Stratification Model Research Articles

Expert Perspectives on Controversies in Castration-Sensitive Prostate Cancer Management: Narrative Review and Report of the First US Prostate Cancer Conference Part 1.

Castration-sensitive prostate cancer (CSPC) is a complex and heterogeneous condition encompassing a range of clinical presentations. As new approaches have expanded management options, clinicians are left with myriad questions and controversies regarding the optimal individualized management of CSPC. The US Prostate Cancer Conference (USPCC) multidisciplinary panel was assembled to address the challenges of prostate cancer management. The first annual USPCC meeting included experts in urology, medical oncology, radiation oncology, and nuclear medicine. USPCC co-chairs and session moderators identified key areas of controversy and uncertainty in prostate cancer management and organized the sessions with multidisciplinary presentations and discussion. Throughout the meeting, experts responded to questions prepared by chairs and moderators to identify areas of agreement and controversy. The USPCC panel discussion and question responses for CSPC-related topics are presented. Key advances in CSPC management endorsed by USPCC experts included the development and clinical utilization of gene expression classifiers and artificial intelligence (AI) models for risk stratification and treatment selection in specific patient populations, the use of advanced imaging modalities in patients with clinically localized unfavorable intermediate or high-risk disease and those with biochemical recurrence, recommendations of doublet or triplet therapy for metastatic CSPC (mCSPC), and consideration of prostate and/or metastasis-directed radiation therapy in select patients with mCSPC. CSPC is a diverse disease with many therapeutic options and the potential for adverse outcomes associated with either undertreatment or overtreatment. Future studies are needed to validate and clinically integrate novel technologies, including genomics, AI, and advanced imaging, to optimize outcomes among patients with CSPC.

Circulating tumor plasma cells and peripheral blood measurable residual disease assessment in multiple myeloma patients not planned for upfront transplant.

Circulating tumor plasma cells (CTPCs) provide a noninvasive alternative for measuring tumor burden in newly diagnosed multiple myeloma (NDMM). Moreover, measurable residual disease (MRD) assessment in peripheral blood (PBMRD) can provide an ideal alternative to bone marrow MRD, which is limited by its painful nature and technical challenges. However, the clinical significance of PBMRD in NDMM still remains uncertain. Additionally, data on CTPC in NDMM patients not treated with transplant arescarce. We prospectively studied CTPC and PBMRD in 141 NDMM patients using highly sensitive multicolor flow cytometry (HS-MFC). PBMRD was monitored at the end of three cycles (PBMRD1) and six cycles (PBMRD2) of chemotherapy in patients with detectable baseline CTPC. Patients received bortezomib-based triplet therapy and were not planned for an upfront transplant. Among baseline risk factors, CTPC ≥ 0.01% was independently associated with poor progression-free survival (PFS) (hazard ratio [HR] = 2.77; p = 0.0047) and overall survival (OS) (HR = 2.9; p = 0.023) on multivariate analysis. In patients with detectable baseline CTPC, undetectable PBMRD at both subsequent time points was associated with longer PFS (HR = 0.46; p = 0.0037), whereas detectable PBMRD at any time point was associated with short OS (HR = 3.25; p = 0.004). Undetectable combined PBMRD (PBMRD1 and PBMRD2) outperformed the serum-immunofixation-based response. On multivariate analysis, detectable PBMRD at any time point was independently associated with poor PFS (HR = 2.0; p = 0.025) and OS (HR = 3.97; p = 0.013). Thus, our findings showed that CTPC and PBMRD assessment using HS-MFC provides a robust, noninvasive biomarker for NDMM patients not planned for an upfront transplant. Sequential PBMRD monitoring has great potential to improve the impact of the existing risk stratification and response assessment models.

Smoking characteristics and years since quitting smoking of US adults diagnosed with lung and bladder cancer: A national health and nutrition examination survey analysis.

Smoking is a recognized risk factor for bladder BC and lung cancer LC. We investigated the enduring risk of BC after smoking cessation using U.S. national survey data. Our analysis focused on comparing characteristics of LC and BC patients, emphasizing smoking status and the latency period from smoking cessation to cancer diagnosis in former smokers. We analyzed data from the National Health and Examination Survey (2003-2016), identifying adults with LC or BC history. Smoking status (never, active, former) and the interval between quitting smoking and cancer diagnosis for former smokers were assessed. We reported descriptive statistics using frequencies and percentages for categorical variables and median with interquartile ranges (IQR) for continuous variables. Among LC patients, 8.9% never smoked, 18.9% active smokers, and 72.2% former smokers. Former smokers had a median interval of 8 years (IQR 2-12) between quitting and LC diagnosis, with 88.3% quitting within 0-19 years before diagnosis. For BC patients, 26.8% never smoked, 22.4% were active smokers, and 50.8% former smokers. Former smokers had a median interval of 21 years (IQR 14-33) between quitting and BC diagnosis, with 49.3% quitting within 0-19 years before diagnosis. BC patients exhibit a prolonged latency period between smoking cessation and cancer diagnosis compared to LC patients. Despite smoking status evaluation in microhematuria, current risk stratification models for urothelial cancer do not incorporate it. Our findings emphasize the significance of long-term post-smoking cessation surveillance and advocate for integrating smoking history into future risk stratification guidelines.

Development of a Multivariable Model to Predict the Need for Bone Marrow Sampling in Persons With Monoclonal Gammopathy of Undetermined Significance : A Cohort Study Nested in a Clinical Trial.

Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) are asymptomatic precursor conditions to multiple myeloma and related disorders. Smoldering multiple myeloma is distinguished from MGUS by 10% or greater bone marrow plasma cells (BMPC) on sampling, has a higher risk for progression, and requires specialist management. To develop a multivariable prediction model that predicts the probability that a person with presumed MGUS has 10% or greater BMPC (SMM or worse by bone marrow criteria) to inform the decision to obtain a bone marrow sample and compare its performance to the Mayo Clinic risk stratification model. iStopMM (Iceland Screens, Treats or Prevents Multiple Myeloma), a prospective population-based screening study of MGUS. (ClinicalTrials.gov: NCT03327597). Icelandic population of adults aged 40 years or older. 1043 persons with IgG, IgA, light-chain, and biclonal MGUS detected by screening and an interpretable bone marrow sample. Monoclonal gammopathy of undetermined significance isotype; monoclonal protein concentration; free light-chain ratio; and total IgG, IgM, and IgA concentrations were used as predictors. Bone marrow plasma cells were categorized as 0% to 4%, 5% to 9%, 10% to 14%, or 15% or greater. The c-statistic for SMM or worse was 0.85 (95% CI, 0.82 to 0.88), and calibration was excellent (intercept, -0.07; slope, 0.95). At a threshold of 10% predicted risk for SMM or worse, sensitivity was 86%, specificity was 67%, positive predictive value was 32%, and negative predictive value was 96%. Compared with the Mayo Clinic model, the net benefit for the decision to refer for sampling was between 0.13 and 0.30 higher over a range of plausible low-risk thresholds. The prediction model will require external validation. This accurate prediction model for SMM or worse was developed in a population-based cohort of persons with presumed MGUS and may be used to defer bone marrow sampling and referral to hematology. International Myeloma Foundation and the European Research Council.

Frequency and Clinical Implications of Atrial Fibrillation in Patients with Accessory Pathways

Abstract:
 Atrial fibrillation (AF) is a common arrhythmia associated with various cardiac conditions. This study investigates the frequency of AF in patients with accessory pathways, anomalous electrical connections that can lead to supraventricular tachycardia.
 Objective:
 The primary objectives are to determine the prevalence of AF in patients with accessory pathways, identify associated factors, and assess the impact on clinical outcomes. A comprehensive dataset of 400 patients undergoing supraventricular tachycardia ablation at Hayatabad Medical Complex from 2020 to 2022 was analyzed.
 Method:
 A retrospective study design was employed, utilizing patient records with documented accessory pathway(s) and complete clinical-electrophysiological data. Demographic information, clinical presentation, electrocardiographic and electrophysiological data, ablation procedures, and clinical outcomes were systematically collected. Analytical approaches included frequency calculation, comparative analysis, regression analysis, and outcome assessment.
 Result:
 Out of 400 patients, 3.5% presented with AF at the time of accessory pathway ablation. Age was identified as a significant predictor of AF occurrence, with a mean age of 63.2 years in AF cases compared to 55.8 years in non-AF cases. Recurrence of arrhythmias was observed in 57.1% of patients with AF, emphasizing the need for ongoing monitoring and management.
 Conclusion:
 This study contributes valuable insights into the relatively low prevalence of AF in patients with accessory pathways. While certain factors, including age, may be associated with AF, they do not serve as definitive predictors. Tailored management and adherence to guidelines are crucial for optimizing outcomes in this unique patient population. Future research may explore genetic factors and refine risk stratification models for a deeper understanding of AF in patients with accessory pathways.

Development and internal validation of a nomogram based on peripheral blood inflammatory markers for predicting prognosis in nasopharyngeal carcinoma.

Inflammatory markers, including the product of neutrophil count, platelet count, and monocyte count divided by lymphocyte count (PIV) and the platelet-to-white blood cell ratio (PWR), have not been previously reported as prognostic factors in nasopharyngeal carcinoma (NPC) patients. In order to predict overall survival (OS) in NPC patients, our goal was to create and internally evaluate a nomogram based on inflammatory markers (PIV, PWR). A retrospective study was done on patients who received an NPC diagnosis between January 2015 and December 2018. After identifying independent prognostic indicators linked to OS using Cox proportional hazards regression analysis, we created a nomogram with the factors we had chosen. A total of 630 NPC patients in all were split into training (n = 441) and validation sets (n = 189) after being enrolled in a population-based study in 2015-2018 and monitored for a median of 5.9 years. In the training set, the age, PIV, and PWR, selected as independent predictors for OS via multivariate Cox's regression model, were chosen to develop a nomogram. Both training and validation cohorts had C-indices of 0.850 (95% confidence interval [CI]: 0.768-0.849) and 0.851 (95% CI: 0.765-0.877). Furthermore, compared with traditional TNM staging, our nomogram demonstrated greater accuracy in predicting patient outcomes. The risk stratification model derived from our prediction model may facilitate personalized treatment strategies for NPC patients. Our findings confirmed the prognostic significance of the PWR and PIV in NPC. High PIV levels (>363.47) and low PWR (≤36.42) values are associated with worse OS in NPC patients.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network.

Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies. https://github.com/amazingma/GraphPath.

Commentary on'risk stratification model integrating nutritional and inflammatory factors for predicting 1-year mortality after valvular heart surgery: a retrospective cohort study'.

Commentary on'risk stratification model integrating nutritional and inflammatory factors for predicting 1-year mortality after valvular heart surgery: a retrospective cohort study'.

VLDL and LDL Subfractions Enhance the Risk Stratification of Individuals Who Underwent Epstein-Barr Virus-Based Screening for Nasopharyngeal Carcinoma: A Multicenter Cohort Study.

Serological tests for Epstein-Barr virus (EBV) antibodies have been widely conducted for the screening of nasopharyngeal carcinoma (NPC) in endemic areas. Further risk stratification of NPC can be achieved through plasma lipoprotein and metabolic profiles. A total of 297 NPC patients and 149 EBV-positive participants are enrolled from the NCT03919552 and NCT05682703 cohorts for plasma nuclear magnetic resonance (NMR) metabolomic analysis. Small, dense very low density lipoprotein particles (VLDL-5) and large, buoyant low density lipoprotein particles (LDL-1) are found to be closely associated with nasopharyngeal carcinogenesis. Herein, an NMR-based risk score (NRS), which combines lipoprotein subfractions and metabolic biomarkers relevant to NPC, is developed and well validated within a multicenter cohort. Combining the median cutoff value of the NRS (N50) with that of the serological test for EBV antibodies, the risk stratification model achieves a satisfactory performance in which the area under the curve (AUC) is 0.841 (95% confidence interval: 0.811-0.871), and the positive predictive value (PPV) reaches 70.08% in the combined cohort. These findings not only suggest that VLDL-5 and LDL-1 particles can serve as novel risk factors for NPC but also indicate that the NRS has significant potential in personalized risk prediction for NPC.

Abstract 2560: Genomic biomarkers of survival in patients (pts) with metastatic castrate-sensitive prostate cancer (mCSPC) undergoing androgen deprivation therapy (ADT) intensification (ADTi)

Abstract Background: ADTi with androgen receptor pathway inhibitors (ARPI) with or without docetaxel improves survival outcomes in pts with mCSPC. Even with ADTi, 20% of these pts progress within a year and experience poor overall survival (OS). Identifying baseline genomic biomarkers predicting poor survival outcomes in pts receiving ADTi remains an unmet need. Methods: In this IRB-approved retrospective study, pts with a new diagnosis of mCSPC who underwent comprehensive genomic profiling of primary prostate tissue or metastatic site or cell-free DNA and treated with ADTi (ADT with ARPI or docetaxel) were included. Genomic alterations with an incidence > 5% were included in the analysis. Study endpoints: progression-free survival (PFS) was defined from the start of therapy for mCSPC to progression (per PCWG-2) or death from any cause. OS was defined from the start of therapy for mCSPC to the date of death from any cause or censored at the last follow-up. A multivariable analysis using the Cox proportional hazards model was used, adjusting for age at mCSPC diagnosis, disease volume (high vs. low), Gleason score (≥ 8 vs. < 8), Log2PSA at baseline, and initial timing of metastasis (de novo vs. non de novo). A subgroup analysis by intensification treatment received (ARPI vs. docetaxel) was also performed. Missing baseline characteristics such as volume of disease, Gleason score, and Log2PSA were multiply imputed on 50 chained equations using “mice” package in R. All the analyses were done using R version 4.3.1. Results: A total of 289 pts were included in the study. The baseline characteristics of the cohort will be presented in the meeting. The alterations included in the analysis were TP53 (31.8%), TMPRSS2 (30.4%), PTEN (17%), APC (10.4%), SPOP (9%), RB1 (6.2%), BRCA2 (5.2%), and CDK12 (5.2%). In the multivariable analysis, TP53 (HR 1.87, 95% CI 1.29-2.72, p = 0.001), RB1 (HR 2.56, 95% CI 1.45-4.53, p = 0.001), PTEN (HR 1.77, 95% CI 1.15-2.72, p = 0.009), and BRCA2 (HR 2.68, 95% CI 1.47-4.89, p = 0.002) were associated with significantly shorter PFS while TP53 (HR 1.78, 95% CI 1.1-2.88, p = 0.019), RB1 (HR 4.78, 95% CI 2.53-9.02, p < 0.001), and PTEN (HR 2.37, 95% CI 1.37-4.1, p = 0.003) were associated with significantly worse OS. Results of the subgroup analysis by intensification treatment (ARPI vs. docetaxel) will also be presented in the meeting. Conclusions: In this real-world study, we identified genomic biomarkers that could be associated with shorter PFS and OS in pts with mCSPC treated with ADTi. Upon external validation, these results may aid in developing a clinical-genomic risk stratification model, patient counseling, and treatment selection. Citation Format: Arshit Narang, Georges Gebrael, Chadi Hage Chehade, Nicolas Sayegh, Yeonjung Jo, Beverly Chigarira, Nishita Tripathi, Ayana Srivastava, Clara Tandar, Benjamin L. Maughan, Umang Swami, Neeraj Agarwal. Genomic biomarkers of survival in patients (pts) with metastatic castrate-sensitive prostate cancer (mCSPC) undergoing androgen deprivation therapy (ADT) intensification (ADTi) [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 2560.

Abstract 6250: The RNA editing landscape of lung cancer in never smokers

Abstract Adenosine-to-Inosine (A-to-I) editing represents a crucial RNA modification mechanism, holding the potential to drive carcinogenesis. This sophisticated post-transcriptional process is catalyzed by a series of ADAR (adenosine deaminase acting on RNA) family members, which can profoundly reshape the cellular epitranscriptomic landscape. While previous explorations have illuminated the important role of A-to-I editing in lung cancer tumorigenesis and prognosis, most studies have focused on lung cancer from smokers. To investigate the contribution on RNA-editing in lung cancers in never smokers (LCINS) , we embarked on a rigorous dissection of A-to-I RNA editing events in 603 LCINS. Harnessing transcriptome and whole-genome sequencing technologies, we identified 15,880 A-to-I sites, of which 75% were positively linked to the enzymatic activity of ADAR1. 92.80% of detected editing sites were located at Alu repetitive regions, with the remainder in non_Alu repeat (4.1%) and non-repeat regions (3.1%). The global RNA editing levels, measured as Alu Editing Index (AEI), were found to be significantly associated with ADAR1 expression, Tumor Mutational Burden, and Neoantigens Burden. A subtle correlation between global RNA editing levels and tumor stage was also noted. Delving into cancer-associated editing events, we identified 5,539 differentially edited sites in tumors compared with normal tissues, which were located at 823 genes, including several key tumor suppressors and oncogenes, like MDM2, BRAF, TP53, BRCA2, ATM, and VHL. Subsequent pathway analysis identified TGF-beta, Notch Signaling, and DNA Damage Response as the key pathways for cancer progression. Using the characterized A-to-I editing profiles, we built a prognostic risk stratification model, with high risk scores significantly linked to poor survival outcomes, even after adjusting for other clinical factors. Our study provides a comprehensive A-to-I editing landscape of lung cancer in never smokers, highlighting the potential roles of RNA modifications in the pathogenesis of this lethal disease. Citation Format: Jian Sang, Wei Zhao, Tongwu Zhang, Maria Teresa Landi. The RNA editing landscape of lung cancer in never smokers [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 6250.

Abstract 7642: Activated FAP+ cancer-associated fibroblasts in non-muscle invasive bladder cancer are associated with progression to muscle-invasive disease

Abstract The majority of patients with bladder cancer initially present with non-muscle-invasive disease (NMIBC). While NMIBC may be cured with transurethral resection +/- intravesical therapies, approximately 20% of patients develop progression to muscle-invasive bladder cancer (MIBC) which requires life-altering treatments such as cystectomy and has a high risk of lethal metastatic recurrence. Current risk stratification for NMIBC relies on clinicopathologic features that overlook the potential role of the tumor microenvironment (TME) in bladder cancer pathogenesis. We sought to delineate the stromal and immune landscape alterations within the TME upon bladder cancer progression and to identify TME-related features that could enhance current prognostic approaches. Analyzing by single-cell RNA sequencing and multiplexed imaging of 22 untreated NMIBC and MIBC specimens, coupled with publicly available datasets, we discovered significant transcriptional variability among cancer-associated fibroblasts (CAFs) and macrophages across patients. This inter-patient variability was attributable to the extensive remodeling of these cell types during cancer progression. Notably, a specific subset of CAF, characterized by ITGA8+F3+ markers, was found to be highly enriched in NMIBC lesions and transcriptionally related to sub-urothelial fibroblasts of the healthy bladder, whereas a FAP+ CAF subset enriched for core matrisome related genes was dominant in MIBC lesions. We developed a FAP+ CAF signature which was then projected onto a large public dataset of bulk-RNAseq data from patients with NMIBC demonstrating that the FAP+ CAF signature was significantly associated with progression to MIBC independently of contemporary risk stratification scores. The presence of FAP+ CAFs in a subset of NMIBC specimens was orthogonally validated using immunohistochemistry. Importantly, areas with FAP staining were observed in close contact with individual or small groups of tumor cells exhibiting an invasive phenotype and partial epithelial-mesenchymal transition, likely explaining the higher risk of progression to MIBC. Our study improves our understanding of fibroblasts remodeling during bladder cancer progression and highlights the potential of integrating CAF-derived biomarkers into NMIBC risk stratification models. Citation Format: Abdenour Abbas, Shrisha Maskey, Evan Cheng, Ziao Li, Koushik Ganesh, Sudeh Izadmehr, Serena Janho dit Hreich, Benjamin Demaille, Jeremy Mesple, Philemon Sirven, Clementine Krucker, Jacqueline Fontugne, Isabelle Bernard-Pierrot, Yves Allory, Miriam Merad, Julien Adam, John Sfakianos, Ephraim Kenigsberg, Matthew Galsky, Helene Salmon. Activated FAP+ cancer-associated fibroblasts in non-muscle invasive bladder cancer are associated with progression to muscle-invasive disease [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 7642.

Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy

Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients’ preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71–0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Molecular Pathology of Pancreatic Cystic Lesions with a Focus on Malignant Progression.

The malignant progression of pancreatic cystic lesions (PCLs) remains understudied with a knowledge gap, yet its exploration is pivotal for effectively stratifying patient risk and detecting cancer at its earliest stages. Within this review, we delve into the latest discoveries on the molecular level, revealing insights into the IPMN molecular landscape and revised progression model, associated histologic subtypes, and the role of inflammation in the pathogenesis and malignant progression of IPMN. Low-grade PCLs, particularly IPMNs, can develop into high-grade lesions or invasive carcinoma, underscoring the need for long-term surveillance of these lesions if they are not resected. Although KRAS and GNAS remain the primary oncogenic drivers of neoplastic development in IPMNs, additional genes that are important in tumorigenesis have been recently identified by whole exome sequencing. A more complete understanding of the genes involved in the molecular progression of IPMN is critical for effective monitoring to minimize the risk of malignant progression. Complicating these strategies, IPMNs are also frequently multifocal and multiclonal, as demonstrated by comparative molecular analysis. Algorithms for preoperative cyst sampling and improved radiomic techniques are emerging to model this spatial and temporal genetic heterogeneity better. Here, we review the molecular pathology of PCLs, focusing on changes associated with malignant progression. Developing models of molecular risk stratification in PCLs which can complement radiologic and clinical features, facilitate the early detection of pancreatic cancer, and enable the development of more personalized surveillance and management strategies are summarized.

Developing Novel Genomic Risk Stratification Models in Soft Tissue and Uterine Leiomyosarcoma.

Leiomyosarcomas (LMS) are clinically and molecularly heterogeneous tumors. Despite recent large-scale genomic studies, current LMS risk stratification is not informed by molecular alterations. We propose a clinically applicable genomic risk stratification model. We performed comprehensive genomic profiling in a cohort of 195 soft tissue LMS (STLMS), 151 primary at presentation, and a control group of 238 uterine LMS (ULMS), 177 primary at presentation, with at least 1-year follow-up. In STLMS, French Federation of Cancer Centers (FNCLCC) grade but not tumor size predicted progression-free survival (PFS) or disease-specific survival (DSS). In contrast, in ULMS, tumor size, mitotic rate, and necrosis were associated with inferior PFS and DSS. In STLMS, a 3-tier genomic risk stratification performed well for DSS: high risk: co-occurrence of RB1 mutation and chr12q deletion (del12q)/ATRX mutation; intermediate risk: presence of RB1 mutation, ATRX mutation, or del12q; low risk: lack of any of these three alterations. The ability of RB1 and ATRX alterations to stratify STLMS was validated in an external AACR GENIE cohort. In ULMS, a 3-tier genomic risk stratification was significant for both PFS and DSS: high risk: concurrent TP53 mutation and chr20q amplification/ATRX mutations; intermediate risk: presence of TP53 mutation, ATRX mutation, or amp20q; low risk: lack of any of these three alterations. Longitudinal sequencing showed that most molecular alterations were early clonal events that persisted during disease progression. Compared with traditional clinicopathologic models, genomic risk stratification demonstrates superior prediction of clinical outcome in STLMS and is comparable in ULMS.

Water renewal and stratification modelling in small estuaries

Water renewal and flushing in small, intermittently open or closed estuaries is receiving increasing attention particularly in light of the climate change induced alterations in run-off, wave and sediment transport conditions along coasts. The challenges of predicting the stratification-circulation state and the balance between tidal or freshwater flushing in response to the mouth dynamics of small, wave-dominated estuaries is the focus of the paper. Such predictions are required for determining estuary freshwater requirements or establishing an estuary's capacity to maintain sound water quality under pollutant discharges. Advances in simulating changes in stratification-circulation over long time scales are limited. Instead attention has focused on generating indices of stratification or water quality state using heuristic methods. In this paper, systems dynamics modelling is applied to simulate the non-linear response of the estuary to changes in river and marine water fluxes. The estuary is modelled as a basin with a specified water volume to water level relationship, connected to the sea by a channel with variable sill height, but fixed width. The direction and magnitude of the flow through the mouth determines whether the sill height erodes or accretes and hence the mouth dynamics (see Slinger, 2017). The tidal flux through the mouth co-determines the volumetric exchange of salt, influencing both the stratification state of the estuary and the degree of tidal or freshwater flushing. This is also influenced by run-off. The resulting dynamic balance is captured in two bulk indices, the Estuarine Richardson number and the bulk densimetric Froude number. Using measured data from the Great Brak Estuary, South Africa, the model is calibrated. Model simulations demonstrate the importance of tidal flushing and concomitant mouth breaching for water renewal as freshwater flushing declines under scenarios of increased water abstraction. Although the estuary remains partially mixed, there is increased average salinity and a more uniform the water column. Water releases and mouth breaching bring about a more natural stratification-circulation state, but these effects are short-lived.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study

BackgroundThe Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants’ baseline characteristics, and preliminary DR progression rates at the first annual follow-up.MethodsDRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated.ResultsOf 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55–67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline.ConclusionsIn our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

Thrombosis in Acute Promyelocytic Leukemia: The Current Understanding.

Despite enormous improvement in the management of patients with acute promyelocytic leukemia (APL), the distinctive coagulopathy observed at presentation in affected patients is often life-threatening. While hemorrhagic manifestations are well known and described in this setting, APL-related thromboses are underappreciated. Data regarding this complication are scarce showing variable incidence. Furthermore, risk factors for thrombosis are inconsistent and unreliable; so, differentiation of increased risk of hemorrhage from an increased thrombotic risk is quite difficult in the absence of adequate predictive scores. Besides, prophylactic use of anticoagulants and recombinant thrombomodulin are a matter of ongoing debate. Also, due to the common feature of thrombocytopenia and other hemorrhagic risks, patients with APL are excluded from trials analyzing anticoagulant prophylaxis in cancers; so, data from prospective trials are lacking. A detailed analysis of thrombotic risks in APL with the development of a reliable risk stratification model is needed to further improve the care of APL patients.

[Artículo traducido] Modelo de estratificación de riesgo de atención farmacéutica para pacientes oncológicos con neoplasias sólidas o hematológicas

ObjectiveWe aimed to develop of a risk stratification model for the pharmaceutical care (PC) of patients with solid or hematologic neoplasms who required antineoplastic agents or supportive treatments. MethodThe risk stratification model was collaboratively developed by oncology pharmacists from the Spanish Society of Hospital Pharmacy (SEFH). It underwent refinement through three workshops and a pilot study. Variables were defined, grouped into four dimensions, and assigned relative weights. The pilot study collected and analyzed data from participating centers to determine priority levels and evaluate variable contributions. The study followed the Kaiser Permanente pyramid model, categorizing patients into three priority levels: Priority 1 (intensive PC, 90th percentile), Priority 2 (60th-90th percentiles), and Priority 3 (60th percentile). Cut-off points were determined based on this stratification. Participating centers recorded variables in an Excel sheet, calculating mean weight scores for each priority level and the total risk score. ResultsThe participants agreed to complete a questionnaire that comprised 22 variables grouped into 4 dimensions: demographic (maximum score =11); social and health variables and cognitive and functional status (maximum = 19); clinical and health services utilization (maximum = 25); and treatment-related (maximum = 41). From the results of applying the model to the 199 patients enrolled, the cutoff points for categorization were 28 or more points for priority 1, 16 to 27 points for priority 2 and less than 16 for priority 3; more than 80% of the total score was based on the dimensions of ‘clinical and health services utilization’ and ‘treatment-related’. Interventions based on the pharmaceutical care model were recommended for patients with solid or hematological neoplasms, according to their prioritization level. ConclusionThis stratification model enables the identification of cancer patients requiring a higher level of pharmaceutical care and facilitates the adjustment of care capacity. Validation of the model in a representative population is necessary to establish its effectiveness.