cross-sectionally recruited 9501 preschool-aged children from 30 kindergartens in Beijing and Tangshan. Overweight and obesity are defined according to the World Health Organization (WHO), International Obesity Task Force (IOTF), and China criteria.ResultsAfter multivariable adjustment, eating speed, sleep duration, birthweight, and paternal body mass index (BMI) were consistently and significantly associated with childhood overweight and obesity under three growth criteria at a significance level of 5%. Additional fast food intake frequency, maternal BMI, gestational weight gain (GWG) and maternal pre-pregnancy BMI were significant factors for overweight (WHO criteria) and obesity (both IOTF and China criteria). Importantly, there were significant interactions between parental obesity and eating speed for childhood obesity. Finally, for practical reasons, risk nomogram models were constructed for childhood overweight and obesity based on significant factors under each criterion, with good prediction accuracy.ConclusionOur findings indicated a synergistic association of lifestyle, fetal and neonatal, and family-related factors with the risk of experiencing overweight and obesity among preschool-aged children.  相似文献   
7.
目的 探究结直肠间质瘤预后相关因素,并通过列线图预测该肿瘤生存概率,为指导临床评估预后提供依据.方法: 通过监测流行病学和最终结果(surveillance, epidemiology, and end results, SEER)数据库获取1992年1月至2015年12月结直肠间质瘤临床病理及预后相关资料,对入组患者进行生存分析,将分析得到的独立预后因素绘制成列线图,之后采用校准曲线评估列线图预测生存准确性.结果: 546例结直肠间质瘤患者被纳入研究.中位发病年龄64岁,区域淋巴结转移率9.4%.546例患者多因素生存分析显示发病年龄 > 64岁,未婚/离婚,结肠间质瘤(与直肠间质瘤相比),非手术治疗,组织分化级别高,区域淋巴结转移及远处转移具有更差的肿瘤特异性生存和总生存(P均<0.05), 美国东部地区诊治患者比西部地区患者具有更长的总生存时间(P = 0.027),以上独立预后因素预测肿瘤特异性生存率和总生存率的C指数分别为0.76(95%CI: 0.72-0.80)和0.75(95%CI: 0.72-0.78).在174例组织分化级别和肿瘤部位明确的患者中,影响肿瘤特异性生存和总生存的独立预后因素为年龄,组织分化级别和是否行手术治疗(P均<0.05),而肿瘤部位仅与肿瘤特异性生存显著相关(P = 0.041),未证实与总生存显著相关(P = 0.057),采用这4个预后影响因素预测546例患者肿瘤特异性生存率和总生存率的C指数分别是0.71(95%CI: 0.66-0.75)和0.73(95%CI: 0.70-0.77), 能较准确预测结直肠间质瘤患者总生存率.结论: 结直肠间质瘤预后受多个临床病理因素影响,列线图能为预测结直肠间质瘤患者生存率提供依据.  相似文献   
8.
The temperature-based nomogram method for estimation of the time period since death was used at the scene of death as the primary method within a compound method in 72 consecutive cases. The situation and cooling conditions inspected and evaluated by the forensic pathologist at the scene are described as far as necessary to enable handling of the method. A comparison of the estimated period since death with the period determined by the police investigations demonstrates the reliability of the method. There were no contradictions in any of the 60 cases between the period of death estimated by this method and that determined by the police investigations. The criminal investigations were effectively supported in the earliest stages in 11 cases despite the fact that the period estimated was of considerable duration.  相似文献   
9.
BackgroundPrimary central nervous system lymphoma is a rare and highly aggressive type of non-Hodgkin lymphoma. This study used population-based data to evaluate the clinical characteristics and prognostic factors of primary central nervous system lymphoma and develop a prediction model to estimate survival.MethodsPatients’ data were extracted from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors were identified using univariate and multivariate Cox regression analyses. Conditional survival estimates were calculated as CS(x y) = S(x + y)/S(X), and a nomogram was built to predict patient prognosis.ResultsIn total, 2563 patients with primary central nervous system lymphoma were included. Multivariate Cox analysis showed that age at diagnosis, sex, histology, tumor site, surgery, chemotherapy, and marital status were independent prognostic factors of overall survival. The 1-year conditional survival increased with time, and our nomogram model showed favorable discriminative ability.ConclusionAt the population level, our study found that gross total resection and chemotherapy improved the prognosis of patients with primary central nervous system lymphoma. However, the prognosis of black patients was poor. Conditional survival provided a more accurate and dynamic survival estimate. Moreover, our nomogram had a good performance and could help predict the overall survival of these patients.  相似文献   
10.
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。  相似文献   
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