Browsing by Subject "Nomogram"

Now showing 1 - 4 of 4
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    Classification and prediction of cognitive trajectories of cognitively unimpaired individuals
    (Frontiers Media, 2023-03-13) Kim, Young Ju; Kim, Si Eun; Hahn, Alice; Jang, Hyemin; Kim, Jun Pyo; Kim, Hee Jin; Na, Duk L.; Chin, Juhee; Seo, Sang Won; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Objectives: Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods: A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results: Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion: Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
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    Development and Validation of a Nomogram for Forecasting Survival of Alcohol Related Hepatocellular Carcinoma Patients
    (Frontiers Media, 2022-11-11) Yan, Tao; Huang, Chenyang; Lei, Jin; Guo, Qian; Su, Guodong; Wu, Tong; Jin, Xueyuan; Peng, Caiyun; Cheng, Jiamin; Zhang, Linzhi; Liu, Zherui; Kin, Terence; Ying, Fan; Liangpunsakul, Suthat; Li, Yinyin; Lu, Yinying; Medicine, School of Medicine
    Background: With the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma. Methods: The retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve. Results: In sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model. Conclusion: The nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.
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    Incidence Trends, Clinicopathologic Characteristics, and Overall Survival Prediction in Retinoblastoma Children: SEER Prognostic Nomogram Analysis
    (Oxford University Press, 2024) Guo, Xiaohong; Wang, Li; Beeraka, Narasimha M.; Liu, Chunying; Zhao, Xiang; Zhou, Runze; Yu, Huiming; Fan, Ruitai; Liu, Junqi; Pediatrics, School of Medicine
    Background: Retinoblastoma is the most common intraocular malignant tumor occurring among children, with an incidence rate of 1/15 000. This study built a joinpoint regression model to assess the incidence trend of retinoblastoma from 2004 to 2015 and constructed a nomogram to predict the overall survival (OS) in children. Materials and methods: Patients less than 19 years diagnosed with retinoblastoma from 2004 to 2015 were selected from the SEER database. Joinpoint regression analysis (version 4.9.0.0) was performed to evaluate the trends in retinoblastoma incidence rates from 2004 to 2015. Cox Regression Analysis was applied to investigate prognostic risk factors that influence OS. Results: Joinpoint regression revealed that retinoblastoma incidence exhibited no significant increase or decrease from 2004 to 2015. As per the multiple Cox regression, tumor size, laterality, and residence (rural-urban continuum code) were correlated with OS and were used to construct a nomogram. The nomogram exhibited a good C-index of 0.71 (95% CI, 0.63 to 0.79), and the calibration curve for survival probability demonstrated that the predictions corresponded well with actual observations. Conclusions and relevance: A prognostic nomogram integrating the risk factors for retinoblastoma was constructed to provide comparatively accurate individual survival predictions. If validated, this type of assessment could be used to guide therapy in patients with retinoblastoma.
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    A New Prognostic Model Covering All Stages of Intrahepatic Cholangiocarcinoma
    (Xia & He Publishing, 2022) Zhou, Shuang-Nan; Lu, Shan-Shan; Ju, Da-Wei; Yu, Ling-Xiang; Liang, Xiao-Xiao; Xiang, Xiao; Liangpunsakul, Suthat; Roberts, Lewis R.; Lu, Yin-Ying; Zhang, Ning; Medicine, School of Medicine
    Background and aims: Intrahepatic cholangiocarcinoma (ICC) is the second most common primary hepatic malignancy that causes a poor survival. We aimed to identify its prognostic factors and to develop a nomogram that will predict survival of ICC patients among all stages. Methods: A total of 442 patients with pathology-proven ICC registered at the Fifth Medical Center of PLA General Hospital between July 2007 and December 2019 were enrolled. Subjects were followed for survival status until June 30, 2020. A prognostic model visualized as a nomogram was constructed in the training cohort using multivariate cox model, and was then validated in the validation cohort. Results: The median age was 55 years. With a median follow-up of 50.4 months, 337 patients died. The median survival was 11.6 months, with 1-, 3- and 5-year survival rates of 48.3%, 22.7% and 16.2%, respectively. Factors associated with overall survival were multiple tumors, lymph node involvement, vascular invasion, distant metastasis, decreased albumin, elevated lactate dehydrogenase (LDH), decreased iron, elevated fibrinogen, elevated CA125 and elevated CA19-9. A nomogram predicting survival of ICC patients at the time of diagnosis achieved a Harrel's c-statistic of 0.758, significantly higher than the 0.582 of the TNM stage alone. Predicted median survivals of those within the low, mid and high-risk subgroups were 35.6, 12.1 and 6.2 months, respectively. Conclusions: A nomogram based on imaging data and serum biomarkers at diagnosis showed good ability to predict survival in patients with all stages of ICC. Further studies are needed to validate the prognostic capability of our new model.