Predictor Of Clinical Outcome Research Articles

Prognostic Impact of CYP2C19 Genotypes on Long-Term Clinical Outcomes in Older Patients After Percutaneous Coronary Intervention.

Carriers of CYP2C19 loss-of-function alleles have increased adverse events after percutaneous coronary intervention, but limited data are available for older patients. We aimed to evaluate the prognostic impact of CYP2C19 genotypes on clinical outcomes in older patients after percutaneous coronary intervention. The study included 1201 older patients (aged ≥75 years) who underwent percutaneous coronary intervention and received clopidogrel-based dual antiplatelet therapy in South Korea. Patients were grouped on the basis of CYP2C19 genotypes. The primary outcome was 3-year major adverse cardiac events, defined as a composite of cardiac death, myocardial infarction, and stent thrombosis. Older patients were grouped into 3 groups: normal metabolizer (36.6%), intermediate metabolizer (48.1%), and poor metabolizer (15.2%). The occurrence of the primary outcome was significantly different among the groups (3.1, 7.0, and 6.2% in the normal metabolizer, intermediate metabolizer, and poor metabolizer groups, respectively; P=0.02). The incidence rate of all-cause death at 3 years was greater in the intermediate metabolizer and poor metabolizer groups (8.1% and 9.2%, respectively) compared with that in the normal metabolizer group (3.5%, P=0.03) without significant differences in major bleeding. In the multivariable analysis, the intermediate metabolizer and poor metabolizer groups were independent predictors of 3-year clinical outcomes. In older patients, the presence of any CYP2C19 loss-of-function allele was found to be predictive of a higher incidence of major adverse cardiac events within 3 years following percutaneous coronary intervention. This finding suggests a need for further investigation into an optimal antiplatelet strategy for older patients. URL: https://clinicaltrials.gov. Identifier: NCT04734028.

Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Performance on clinical outcomes, activities of daily living anduser experience on head-mounted displays for people withvision impairment.

To compare the objective performance, acceptance and usability of head-mounted displays (HMDs) to provide evidence-based data that could be used to increase the efficiency of device referrals based upon a person's vision loss and functional needs. A cross-sectional, counterbalanced, individually controlled crossover study was performed on 15 adults with various eye conditions. Performance was measured when using four HMDs: eSight4, Eyedaptic EYE3, Eyedaptic EYE4 and IrisVision Inspire. Performance on clinical visual acuity tests and contrast were assessed, as well as vision-related activities of daily living (ADL) which were divided into three categories: Reading, Searching & Identifying and Eye-hand Coordination. User-experience was also assessed. Logistic regression analyses, Friedman one-way repeated measure analyses of variance by ranks and multivariate permutation testing were used for analysis. There was a significant improvement in visual acuity when using all devices. For contrast tasks, only the eSight4 and Eyedaptic EYE3 improved performance relative to baseline. For most Reading and Searching & Identifying tasks, the odds of being able to perform the tasks were significantly higher while using the devices. However, the actual performance with most devices (e.g., number of words read or reading speed) did not improve significantly over baseline for most tasks. For the Eye-hand Coordination tasks, participants performed equivalent to or significantly poorer than baseline when using the devices. No demographic or clinical predictors of outcomes were identified. Participants expressed dissatisfaction with the devices' effectiveness, acceptability and usability. While performance on clinical tests was better when using the devices, performance on most real-world ADLs was equal to or worse than baseline. No single device improved performance on all tasks, and performance on any one task was not improved with all the devices. The overall dissatisfaction with the devices paralleled the lack of objective improvement in the performance of real-world tasks.

Post-traumatic hyperoxia after pediatric TBI

ABSTRACT Objective Hyperoxia has been suggested as a mechanism for secondary injury following adult traumatic brain injury (TBI), but its effects have not been well described in pediatric patients. Methods Pediatric (≤18yo) TBI patients were identified in a prospective institutional registry from October 2008 to April 2022. The first, highest, and the Area Under the Curve (AUC) PaO2 in the first 24 hours were collected and calculated for each patient from arterial blood gas reports after admission to the ICU. Neurological outcome after 6 months was measured using dichotomized modified Rankin Scale (mRS) and Glasgow Outcome Scale – Extended (GOS-E). Multivariable logistic regression models were used to determine if the three measurements for hyperoxia predicted an unfavorable outcome after controlling for well-established clinical and imaging predictors of outcome. Results We identified 98 pediatric patients with severe accidental TBI during the study period. Hyperoxia (PaO2 > 300 mmHg) occurred in 33% of the patients. The presence of elevated PaO2 values, determined by all three evaluations of hyperoxia, was not associated with unfavorable outcome after 6 months. Conclusion Utilizing multiple methods to assess exposure, hyperoxia was present in a substantial number of patients with severe TBI but was not associated with an unfavorable outcome.

Sociodemographic Disparities and Postoperative Outcomes Following Cranial Vault Remodeling for Craniosynostosis: Analysis of the 2012 to 2021 NSQIP-Pediatric Database.

The objective of this study was to assess whether race and ethnicity are independent predictors of inferior postoperative clinical outcomes, including increased complication rates, extended length of stay (LOS), and unplanned 30-day readmission following cranial vault repair for craniosynostosis. A retrospective cohort study was performed using the American College of Surgeons National Surgical Quality Improvement Program-Pediatric database. Pediatric patients under 2 years of age undergoing cranial vault repair for craniosynostosis between 2012 and 2021 were identified using the International Classification of Diseases-9/10 and Current Procedural Terminology codes. Patients were dichotomized into 4 cohorts: non-Hispanic White (NHW), non-Hispanic Black (NHB), Hispanic, and other. Only patients with available race and ethnicity data were included in this study. Patient demographics, comorbidities, surgical variables, postoperative adverse events, and hospital resource utilization were assessed. Multivariate logistic regression analysis was used to assess the impact of race on complications, extended LOS, and unplanned readmissions. In our cohort of 7764 patients, 72.80% were NHW, 8.44% were NHB, 15.10% were Hispanic, and 3.67% were categorized as "other." Age was significantly different between the 4 cohorts (P<0.001); NHB patients were the oldest, with an average age of 327.69±174.57 days old. Non-Hispanic White experienced the least adverse events while NHB experienced the most (P=0.01). Total operative time and hospital LOS were shorter for NHW patients (P<0.001 and P<0.001, respectively). Rates of unplanned 30-day readmission, unplanned reoperation, and 30-day mortality did not differ significantly between the 4 cohorts. On multivariate analysis, race was found to be an independent predictor of extended LOS [NHB: adjusted odds ratio: 1.30 (1.04-1.62), P=0.021; other: 2.28 (1.69-3.04), P=0.005], but not of complications or readmission. Our study demonstrates that racial and ethnic disparities exist among patients undergoing cranial vault reconstruction for craniosynostosis. These disparities, in part, may be due to delayed age of presentation among non-Hispanic, non-White patients. Further investigations to elucidate the underlying causes of these disparities are necessary to address gaps in access to care and provide equitable health care to at-risk populations.

Defining the correlates of lymphopenia and independent predictors of poor clinical outcome in adults hospitalized with COVID-19 in Australia

Lymphopenia is a common feature of acute COVID-19 and is associated with increased disease severity and 30-day mortality. Here we aim to define the demographic and clinical characteristics that correlate with lymphopenia in COVID-19 and determine if lymphopenia is an independent predictor of poor clinical outcome. We analysed the ENTER-COVID (Epidemiology of hospitalized in-patient admissions following planned introduction of Epidemic SARS-CoV-2 to highly vaccinated COVID-19 naïve population) dataset of adults (N = 811) admitted for COVID-19 treatment in South Australia in a retrospective registry study, categorizing them as (a) lymphopenic (lymphocyte count < 1 × 109/L) or (b) non-lymphopenic at hospital admission. Comorbidities and laboratory parameters were compared between groups. Multiple regression analysis was performed using a linear or logistic model. Intensive care unit (ICU) patients and non-survivors exhibited lower median lymphocyte counts than non-ICU patients and survivors respectively. Univariate analysis revealed that low lymphocyte counts associated with hypertension and correlated with haemoglobin, platelet count and negatively correlated with urea, creatinine, bilirubin, and aspartate aminotransferase (AST). Multivariate analysis identified age, male, haemoglobin, platelet count, diabetes, creatinine, bilirubin, alanine transaminase, c-reactive protein (CRP) and lactate dehydrogenase (LDH) as independent predictors of poor clinical outcome in COVID-19, while lymphopenia did not emerge as a significant predictor.

Distance to thrombus, ischemic lesion volume and clinical outcome after thrombectomy for M1 middle cerebral artery occlusion.

Stroke resulting from occlusion of the middle cerebral artery (MCA) can have devastating consequences, potentially leading to aloss of independence. This study aimed to investigate the relationship between the distance to the thrombus (DT) and both ischemic lesion volume (ILV) and clinical outcomes. We retrospectively evaluated patients with thromboembolic MCA M1 segment occlusion who underwent neurovascular imaging followed by endovascular thrombectomy (EVT) at two comprehensive stroke centers over a3-year period (2018-2020). Preinterventional computed tomography (CT) or magnetic resonance (MR) angiography was used to measure DT, defined as the distance from the carotid‑T bifurcation to the proximal surface of the M1 occlusion. Postinterventional CT or MR imaging was employed to determine the ILV and clinical outcomes were assessed using the modified Rankin scale (mRS) at 3 months. There were 346 patients evaluated. The median DT was 9.4 mm (interquartile range, IQR 6.0-13.7 mm) and the median ILV was 13.9 ml (IQR 2.2-53.1 ml). After adjustment, an increase in DT was associated with adecrease in odds for a larger ILV (odds ratio, OR 0.96, 95% confidence interval, CI 0.92-0.99, p = 0.041). Through this association, more distal thrombi were associated with good clinical outcome (mRS 0-2; clinical outcome available in 282 patients, p = 0.018). The ILV was inversely associated with better clinical outcome OR 0.52 (95% CI 0.40-0.67). Based on the findings, DT was identified as an independent albeit weak predictor for ILV and clinical outcomes in patients with MCA M1 occlusion who underwent EVT.

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment

ObjectivesAccurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI.MethodsWe recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network.ResultsThe proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (−).ConclusionThe proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient’s outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

BackgroundSignal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.MethodsaSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.ResultsA total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.ConclusionsMSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

CatLet score and clinical CatLet score as predictors of long-term outcomes in patients with acute myocardial infarction presenting later than 12 hours from symptom onset

Background Our recently developed C oronary A rtery T ree description and L esion E valua T ion (CatLet) angiographic scoring system is unique in its description of the variability in the coronary anatomy, the degree of stenosis of a diseased coronary artery, and its subtended myocardial territory, and can be utilized to predict clinical outcomes for patients with acute myocardial infarction (AMI) presenting ≤12 h after symptom onset. The current study aimed to assess whether the Clinical CatLet score (CCS), as compared with CatLet score (CS), better predicted clinical outcomes for AMI patients presenting >12 h after symptom onset. Methods CS was calculated in 1018 consecutive AMI patients enrolled in a retrospective registry. CCS was calculated by multiplying CS by the ACEF I score (age, creatinine, and left ventricular ejection fraction). Primary endpoint was major adverse cardiac events (MACEs) at 4-year-follow-up, a composite of cardiac death, myocardial infarction, and ischemia-driven revascularization. Results Over a 4-year follow-up period, both scores were independent predictors of clinical outcomes after adjustment for a broad spectrum of risk factors. Areas-under-the-curve (AUCs) for CS and CCS were 0.72(0.68–0.75) and 0.75(0.71–0.78) for MACEs; 0.68(0.63–0.73) and 0.78(0.74–0.83) for all-cause death; 0.73(0.68–0.79) and 0.83(0.79–0.88) for cardiac death; and 0.69(0.64–0.73) and 0.75(0.7–0.79) for myocardial infarction; and 0.66(0.61–0.7) and 0.63(0.58–0.68) for revascularization, respectively. CCS performed better than CS in terms of the above-mentioned outcome predictions, as confirmed by the net reclassification and integrated discrimination indices. Conclusions CCS was better than CS to be able to risk-stratify long-term outcomes in AMI patients presenting >12 h after symptom onset. These findings have indicated that both anatomic and clinical variables should be considered in decision-making on management of patients with AMI presenting later.

Real-world clinical outcome related to veno-arterial extracorporeal membranous oxygenator: a single-center experience

Abstract Funding Acknowledgements None. Background Veno-arterial extracorporeal membranous oxygenator (VA-ECMO) is one of the most powerful devices that rapidly restore sufficient organ perfusion in patients with cardiogenic shock. Despite abundant experiences of successful resuscitation with VA-ECMO, evidences for clinical benefit of VA-ECMO are still lacking. Purpose We summarised clinical outcomes related to VA-ECMO and investigated predictors regarding survival at discharge. Methods Patients who treated with peripheral VA-ECMO between 2006 and 2022 were included from a Hospital in South Korea. Eligible patients were analysed in stratification with ECMO initiation year (year 2006–2010, year 2011–2016, and year 2017–2022). Survival status at discharge were investigated. Results Among total of 693 patients included, 223 (32.2%) were survived at discharge. Survivors had stayed in hospital for median 28 (19–52) days. The overall volume of ECMO initiation (86 runs vs. 250 runs vs. 357 runs) and the rate of extracorporeal CPR (3.5% vs. 18.4% vs. 38.1%) have increased over time. The median duration of VA-ECMO treatment has increased over time (52.6 hours vs. 63.6 hours vs. 85.8 hours). The serum lactate test has been performed more frequently over time (15.1% vs. 79.6% vs. 99.2%). Among 470 patients who died in the index hospitalization, 154 (32.8%) patients died in the first 24 hours after initiation of VA-ECMO. In a multivariate regression model, age over 70 (OR, 0.54; 95% CI, 0.32–0.89), extracorporeal CPR (OR, 0.42; 95% CI, 0.24–0.71), and lactate level ≥8.0 mmol/L (OR, 0.24; 95% CI, 0.15–0.37) were associated with unfavorable outcome while hemoglobin was a predictor of favorable clinical outcome (OR, 1.16; 95% CI, 1.07–1.25). Conclusion The volume of VA-ECMO has increased and clinical severity has also become higher than before. The rate of survival at discharge after VA-ECMO treatment remains stable; however, the rate of patients who died in the first 24 hours is still high. Age, extracorporeal CPR, hemoglobin and lactate levels were predictors of clinical outcome after VA-ECMO treatment.

Heart Failure with Mildly Reduced Ejection Fraction—A Phenotype Waiting to Be Explored

Heart failure (HF) presents a significant global health challenge recognised by frequent hospitalisation and high mortality rates. The assessment of left ventricular (LV) ejection fraction (EF) plays a crucial role in diagnosing and predicting outcomes in HF, leading to its classification into preserved (HFpEF), reduced (HFrEF), and mildly reduced (HFmrEF) EF. HFmrEF shares features of both HFrEF and HFpEF but also exhibits distinct characteristics. Despite advancements, managing HFmrEF remains challenging due to its diverse presentation. Large-scale studies are needed to identify the predictors of clinical outcomes and treatment responses. Utilising biomarkers for phenotyping holds the potential for discovering new treatment targets. Given the uncertainty surrounding optimal management, individualised approaches are imperative for HFmrEF patients. This chapter examines HFmrEF, discusses the rationale for its re-classification, and elucidates HFmrEF’s key attributes. Furthermore, it provides a comprehensive review of current treatment strategies for HFmrEF patients.

Effect of Tumor Regression Grade on Survival and Disease-Free Interval in Patients Operated on for Locally Advanced Rectal Cancer

Introduction: Colorectal cancer is the fourth leading cause of cancer-related death in both men and women in our population. In this regard, rectal cancer accounts for more than half of colorectal cancer deaths, and its incidence is expected to increase in the coming years. There have been significant changes in neoadjuvant therapy regimens, with promising results, as demonstrated by the recent RAPIDO and PRODIGE23 studies. Around 40% of patients diagnosed with locally advanced rectal cancer show some degree of response to neoadjuvant treatment, with complete tumor regression observed in up to one in five patients. Materials and Methods: Retrospective observational study. A total of 181 patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery were analyzed. Clinical and pathological data were collected from the patients, including assessment of tumor regression through histopathological studies after surgery. The Mandard tumor regression grading system was used to categorize tumor response into different grades. Results: The results showed a significant association between the degree of tumor regression and several important clinical outcomes. Specifically, patients with higher tumor regression had significantly better disease-free survival than those with less regression (p = 0.004). In addition, tumor regression was also correlated with the incidence of local recurrence (p = 0.018) and distant metastasis (p = 0.032). These associations suggest that tumor responsiveness to neoadjuvant therapy may influence the long-term progression of the disease. Regarding tumor deposits and the presence of lymphadenopathy, these factors were also found to be significantly associated with clinical outcomes. Patients with tumor deposits had a higher incidence of local recurrence (p = 0.025) and distant metastases (p = 0.041), while the presence of lymphadenopathy increased the risk of local recurrence (p = 0.013). These findings highlight the importance of evaluating not only tumor regression but also other pathological markers to predict prognosis and guide clinical management. Conclusions: The degree of tumor regression was not an independent predictor of survival compared to other variables such as nodal stage and presence of tumor deposits. This indicates that while tumor regression is an important factor, other elements also play a crucial role in determining the prognosis of patients with locally advanced rectal cancer. This study provides additional evidence for the importance of tumor regression, tumor deposits, and lymphadenopathy as predictors of clinical outcomes in patients with rectal cancer treated with neoadjuvant chemoradiotherapy.

The potential of triglyceride-glucose index in improving the prediction of post-percutaneous coronary intervention clinical outcomes: a systematic review and meta-analysis

Introduction Despite percutaneous coronary intervention is the current preferred reperfusion modality, the incidence of major adverse cardiovascular events (MACE) is still high. Currently, GRACE score is used for predicting PCI outcomes. The TyG (triglyceride-glucose) index, a potential predictor based on insulin resistance of cardiovascular disease, has not been considered in the GRACE score. Objective To assess the potential of the TyG index in predicting cardiovascular adverse clinical outcomes in patients undergoing PCI. Method Following PRISMA guidelines, the authors reviewed literature from five databases. We included studies investigating post-PCI outcomes based on the TyG index level. Effect size was estimated using RevMan to calculate pooled odds ratio and mean difference, and R software to perform diagnostic test accuracy. Results Overall, 31,671 post-PCI patients with acute coronary syndrome were included in this study. A significant difference in TyG index was found between patients who experienced MACE and those who did not [MD: 0.3 (0.2–0.4), p < .05]. Higher TyG index was strongly correlated with higher MACE [OR: 2.01 (1.45–2.77), p < .05], especially revascularization [OR: 2.40 (1.72–3.36), p < .05], followed by myocardial infarction [OR: 1.80 (2.41–2.86), p < .05], death [OR: 1.36 (0.86–2.15), p = .19], and stroke [OR: 1.09 (0.79–1.50), p = .59]. Tyg Index showed 55% sensitivity, 60% specificity, and 0.612 accuracy. The addition of the TyG index to the GRACE scoring improved the predictive model’s AUC (0.746 vs. 0.809). Conclusion This systematic review and meta-analysis comprises evidence-based results for the correlation of TyG index and post-PCI outcomes, demonstrating modest sensitivity, specificity, accuracy, and enhanced predictive value of GRACE score.

Corrigendum: Identifying predictors of clinical outcomes using the projection-predictive feature selection—a proof of concept on the example of Crohn's disease

Corrigendum: Identifying predictors of clinical outcomes using the projection-predictive feature selection—a proof of concept on the example of Crohn's disease

Diagnosis and Management of MASLD: An Metabolic Perspective of a Multisystem Disease

Metabolic dysfunction–associated steatotic liver disease (MASLD) has attained epidemic proportions in recent years, affecting nearly one-third of the general population and two-thirds of patients with type 2 diabetes (T2DM). It encompasses a wide spectrum of pathophysiological entities, ranging from simple steatosis to steatohepatitis to fibrosis and cirrhosis, with both hepatic and extrahepatic implications, including cardiovascular disease. The possibility of MASLD should be considered in high-risk groups such as T2DM, obesity, metabolic syndrome, polycystic ovarian syndrome (PCOS), etc., with/ without evidence of hepatic steatosis on imaging or persistently raised liver enzymes. Initial assessment should include a comprehensive evaluation of comorbidities, exclusion of secondary causes of steatosis, and fibrosis risk stratification. The presence of NASH and fibrosis is the most important predictor of adverse clinical outcomes, and non-invasive risk assessment has been an area of intense research in recent decades. FIB-4 can be used as a primary tool to effectively rule out significant fibrosis, followed by vibration-controlled transient elastography (VCTE) or enhanced liver fibrosis (ELF) tests as secondary tools if the FIB-4 score is indeterminate or high. The management of MASLD patients has shifted from a hepatocentric to a multicentric approach, with a focus on lifestyle interventions, disease-modifying pharmacotherapy in selected patients, and optimized management of comorbidities and complications.

Kenya Psychosis-Risk Outcomes Study (KePROS): Development of an Accelerated Medicine Partnership Schizophrenia (AMP SCZ)-Aligned Project in Africa

Abstract Background and Hypothesis The Accelerating Medicines Partnership Schizophrenia (AMP SCZ) funds a longitudinal study of 43 research sites across 5 continents to develop tools to stratify developmental trajectories of youth at clinical high risk for psychosis (CHR) and identify homogenous targets for future clinical trials. However, there are no sites in Africa, leaving a critical gap in our knowledge of clinical and biological outcomes among CHR individuals. Study Design We describe the development of the Kenya Psychosis-Risk Outcomes Study (KePROS), a five-year NIH-funded project in Kenya designed to harmonize with AMP SCZ. The study will recruit over 100 CHR and 50 healthy participants and conduct multiple clinical and biomarker assessments over two years. Capacity building is a key component of the study, including the construction of an EEG laboratory and the upgrading of a local 3T MRI machine. We detail community recruitment, study methodologies and protocols, and unique challenges with this pioneering research in Africa. Study Results This paper is descriptive only. Planned future analyses will investigate possible predictors of clinical outcomes and will be compared to results from other global populations. Conclusions KePROS will provide the research community with a rich longitudinal clinical and biomarker dataset from an African country in the developing Global South, which can be used alongside AMP SCZ data to delineate CHR outcome groups for future treatment development. Training in mental health assessment and investment in cutting-edge biomarker assessment and other technologies is needed to facilitate the inclusion of African countries in large-scale research consortia.

Abstract PO4-17-06: Real World Data: A single institute experience with neoadjuvant endocrine therapy for ER+ breast cancer — clinical response rates, clinical predictors of response and long-term outcomes following breast conserving surgery

Abstract Background: Neoadjuvant endocrine therapy (NET) in postmenopausal women (PMW) with large or locally advanced oestrogen receptor (ER)-rich breast cancer allows more women to be treated by breast-conserving surgery (BCS). A comprehensive analysis of the factors affecting clinical response to NET and the long-term safety of this strategy has been studied. Patients: A retrospective cohort was studied of 435 PMW (median age: 77 years; range: 50-98) with ER-positive breast cancer treated with NET (aromatase inhibition) (median duration: 6.8 months; range 3-43 months), between 2001-15. Clinical response was monitored throughout NET treatment with periodic 3D ultrasound measurements of the primary tumour. All patients went on to have surgery, 55% had adjuvant radiotherapy and all had adjuvant ET for at least 5 years. All patients had routine follow-up (median follow-up 12.3 years). To-date, this represents the largest cohort of patients treated with &amp;gt;3 months NET. Results: Clinical response to NET was excellent, with only 3.2% of tumours progressing on-treatment based on RECIST 1.1 criteria. Mean percentage reduction in tumour on NET was 71%. Response rate (partial/complete response) was 59% and response continued until 12 months and then levelled off. Following NET, over 92% of patients were suitable for BCS and 61% achieved clinical TNM down-staging. Multivariate analysis revealed that only ER level (response highest in patients with Allred scores 7 and 8), and percentage change in tumour volume by 6 weeks were significant predictors of overall clinical response to NET. Clinical response to NET was not associated with node status or tumour grade. BCS following NET had a local disease control rate of 89% (95%CI±0.06) at 10 years, but actuarial local recurrence at 10 years was only 7% in those having radiotherapy compared with 30% in the group who did not receive radiotherapy (P&amp;lt; 0.0001). In addition, the overall recurrence rate (P=0.028) was improved by radiotherapy irrespective of nodal status. Radiotherapy did not improve overall breast cancer-specific survival (BCSS). Systemic recurrence and BCSS was higher with greater nodal involvement. Most patients in this study died of other causes (14-year crude all-cause mortality was 71%). Conclusion: NET followed by BCS is an effective strategy in PMW with ER-rich breast cancer. Excellent clinical response rates are seen even in women with node-positive or high-grade disease. Less than 5% showed evidence of progression. Early response to NET predicts for long-term tumour volume reduction and NET can achieve continued tumour shrinkage for up to 1 year. NET followed by BCS appears a safe treatment approach and offers good long-term outcomes when BCS is followed by adjuvant radiotherapy. Citation Format: Arran K Turnbull, Carlos Martinez-Perez, Charlene Kay, Rebecca Swan, Lorna Renshaw, J Michael Dixon. Real World Data: A single institute experience with neoadjuvant endocrine therapy for ER+ breast cancer — clinical response rates, clinical predictors of response and long-term outcomes following breast conserving surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-17-06.

Abstract PO1-07-04: Multiomics analysis reveals the landscape of PD-L1 expression in triple-negative breast cancer

Abstract Background: Tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) remain controversial in predicting clinical outcomes of triple-negative breast cancer (TNBC), as outcomes do not always correlate with the expression of these biomarkers. Genomic and transcriptomic alterations that might contribute to the expression of these biomarkers remain incompletely uncovered. Methods: We evaluated PD-L1 immunohistochemistry (IHC) scores (SP142 and 28-8) and TILs in our TNBC multiomics dataset and two immunotherapy clinical trial cohorts. Then, we analyzed genomic and transcriptomic alterations correlated with TILs, PD-L1 expression and patient outcomes. Results: Despite TILs serving as a decent predictor for TNBC clinical outcomes, there remained exceptions. Our study revealed that several genomic alterations were correlated with unexpected events. Particularly, PD-L1 expression may cause a paradoxical relationship between TILs and prognosis in certain patients. Consequently, we classified TNBCs into four groups based on PD-L1 and TIL levels. The TIL-PD-L1+ and TIL+PD-L1- groups were not typical “hot” tumors; both were associated with worse prognoses and lower immunotherapy efficacy than TIL+PD-L1+ tumors. Copy number variation of PD-L1 and oncogenic signaling activation were correlated with PD-L1 expression in the TIL-PD-L1+ group, whereas GSK3B-induced degradation might cause undetectable PD-L1 expression in the TIL+PD-L1- group. These factors have the potential to affect the predictive function of both PD-L1 and TILs. Conclusions: Several genomic and transcriptomic alterations may cause paradoxical effects among TILs, PD-L1 expression and prognosis in TNBC. Investigating and targeting these factors will advance precision immunotherapy for TNBC patients. Keywords: Triple-negative breast cancer, PD-L1 expression, Tumor-infiltrating lymphocytes, Multiomics, Immunotherapy Citation Format: Han Wang, Xiao-Hong Ding, Cheng-Lin Liu, Yi Xiao, Ruo-Hong Shui, Yan-Ping Li, Chen Chen, Wen-Tao Yang, Zhi-Ming Shao, Yi-Zhou Jiang. Multiomics analysis reveals the landscape of PD-L1 expression in triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-04.