Clinical Risk Model Research Articles

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening—Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

ObjectiveArterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).MethodsThis study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.ResultsPheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc (‘ntreeA’) was associated with both AAA (β = −0.36, P = 6.75e−10) and ICA (β = −0.11, P = 5.51e−06). In addition, the mean angles between each artery branch (‘curveangle_mean_a’) were commonly associated with 4 MFS genes (FBN1: β = −0.10, P = 1.63e−12; COL16A1: β = −0.07, P = 3.14e−09; LOC105373592: β = −0.06, P = 1.89e−05; C8orf81/LOC441376: β = 0.07, P = 1.02e−05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780–0.838], which was similar to the clinical risk model (0.806 [0.778–0.834]) but higher than the baseline model (0.739 [0.733–0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727–0.869) for the aneurysm-RVF model, 0.795 (0.718–0.871) for the clinical risk model and 0.719 (0.620–0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5–48.8], P = 1.02e−05).ConclusionWe identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.Graphical abstract

The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy

To date, the combined effect of polygenic risk score (PRS) and prostate health index (phi) on PCa diagnosis in men undergoing prostate biopsy has never been investigated. A total of 3166 patients who underwent initial prostate biopsy in three tertiary medical centers from August 2013 to March 2019 were included. PRS was calculated on the basis of the genotype of 102 reported East-Asian-specific risk variants. It was then evaluated in the univariable or multivariable logistic regression models that were internally validated using repeated 10-fold cross-validation. Discriminative performance was assessed by area under the receiver operating curve (AUC) and net reclassification improvement (NRI) index. Compared with men in the first quintile of age and family history adjusted PRS, those in the second, third, fourth, and fifth quintiles were 1.86 (odds ratio, 95% confidence interval (CI): 1.34-2.56), 2.07 (95%CI: 1.50-2.84), 3.26 (95%CI: 2.36-4.48), and 5.06 (95%CI: 3.68-6.97) times as likely to develop PCa (all p < 0.001). Adjustment for other clinical parameters yielded similar results. Among patients with prostate-specific antigen (PSA) at 2-10 ng/mL or 2-20 ng/mL, PRS still had an observable ability to differentiate PCa in the group of prostate health index (phi) at 27-36 (Ptrend < 0.05) or >36 (Ptrend ≤ 0.001). Notably, men with moderate phi (27-36) but highest PRS (top 20% percentile) would have a comparable risk of PCa (positive rate: 26.7% or 31.3%) than men with high phi (>36) but lowest PRS (bottom 20% percentile positive rate: 27.4% or 34.2%). The combined model of PRS, phi, and other clinical risk factors provided significantly better performance (AUC: 0.904, 95%CI: 0.887-0.921) than models without PRS. Adding PRS to clinical risk models could provide significant net benefit (NRI, from 8.6% to 27.6%), especially in those early onset patients (NRI, from 29.2% to 44.9%). PRS may provide additional predictive value over phi for PCa. The combination of PRS and phi that effectively captured both clinical and genetic PCa risk is clinically practical, even in patients with gray-zone PSA.

Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk

PurposeWe compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS).MethodsUsing nested case–control data from the Nurses’ Health Study, we compared the models’ association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk.ResultsThe odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035).ConclusionBRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve= 0.89) to existing clinical risk models (area under the curve= 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer.

Colorectal cancer (CRC) ranks second for mortality and third for morbidity among the most commonly diagnosed cancers worldwide. We aimed to investigate the heterogeneity and convergence of tumor microenvironment (TME) in CRC. We analyzed the single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database and identified 8 major cell types and 25 subgroups derived from tumor, para-tumor and peripheral blood. In this study, we found that there were significant differences in metabolic patterns, immunophenotypes and transcription factor (TF) regulatory patterns among different subgroups of each major cell type. However, subgroups manifested similar lipid metabolic patterns, immunosuppressive functions and TFs module at the end of the differentiation trajectory in CD8+ T cells, myeloid cells and Fibroblasts. Meanwhile, TFs regulated lipid metabolism and immunosuppressive ligand-receptor pairs were detected by tracing the differentiation trajectory. Based on the cell subgroup fractions calculated by CIBERSORTx and bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA), we constructed an immune risk model and clinical risk model of CRC which presented excellent prognostic value. This study identified that the differentiation was accompanied by remodeling of lipid metabolism and suppression of immune function, which suggest that lipid remodeling may be an important trigger of immunosuppression. More importantly, our work provides a new perspective for understanding the heterogeneity and convergence of the TME and will aid the development of prognosis and immunotherapies of CRC patients.

Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model.

Pancreatic cancer has the poorest 5-year survival rate of any major solid tumor, but when diagnosed at an early stage, survival rates improve. Population screening is impractical because pancreatic cancer is rare with a lifetime risk of 1.7%, but accurate risk stratification in the general population could enable health care providers to focus early detection strategies to at-risk individuals. Here, we validate a combined risk prediction model that integrates a polygenic risk score and a clinical risk model. Using the UK Biobank, we conducted a prospective cohort study assessing 10-year pancreatic cancer risks based on a polygenic risk score, a clinical risk score, and a combined risk score. We assessed the association, discrimination, calibration, cumulative hazards, and standardized incidence ratios compared to population incidence rates for the risk scores. We also conducted net reclassification analyses. While all of the risk scores discriminated well between affected and unaffected participants, the combined risk score - with a Harrell's C-index of 0.714 (95% confidence interval [CI]= 0.698, 0.730) - discriminated better than both the polygenic risk score (P= .001) and the clinical risk score (P= .02). In terms of calibration, there was no problem with dispersion for the combined risk score (β= 0.952, 95% CI= 0.865-1.039, P= .3) and overall there was a small overestimation of risk (α=-0.089, 95% CI=-0.156 to-0.021, P= .009). Participants in the top decile of 10-year risk were at 1.413 (95% CI= 1.242-1.607) times population risk. The combined risk score was able to identify individuals at substantially increased risk of pancreatic cancer and to whom targeted screening could be useful.

Полиморфизм rs5219 гена KCNJ11 в прогнозировании риска развития сахарного диабета 2 типа

The purpose — to study the possibility of using rs9939609 T/A polymorphisms of the FTO gene, rs5219 C/T of the KCNJ11 gene, rs1801282 C/G of the PPARG gene, rs8192678 G/A of the PPARGC1A gene, rs12255372 G/T and rs7903146 C/T of the TCF7L2 gene as markers for predicting the risk of developing type 2 diabetes mellitus in patients with T2DM risk factors. Material and methods. 112 overweight/obese patients from the Republic of Tatarstan (79% women and 21% men) aged 22 to 79 years participated in a single–center observational cohort 3-year prospective study. All patients were clinically examined, glucose tolerance test was performed, the level of glycated hemoglobin was determined, and polymorphisms rs9939609 T/A of the FTO gene, rs5219 C/T of the KCNJ11 gene, rs1801282 C/G of the PPARG gene, rs8192678 G/A of the PPARGC1A gene, rs12255372 G/T and rs7903146 C/T of the TCF7L2 gene were studied using PCR in real-time mode. Statistical analysis was performed with R 4.1.0 software. Univariable and multivariable logistic regression modeling were performed to evaluate association between outcomes and possible predictors. Area under the ROC-curve and Nagelkerke pseudo R-squared was used to compare prognostic performance of predictors. Results. According to the result of logistic regression (p=0.003), the carrier of the T allele KCNJ11 rs5219 is an independent risk factor for T2DM regardless of gender, age, waist circumference (WC), waist–hip ratio (WHR) and waist-to-height ratio (WHtR) and can be attributed as a marker of increased risk of T2DM. The inclusion of this polymorphism into T2DM clinical risk models, taking into account gender, age, maternal obesity, WHR and WHtR indices showed an increase of AUC from 0.74 to 0.78 (p=0.012) and from 0.73 to 0.79 (p=0.0056), respectively. Conclusion. The rs5219 polymorphism of the KCNJ11 gene can be used as an independent marker for predicting the risk of developing type 2 diabetes mellitus. The inclusion of this polymorphism in the risk model, which takes into account, in addition to the maternal obesity, the indices of WHtR or WHR, improves its predictive ability.

Renin Trajectories and Outcome in Stable Heart Failure with Reduced Ejection Fraction (HFrEF) on Contemporary Therapy: A Monocentric Study from an Austrian Tertiary Hospital Outpatient Clinic

Introduction. The renin-angiotensin system (RAS) is the main target of neurohumoral therapy in heart failure with reduced ejection fraction (HFrEF) effectively reducing mortality. Reasonably, renin might serve as a biomarker for risk prediction and therapy response. Renin indeed bears some additional value to clinical risk models, albeit the effect is not pronounced. Whether assessing renin trajectories can overcome the weaknesses of single renin measurements has not been reported. Methods. A total of 505 patients with stable HFrEF were enrolled prospectively and followed through routine clinical visits. Active plasma renin concentration was documented up to 5 years. Changes in renin were analyzed throughout the disease course, and survival was compared for different renin trajectories within the first year. Results. Baseline renin levels were not related to all-cause mortality (crude HR for an increase of 100 μiE/ml: 1.01 (95% CI: 0.99–1.02), [Formula: see text]) but associated with unplanned HF hospitalizations (crude HR: 1.01 (95% CI: 1.00-1.02), [Formula: see text]). Renin increased during the disease course from baseline to 1-year and 2-year FUP (122.7 vs. 185.6 μIU/ml, [Formula: see text], and 122.7 vs. 258.5 μIU/ml, [Formula: see text]). Both survival and unplanned HF hospitalization rates were comparable for different renin trajectories at 1-year FUP ([Formula: see text], [Formula: see text]). Conclusions. Intriguingly, renin is not a good biomarker to indicate prognosis in HF, while renin trajectories over a 1-year period do not have an additional value. Rapid physiologic plasma renin variations, but also opposing effects of angiotensinogen-derived metabolites under presence of RAS blockade, might obscure the predictive ability of renin.

Circulating Tumor Plasma Cells in the Screened Istopmm Smoldering Multiple Myeloma Cohort

Background The presence and number of circulating tumor plasma cells (CTPCs) in peripheral blood (PB) as detected by flow cytometry has previously been associated with increased risk of progression from smoldering multiple myeloma (SMM) to multiple myeloma (MM). CTPCs may therefore be a marker for tumor burden and/or tumor aggressiveness in SMM that can, if the correlation to progression can be verified, potentially spare patients from bone marrow biopsies. However, the rate of detectable CTPCs in the overall SMM population is not known and the dynamics of CTPC numbers between diseases stages and risk categories in a screened cohort of individuals with SMM have not been studied. Aim To investigate the detection and quantification of CTPCs in individuals with SMM diagnosed in the screened population-based Iceland Screens, Treats, or Prevents Multiple Myeloma study (iStopMM), and to associate the findings to known clinical risk models of progression from SMM to MM. Methods Individuals diagnosed with SMM, monoclonal gammopathy of undetermined significance (MGUS) and MM in the iStopMM study were included. Next-generation flow (NGF) cytometry was used to assess CTPC numbers in PB and the limit of detection (LOD) was set at ≥20 tumor PC. An optimal cutoff value of the percentage of CTPC to discriminate between SMM and MM was found using receiver operating characteristic (ROC) curve analysis of individuals with CTPCs detected. In a subgroup of individuals with SMM, interphase fluorescent in situ hybridization (FISH) analysis was performed. The difference in frequency of CTPC was tested between risk groups according to the PETHEMA and Mayo 2/20/20 models, the groups with, and without high-risk FISH abnormalities and between clinical entities (MGUS, SMM and MM). The Mann-Whitney U test or the Kruskal-Wallis tests were used to assess differences observed between two or more than two groups, respectively. Results A total of 93 individuals with SMM, 29 with MM, and 103 with MGUS were included. CTPCs were detected in 64 (69%) of individuals with SMM, with a median frequency of 0.0009% (range 0.0002-0.078%), or 0.054 CTPC/μL (range 0.011-3.99 CTPC/μL). The median sensitivity reached for samples with undetectable CTPCs was 2.3x10-6 (range 4.8-1.7x10-6). The detection rate and concentration of CTPCs increased from MGUS to SMM and MM (p<0.001 for both, figure) and from low to higher risk categories according to both risk models (table and figure). The increase in frequency of CTPCs according to risk score was only significant for the PETHEMA model (p<0.001), but not the Mayo 2/20/20 model (p=0.19). However, when low-risk individuals were compared to the intermediate/high risk groups according to the Mayo 2/20/20 model, the difference reached statistical significance (p=0.05). An optimal cutoff of 0.0017% CTPCs for SMM vs. MM was found with a sensitivity of 0.70, specificity of 0.69, and area under the curve 0.76 (figure). In total, 20 (22%) individuals with SMM had CTPC over the cutoff. This proportion increased with higher Mayo 2/20/20 risk categories (20% in low-risk, 23% in intermediate-risk and 25% in high-risk SMM) and PETHEMA risk categories (18% in low-risk, 23% in intermediate-risk and 30% in high-risk SMM). In the 40 individuals with SMM who underwent FISH, 12 (30%) had high-risk FISH abnormalities (del(17p), t(4;14) or t(14;16)) present, and of those 75% had CTPCs detected vs. 71% in those without high-risk FISH abnormalities, no difference in CTPC frequency was found between the groups (p=0.11) Conclusion CTPCs are a potential marker of tumor burden or tumor aggressiveness in SMM and are present in 69% of the overall population with SMM. Both the detection and numbers of CTPCs increase progressively from MGUS, to SMM, and MM. A similar trend was observed with increasing risk score in SMM, according to two established risk stratification models. These results indicate that NGF of PB to detect CTPCs might have a role in the management of SMM. Future studies with serial monitoring of CTPCs will help to further define the clinical value of CTPCs in relation to the risk of progression, and to define how CTPC monitoring impacts the role of established markers, especially to identify individuals with low risk of progression where bone marrow sampling might be omitted. This would focus clinical care and treatments in SMM and MM to those who benefit the most from them while limiting interventions in those who do not need them. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Available Risk Scores Modestly Predict Hemorrhage in Patients with Atrial Fibrillation and Cance

Background: Patients with cancer have an increased risk of developing atrial fibrillation (AF). Patients with cancer who develop AF have an increased risk of cardiovascular-related death at 12-months. Use of anticoagulant therapy may benefit these patients. However, patients with cancer are at increased risk of anticoagulant-related bleeding. Several risk prediction models are available to estimate the risk of anticoagulant-related bleeding in patients with AF. However, these models were developed predominately in patients without cancer. Thus, the utility of these models in cancer is unknown. We aimed to evaluate the performance of existing bleeding risk models in a cohort of patients with active cancer and AF starting on anticoagulant therapy. Methods: Using a nationwide cohort of US Veterans, we identified patients with active cancer and AF with a new prescription for anticoagulant therapy between 2012 and 2018. Cancer was defined as active if the diagnosis of AF occurred within 3 months prior to up to 6 months after new cancer diagnosis or in the setting of incurable hematological cancers (e.g., multiple myeloma, chronic myelogenous leukemia) or metastatic solid tumors. We identified anticoagulant-related major bleeding (MB) within 6 months of anticoagulant start by the presence of inpatient ICD-9/10 codes in any position as previously validated. Patients were censored 30-days after last anticoagulant prescription. We evaluated the predictive performance of six models. The association per standard deviation increase in the score and MB within 6 months of anticoagulant start was estimated using Fine-Gray subdistribution survival analysis to account for the competing risk of death≥÷A sensitivity analysis limiting inpatient ICD-9/10 codes for MB to the first two positions was also performed. We evaluated model discrimination using Harrell's c-statistic. Results: 5716 patients met inclusion criteria into the cohort. The most common types of cancer included: hematological malignancies (24%), lung (20%), prostate (18%), non-prostate genitourinary (11%), and gastrointestinal (10%) cancer. Mean age at the start of anticoagulant therapy was 73.4 years and median overall survival 41.1 months. Of the cohort, anticoagulant therapy was as follows: 2040 warfarin, 179 LMWH, 5 fondaparinux, and 3495 DOAC. 328 (5.7%) patients had a MB within 6 months of anticoagulant start and 439 (7.7%) within 12 months. The most common type of bleeds within the first 6 months of anticoagulant therapy was gastrointestinal (47%), followed by hematuria (28%) and hemoptysis (13.7%). 2.4% of bleeding events (n=8) were intracranial hemorrhage. The increase in hazard of bleeding per each standard deviation increase in bleeding score is in Table 1. For model discrimination, Harrell's c-statistic for each score is also listed in Table 1. Conclusions: In this cohort of 5716 patients with active cancer and atrial fibrillation starting on anticoagulant therapy, available clinical risk models moderately predicted bleeding. The addition of cancer-specific risk factors might improve accuracy of the available models. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Development and external validation of multivariable risk models to predict incident and resolved neuropathic pain: a DOLORisk Dundee study

Neuropathic pain is difficult to treat, and an understanding of the risk factors for its onset and resolution is warranted. This study aimed to develop and externally validate two clinical risk models to predict onset and resolution of chronic neuropathic pain. Participants of Generation Scotland: Scottish Family Health Study (GS; general Scottish population; n = 20,221) and Genetic of Diabetes Audit and Research in Tayside Scotland (GoDARTS; n = 5236) were sent a questionnaire on neuropathic pain and followed- -up 18 months later. Chronic neuropathic pain was defined using DN4 scores (≥ 3/7) and pain for 3 months or more. The models were developed in GS using logistic regression with backward elimination based on the Akaike information criterion. External validation was conducted in GoDARTS and assessed model discrimination (ROC and Precision-Recall curves), calibration and clinical utility (decision curve analysis [DCA]). Analysis revealed incidences of neuropathic pain onset (6.0% in GS [236/3903] and 10.7% in GoDARTS [61/571]) and resolution (42.6% in GS [230/540] and 23.7% in GoDARTS [56/236]). Psychosocial and lifestyle factors were included in both onset and resolved prediction models. In GoDARTS, these models showed adequate discrimination (ROC = 0.636 and 0.699), but there was evidence of miscalibration (Intercept = − 0.511 and − 0.424; slope = 0.623 and 0.999). The DCA indicated that the models would provide clinical benefit over a range of possible risk thresholds. To our knowledge, these are the first externally validated risk models for neuropathic pain. The findings are of interest to patients and clinicians in the community, who may take preventative or remedial measures.

Abstract 14815: Clinical Prediction Model of Growth Rate in Type B Aortic Dissection Using 4D Flow MRI

Introduction: Chronic type B aortic dissection is often associated with aneurysmal degeneration and rupture of the descending aorta (DAo). Early identification of patients at risk for aneurysmal degeneration could identify candidates for prophylactic surgical intervention. Clinical and static imaging measures (age, early aortic dilation, etc.) have been unable to reliably predict DAo growth rate (GR). Importantly, these measures do not account for dissection hemodynamics, an important driver of GR. We therefore aimed to build a multivariate linear regression model that combines baseline clinical, morphologic, and hemodynamic metrics to predict GR. Methods: Patients who underwent 4D-flow MRI and had ≥9 months of imaging follow-up were identified. Baseline clinical features were obtained from medical records. Morphologic metrics were assessed on baseline imaging and GR (mm/y) was calculated by comparison to follow-up scans. Baseline 4D-flow MRI was used to measure the following parameters in the true lumen: pulse wave velocity (PWV), peak velocity (PV), and forward flow. False lumen thrombus (FLT) was assessed in percentage quartiles (0, 1-25%, 26-50%, 51-75%, &gt;75%) and scored from 0 to 4. Stepwise multivariate regression was conducted to identify a model for best prediction of GR (Figure 1). Results: Thirty-four patients were included (70.0 ± 16.5y; 21 M). On multivariate analysis, we detected a model that significantly correlates with GR (r 2 = 0.55 p &lt; 0.001) by using PWV, FLT, and PV as predictors. In fact, PWV was the strongest predictor overall (r = - 0.54 p = 0.001). Clinical features and other imaging measures showed no association with GR and hence were excluded by stepwise regression analysis. Conclusions: The regression model identified two hemodynamic parameters and FLT as the strongest predictors of GR. This finding suggests that a multitude of hemodynamic factors impact GR and that 4D flow parameters have the potential to improve clinical risk models.

Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease

BackgroundThe value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. MethodsAn observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case–control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold.ResultsIn the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634–0.646), 0.718 (95% CI, 0.713–0.723), and 0.753 (95% CI, 0.748–0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and − 0.023 (95% CI, − 0.025 to − 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category.ConclusionsAddition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.

A clinical risk model for assessing the survival of patients with stage IA-IIA non-small cell lung cancer after surgery.

The survival of patients with stage IA-IIA non-small cell lung cancer (NSCLC) after surgery is heterogeneous. This study aimed to construct a prognostic risk model to predict the overall survival (OS) of these patients. Data from patients (n=9,914) from the Surveillance Epidemiology and End Results (SEER) database were analyzed. The cases were randomly divided into the training and the validation groups. Patients from the Shanghai Pulmonary Hospital (n=270) were also included as an external cohort. Independent significant factors affecting survival in the training cohort were used to construct a nomogram. The precision was evaluated using the concordance index (C-index) and calibration plots. The X-tile software was used to confirm the optimal cut-off value to classify the patients. Sex, age at diagnosis, tumor size, visceral pleura invasion (VPI), tumor grade, and the number of examined lymph nodes were deemed independent prognostic factors and were selected to establish the nomogram. The C-indices of the nomogram for predicting OS were 0.671 [95% confidence interval (CI): 0.653-0.689] in the training group, and 0.668 (95% CI: 0.650-0.687) and 0.707 (95% CI: 0.651-0.763) in the validation and the testing groups, respectively. The cut-off value of risk points was 106.0, which stratified the patients into high-risk and low-risk groups. The high-risk patients had shorter 5-year OS than low-risk patients (P<0.001). The established nomogram could evaluate the survival in patients with stage IA-IIA NSCLC after surgery and may provide prognostic information for clinicians to make decisions in the management of adjuvant therapy.

Prognostic implication of unrecognized myocardial infarction in patients with non-ST-segment-elevation acute coronary syndrome

Abstract Background Prognostic value of unrecognized non-infarct-related territory (non-IR) myocardial infarction (UMI) in patients with non-ST-segment-elevation acute coronary syndrome (NSTE-ACS) remains to be elucidated. Purpose This study sought to evaluate the prevalence of non-IR UMI and its prognostic value in patients with first NSTE-ACS presentation. Methods This retrospective single-center analysis was conducted in patients with NSTE-ACS without prior history of coronary artery disease, who underwent uncomplicated urgent percutaneous coronary intervention (PCI) within 48 hours of admission between August 2014 and January 2018. All patients underwent postprocedural cardiac magnetic resonance imaging (CMR) within 30 days after PCI. Non-IR UMI was defined as the presence of non-IR delayed gadolinium enhancement with an ischemic distribution pattern. We investigated the association of non-IR UMI, other CMR findings and baseline clinical characteristics with major adverse cardiac events (MACE), defined as all-cause death, non-fatal myocardial infarction, ischemic stroke, late revascularization and hospitalization for congestive heart failure. Results A total of 168 NSTE-ACS patients were included (124 males (73.8%); 66±11 years). Non-IR UMI was detected in 28 patients (16.7%). During a median follow-up of 32 months (15–58), MACE occurred in 10 (35.7%) patients with non-IR UMI, and 20 (14.3%) patients without (P=0.013). Patients with MACE showed higher frequency of non-IR UMI in RCA territory and multi vessel disease, higher level of NT-proBNP at admission, higher Genisini score, and greater extent of UMI. Cox's proportional hazards analysis showed that the presence of non-IR UMI was an independent predictor of MACE (HR 2.34, 95% CI 1.02–5.37, P=0.045), after adjusting confounding factors, such as multi vessel disease and serum levels of NT-proBNP at admission. The discriminant efficacy (IDI and NRI) of predicting MACE was significantly improved when the presence of non-IR UMI added to the reference clinical risk model. Kaplan-Meier analysis revealed that patients with non-IR UMI were significantly associated with poor prognosis. (Figure 1). Conclusions In patients with NSTE-ACS undergoing urgent PCI, the prevalence of non-IR UMI was 16.7%. Non-IR UMI provided prognostic information independent of conventional risk factors. Funding Acknowledgement Type of funding sources: None.

Initiation of the SGLT2 inhibitor canagliflozin to prevent kidney and heart failure outcomes guided by HbA1c, albuminuria, and predicted risk of kidney failure

BackgroundSodium glucose co-transporter-2 (SGLT2) inhibitors reduce the risk of kidney and heart failure events independent of glycemic effects. We assessed whether initiation of the SGLT2 inhibitor canagliflozin guided by multivariable predicted risk based on clinical characteristics and novel biomarkers is more efficient to prevent clinical outcomes compared to a strategy guided by HbA1c or urinary-albumin-creatinine ratio (UACR) alone.MethodsWe performed a post-hoc analysis of the CANVAS trial including 3713 patients with available biomarker measurements. We compared the number of composite kidney (defined as a sustained 40% decline in eGFR, chronic dialysis, kidney transplantation, or kidney death) and composite heart failure outcomes (defined as heart failure hospitalization or cardiovascular (CV) death) prevented per 1000 patients treated for 5 years when canagliflozin was initiated in patients according to HbA1c ≥ 7.5%, UACR, or multivariable risk models consisting of: (1) clinical characteristics, or (2) clinical characteristics and novel biomarkers. Differences in the rates of events prevented between strategies were tested by Chi2-statistic.ResultsAfter a median follow-up of 6.1 years, 144 kidney events were recorded. The final clinical model included age, previous history of CV disease, systolic blood pressure, UACR, hemoglobin, body weight, albumin, estimated glomerular filtration rate, and randomized treatment assignment. The combined biomarkers model included all clinical characteristics, tumor necrosis factor receptor-1, kidney injury molecule-1, matrix metallopeptidase-7 and interleukin-6. Treating all patients with HbA1c ≥ 7.5% (n = 2809) would prevent 33.0 (95% CI 18.8 to 43.3 ) kidney events at a rate of 9.6 (95% CI 5.5 to 12.6) events prevented per 1000 patients treated for 5 years. The corresponding rates were 5.8 (95% CI 3.4 to 7.9), 16.6 (95% CI 9.5 to 22.0) (P < 0.001 versus HbA1c or UACR approach), and 17.5 (95% CI 10.0 to 23.0) (P < 0.001 versus HbA1c or UACR approach; P = 0.54 versus clinical model). Findings were similar for the heart failure outcome.ConclusionInitiation of canagliflozin based on an estimated risk-based approach prevented more kidney and heart failure outcomes compared to a strategy based on HbA1c or UACR alone. There was no apparent gain from adding novel biomarkers to the clinical risk model. These findings support the use of risk-based assessment using clinical markers to guide initiation of SGLT2 inhibitors in patients with type 2 diabetes.

External validation of a risk score model for predicting major clinical events in adults after atrial switch

A risk model has been proposed to provide a patient individualized estimation of risk for major clinical events (heart failure events, ventricular arrhythmia events, all-cause mortality) in patients with transposition of the great arteries corrected by an atrial switch operation (D-TGA). The aim of this study was to externally validate the model. A retrospective, multicentric, longitudinal cohort of 417 patients with D-TGA (median age 24 years [interquartile range 18–30], 63% male) independent of the model development and internal validation cohort was studied. Data on risk model predictors (age > 30 years, prior ventricular arrhythmia, age > 1 year at repair, moderate or greater right ventricular dysfunction, severe tricuspid regurgitation, and mild or greater left ventricular dysfunction) were collected from the time of baseline clinical evaluation. The performance of the prediction model in predicting risk at 5 years was assessed. Twenty-eight patients (6.7%) met the major clinical events endpoint within 5 years, with an overall incidence rate of 1.42 per 100 patient-years [95% confidence interval (CI) 0.94–2.05]. Model validation showed a good discrimination between high and low 5-year risk patients (Harrell's C-index of 0.710 (95% CI 0.66 to 0.75)) but tended to overestimate this risk (calibration slope of 0.42 (95% 0.061–0.78); Fig. 1 ). We reported the first external validation of a clinical events risk model in a large D-TGA patient population. Although a good discrimination, the model tends to overestimate the 5-year risk. The development of new risk models is needed to individualize risk predictions in D-TGA patients.

Experimental verification and validation of immune biomarkers based on chromatin regulators in ischemic stroke.

Ischemic stroke (IS) is a disease characterized by rapid progression and high mortality and disability rates. Its pathophysiological process is inseparable from immune dysfunction. Recently, chromatin regulators (CRs) have been described as a class of enzymes that can recognize, form, and maintain the epigenetic state of an organism, and are closely associated with immune regulation. Nevertheless, the role of CR-related genes in IS has not been fully elucidated. In this study, seven CR-related immune biomarkers in the GSE58294 and GSE22255 datasets were identified by combining differential gene expression analysis, weighted correlation network analysis, and single sample gene set enrichment analysis. After experimental validation using quantitative polymerase chain reaction, four genes (DPF2, LMNB1, MLLT3, and JAK2) were screened as candidate immune biomarkers. These four biomarkers demonstrated good predictive power in the clinical risk model (area under the curve, 0.775). Molecular docking simulations revealed that mevastatin, WP1066, cladribine, trichostatin A, mequitazine, and zuclomiphene may be potential immunomodulatory drugs for IS. Overall, the results of this study contribute to the identification of CR-related immune therapeutics target in IS and provide an important reference for further research.

Association of atrial depolarization variability and cardiac autonomic regulation with sudden cardiac death in coronary artery disease.

To evaluate the prognostic significance of the temporal variability of P-wave morphology, specifically in relation to cardiac autonomic regulation. We analyzed the standard deviation of P-wave residuum (PWRSD) from five consecutive beats of the standard 12-lead ECG in 1236 patients with angiographically verified coronary artery disease (CAD). We evaluated the prognostic value of PWRSD, of PWRSD and PWR in relation to the 24 h standard deviation of normal-to-normal intervals (PWRSD/SDNN and PWR/SDNN). After 8.7 ± 2.2 years of follow-up on average, 43 patients (3.5%) experienced sudden cardiac death (SCD) or were resuscitated from sudden cardiac arrest (SCA), 34 (2.8%) succumbed to non-sudden cardiac death (NSCD) and 113 (9.1%) to non-cardiac death (NCD). In the Cox regression analysis, PWRSD (≥0.002727) had a significant univariate (uv) [hazard ratio (HR): 4.27, 95% confidence interval (CI): 2.26-8.08, P = 0.000008] and multivariate (mv) (HR: 2.58, 95% CI: 1.31-5.08, P = 0.006) association with SCD/SCA but not with NSCD (uv P = 0.76, mv P = 0.33) or NCD (uv P = 0.57, mv P = 0.66). All the studied P-morphology parameters retained a significant association with the risk of SCD/SCA after relevant adjustment (mv P-values from 0.00003 to <0.05) but not with NSCD or NCD. When dichotomized PWRSD, PWR, PWRSD/SDNN, and PWR/SDNN were added to the clinical risk model for SCD/SCD, the C-index increased from 0.799 to 0.834 and integrated discrimination index and net reclassification index improved significantly (P < 0.001). Variability of P-morphology representing temporo-spatial heterogeneity of atrial depolarization, specifically when combined with cardiac autonomic regulation, independently predicts the risk of SCD in patients with CAD.