Prognostic Score Research Articles

The efficacy of different biomarkers and endpoints to refine referrals for suspected prostate cancer: the TARGET study (Tiered integrAted tests for eaRly diaGnosis of clinically significant ProstatE Tumours).

The majority of men referred with a raised PSA for suspected prostate cancer will receive unnecessary tertiary investigations including MRI and biopsy. Here, we compared different types of biomarkers to refine tertiary referrals and when different definitions of clinically significant cancer were used. Data and samples from 798 men referred for a raised PSA (≥ 3ng/mL) and investigated through an MRI-guided biopsy pathway were accessed for this study. Bloods were acquired pre-biopsy for liquid biomarkers and germline DNA. Variables explored included PSA + Age (base model), free/total PSA (FTPSA), Prostate Health Index (phi), PSA density (PSAd), polygenic risk score (PRS) and MRI (≥ LIKERT 3). Different diagnostic endpoints for significant cancer (≥ grade group 2 [GG2], ≥ GG3, ≥ Cambridge Prognostic Group 2 [CPG2], ≥ CPG3) were tested. The added value of each biomarker to the base model was evaluated using logisticregression models, AUC and decision curve analysis (DCA) plots. The median age and PSA was 65years and 7.13ng/mL respectively. Depending on definition of clinical significance, ≥ grade group 2 (GG2) was detected in 57.0% (455/798), ≥ GG3 in 27.5% (220/798), ≥ CPG2 in 61.6% (492/798) and ≥ CPG3 in 42.6% (340/798). In the pre-MRI context, the PSA + Age (base model) AUC for prediction of ≥ GG2, ≥ GG3, ≥ CPG2 and ≥ CPG3 was 0.66, 0.68, 0.70 and 0.75 respectively. Adding phi and PSAd to base model improved performance across all diagnostic endpoints but was notably better when the composite CPG prognostic score was used: AUC 0.82, 0.82, 0.83, 0.82 and AUC 0.74, 0.73, 0.79, 0.79 respectively. In contrast, neither FTPSA or PRS scores improved performance especially in detection of ≥ GG3 and ≥ CPG3 disease. Combining biomarkers did not alter results. Models using phi and PSAd post-MRI also improved performances but again benefit varied with diagnostic endpoint. In DCA analysis, models which incorporated PSAd and phi in particular were effective at reducing use of MRI and/or biopsies especially for ≥ CPG3 disease. Incorporating phi or PSAd can refine and tier who is referred for tertiary imaging and/or biopsy after a raisedPSA test. Incremental value however varied depending on the definition of clinical significance and was particularly useful when composite prognostic endpoints are used.

Prognostic risk score and index including the platelet-to-lymphocyte ratio and lactate dehydrogenase in patients with metastatic or unresectable urothelial carcinoma treated with immune checkpoint inhibitors.

Avelumab and pembrolizumab are administered after platinum-based chemotherapy for the treatment of metastatic urothelial carcinoma. We explored the prognostic factors and risk scores for predicting the outcomes of metastatic or unresectable urothelial carcinoma at the start of treatment with immune checkpoint inhibitors. This retrospective study included patients with metastatic or unresectable urothelial carcinoma treated with avelumab or pembrolizumab after platinum-based chemotherapy between January 2017 and December 2022. Prognostic factors, including patient and tumor characteristics and blood data at the initiation of immune checkpoint inhibitor therapy, were examined. This study included 36 and 207 patients treated with avelumab and pembrolizumab, respectively, for metastatic or unresectable urothelial carcinoma. Eastern Cooperative Oncology Group performance status, presence of visceral metastases, platelet-to-lymphocyte ratio and lactate dehydrogenase levels were independent prognostic factors for predicting overall survival. The median overall survival of patients in the risk-score model was 58.5months (score zero), 27.9months (one), 13.1months (two) and 3.9months (three or higher). The C-index for overall survival was 0.718 for the newly developed risk score compared with 0.679 for the Bellmunt score and 0.703 for the Bellmunt-C-reactive protein score. Additionally, the C-index for overall survival using the immune prognostic index derived from lactate dehydrogenase and the platelet-to-lymphocyte ratio was 0.646 compared with 0.615 for the Lung Immune Prognostic Index. A risk score that includes the platelet-to-lymphocyte ratio and lactate dehydrogenase may serve as a useful model for predicting prognosis following the initiation of immune checkpoint inhibitors in patients with metastatic or unresectable urothelial carcinoma.

Accounting for Red Cell Distribution Width Improves Risk Stratification by Commonly Used Mortality/Deterioration Risk Scores in Adult Patients Hospitalized Due to COVID-19.

Higher red blood cell distribution width (RDW) levels have gained attention in the prognostication of many chronic metabolic and malignant diseases, as well as coronavirus disease 2019 (COVID-19). We aimed to evaluate whether accounting for RDW might contribute to risk stratification when added to commonly used risk scoring systems in adult COVID-19 patients. We retrospectively analyzed a cohort of 3212 non-critical COVID-19 patients hospitalized in a tertiary-level institution from March 2020 to June 2021. Admission RDW values were considered normal if they were ≤14.5% in males or ≤16.1% in females. The Modified Early Warning Score (MEWS), International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium score (ISARIC 4C), and Veterans Health Administration COVID-19 (VACO) index were evaluated as prognostic scores. RDW exceeded the upper limit in 628 (19.6%) of the patients. When RDW was accounted for, risks of the predicted outcomes were considerably different within the same MEWS, 4C score, and VACO index levels. The same patterns applied equally to patients who started, and those who did not start, remdesivir before deterioration. RDW may be a useful tool for stratifying risk when considered on top of commonly used prognostic scores in non-critical COVID-19 patients.

12234 Gα13 Promotes Clonogenic Growth By Increasing Tolerance To Oxidative Metabolic Stress In Prostate Cancer Cells

Abstract Disclosure: D. Wu: None. W. Lim: None. X. Chai: None. V.P. Seshachalam: None. S.A. Rasheed: None. S. Ghosh: None. P.J. Casey: None. Gα13 and Gα12 are members of the G12 family of Gα proteins that, along with their associated Gβγ subunits, mediate signaling from specific G protein-coupled receptors. Analyses of tumor specimens indicate that Gα13 expression correlates with patient survival in several solid cancers. We previously reported a role for Gα13 in cell migration and invasion in prostate cancer cell lines. Interestingly, patient data supports the notion that mitochondrial superoxide dismutase 2 (SOD2) correlates with prostate cancer risk and prognostic Gleason scores. Gα13 lower-expressing LNCaP cells also have lower SOD2 protein levels compared to the Gα13 higher-expressing PC3 cells. Hence, we explored the role of Gα13 in prostate tumorigenesis, and its effect on cellular processes such as cell survival and mitochondrial metabolism, in human prostate cancer cell lines PC3 and LNCaP. We stably knocked-down GNA13 expression in PC3 cells and overexpressed GNA13 in LNCaP cells. We found that Gα13 promoted anchorage-independent cell growth in PC3 and LNCaP cell lines, as assessed by soft agar colony formation, spheroid formation, and xenograft tumor growth. Whole-genome transcriptome analyses suggested that Gα13-regulated genes are functionally enriched in the mitochondria compartment and that GNA13 expression positively correlated to SOD2 transcript levels in PC3 cells. We found that silencing GNA13 sensitized PC3 cells to long-term oxidative metabolic stress when cultured in media containing non-glycolytic metabolites, namely galactose, glutamate plus malate, and succinate. Furthermore, Gα13 levels impacted the abundance of SOD2 protein in the mitochondria, as well as SOD2 promoter activity and mRNA expression. In addition, silencing GNA13 increased mitochondrial superoxide levels in PC3 cells when cultured in galactose medium, as assessed by MitoSOX staining and flow cytometry. Moreover, overexpression of SOD2 could rescue the effect of Gα13 loss on suppression of anchorage-independent cell growth in PC3 cells. Importantly, anchorage-independent cell growth was not rescued when a catalytically-inactive SOD2(Q167A) mutant was expressed. Likewise, stable knockdown of SOD2 suppressed the effect of overexpression of Gα13 on anchorage-independent cell growth in LNCaP cells. We propose a novel biological route of Gα13-mediated anchorage-independent growth and response to oxidative metabolic stress through regulation of SOD2 expression in prostate cancer cells. Given that SOD2 protein levels correlate with prostate cancer Gleason grade, identifying the upregulated GPCRs that signal through Gα13 in prostate cancer could lead to novel preventive or therapeutic strategies. Presentation: 6/1/2024

The role of 1q abnormalities in multiple myeloma: Genomic insights, clinical implications, and therapeutic challenges

Chromosome 1q copy number variations, collectively termed +1q, are 1 of the most common cytogenetic abnormalities in multiple myeloma. 1q abnormalities are associated with overexpression of a high-risk gene signature promoting cell proliferation, apoptosis resistance, genomic instability, and treatment resistance, and acquisition or expansion of +1q subclones mediate disease development and relapse. While there remains significant controversy as to whether the presence of +1q is itself an independent driver of poor prognosis or is simply a marker of other high-risk features, +1q has recently been incorporated into multiple prognostic scoring models as a new high-risk cytogenetic abnormality. In this review, we present possible underlying genetic mechanisms of high-risk disease in +1q myeloma, implications for subclonal development, its role in modifying the tumor microenvironment, current evidence for clinical significance in newly-diagnosed and relapsed patients, and current controversies in +1q classification and prognostication.

Identification of KLHL17 as a prognostic marker for prostate cancer based on single-cell sequencing and in vitro/in vivo experiments

BackgroundProstate cancer (PCa) is a leading cause of cancer-related mortality among men, characterized by significant heterogeneity that complicates diagnosis and treatment.Methods and resultsTo enhance our understanding of PCa, we utilized single-cell RNA sequencing (scRNA-seq) data to identify distinct malignant epithelial cell subpopulations and their molecular characteristics. By integrating scRNA-seq data with bulk RNA-seq data, we constructed a prognostic risk score model. The influence of key genes identified in the risk score on PCa was validated through both in vitro and in vivo experiments. Our study revealed eight unique malignant epithelial cell clusters, each exhibiting distinct molecular characteristics and biological functions. KEGG and GO enrichment analyses highlighted their involvement in various pathways. The prognostic risk score model demonstrated strong predictive power for patient outcomes, particularly in predicting progression-free survival (PFS). Notably, KLHL17, identified as a high-risk gene, was found to significantly impact PCa cell proliferation, migration, invasion, and apoptosis upon knockdown. This finding was further validated in vivo using a subcutaneous xenograft tumor model, where reduced KLHL17 expression led to decreased tumor growth.ConclusionOur research provides a comprehensive analysis of PCa heterogeneity and highlights KLHL17 as a potential therapeutic target.

Prognostic score-based model averaging approach for propensity score estimation

BackgroundPropensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis.MethodsWe conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment.ResultsThe simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates.DiscussionThe prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data.ConclusionsUsing the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

Analysis of treatment benefits and prognostic factors for posttransplant HCC recurrence in a large Euro-American-Asian cohort.

Posttransplant HCC recurrence significantly impacts survival, yet its management is challenging due to limited evidence. With recent advancements in HCC treatment, updated data on managing recurrent diseases are needed. In this retrospective study across 6 centers (2000-2022), we employed Cox proportional-hazards regression and log-rank tests to assess survival differences. A prognostic score model was developed to categorize patient survival. The efficacy of tyrosine kinase inhibitors was evaluated through propensity score matching. In our study, 431 of 3349 (14%) patients with HCC who underwent transplantation developed recurrence within a median interval of 18 (IQR: 9-32) months. One hundred forty-seven (34%) underwent curative-intent treatments, 207 (48%) received palliative treatments, and 77 (18%) were given best-supportive care. Patients undergoing curative-intent treatments had better survival from the time of recurrence with a median survival of 45 (95% CI: 36-63) months and 1/3/5-year survival of 90%/56%/43% compared to those receiving noncurative treatments (median: 11 [95% CI: 10-13] mo, 1/3/5-y survival of 46%/10%/7%, log-rank p < 0.001). Patients with recurrence diagnosed in the era 2018-2022 showed improved survival over the previous era (HR 0.64 [95% CI: 0.47-0.86]). Multivariable analysis identified 5 prognostic factors: ineligibility for curative-intent treatment (HR: 3.5 [95% CI: 2.7-4.6]), recurrence within 1 year (HR: 1.7 [95% CI: 1.3-2.1]), poor tumor differentiation (HR: 1.5 [95% CI: 1.1-1.9]), RETREAT score ≥3 (HR: 1.4 [95% CI: 1.1-1.8]), and alpha-fetoprotein at recurrence ≥400ng/mL (HR: 1.4 [95% CI: 1.1-1.9]). These factors contributed to a prognostic scoring system (0-9) that stratified patients into 3 prognosis groups. Both propensity score-matched analysis and multivariable regression indicated that lenvatinib was not statistically superior to sorafenib in terms of efficacy. Curative-intent treatments should be advocated for patients with posttransplant recurrence whenever possible. Prognostic factors linked to aggressive tumor biology significantly influence survival. Advancements in HCC management have improved survival outcomes over the past 5 years.

Causal effect estimation in survival analysis with high dimensional confounders.

With the ever advancing of modern technologies, it has become increasingly common that the number of collected confounders exceeds the number of subjects in a data set. However, matching based methods for estimating causal treatment effect in their original forms are not capable of handling high-dimensional confounders, and their various modified versions lack statistical support and valid inference tools. In this article, we propose a new approach for estimating causal treatment effect, defined as the difference of the restricted mean survival time (RMST) under different treatments in high-dimensional setting for survival data. We combine the factor model and the sufficient dimension reduction techniques to construct propensity score and prognostic score. Based on these scores, we develop a kernel based doubly robust estimator of the RMST difference. We demonstrate its link to matching and establish the consistency and asymptotic normality of the estimator. We illustrate our method by analyzing a dataset from a study aimed at comparing the effects of two alternative treatments on the RMST of patients with diffuse large B cell lymphoma.

The SpO2/FiO2 Ratio Combined with Prognostic Scores for Pneumonia and COVID-19 Increases Their Accuracy in Predicting Mortality of COVID-19 Patients

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study’s primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p &lt; 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p &lt; 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy.

Why effect sizes are systematically larger for progression-free survival than overall survival in cancer drug trials: prognostic scores as a way forward

Cancer drugs have accumulated the most approvals over the past years. Overall survival (OS) is considered the gold standard for cancer trial outcomes. However, its use has declined over the past years, in favor of surrogate endpoints, such as progression-free survival (PFS). PFS allows to assess outcomes earlier and, thus, accelerates approval of cancer drugs. Previous studies have demonstrated a poor correlation between PFS and OS. Using simulation models, we examined why PFS usually overestimates survival benefit. We created a publicly accessible web application that allows users to run the simulations with different parameter settings. Based on the findings, we propose that assessment of preliminary evidence should be based on a combination of OS result and prognostic scores that reflect the health status of surviving patients.

Positive Lymph Node Ratio as a new prognostic score in Geriatric patients with operated gastric cancer

ObjectiveThe pivotal prognostic determinant for recurrence and survival in surgically treated gastric cancer (GC) patients remains the lymph node status. Despite the adoption of D2 lymph node dissection as the standard approach in recent years, its association with increased morbidity in elderly patients raises concerns. This study aims to explore the prognostic significance of the Positive Lymph Node Ratio (PLNR) score in the context of recurrence and survival among elderly patients with surgically treated GC. Material and methodA retrospective review of files about surgically treated patients with GC was conducted. The prognostic impact of the PLNR score on overall survival (OS) was assessed through Receiver Operating Characteristic (ROC) analysis. ResultsThe cut-off value for the PLNR, determined through ROC analysis, was identified as 0.138. This value serves as a crucial threshold, as it distinguishes patients with a higher risk of poor outcomes. Patients with a PLNR score of 0.138 or below exhibited a median OS of 111 months, whereas those with a PLNR score above 0.138 had a significantly lower median OS of 22 months (p = 0.004). ConclusionOur findings revealed that the PLNR emerged as an independent predictor of survival and recurrence in patients undergoing GC resection.However, it's important to note that while valuable, the PLNR system has limitations. It does not encompass the T stage, a key factor in cancer staging. Therefore, it cannot be a direct substitute for the comprehensive information TNM staging provides. It should be used as a supplementary tool in predicting prognosis, particularly in elderly patients unsuitable for standard lymph node dissection.

Development of a Prognostic Risk Score to Predict Early Mortality in Incident Elderly Japanese Patients on Hemodialysis

Development of a Prognostic Risk Score to Predict Early Mortality in Incident Elderly Japanese Patients on Hemodialysis

Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study.

Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP. This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients. The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients. This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.

Upfront Management of Blastoid Variant Mantle Cell Lymphoma: Making the Case for 2nd/3rd-Generation Bruton Tyrosine Kinase Inhibitor-Based Therapies.

Blastoid variant-mantle cell lymphoma (BV-MCL) represents an aggressive subset of patients with no established standard of care treatment approach. We performed a retrospective analysis of the clinical characteristics and outcomes of 17 de novo BV-MCL patients treated at our institution between May 2009 and September 2023. In addition, we reviewed the literature supporting 2nd/3rd generation Bruton's Tyrosine-kinase (BTKi)-based therapies for upfront management. The complete response rate to frontline and salvage therapy was 66.7% and 25%, respectively. Two-year overall survival (OS) was low at 62.5% (95% CI, 34.7%-81.1%). Variables associated with higher OS included receipt of consolidative HSCT (p = 0.017), TP53-wild type (p = 0.031), intermediate- versus high-risk Mantle Cell Lymphoma Prognostic Index score (p = 0.026), and achieving complete response with induction therapy (p = 0.027). Treatment outcomes with chemo-immunotherapy in BV-MCL patients are poor, especially among TP53-mutated patients. Recent findings describing positive outcomes with novel BTKi-based therapies are encouraging and should be considered in this high-risk population.

Health care contact days in older adults enrolled in SWOG cancer clinical trials.

64 Background: Health care contact days—days with health care contact outside the home—are a measure of how much of a patient’s life is consumed by health care. This number of contact days are especially relevant for older adults with advanced cancer facing limited survival. One concern is that patients with different sociodemographic and clinical backgrounds may be at particular risk of increased contact days. Thus, there is a critical need to understand factors associated with contact days. Methods: We linked data from 6 SWOG trials in advanced cancer to Medicare claims data. These trials encompassed a variety of primary cancer types [prostate (S9916, S0421), lung (S0003, S0124, S0819), and pancreas (S0205)]. Health care contact days (days with ambulatory, emergency department, inpatient, or facility-based care) in the first 12 months were calculated using Medicare database files. A day with &gt;1 source of contact counted as 1. We examined sociodemographic (age, race, ethnicity, sex, insurance status, rurality, area deprivation index) and clinical (prognostic risk, cancer type, performance status) predictors of contact days, using negative binomial regression, with a log link and clustering by study ID, including an offset variable for duration of observation. We examined predictors in univariate and multivariable analysis. We report relative risk (RRs) estimates and 95% CI. Results: We included 1,429 patients (median age, 71 years, 7.6% Black, 2.7% Hispanic, 21.4% female, and 23.5% rural residence). Of these, 741 (51.9%) patients died in the first year. The median number of contact days was 48, out of a median of 350 days of observation. At a trial level, median percentage of contact days ranged from 14% (prostate) to 27% (lung). The most common sources of contact days were ambulatory clinician visits (median=17), tests (median=12), and treatments (median=11). In multivariable regression, factors associated with increased contact days included increasing age (per year, RR=1.02 [1.01-1.02], p&lt;.001), insurance status (Medicare alone or with Private or Medicaid insurance, vs Military/VA, none, or other insurance, RR=2.47 [2.16-2.83], p&lt;.001), higher prognostic risk score (RR=1.14 [1.04-1.25], p=.004), and type of cancer (pancreas, RR=1.69 [1.51-1.89] and lung, RR=1.69 [1.54-1.85] vs prostate, p&lt;.001 for both). Conclusions: This novel linkage of trial and claims data to evaluate contact days and associated factors found that older trial participants spent up to one in four days with health care contact. Ambulatory appointments were the most common source of contact days. Encouragingly, rural residence and neighborhood deprivation were not associated with contact days, increasing confidence that participants from diverse geographic areas may receive equitable care in trials. These data highlight the frequency of contact days for trial participants, while identifying factors to consider when benchmarking contact days.

S1950 A Comparative Study of Prognostic Scores for Predicting 28-Day Mortality in Patients Having Acute on Chronic Liver Failure

S1950 A Comparative Study of Prognostic Scores for Predicting 28-Day Mortality in Patients Having Acute on Chronic Liver Failure

TCT-150 Impact of Naples Prognostic Score on Clinical Outcomes After Transcatheter Aortic Valve Replacement in Patients With Severe Aortic Stenosis: A Systematic Review and Meta-Analysis With Reconstructed Time-to-Event Data

TCT-150 Impact of Naples Prognostic Score on Clinical Outcomes After Transcatheter Aortic Valve Replacement in Patients With Severe Aortic Stenosis: A Systematic Review and Meta-Analysis With Reconstructed Time-to-Event Data

A Predictive Model for Disseminated Intravascular Coagulopathy in Sepsis: An Observational Study.

Sepsis remains a significant global health challenge due to its high morbidity and mortality rates. Disseminated Intravascular Coagulopathy (DIC) represents a critical complication of sepsis, contributing to increased mortality and economic burden. Despite various prognostic scoring systems, there is a lack of a specific model for DIC prediction in sepsis patients. This observational study included 336 sepsis patients. Clinical and laboratory data were collected, and prognoses were defined according to established criteria. We enrolled 336 patients, with 304 in the non-DIC group and 32 in the DIC group. Patients with DIC had notably lower platelet (PLT) and higher levels of prothrombin time (PT), lactate (LAC), and procalcitonin (PCT) compared to those without DIC. Univariate and multivariate analyses identified risk factors associated with the DIC, showing that PLT (OR = 0.985, 95% CI 0.978-0.993, p < 0.001), PT level (OR = 1.140, 95% CI 1.004-1.295, p = 0.044), and LAC (OR = 1.101, 95% CI 0.989-1.226, p = 0.078) were related factors. A risk model was established, and its sensitivity and specificity in predicting DIC among sepsis patients were assessed by comparing it to the SOFA score. The area under the ROC curve for the model was 0.850, while the SOFA score was 0.813. With a model score >-2.12, the sensitivity for predicting DIC was 84.4%, and the specificity was 75.0%. Our study introduces a predictive model for DIC detection in sepsis patients, emphasizing the need for clinicians to focus on patients with high model scores for timely intervention.

TCT-809 Examining Prognostic Scores for Short-Term Major Adverse Cardiovascular Events Prediction After Primary PCI: A Comparative Analysis

TCT-809 Examining Prognostic Scores for Short-Term Major Adverse Cardiovascular Events Prediction After Primary PCI: A Comparative Analysis