Additional Risk Stratification Research Articles

Risk Stratification of In-Hospital Mortality in Patients with High-Risk Pulmonary Embolism.

Objective. To identify factors associated with in-hospital mortality and to develop and validate a formula for additional risk of early mortality in patients (pts) with high-risk pulmonary embolism (PE). Materials and Methods. The study consisted of two phases. In Phase I, a retrospective analysis was conducted on the medical records of 113 pts with high-risk PE who were consecutively hospitalized at Kharkiv City Clinical Hospital No. 8 from 01.01. 2017 to 01.01.2023. The mean age of pts was 65.14±14.13 years, with 45 (38.8%) males and 68 (60.2%) females. Based on the course of the disease, the pts were divided into two groups: Group 1 – 63 pts (55.8%) who improved and were discharged, and Group 2 – 50 pts (44.2%) who died during hospitalization. Phase II of the study involved a multicenter cohort (9 centers in Ukraine that provided data on pts with high-risk pulmonary embolism hospitalized from January 2023 to December 2023). This phase included 75 pts with high-risk PE, with mean age of 60.60±13.40 years; 38 (50.7%) were male, and 37 (49.3%) were female. Group I consisted of 65 patients (80%) discharged with improvement, Group II comprised 15 patients (20%) who died during hospitalization. Clinical and anamnestic and laboratory-instrumental data were analyzed, statistical analysis of the resulting figures was performed. Results and Discussion. In Phase I of the study, a formula for additional risk stratification of early mortality in pts with high-risk PE was developed. Using ROC analysis, the sensitivity of the resulting prognostic formula was found to be 64.0%, and the specificity was 92.1%. Based on this formula, we proposed a simplified scale called SBAFS (S - saturation, B - bifurcation, A - arterial hypotension, F - fraction of ejection, S - female sex). In Phase II of the study, the sensitivity and specificity of the obtained formula were validated using ROC analysis: the sensitivity was 93.3%, and the specificity was 74.6%, confirming the results of Phase I. When evaluating the proposed simplified SBAFS scale, it was also found that in patients of Group II, the mean score on the scale was (2.53±0.83) and was higher than that in Group I: (1.57±0.96), p=0.0006. Conclusions. The main factors associated with the risk of in-hospital mortality in pts with high-risk PE are decreased oxygen saturation, thrombus localization in the bifurcation of the pulmonary artery as determined by СТPA angiography, arterial hypotension, female sex, and decreased left ventricular ejection fraction as assessed by echocardiography. The previously proposed SBAFS formula confirmed its significance upon validation involving data on PE pts from 9 regions of Ukraine, with a sensitivity of 93% and specificity of 75%.

Incremental prognostic value of 18F-FDG myocardial ischemic memory imaging for major adverse cardiovascular events in patients with suspected unstable angina

BackgroundThe additional prognostic value of 18F-FDG PET myocardial ischemic memory imaging for patients with suspected unstable angina (UA) is not well established. This study aimed to determine whether 18F-FDG PET imaging provides incremental prognostic information for predicting major adverse cardiac events (MACE) compared to clinical risk factors, GRACE score, and coronary artery calcium score (CACS) in suspected UA patients. MethodsIn this post-hoc analysis of a prospective study, 265 suspected UA patients (62.3% male, mean age 65.0±9.4 years) were enrolled. 18F-FDG positive was defined as focal or focal on diffuse uptake patterns. MACE included cardiovascular death, acute myocardial infarction, heart failure, rehospitalization for UA, and stroke. Multivariable Cox regression was used to identify predictors of MACE, and the incremental prognostic value of 18F-FDG PET imaging was assessed using C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). ResultsOver a median follow-up of 25 months, 51 patients (19.2%) experienced MACE. 18F-FDG positive (HR=3.220, 95% CI: 1.630-6.360, P<0.001) , as well as 18F-FDG standardized uptake ratio (SUR) (HR=1.330, 95%CI: 1.131-1.564, P=0.0006) and Extent (HR=1.045, 95%CI: 1.028-1.062, P<0.0001), were independent predictors of MACE. The addition of 18F-FDG PET imaging significantly improved risk stratification beyond clinical factors, the GRACE score, and CACS, with improved C-index (0.769 vs 0.688, P=0.045), NRI (0.324, P=0.020), and IDI (0.055, P=0.027). Conclusion18F-FDG PET myocardial ischemic memory imaging significantly improves prognostic assessment for suspected UA patients, providing valuable additional risk stratification beyond clinical risk factors, GRACE score, and CACS.

Improving the prediction of major adverse cardiovascular events in patients with hypertrophic cardiomyopathy by adding plasma SVEP1 to conventional clinical model including NT-proBNP

Abstract Background Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease with a prevalence between 1 in 200 to 500 [1]. HCM can lead to major adverse cardiovascular events (MACE) such as heart failure (HF) hospitalization and cardiac death [2,3]. A protein named "sushi, von Willebrand factor type A, EGF and pentraxin domain containing 1" (SVEP1) is a large extracellular matrix protein whose activity can be measured in plasma [4,5]. SVEP1 promotes inflammation and atherosclerosis [4,5]. SVEP1 is associated with risk of HF hospitalization and cardiac death in patients with HF with reduced ejection fraction [6]. However, no prior study has assessed the prognostic value of SVEP1 in patients with HCM. Purpose To test the hypothesis that the addition of SVEP1 improves the predictive performance of the conventional model based on clinical risk factors plus NT-proBNP for future MACE in patients with HCM. Methods We performed a multicenter prospective cohort study of patients with HCM [7]. The outcome was MACE, defined as a composite of HF hospitalization or cardiac death [6]. After dividing the cohort into four groups using the median values of SVEP1 and NT-proBNP, we compared the risk of the outcome event using the Cox proportional hazards model adjusting for 15 clinical risk factors known to be associated with MACE in HCM (Figure 1) [8-11]. We also developed the Lasso-regularized Cox proportional hazards model to predict the time to first MACE by adding SVEP1 to the 15 clinical risk factors with or without NT-proBNP in the training cohort (randomly selected 75% of the cohort). We then compared the predictive performance in the test cohort (25% of the cohort) using the C-statistic of each model. Results During a median follow-up of 1.9 (25 percentile-75 percentile, 0.7-4.3) years, the outcome event occurred in 63 (10%) of 610 patients. The four groups stratified by the median of SVEP1 and NT-proBNP levels had different risks of the outcome event (log-rank p&amp;lt;0.001: Figure 1). Even in the groups with lower-than-median NT-proBNP levels, the high-SVEP1 group had higher risks of MACE compared with the low-SVEP1 group (adjusted hazard ratio 4.52, p=0.042: Figure 1). In the prediction of time to first MACE, the addition of SVEP1 improved the C-statistic of the clinical model (p&amp;lt;0.001) and that of the clinical plus NT-proBNP model (p=0.01: Figure 2). The clinical plus SVEP1 model also outperformed the clinical plus NT-proBNP model (p=0.005: Figure 2). Conclusions In the present multicenter prospective cohort study of 610 patients with HCM, SVEP1 improved MACE risk stratification when added to the conventional model consisting of clinical risk factors plus NT-proBNP. SVEP1 also exhibited a superior predictive ability than NT-proBNP to predict MACE in patients with HCM. These data collectively indicate that SVEP1 provides additional risk stratification and may be a better biomarker than NT-proBNP for the prognostication of patients with HCM.Figure 1.Kaplan-Meier curves for MACEFigure 2.Time-dependent AUROC curves

Interplay between family History and polygenic risk for coronary heart disease: a cohort study among over 60 thousand individuals

Abstract Background The degree to which background genetic risk adds to self-report of family history of coronary heart disease (CHD) remains an important question. Methods We utilized data from the multiethnic Genetic Epidemiology Resource in Adult Health and Aging (GERA) cohort of 60,070 Kaiser Permanente of Northern California (KPNC) members (mean ± SD age=59 ± 9 years; 67% female; 18% non-white). We characterized the cohort at baseline in 2003-2007. Excluding those with missing self-report of family history of CHD (n=1,718) resulted in a final sample of 61,352. We stratified the cohort into not having (n=42,866; 70%) and having (n=18,486; 30%) self-reported family history of CHD and then (within stratum of family history of CHD) by three groups of CHD polygenic risk (low: quintile 1; intermediate: quintiles 2, 3 and 4 combined; and high: quintile 5) using a validated 12-SNP polygenic risk score (PRS) for CHD (CARDIO inCode-Score®). Incident CHD was based on ICD-9/10 codes for angina pectoris, myocardial infarction, revascularization procedures or CHD death (n=3,040) through 12/31/2022; mean follow-up was 14.5 years. Results Age-adjusted CHD incidence rates per 10,000 person years were estimated using Poisson regression (accounting for death and health plan disenrollment) by absence/presence of family history of CHD and polygenic risk groups. As shown in the Figure, polygenic risk provided additional risk stratification within categories of family history. The hazard ratio of high PRS (vs low PRS) adjusted for age, sex, 10 principal components of ancestry, smoking, body mass index, diabetes, hypertension, total cholesterol/HDL ratio and cholesterol lowering therapy was 1.62 (95% CI, 1.39-1.87; p&amp;lt;0.001) in those with no family history of CHD, and 1.66 (95% CI, 1.37-2.00; p&amp;lt;0.001) in those with family history of CHD (p-interaction PRS continuous*family history of CHD=0.93). Conclusions Our results show that polygenic risk predicts future CHD regardless of the presence of family history of CHD. Thus, it is not enough to rely of family history to fully characterize potential genetic burden. These data are therefore important for the future of precision medicine.

Associations of traditional healthy lifestyle and sleep quality with metabolic dysfunction-associated fatty liver disease: two population-based studies

AimsTo examine whether an extended lifestyle metrics incorporating sleep quality improves risk stratification for metabolic dysfunction-associated fatty liver disease (MAFLD), at-risk metabolic dysfunction-associated steatohepatitis (MASH) and significant fibrosis.MethodsA total of 5011 participants with abdominal ultrasound from Imaging sub-cohort of South China Cohort (ISSCC) and 3672 participants underwent vibration controlled transient elastography from US National Health and Nutrition Examination Survey (US NHANES) were included. Liver Essential 5 was constructed by incorporating sleep quality into traditional healthy lifestyles (HLS).ResultsA total of 4.66–17.72% of the association between traditional HLS and MAFLD was mediated by sleep quality regardless of the detection techniques, and their joint associations on MAFLD were significant in both cohorts. ORs for individuals with poor sleep and unfavorable HLS were 1.72 (1.29–2.30) in ISSCC and 2.25 (1.55–3.26) in US NHANES, respectively. Around half of the participants previously considered as following a favorable HLS were re-classified by Liver Essential 5 with significantly higher prevalences of MAFLD in both cohorts (P < 0.001). Similar results were also found on at-risk MASH and significant fibrosis in US NHANES. ORs of participants with per one increment increase in Liver Essential 5 were 0.82 (0.77–0.89) and 0.79 (0.70–0.88) for MAFLD in ISSCC and US NHANES, 0.62 (0.48–0.78) for at-risk MASH and 0.78 (0.65–0.93) for significant fibrosis.ConclusionsLiver Essential 5, which incorporates sleep quality and traditional lifestyle factors, provides additional risk stratification for MAFLD-related outcomes.

Acute Myocardial Injury in Spontaneous Intracerebral Hemorrhage: A Secondary Observational Analysis of the FAST Trial.

Acute myocardial injury is associated with poor outcomes in patients with acute ischemic stroke, but its prognostic significance in patients with spontaneous intracerebral hemorrhage remains unclear. We investigated whether acute myocardial injury and the direction of the cardiac troponin I (cTnI) change (rising versus falling) affect post-intracerebral hemorrhage outcomes. We re-analyzed the FAST (Factor-Seven-for-Acute-Hemorrhagic-Stroke) trial. Acute myocardial injury was defined as at least 1 cTnI value above the upper reference limit with a rise/fall of >20%. Logistic regression tested for associations (1) between acute myocardial injury (presence versus absence) and poor outcome (modified Rankin Scale 4-6) and mortality at 15 and 90 days; (2) among 3 groups (rising versus falling versus no acute myocardial injury) and outcomes. Among the 841 FAST participants, 785 patients were included. Acute myocardial injury was detected in 29% (n=227); 170 had rising cTnI. At 15 and 90 days, respectively, those with acute myocardial injury had higher odds of poor outcome (adjusted odds ratio) ([aOR] 2.3 [95% CI, 1.3-3.9]); and adjusted odds ratio 2.5 [95% CI, 1.6-3.9];, and higher odds of mortality (adjusted odds ratio 2.4 [95% CI, 1.4-4.3]; and adjusted odds ratio 2.2 [CI, 1.3-3.6]) than patients without. There was no interaction between FAST group assignment and myocardial injury, and associations between myocardial injury and outcomes were consistent across group assignments. Rising cTnI was associated with the highest risk of poor outcomes and mortality. In this secondary analysis of the FAST trial, acute myocardial injury was common and associated with poor outcomes. The direction of the cTnI change might provide additional risk stratification after intracerebral hemorrhage.

Neutrophil-to-lymphocyte ratio and prognostic nutritional index in predicting composite endpoint of early safety following transcatheter aortic valve replacement.

The incidence of composite endpoint of early safety (CEES) after transcatheter aortic valve replacement (TAVR) has been a topic of focus within the cardiovascular field due to its impact on long-term patient outcomes. Timely prophylactic interventions are crucial for patients identified as high risk for CEES through preoperative risk stratification. This study aimed to explore the connection between inflammatory and nutritional markers, specifically the neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI), and CEES occurrence. A cohort of 134 patients undergoing TAVR in a single center was studied. The study endpoint was the occurrence of CEES, which was defined according to the Valve Academic Research Consortium 3. The CEES was reached in 25.4% of patients at 30 days. A high NLR was associated with a 5.55-fold increased risk of CEES (95% confidence interval (95% CI): 1.52-20.29; p < 0.05), while a low PNI was linked to a 4.43-fold increased risk (95% CI: 1.55-12.65; p < 0.01). Combining NLR and PNI provided additional risk stratification for high-risk patients (hazard ratio (HR), 95% CI: 2.24-43.37; p < 0.005). A high NLR and low PNI were shown to be significant predictors of CEES following TAVR. These findings underscore the significance of NLR and PNI in the risk assessment of TAVR patients, offering valuable insights for preventive measures.

Precision in prostate cancer detection: integrating prostate-specific antigen density (PSAD) and the Prostate Imaging Reporting and Data System (PI-RADS) to provide additional risk stratification for a more accurate diagnostic decision.

This study focuses on integrating prostate-specific antigen density (PSAD) and Prostate Imaging Reporting and Data System (PI-RADS) for enhanced risk stratification in biopsy-naïve patients. A prospective study was conducted on 339 patients with suspected prostate cancer, utilizing PSAD and PI-RADS in combination. Logistic regression models were employed, and receiver operating characteristic (ROC) analysis performed to evaluate predictive performance. The patient cohort underwent multiparametric MRI, targeted biopsy, and systematic biopsy. When patients were stratified into four PSAD risk groups, the rate of clinically significant prostate cancer (csPCa) increased significantly with higher PSAD levels. Logistic regression confirmed the independent contribution of PI-RADS and PSAD, highlighting their role in the prediction of csPCa. Combined models showed superior performance, as evidenced by the area under the curve (AUC) for PI-RADS category and PSAD (0.756), which exceeded that of the individual predictors (PSA AUC, 0.627, PI-RADS AUC 0.689, PSAD AUC 0.708). This study concludes that combining PSAD and PI-RADS improves diagnostic accuracy and predictive value for csPCa in biopsy-naïve men, resulting in a promising strategy to provide additional risk stratification for more accurate diagnostic decision in biopsy-naïve patients, especially in the PI-RADS 3 group.

Papillary Muscle Infarction by Cardiac MRI in Patients With Mitral Regurgitation.

Papillary muscle (PM) infarction (PMI) detected by cardiac magnetic resonance imaging (CMR) is associated with poor outcomes. Whether PM parameters provide more value for mitral regurgitation (MR) management currently remains unclear. Therefore, we examined the prognostic value of PMI using CMR in patients with MR. Between March 2018 and July 2023, we retrospectively enrolled 397 patients with MR undergoing CMR. CMR was used to detect PMI qualitatively and quantitively. We also collected baseline clinical, echocardiography, and follow-up data. Of the 397 patients with MR (52.4 ± 13.9 years), 117 (29.5%) were assigned to the PMI group, with 280 (70.5%) in the non-PMI group. PMI was demonstrated more in the posteromedial PM (PM-PM, 98/117) than in the anterolateral PM (AL-PM, 45/117). Compared with patients without PMI, patients with PMI had a decreased AL-PM (41.5 ± 5.4 vs. 45.6 ± 5.3)/PM-PM diastolic length (35.0 ± 5.2 vs. 37.9 ± 4.0), PM-longitudinal strain (LS, 20.4 ± 6.1 vs. 24.9 ± 4.6), AL-PM-LS (19.7 ± 6.8 vs. 24.7 ± 5.6)/PM-PM-LS (21.2 ± 7.9 vs. 25.2 ± 6.0), and increased inter-PM distance (25.7 ± 8.0 vs. 22.7 ± 6.2, all p < 0.001). Multiple logistic regression analyses identified male sex (odds ratio [OR] = 3.65, 95% confidence interval = 1.881-7.081, p < 0.001) diabetes mellitus (OR/95% CI/p = 2.534/1.13-5.68/0.024), AL-PM diastolic length (OR/95% CI/p = 0.841/0.77-0.92/< 0.001), PM-PM diastolic length (OR/95% CI/p = 0.873/0.79-0.964/0.007), inter-PM distance (OR/95% CI/p = 1.087/1.028-1.15/0.003), AL-PM-LS (OR/95% CI/p = 0.892/0.843-0.94/< 0.001), and PM-PM-LS (OR/95% CI/p = 0.95/0.9-0.992/0.021) as independently associated with PMI. Over a 769 ± 367-day follow-up, 100 (25.2%) patients had arrhythmia. Cox regression analyses indicated that PMI (hazard ratio [HR]/95% CI/p = 1.644/1.062-2.547/0.026), AL-PM-LS (HR/95% CI/p = 0.937/0.903-0.973/0.001), and PM-PM-LS (HR/95% CI/p = 0.933/0.902-0.965/< 0.001) remained independently associated with MR. The CMR-derived PMI and LS parameters improve the evaluation of PM dysfunction, indicating a high risk for arrhythmia, and provide additive risk stratification for patients with MR.

Recurrent stroke prediction by applying a stroke polygenic risk score in the Japanese population.

Recently, various polygenic risk score (PRS)-based methods were developed to improve stroke prediction. However, current PRSs (including cross-ancestry PRS) poorly predict recurrent stroke. Here, we aimed to determine whether the best PRS for Japanese individuals can also predict stroke recurrence in this population by extensively comparing the methods and maximizing the predictive performance for stroke onset. We used data from the BioBank Japan (BBJ) 1st cohort (n=179,938) to derive and optimize the PRSs using a 10-fold cross-validation. We integrated the optimized PRSs for multiple traits, such as vascular risk factors and stroke subtypes to generate a single PRS using the meta-scoring approach (metaGRS). We used an independent BBJ 2nd cohort (n=41,929) as a test sample to evaluate the association of the metaGRS with stroke and recurrent stroke. We analyzed recurrent stroke cases (n=174) and non-recurrent stroke controls (n=1,153) among subjects within the BBJ 2nd cohort. After adjusting for known risk factors, metaGRS was associated with stroke recurrence (adjusted OR per SD 1.18 [95% CI: 1.00-1.39, p=0.044]), although no significant correlation was observed with the published PRSs. We administered three distinct tests to consider the potential index event bias; however, the outcomes derived from these examinations did not provide any significant indication of the influence of index event bias. The high metaGRS group without a history of hypertension had a higher risk of stroke recurrence than that of the low metaGRS group (adjusted OR 2.24 [95% CI: 1.07-4.66, p=0.032]). However, this association was weak in the hypertension group (adjusted OR 1.21 [95% CI: 0.69-2.13, p=0.50]). The metaGRS developed in a Japanese cohort predicted stroke recurrence in an independent cohort of patients. In particular, it predicted an increased risk of recurrence among stroke patients without hypertension. These findings provide clues for additional genetic risk stratification and help in developing personalized strategies for stroke recurrence prevention.

18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer

Background[18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making.MethodsIn total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups.ResultsAll AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles.ConclusionsLow and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

Review and targeted lipidomic biomarkers in atherosclerosis

Ischemic cardiovascular events (ischemic stroke, myocardial infarction) are the most common complications of cardiovascular diseases. One of the leading mechanisms of these complications is atherosclerosis. Lipids play an important role in plaque development. Recently, the lipidome has been of greatest interest, since it may have a prognostic value in atherosclerosis development. With an increase in the circulation of proatherogenic lipidomic biomarkers, the risk of atheroscle­rosis destabilization and ischemic complications increases.The conducted studies made it possible to create additional risk stratification scales, for example, Cardiovascular Event Risk Test (CERT) 1 and 2. They make it possible to estimate the residual risk in patients taking statins. The lipidome examination in extracranial artery atherosclerosis can identify those plaques that have signs of instability, and therefore are dangerous for ischemic stroke development.The review describes the features of plasma lipidome in various cardiovascular diseases associated with atherosclerosis.

Abstract 7669: Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category

Abstract Background: Current tools for predicting prostate cancer biochemical recurrence (BCR) after RP often depend on parameters determined by pathologists, such as tumor grade, which is known to vary between reviewers. Genomic risk classifiers provide useful information but require substantial tissue, are costly, and are not commonly accessible in many medical centers. The study presents an artificial intelligence (AI) model for automated risk assessment of biochemical recurrence (BCR) by analyzing nuclear morphologic patterns from Pca archival H&amp;E slides. Nuclear morphometric features are essential for disease diagnosis and gaining insights into cellular functions. Specifically, we sought to evaluate whether the computational pathology approach could provide additional granular risk stratification within the low-risk group identified by Decipher. Methods: The study employed two cohorts from the University of Pennsylvania D1 and D2. D1 comprised 180 patients, while D2 comprised 175 patients, including 63 individuals from the Decipher low-risk group. None of the patients in either cohort had received any pre-operative treatments. Within the Decipher cohort, a tumor segmentation model was created using the U-net deep learning network. The HoVer-Net model was used to both segment and classify nuclei. The feature extracted included a combination of nuclear texture and quantitative measurements of the spatial arrangement of nuclei. The eleven most significant features, selected via the least absolute shrinkage and selection operator (LASSO), were utilized to build a Cox regression model for predicting the risk of BCR in D1. In D2, patients were classified as either low-risk or high-risk for BCR based on the median risk score derived from D1. Results: Patients from D1 identified as high risk by the AI model experienced shorter survival, with a median BCR time of 27 months, in contrast to 55 months for those classified as low risk. The AI model demonstrated significant prognostic value for BCR in both D1 (HR = 6.82, 95% confidence interval [CI]: 3.54-13.12, p &amp;lt; 0.0019), and D2 (HR = 2.72, 95% CI: 1.53-4.82, p &amp;lt; 0.0032). Furthermore, the model effectively stratified the Decipher low-risk group in D2 into distinct low and high-risk categories (HR = 4.52, 95% confidence interval [CI]: 1.70-12.05, p &amp;lt; 0.0085). The most prognostic features identified included five related to nuclei shape diversity (Minor Axis Length, Eccentricity, Equivalent Diameter, Solidity, and Circularity) in conjunction with nine nuclear texture features. Conclusions: The AI model's findings suggest that a digital image-based prognostic classifier for prostate cancer could serve as an alternative or complementary method to molecular-based companion risk tests. Additional, independent multi-site and prospective validation of these findings are warranted. Citation Format: Kamal Hammouda, Tilak Pathak, Tuomas Mirtti, Priti Lal, Shilpa Gupta, Anant Madabhushi. Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7669.

Low Muscle Mass by Preprocedural Computed Tomography Is Associated With Worse Short-Term Outcomes in Transcatheter Aortic Valve Replacement Recipients

Low muscle mass (LMM) is associated with worse outcomes in various clinical situations. Traditional frailty markers have been used for preoperative risk stratification in patients who underwent transcatheter aortic valve replacement (TAVR). However, preoperative imaging provides an opportunity to directly quantify skeletal muscle mass to identify patients at higher risk of procedural complications. We reviewed all TAVR recipients from January to December 2018 and included subjects with preprocedural chest computed tomography. Multi-slice automated measurements of skeletal muscle mass were made from the ninth to twelfth thoracic vertebrae and normalized by height squared to obtain skeletal muscle index (cm2/m2). LMM was defined as the lowest gender-stratified skeletal muscle index tertile. Strength testing was collected during pre-TAVR evaluation. Primary outcome was a composite of perioperative complications, 1-year rehospitalization, or 1-year mortality. In our cohort, 238 patients met inclusion criteria, and 80 (33.6%) were identified to have LMM. Patients with LMM were older with lower body mass index, decreased grip strength, lower hemoglobin A1c, and higher N-terminal pro-brain natriuretic peptide. They had greater rates of the composite outcome and 2-year all-cause mortality, which remained significant on multivariable adjustment (hazard ratio 1.71, 95% confidence interval 1.05 to 2.78, p = 0.030 and hazard ratio 2.31, 95% confidence interval 1.02 to 5.24, p = 0.045, respectively) compared with patients without LMM; there was no significant difference in 5-year all-cause mortality. In conclusion, LMM was associated with an increase in the primary composite outcome and 2-year all-cause mortality in TAVR recipients. Using automatic muscle processing software on pre-TAVR computed tomography scans may serve as an additional preoperative risk stratification tool.

Risk of Clinically Significant Prostate Cancer after a Nonsuspicious Prostate MRI-A Comparison with the General Population.

We compare the risk of clinically significant (csPCa; ISUP Grade Group ≥ 2) and insignificant prostate cancer (isPCa; ISUP Grade Group 1) in men with a nonsuspicious prostate MRI (nMRI; PI-RADS ≤ 2) with the general population, and assess the value of PSA density (PSAD) in stratification. In this retrospective population-based cohort study we identified 1,682 50-79-year-old men, who underwent nMRI at HUS (2016-2019). We compared their age-standardized incidence rates (IR) of csPCa and the odds of isPCa to a local age- and sex-matched general population (n = 230,458) during a six-year follow-up. Comparisons were performed by calculating incidence rate ratios (IRR) and ORs with 95% confidence intervals (CI). We repeated the comparison for the 920 men with nMRI and PSAD < 0.15 ng/mL/cm3. Compared with the general population, the IR of csPCa was significantly higher after nMRI [1,852 vs. 552 per 100,000 person-years; IRR 3.4 (95% CI, 2.8-4.1)]. However, the IR was substantially lower if PSAD was low [778 per 100,000 person-years; IRR 1.4 (95% CI, 0.9-2.0)]. ORs for isPCa were 2.4 (95% CI, 1.7-3.5) for all men with nMRI and 5.0 (95% CI, 2.8-9.1) if PSAD was low. Compared with the general population, the risk of csPCa is not negligible after nMRI. However, men with nMRI and PSAD <0.15 ng/mL/cm3 have worse harm-benefit balance than men in the general population. Prostate biopsies for men with nMRI should be reserved for cases indicated by additional risk stratification. See related In the Spotlight, p. 641.

Preoperative Frailty and Malnutrition in Surgical Oncology Patients Predicts Higher Postoperative Adverse Events and Worse Survival: Results of a Blinded, Prospective Trial.

Frailty, a multidimensional state leading to reduced physiologic reserve, is associated with worse postoperative outcomes. Despite the availability of various frailty tools, surgeons often make subjective assessments of patients' ability to tolerate surgery. The Risk Analysis Index (RAI) is a validated preoperative frailty assessment tool that has not been studied in cancer patients with plans for curative-intent surgery. In this prospective, surgeon-blinded study, patients who had abdominal malignancy with plans for resection underwent preoperative frailty assessment with the RAI and nutrition assessment by measurement of albumin, prealbumin, and C-reactive protein (CRP). Postoperative outcomes and survival were assessed. The study included 220 patients, 158 (72%) of whom were considered frail (RAI ≥21). Frail patients were more likely to be readmitted within 30 and 90 days, (16% vs. 3% [P = 0.006] and 16% vs. 5% [P = 0.025], respectively). Patients with abnormal CRP, prealbumin, and albumin experienced higher rates of unplanned intensive care unit admission (CRP [27% vs. 8%; P < 0.001], albumin [30% vs. 10%; P < 0.001], prealbumin [29% vs. 9%; P < 0.001]) and increased postoperative mortality at 90 and 180 days. Survival was similar for frail and non-frail patients. In the multivariate analysis, frailty remained an independent risk factor for readmission (hazard ratio, 5.58; 95% confidence interval, 1.39-22.15; P = 0.015). In the post hoc analysis using the pre-cancer RAI score, the postoperative outcomes did not differ between the frail and non-frail patients. In conjunction with preoperative markers of nutrition, the RAI may be used to identify patients who may benefit from additional preoperative risk stratification and increased postoperative follow-up evaluation.

Obesity-Related Differences in Pathomechanism and Outcomes in Patients With HFpEF: A CMR Study

BackgroundClinical significance of an integrated evaluation of epicardial adipose tissue (EAT) and the right ventricle (RV) in heart failure with preserved ejection fraction (HFpEF) is unknown. ObjectivesThe authors investigated the potential of EAT and RV quantification for obesity-related pathophysiology and risk stratification in obese HFpEF patients using cardiovascular magnetic resonance (CMR). MethodsA total of 150 patients (obese, body mass index ≥30 kg/m2; n = 73, nonobese, body mass index <30 kg/m2; n = 77) with a clinical diagnosis of HFpEF undergoing CMR were retrospectively identified. EAT volume surrounding both ventricles were quantified with manual delineation on cine images. Total RV volume (TRVV) was calculated as the sum of RV cavity and mass at end-diastole. The endpoint was the composite of all-cause mortality and first HF hospitalization. ResultsDuring a median follow-up of 46 months, 39 nonobese patients (51%) and 32 obese patients (44%) experienced the endpoint. EAT was a prognostic biomarker regardless of obesity and was independently correlated with TRVV. In obese HFpEF, EAT correlated with RV longitudinal strain (r = 0.32, P = 0.006), and increased amount of EAT and TRVV was associated with greater left ventricular end-diastolic eccentric index (r = 0.36, P = 0.002). The integration of RV quantification into EAT provided improved risk stratification with a C-statistic increase from 0.70 to 0.79 in obese HFpEF. Obese patients with EAT<130 ml and TRVV<180 ml had low risk (annual event rate 3.2%), while those with increased EAT ≥130 ml and TRVV ≥180 ml had significantly higher risk (annual event rate 11.8%; P < 0.001). ConclusionsCMR quantification of EAT and RV structure provides additive risk stratification for adverse outcomes in obese HFpEF.

Inter-atrial block and atrial fibrillation predict cardiac adverse events in heart failure with preserved ejection fraction

Abstract Background Atrial fibrillation (AF) is a prognostic risk-factor in heart failure with preserved ejection fraction (HFpEF). Earlier stages of atrial dysfunction identified by the ECG, particularly inter-atrial block (IAB), may provide additional risk predictions in HFpEF patients in sinus rhythm. Purpose To assess the prevalence of IAB in patients diagnosed with HFpEF compared to non-HFpEF, and to evaluate the prognostic association of IAB and AF in patients diagnosed with HFpEF. Methods Consecutive symptomatic patients referred for a systematic diagnostic work-up to rule-in or rule-out HFpEF were included. The diagnosis of HFpEF was based on current guidelines (including HFA-PEFF and H2FPEF algorithms) and confirmed by consensus of multiple experienced HF specialists. Patients in whom HFpEF was ruled-out were included as non-HFpEF. We excluded patients with cardiac valve interventions before or up to 1 year after baseline, or with paced rhythm during IAB assessment. ECGs at baseline were assessed in detail for P-wave duration, morphology, and presence of IAB using digital calipers by investigators blinded to clinical data. IAB was defined based on a P-wave duration of &amp;gt;120ms. Patients with HFpEF and non-HFpEF were grouped into either i) AF based on ECG or medical history, ii) IAB based on ECG, or iii) no IAB or AF based on ECG. Adverse events were evaluated only in HFpEF patients and included HF hospitalization, cardiac or sudden death, and a composite of both. Results 372 patients with HFpEF and 68 non-HFpEF were included (females: 67.7% vs. 64.7%, p = 0.726; mean age: 75±7 vs. 67±10 years, p&amp;lt;0.001). AF was more common in HFpEF than non-HFpEF (64.8% vs. 23.5%, p&amp;lt;0.001). In sinus rhythm, presence of IAB was comparable between HFpEF and non-HFpEF (87.0% vs. 80.8%). During a mean follow-up of 3.2±1.6 years in HFpEF patients, more adverse cardiac events were observed when AF was present (23.7%, HR 10.6 [95%CI 1.5-1334.4]) compared to IAB (10.5%, HR 4.2 [95%CI 0.56-542.6]) and no IAB (0%), p = 0.0004 (Figure 1). Pairwise comparisons confirmed these observations: AF vs. IAB p = 0.006, AF vs. no IAB p = 0.031. In HFpEF patients with sinus rhythm, a trend was found for more adverse events when IAB was present versus no IAB (p = 0.06). Conclusion Patients with HFpEF have a higher prevalence of AF and similar prevalence of IAB compared to symptomatic patients ruled-out for HFpEF. Presence of IAB or AF in HFpEF may predispose to adverse cardiac events in patients without severe valve disease. IAB may enrich cardiac risk stratification in addition to AF in HFpEF.Survival curves for no IAB, IAB, and AF

Incremental prognostic value of metabolic dysfunction-associated fatty liver disease over coronary computed tomography angiography findings in patients with suspected coronary artery disease

Abstract Background Fatty liver caused by nutritional metabolic disorders regardless of other chronic liver diseases has been proposed as a new liver disease concept, metabolic dysfunction-associated fatty liver disease (MAFLD). With the alteration from non-alcoholic fatty liver disease to MAFLD, MAFLD can be recognized as higher CVD risk. However, the association of MAFLD with adverse coronary CT angiography (CCTA) findings, such as the presence of obstructive and high-risk plaque, and cardiovascular events has not been fully elucidated. Purpose This study was aimed to investigate additional risk stratification benefits of MAFLD diagnosed by hepatic steatosis assessed during CCTA in patients with suspected coronary artery disease (CAD). Methods A total of 2289 patients without a history of CAD who underwent CCTA in our institute from August 2011 and December 2016 were enrolled. Hepatic steatosis, defined on CT as a hepatic-to-spleen attenuation ratio of &amp;lt;1.0, was examined just before the evaluation of CCTA. Adverse CCTA findings were defined as obstructive and/or high-risk plaque. The major adverse cardiovascular events (MACE) were composite coronary events including cardiovascular death, acute coronary syndrome, and late coronary revascularization. The incremental predictive value of MAFLD was evaluated using the global chi-square values and C-statistic. Results Among 2289 patients (60% men, mean age 68 years), MAFLD was identified in 382 (17%) patients. The presence of adverse CCTA findings in patients with MAFLD was significantly greater than that in patients without MAFLD (40% and 35%, p=0.039). During a median follow-up of 4.4 years, 103 (4.4%) MACE were observed. In the multivariate Cox regression analysis, MAFLD was significantly associated with MACE in a model included traditional risk factors and adverse CCTA findings (HR 1.73, 95%CI 1.10–2.72, P = 0.017). The Kaplan–Meier analysis showed that patients with both MAFLD and adverse CCTA findings had highest event rates compared with patients without MAFLD or adverse CTA findings (log rank test, p=0.004). By adding MAFLD to a prediction model including adverse CCTA findings, the global chi-square values and C-statistic significantly increased from 80.6 to 86.2 (P = 0.017) and 0.72 to 0.74 (P = 0.044), respectively. Conclusions MAFLD concurrently assessed during CCTA enables more accurate detection of patients at higher risk of MACE In patients with suspected stable CAD.

Biomarker-based clinical subphenotypes of cardiac surgery associated acute kidney injury and their association with in-hospital treatments and outcomes: a data-driven cluster analysis

Abstract Background Cardiac Surgery-Associated Acute Kidney Injury (CSA-AKI) is a prevalent and detrimental complication after cardiac surgery. Its etiology, pathophysiology, and clinical features are often obscure and heterogenous, with varying prognosis and therapeutic requirements. Cluster analysis has been increasingly used to profile complex diseases, but no prior studies have applied it to the CSA-AKI population. Purpose We aim to employ an unsupervised, data-driven cluster analysis approach utilizing routinely available and previously reported perioperative biomarkers to identify CSA-AKI subphenotypes that exhibit varying treatment requirements and in-hospital outcomes. Methods Patients diagnosed with CSA-AKI in accordance with the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, who underwent coronary bypass grafting surgery in a single institution and had comprehensive perioperative data, were included (n=7 796). Cluster analysis based on 12 routinely available perioperative biomarkers was performed to partition CSA-AKI subphenotypes. Treatment requirements for vasoactive supports, diuretics, and dialysis, as well as AKI duration and in-hospital mortality rates, were compared among the identified subphenotypes. Results Three distinct clusters of patients with CSA-AKI were identified with different clinical characteristics, treatment needs, and in-hospital outcomes. Cluster 1(n=2 452, 31.5%) was characterized by normal NT-proBNP level, less comorbidities, predominantly isolated surgery, and better prognosis. Cluster 3 (n=1 060, 13.6%) was distinguished by extremely inflammatory states (significantly higher hsCRP level), more comorbidities, more complex surgery, and worse prognosis. Cluster 2 (n=4 284, 55.0%) was considered the intermediate cluster. CSA-AKI patients in cluster 3 exhibited significantly higher in-hospital mortality rate, longer AKI duration and hospital stay, and increased requirements for vasoactive supports, diuretics, and dialysis compared with the other two clusters. Notably, according to the current KDIGO criteria, 87.4% of cluster 3 patients are classified as only stage 1. Conclusions Three clinical subphenotypes of CSA-AKI were identified based on routine perioperative biomarkers, which demonstrated varying treatment requirements and in-hospital outcomes. This cluster analysis augments the current severity-based staging system for general AKI, providing additional risk stratification and treatment implications that are more tailored to cardiac surgery patients.