Abstract WP158: Validation of a Preliminary Prediction Model with MR Plaque Imaging to Estimate Risk for New Ischemic Brain Lesions after Carotid Endarterectomy or Stenting

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Kohkichi Hosoda ◽  
Nobuyuki Akutsu ◽  
Atsushi Fujita ◽  
Eiji Kohmura
Keyword(s):  
Prediction Model ◽  
Carotid Stenosis ◽  
Predictive Value ◽  
Signal Intensity ◽  
Carotid Plaque ◽  
Brain Lesions ◽  
Black Blood ◽  
Training Set ◽  
Ischemic Brain ◽  
Validation Set

[Objective] Recently, we reported a preliminary prediction model with carotid plaque MRI to estimate risk for new ischaemic brain lesions after CEA or CAS. The objective of this study was to validate this model in new set of patients with carotid stenosis. [Methods] One hundred four patients with carotid stenosis undergoing treatment (63 CEA, 41 CAS) were used as a training set for construction of a preliminary prediction model to estimate risk for new ischemic brain lesions after CEA or CAS. T1 and T2 signal intensity of carotid plaque were measured on black-blood MRI. Associations among MRI findings, treatment, clinical factors, and occurrence of new ischemic lesions on DWI 1 day after treatment were studied by logistic regression. The validity of the prediction model was examined using a new set of patients with carotid stenosis (n = 43) as a validation set. [Results] In the training set, new DWI lesions after treatment were observed in 25 patients (24%). The model demonstrated that T1-signal intensity and CAS were positively associated with new lesions on post-treatment DWI scans, and T2 signal intensity was negatively associated (Fig. 1). The C-index was 0.79, which indicated some predictive value. In the validation set, new DWI lesions after treatment were observed in 10 patients (23%). However, C-index was 0.6 and positive predictive value was 33% (Fig. 2), which suggested overfitting of our model and/or differences in case-mix between the training set and validation set. [Conclusions] Our preliminary prediction model may provide some useful information for decision-making regarding treatment strategy, but needs further collection of patients to improve its predictive value.

scholarly journals The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Pokpong Piriyakhuntorn ◽  
Adisak Tantiworawit ◽  
Thanawat Rattanathammethee ◽  
Chatree Chai-Adisaksopha ◽  
Ekarat Rattarittamrong ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Microcytic Anemia ◽  
Deficiency Anemia ◽  
Operating Characteristics ◽  
Training Set ◽  
Distribution Width ◽  
Validation Set ◽  
Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

A pharmacogenetic model predicting low paclitaxel clearance based on the DMET platform.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 2597-2597
Author(s):  
Annemieke J.M. Nieuweboer ◽  
Anne-Joy M. de Graan ◽  
Laure Elens ◽  
Marcel Smid ◽  
John W. M. Martens ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Prediction Model ◽  
Predictive Value ◽  
Plasma Concentrations ◽  
Sampling Strategy ◽  
High Sensitivity ◽  
Cancer Drug ◽  
Drug Metabolizing Enzyme ◽  
Validation Set ◽  
Metabolizing Enzyme

2597 Background: Paclitaxel (PTX) is a commonly used cytotoxic agent. It is metabolized by P450 cytochrome iso-enzymes CYP3A4 and CYP2C8 and has high interindividual variability in pharmacokinetics (PK) and toxicity. Here, we present a genetic prediction model to identify patients with low PTX clearance (CL) using the new Drug-Metabolizing Enzyme and Transporter (DMET; Affymetrix) platform, capable of detecting 1,936 genetic variants (SNPs) in 225 genes. Methods: In a PK study, 270 Caucasian cancer patients were treated with PTX. PK parameters were determined using a limited sampling strategy. HPLC or LC-MS/MS were used to determine PTX plasma concentrations and non-linear mixed effects modelling (NONMEM) was used to estimate individual unbound CL from previously developed PK population models. Subsequently, the cohort of patients was randomly split into a training and validation set. In all patients, the presence of SNPs in metabolic enzymes and transporters was determined using the DMET platform. Selected SNPs were subsequently validated in the validation set. Results: Baseline characteristics were comparable in both sets. The mean CL of the total cohort was 488 ± 149 L/h and the threshold for low CL was set at 339 L/h (1 SD < total mean CL). 14 SNPs were selected to be included in the prediction model and validated in the validation set. For none of these 14 SNPs, evidence for a biological plausible link to taxane metabolism exists. The developed prediction model had a sensitivity of 95% to identify low PTX CL, a positive predictive value of 22% and remained significantly associated with low CL after multivariate analysis correcting for age, gender and Hb levels at start of therapy (P=0.024). Conclusions: This is the first considerably-sized application of the DMET platform to explain PK variability of a widely used anti-cancer drug. Although this validated prediction model for PTX CL had a high sensitivity, its positive predictive value is too low to be of direct clinical use. Likely, genetic variability in DMET genes alone does not sufficiently explain PTX CL, as for example environmental factors may also influence PTX metabolism.

scholarly journals A novel ferroptosis-related genes model for prognosis prediction of lung adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Cox Regression ◽  
Risk Group ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Key Genes ◽  
Validation Set ◽  
Prognostic Prediction

Abstract Background Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD). Methods RNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well. Results High-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results. Conclusions This study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.

scholarly journals CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yinghao Meng ◽  
Hao Zhang ◽  
Qi Li ◽  
Fang Liu ◽  
Xu Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Predictive Value ◽  
Tumor Stroma ◽  
Ductal Adenocarcinoma ◽  
Gradient Boosting ◽  
Rank Test ◽  
Training Set ◽  
Extreme Gradient Boosting ◽  
Validation Set

PurposeTo develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor–stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsIn this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.ResultsWe observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.ConclusionsThe CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

scholarly journals Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Aihua Wu ◽  
Zhigang Liang ◽  
Songbo Yuan ◽  
Shanshan Wang ◽  
Weidong Peng ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Diagnostic Accuracy ◽  
Prediction Model ◽  
Scoring System ◽  
Malignant Pleural Effusion ◽  
Diagnostic Performance ◽  
External Validation ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set

BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

scholarly journals Comprehensive Evaluation of Cerebral Hemodynamics and Oxygen Metabolism in Revascularization of Asymptomatic High-Grade Carotid Stenosis

2021 ◽  
Author(s):  
Bernardo Crespo Pimentel ◽  
Jan Sedlacik ◽  
Julian Schröder ◽  
Marlene Heinze ◽  
Leif Østergaard ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Carotid Stenosis ◽  
Transit Time ◽  
Carotid Artery Stenosis ◽  
Oxygen Metabolism ◽  
Brain Lesions ◽  
Artery Stenosis ◽  
High Grade ◽  
Ischemic Brain ◽  
Revascularization Procedures

Abstract Introduction Revascularization procedures in carotid artery stenosis have shown a positive effect in the restoration of cerebral oxygen metabolism as assessed by T2’ (T2 prime) imaging as well as capillary homeostasis by measurement of capillary transit time heterogeneity (CTH); however, data in patients with asymptomatic carotid stenosis without manifest brain lesions are scarce. Patients and Methods The effect of revascularization on the hemodynamic profile and capillary homeostasis was evaluated in 13 patients with asymptomatic high-grade carotid stenosis without ischemic brain lesions using dynamic susceptibility contrast perfusion imaging and oxygenation-sensitive T2’ mapping before and 6–8 weeks after revascularization by endarterectomy or stenting. The cognitive performance at both timepoints was further assessed. Results Perfusion impairment at baseline was accompanied by an increased CTH (p = 0.008) in areas with a time to peak delay ≥ 2 s in the affected hemisphere compared to contralateral regions. Carotid intervention improved the overall moderate hemodynamic impairment at baseline by leading to an increase in normalized cerebral blood flow (p = 0.017) and a decrease in mean transit time (p = 0.027), oxygen extraction capacity (OEC) (p = 0.033) and CTH (p = 0.048). The T2’ values remained unchanged. Conclusion This study presents novel evidence of a state of altered microvascular function in patients with high-grade carotid artery stenosis in the absence of ischemic brain lesions, which shows sustained normalization after revascularization procedures.

scholarly journals T203. RELEVANT CLOZAPINE BLOOD LEVELS USING A POINT OF CARE TEST (POCT)

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S309-S309
Author(s):  
Deanna Kelly ◽  
Ann Marie Kearns ◽  
Matthew Glassman ◽  
Matthew Atkins ◽  
Philip McQuire
Keyword(s):  
Whole Blood ◽  
Predictive Value ◽  
Patient Adherence ◽  
Point Of Care ◽  
Target Range ◽  
Blood Levels ◽  
Clinical Implementation ◽  
Training Set ◽  
Average Bias ◽  
Validation Set

Abstract Background Clozapine is one of the most underused medications in psychiatry for many reasons including mandatory blood testing, fear of serious side-effects, lack of patient adherence. A critical barrier to adoption could be addressed with the ability to measure clozapine at point-of-care (POC) from a fingerstick. Current practice of clozapine measurements, however, was developed based on serum levels. Therefore, meaningful POC results must be reported as the serum equivalent. We evaluated a new immunoassay method to measure clozapine in whole blood to establish standardization to serum, and to assess the ability of the POCT to detect differences in patients’ clozapine levels compared to an existing laboratory method. Methods A whole blood POCT (MyCare® Insite Clozapine Test on the MyCare Insite)* immunoassay was compared to liquid chromatography tandem mass-spectrometry (LC-MS/MS) in serum with 95 matched patient samples. Passing-Bablok regression was used to compare results and establish calibrator values to standardize the POCT to report whole blood results as equivalent to serum. The standardization was validated by a method comparison to LC-MS/MS with 304 samples collected from patients with schizophrenia who were being treated with clozapine. Serial blood levels were taken for 13 patients to compare deviation from baseline for POCT and LC-MS/MS results. To detect a discordant difference in clozapine levels, the difference to the preceding value was calculated for 73 sequential samples of the 86 total results. Because of high intra-patient variability changes of &gt; ±50% were considered significant. Results There was good correlation (R = 0.9) between the POCT and LC-MS/MS in the training set (N=95). Passing-Bablok statistics were: slope = 1.02, intercept = -2.3, R = 0.9, average bias -17.7 (-3.8%). The average values (± SD) were 479.7 (± 181.5) ng/mL for LC-MS/MS and 462.0 (± 199.1) ng/mL for POCT. The Passing-Bablok regression of the validation set (N=304), using the reassigned calibrator values as the training set, gave a slope = 0.971, intercept = -21.2, R = 0.9, mean values (± SD) of 445.6 (± 242.4) for LC-MS/MS and, 412.6 (± 245.7) for the POCT, average bias was -33.0 (-7.7%). Bias between POCT and LC-MS/MS for 12 individuals ranged from -22% to 22%. One patient with five sequential measurements had a total bias of -34% with 4 of 5 results, agreeing with assignment in or out of the presumptive target range of 350 – 600 ng/mL. The frequency of &gt;±50% change in clozapine levels was &lt;5%. Ninety percent (66 of 73) of results agreed, selectivity = 50%, specificity = 94%, positive predictive value (PPV) = 42.9%, negative predictive value (NPV) = 95.5%. Seven samples had a 50% change by one method and not the other. There was only one discrepant sample that was 66% lower with POCT. Discussion Differences in measurement methods are expected. The good correlation and similarity of results between the calibrator assignment training set and the validation set demonstrates the accuracy of the calibrator value assignment. The POCT was highly selective in detecting important changes in clozapine levels of more than 50% which would occur secondary to non-adherence, change in life-style habits or drug-drug interactions. The collection conditions gave consistent levels for most patients, with few large shifts in concentration, thus underestimating the PPV. These data suggest that clozapine levels can be accurately measured from a small volume of capillary blood collected via a fingerstick sample. This method makes blood sampling easier for both patients and clinical staff, and provides a result in a few minutes, at point of care. Its clinical implementation may facilitate the safe and effective use of clozapine in schizophrenia. *CE mark/US RUO

P–364 How to estimate the probability of a live birth after one or more complete IVF cycles? The development of a novel model in a single-center

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
X Kong ◽  
Z Liu ◽  
C Huang ◽  
X Hu ◽  
M Mo ◽  
...  
Keyword(s):  
Prediction Model ◽  
Live Birth ◽  
Birth Rate ◽  
Cox Regression ◽  
Live Birth Rate ◽  
Single Center ◽  
Training Set ◽  
Validation Data ◽  
Specificity And Sensitivity ◽  
Validation Set

Abstract Study question What is the probability of a live birth for an infertile couple after one or more complete cycles of in vitro fertilization (IVF)？ Summary answer The Cox regression and Nomogram model could estimate the chance of a live birth after a complete IVF cycle effectively. What is known already At present, kinds of prediction models have been established for estimating the chance of having a live birth in different centers based on the characteristics of the population. But the predictive value and effectiveness of different models were different. These models were not applicable to every reproductive center. Study design, size, duration A retrospective cohort study was conducted in reproductive center of Shenzhen Zhongshan Urology Hospital from January 2012 to April 2015. 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. In order to verify the efficacy, we conducted stratified sampling for the whole sample according to live birth or not.70% of the patients were divided into training set (N = 3089) and 30% of the patients were divided into validation set (N = 1324). Participants/materials, setting, methods Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for up to five complete IVF cycles. PH test was used for establishing a prediction model. A Cox regression and nomogram model was built on the basis of training set, and ROC curve was used to test the specificity and sensitivity of the prediction model. And then, the validation set was applied to verify the validity of the model. Main results and the role of chance In the fresh embryo transfer cycle, the LBR were 38.7%. In the first to fifth frozen cycle, the optimal estimate and conservative estimate CLBR were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. There was no difference among the characteristics data of training and validation cohorts, which indicates that stratified sampling was reasonable. Based on the results of PH test, the predictive factors of live birth were fertilization technique, infertility factor, serum progesterone level (pg/mL) and luteinizing hormone level (pg/mL) on the day initiated with gonadotropin (R = 0.043, p = 0.059; R = 0.015, p = 0.499), basal follicle-stimulating hormone (R=–0.042, p = 0.069) and BMI(R=–0.035, p = 0.123). We used ROC curve to test the specificity and sensitivity of the prediction model. The AUC was 0.782(p &lt; 0.01,95%CI=76.4–80.1%). Then the model was verified in the validation data. And the AUC was 0.801 (p &lt; 0.01,95%CI=77.4–82.8%). A Nomogram model was built on the basis of possible factors that might influence the live birth rate of training data. The concordance index (C-index) was 0.53. For the validation data, the C-index was 0.525. Limitations, reasons for caution This study was a retrospective analysis of a single-center, which was limited by sample size. Although its efficacy and specificity have been validated internally, further prospective clinical trials are needed to validate its efficacy. Wider implications of the findings: This prediction model can effectively predict the probability of infertile couples having a live birth. Further, this model can also help clinicians to make clinical decisions and provide guidance for patients. Trial registration number Non-clinical trials

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