Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.

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Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.671359 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xin Yan ◽

Zi-Xin Guo ◽

Dong-Hu Yu ◽

Chen Chen ◽

Xiao-Ping Liu ◽

...

Keyword(s):

Adrenocortical Carcinoma ◽

Cox Regression ◽

Characteristic Curve ◽

Copy Number Variations ◽

Gene Model ◽

Cox Regression Analysis ◽

Prognosis Prediction ◽

Model Training ◽

Rare Malignancy ◽

Prognosis Prediction Model

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

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Assessing Dynamic Violence Risk in a High-Risk Treated Sample of Violent Offenders

Assessment ◽

10.1177/1073191118797440 ◽

2018 ◽

Vol 27 (8) ◽

pp. 1886-1900 ◽

Cited By ~ 2

Author(s):

Richard B. A. Coupland ◽

Mark E. Olver

Keyword(s):

Risk Assessment ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Violent Offenders ◽

Violence Risk ◽

Violence Risk Assessment ◽

Change Scores ◽

Assessment And Treatment ◽

General Community

The present study featured an investigation of the predictive properties of risk and change scores of two violence risk assessment and treatment planning tools—the Violence Risk Scale (VRS) and the Historical, Clinical, Risk–20, Version 2 (HCR-20)—in sample of 178 treated adult male violent offenders who attended a high-intensity violence reduction program. The cases were rated on the VRS and HCR-20 using archival information sources and followed up nearly 10 years postrelease. Associations of HCR-20 and VRS risk and change scores with postprogram institutional and community recidivism were examined. VRS and HCR-20 scores converged in conceptually meaningful ways, supporting the construct validity of the tools for violence risk. Receiver operating characteristic curve analyses demonstrated moderate- to high-predictive accuracy of VRS and HCR-20 scores for violent and general community recidivism, but weaker accuracy for postprogram institutional recidivism. Cox regression survival analyses demonstrated that positive pretreatment and posttreatment changes, as assessed via the HCR-20 and VRS, were each significantly associated with reductions in violent and general community recidivism, as well as serious institutional misconducts, after controlling for baseline pretreatment score. Implications for use of the HCR-20 and VRS for dynamic violence risk assessment and management are discussed.

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MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

Journal of Ovarian Research ◽

10.1186/s13048-021-00941-7 ◽

2022 ◽

Vol 15 (1) ◽

Author(s):

Tianping Wang ◽

Haijie Wang ◽

Yida Wang ◽

Xuefen Liu ◽

Lei Ling ◽

...

Keyword(s):

Validation Cohort ◽

Characteristic Curve ◽

Multivariate Logistic Regression Analysis ◽

Predictive Performance ◽

Net Benefit ◽

Prognosis Prediction ◽

Epithelial Ovarian Tumor ◽

System A ◽

Area Under Roc Curve ◽

Prognosis Prediction Model

Abstract Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

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Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

BMC Cancer ◽

10.1186/s12885-021-07972-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chunyu Zhang ◽

Haitao Liu ◽

Pengfei Xu ◽

Yinqiu Tan ◽

Yang Xu ◽

...

Keyword(s):

Network Analysis ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Prognostic Indicator ◽

High Risk Group ◽

Survival Prediction ◽

Prognostic Signature ◽

Who Grade ◽

Cox Analysis

Abstract Background To accurately predict the prognosis of glioma patients. Methods A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. Results By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

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Transcranial Doppler Combined With Quantitative Electroencephalography Brain Function Monitoring for Estimating the Prognosis of Patients With Posterior Circulation Cerebral Infarction

Frontiers in Neurology ◽

10.3389/fneur.2021.600985 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yanting Cao ◽

Xiaonan Song ◽

Lijuan Wang ◽

Yajie Qi ◽

Ying Chen ◽

...

Keyword(s):

Blood Flow ◽

Cerebral Blood Flow ◽

Cerebral Infarction ◽

Roc Curve ◽

Transcranial Doppler ◽

Brain Function ◽

Predictive Accuracy ◽

Posterior Circulation ◽

Quantitative Electroencephalography ◽

Brain Function Monitoring

Posterior circulation cerebral infarction (PCCI) can lead to deceased infratentorial cerebral blood flow (CBF) and metabolism. Neural activity is closely related to regional cerebral blood flow both spatially and temporally. Transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) is a technique that evaluates neurovascular coupling and involves synergy between the metabolic and vascular systems. This study aimed to monitor brain function using TCD-QEEG and estimate the efficacy of TCD-QEEG for predicting the prognosis of patients with PCCI. We used a TCD-QEEG recording system to perform quantitative brain function monitoring; we recorded the related clinical variables simultaneously. The data were analyzed using a Cox proportional hazards regression model. Receiver-operating characteristic (ROC) curve analysis was used to evaluate the cut-off for the diastolic flow velocity (VD) and (delta + theta)/(alpha + beta) ratio (DTABR). The area under the ROC curve (AUROC) was calculated to assess the predictive validity of the study variables. Forty patients (aged 63.7 ± 9.9 years; 30 men) were assessed. Mortality at 90 days was 40%. The TCD indicators of VD [hazard ratio (HR) 0.168, confidence interval (CI) 0.047–0.597, p = 0.006] and QEEG indicators of DTABR (HR 12.527, CI 1.637–95.846, p = 0.015) were the independent predictors of the clinical outcomes. The AUROC after combination of VD and DTABR was 0.896 and showed better predictive accuracy than the Glasgow Coma Scale score (0.75), VD (0.76), and DTABR (0.781; all p < 0.05). TCD-QEEG provides a good understanding of the coupling mechanisms in the brain and can improve our ability to predict the prognosis of patients with PCCI.

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Screening and discrimination of optimal prognostic genes for pancreatic cancer based on a prognostic prediction model

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab296 ◽

2021 ◽

Author(s):

Zhiqin Chen ◽

Haifei Song ◽

Xiaochen Zeng ◽

Ming Quan ◽

Yong Gao

Keyword(s):

Pancreatic Cancer ◽

Prediction Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Molecular Therapy ◽

Clinical Factors ◽

Cox Regression Analysis ◽

Gene Set ◽

Prognosis Prediction ◽

Prognosis Prediction Model

Abstract The prognosis of pancreatic cancer is poor because patients are usually asymptomatic in the early stage and the early diagnostic rate is low. Therefore, in this study, we aimed to identify potential prognosis-related genes in pancreatic cancer to improve diagnosis and the outcome of patients. The mRNA expression profile data from The Cancer Genome Atlas database and GSE79668, GSE62452, and GSE28735 datasets from Gene Expression Omnibus were downloaded. The prognosis-relevant genes and clinical factors were analyzed using Cox regression analysis and the optimal gene sets were screened using the Cox proportional model. Next, the Kaplan-Meier survival analysis was used to evaluate the relationship between risk grouping and patient prognosis. Finally, an optimal gene-based prognosis prediction model was constructed and validated using a test dataset to discriminate the model accuracy and reliability. The results showed that 325 expression variable genes were identified, and 48 prognosis-relevant genes and three clinical factors, including lymph node stage (pathologic N), new tumor, and targeted molecular therapy were preliminarily obtained. In addition, a gene set containing 16 optimal genes was identified and included FABP6, MAL, KIF19, and REG4, which were significantly associated with the prognosis of pancreatic cancer. Moreover, a prognosis prediction model was constructed and validated to be relatively accurate and reliable. In conclusion, a gene set consisting of 16 prognosis-related genes was identified and a prognosis prediction model was constructed, which is expected to be applicable in the clinical diagnosis and treatment guidance of pancreatic cancer in the future.

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The Prognostic Ability of Staging System in Men with Penile Cancer: An Analysis of SEER Database

Nepalese Journal of Cancer ◽

10.3126/njc.v4i1.31787 ◽

2020 ◽

Vol 4 (1) ◽

pp. 68-77

Author(s):

Aditya Jalan ◽

Ravi Kanodia ◽

Sarita Rana Gurung ◽

Rajeev Kumar Malhotra ◽

Umesh Nepal ◽

...

Keyword(s):

Penile Cancer ◽

Cox Regression ◽

Predictive Accuracy ◽

Tnm Classification ◽

Characteristic Curve ◽

Rare Condition ◽

Tnm Stage ◽

Prognostic Models ◽

Ajcc Stage ◽

Free Rate

Background: Penile cancer is now a rare condition. The low incidence of the disease makes a valid estimation of its prognosis difficult. In this study, we made an attempt and propose a nomogram to develop a prognostic rule that could predict the Cancer-Specific Mortality (CSM) free rates in patients with primary penile squalors cell carcinoma of the penis (PPSCC).Methods: This study included 1304 patients diagnosed with PPSCC between the years 2004 & 2011 and treated with penile tumor excision. Subjects were staged as per Surveillance, Epidemiology & End Results stage (SEER), American Joint Committee on Cancer (AJCC), TNM classification and tumor grade (TG). CSM free rates were determined. Univariate and multivariate Cox regression model was used to test the prediction of the CSM free rate. The predictive rule accuracy was created using the receiver operating characteristic curve. Results: The clinico-pathological profile depicts a mean age of 64.66 ± 14.38 yrs. The most common primary site involved was glans penis (n= 483, 37%) and the disease was most commonly diagnosed at AJCC stage I (n= 670, 51.4%) disease. The cumulative 5-year CSM free rates according to Fine & Gray, & Kaplan-Meier methods were 81.8% and 79.8%, respectively. The predictive accuracy as per SEER stage, AJCC stage, TNM stage alone were 68.8%, 70.3%, 72.3%, respectively. When TG was combined, the predictive accuracy increased to 72.8%, 73.1%, and 75.0%, respectively. TNM stage with TG was most accurate in predicting CSM free rate compared to other models. Conclusions: TNM stage with TG and AJCC stage with TG appear to have comparable accuracy to predict the CSM free rate in patients with PPSCC, the TNM stage with TG is the most accurate (75%) method to predict the CSM free rates. The addition of the TG variable improved the accuracy of these prognostic models.

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Using an electronic frailty index to predict adverse outcomes in geriatric COVID-19 patients: data from the Stockholm GeroCovid study

10.1101/2021.12.03.21267254 ◽

2021 ◽

Author(s):

Jonathan K. L. Mak ◽

Maria Eriksdotter ◽

Martin Annetorp ◽

Ralf Kuja-Halkola ◽

Laura Kananen ◽

...

Keyword(s):

Length Of Stay ◽

Hospital Mortality ◽

Cox Regression ◽

Predictive Accuracy ◽

Strong Predictor ◽

Characteristic Curve ◽

Frailty Index ◽

Predictive Ability ◽

Adverse Outcomes ◽

Clinical Frailty Scale

ABSTRACTBackgroundThe Clinical Frailty Scale (CFS) is a strong predictor for worse outcomes in geriatric COVID-19 patients, but it is less clear whether an electronic frailty index (eFI) constructed from routinely collected electronic health records (EHRs) provides similar predictive value. This study aimed to investigate the predictive ability of an eFI in comparison to other frailty and comorbidity measures, using mortality, readmission, and the length of stay as outcomes in geriatric COVID-19 patients.MethodsWe conducted a retrospective cohort study using EHRs from nine geriatric clinics in Stockholm, Sweden, comprising 3,405 COVID-19 patients (mean age 81.9 years) between 1/3/2020 and 31/10/2021. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the CFS, and Hospital Frailty Risk Score (HFRS). Comorbidity was measured using the Charlson Comorbidity Index (CCI). We analyzed in-hospital mortality and 30-day readmission using logistic regression and area under receiver operating characteristic curve (AUC). 30-day and 6-month mortality were modelled by Cox regression, and the length of stay by linear regression.ResultsControlling for age and sex, a 10% increase in the eFI was associated with higher risks of in-hospital mortality (odds ratio [OR]=2.84; 95% confidence interval=2.31-3.51), 30-day mortality (hazard ratio [HR]=2.30; 1.99-2.65), 6-month mortality (HR=2.33; 2.07-2.62), 30-day readmission (OR=1.34; 1.06-1.68), and longer length of stay (β=2.28; 1.90-2.66).The CFS, HFRS and CCI similarly predicted these outcomes, but the eFI had the best predictive accuracy for in-hospital mortality (AUC=0.775).ConclusionsAn eFI based on routinely collected EHRs can be applied in identifying high-risk geriatric COVID-19 patients.

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Development of an electronic frailty index for hospitalized geriatric patients in Sweden

10.1101/2021.11.26.21266912 ◽

2021 ◽

Author(s):

Jonathan K. L. Mak ◽

Sara Hagg ◽

Maria Eriksdotter ◽

Martin Annetorp ◽

Ralf Kuja-Halkola ◽

...

Keyword(s):

Length Of Stay ◽

Hospital Mortality ◽

Geriatric Patients ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Frailty Index ◽

Laboratory Data ◽

Adverse Outcomes ◽

Clinical Frailty Scale

Background: Frailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its predictive ability for adverse outcomes in geriatric patients. Methods: EHRs were extracted for 18,225 geriatric patients with unplanned admissions between 1/3/2020 and 17/6/2021 from nine geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modelled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression. Results: 13,188 patients were included in analyses (mean age 83.1 years). A 10% increment in the eFI was associated with higher risks of in-hospital (odds ratio: 5.34; 95% confidence interval: 4.20-6.82), 30-day (hazard ratio [HR]: 3.28; 2.91-3.69), and 6-month mortality (HR: 2.70; 2.52-2.90) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the best predictive accuracy for in-hospital mortality, yielding an area under receiver operating characteristic curve of 0.813. Higher eFI also predicted a longer length of stay, but had a rather poor discrimination for 30-day readmission. Conclusions: An EHR-based eFI has good predictive accuracy for adverse outcomes, suggesting that it can be used in risk stratification in geriatric patients.

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Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

The Scientific World JOURNAL ◽

10.1155/2013/201976 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 13

Author(s):

Herng-Chia Chiu ◽

Te-Wei Ho ◽

King-Teh Lee ◽

Hong-Yaw Chen ◽

Wen-Hsien Ho

Keyword(s):

Neural Network ◽

Hepatocellular Carcinoma ◽

Artificial Neural Network ◽

Clinical Decision Making ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Predictors Of Mortality ◽

Artificial Neural ◽

Estimation Models

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

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