Significant Subgroups Research Articles

The prognostic value of triglyceride-glucose index to adverse renal outcomes in patients with type 2 diabetes mellitus: results from the cohort study of ACCORD

BackgroundThe triglyceride-glucose (TyG) index is a new and good biomarker of insulin resistance (IR). The prognostic utility of the TyG index for patients with type 2 diabetes mellitus (T2DM) remains uncertain. Our study seeks to elucidate the connection between the TyG index and adverse renal outcomes within a T2DM population, while also examining if these relationships are influenced by subgroup variations.MethodsWe analyzed data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, involving 10,196 T2DM participants, to assess the link between triglyceride-glucose levels and adverse renal outcomes. This evaluation included Restricted Cubic Spline (RCS) analysis, Kaplan–Meier survival analysis, and Multivariate Cox proportional regression. Additionally, we examined the interaction between subgroups concerning adverse renal outcomes.ResultsDuring a 7-year follow-up, 5824 patients (57.1%) experienced worsening renal function, 2309 patients (23.2%) developed albuminuria, and 280 patients (2.7%) advanced to renal failure. After adjusting for a range of confounding variables, triglyceride-glucose levels were significantly linked to both worsening renal function (p < 0.001) and the onset of albuminuria (p = 0.020). Nonetheless, no significant association was observed between triglyceride-glucose levels and renal failure (p = 0.247). Furthermore, there was no significant subgroups interaction to the associations between TyG levels and adverse renal outcomes.ConclusionOur study underscores the significant relationship between the triglyceride-glucose index and the risk of adverse renal outcomes in patients with T2DM. The TyG index, as a readily calculable measure, offers clinicians a valuable tool for anticipating the risk of adverse renal outcomes in this patient population.

Efficient Discovery of Significant Patterns with Few-Shot Resampling

Significant pattern mining is a fundamental task in mining transactional data, requiring to identify patterns significantly associated with the value of a given feature, the target. In several applications, such as biomedicine, basket market analysis, and social networks, the goal is to discover patterns whose association with the target is defined with respect to an underlying population, or process, of which the dataset represents only a collection of observations, or samples. A natural way to capture the association of a pattern with the target is to consider its statistical significance , assessing its deviation from the (null) hypothesis of independence between the pattern and the target. While several algorithms have been proposed to find statistically significant patterns, it remains a computationally demanding task, and for complex patterns such as subgroups, no efficient solution exists. We present FSR, an efficient algorithm to identify statistically significant patterns with rigorous guarantees on the probability of false discoveries. FSR builds on a novel general framework for mining significant patterns that captures some of the most commonly considered patterns, including itemsets, sequential patterns, and subgroups. FSR uses a small number of resampled datasets, obtained by assigning i.i.d. labels to each transaction, to rigorously bound the supremum deviation of a quality statistic measuring the significance of patterns. FSR builds on novel tight bounds on the supremum deviation that require to mine a small number of resampled datasets, while providing a high effectiveness in discovering significant patterns. As a test case, we consider significant subgroup mining, and our evaluation on several real datasets shows that FSR is effective in discovering significant subgroups, while requiring a small number of resampled datasets.

Patterns of emotion-network dynamics are orthogonal to mood disorder status: An experience sampling investigation.

Individuals differ markedly in how they experience the ebb and flow of emotions. In this study, we used daily experience sampling to examine whether these differences reflect the nature and presence of mood disorders or whether they can better be characterized as distinct dynamic emotion profiles that cut-across diagnostic boundaries. We followed 105 individuals in 2019-2020 with diagnoses of major depression, remitted major depression, bipolar disorder, or no history of disorder, over 14 days (n = 6,543 experience-sampling assessments). We applied group iterative multiple model estimation, using both diagnosis-based and data-driven methods to investigate similarities in unfolding within-person emotion-network time-courses. Results did not support diagnosis-based subgroupings but rather revealed two significant data-driven subgroups based on dynamic emotion patterns. These data-driven subgroups did not significantly differ in terms of clinical features or demographics, but did differ on key emotion metrics-instability, granularity, and inertia. These data-driven subgroupings, agnostic to diagnostic status, provide insights into the nature of idiographic emotion-network dynamics that cut-across clinical diagnostic divisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multidimensional subgroup discovery on event logs

Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low-structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real-life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real-life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.

Effect of CYP2C19 Pharmacogenetic Testing on Predicting Citalopram and Escitalopram Tolerability and Efficacy: A Retrospective, Longitudinal Cohort Study.

Background-Various antidepressant agents are metabolized by the CYP2C19 enzyme, including Citalopram and Escitalopram. Variation in CYP2C19 expression might give rise to different plasma concentrations of the active metabolites, potentially affecting both drugs' efficacy and tolerability. Aim-The aim of this study was to evaluate differences in the Escitalopram and Citalopram efficacy and tolerability between different CYP2C19 genotype-based metabolizing categories in outpatients suffering from major depressive disorder (MDD). Methods-In a retrospective, longitudinal cohort study of electronic medical-record data, 283 patients with MDD who were prescribed Escitalopram or Citalopram with the available CYP2C19-genotyping test were enrolled. The primary efficacy end point was adverse drug reactions recorded in the medical files. A proportional-odds, multilevel-regression model for longitudinal ordinal data was used to estimate the relation between the CYP2C19 genotype and adverse drug reactions, adjusting for potential confounding variables and other explanatory variables. Latent-class analysis (LCA) was utilized to detect the presence of clinically significant subgroups and their relation to an individual's metabolizing status for CYP2D6/CYP2C19. Results-With poor CYP2C19 metabolizers as a reference, for each unit difference in the activity score of the CYP2C19 phenotype, the odds ratio for drug intolerability was lowered by 0.73 (95% credible intervals: 0.56-0.89), adjusting for significant covariates. In addition, applying LCA, we identified two qualitatively different subgroups: the first group (61.85%) exhibited multiple side effects, low compliance, and frequent treatment changes, whereas the second group (38.15%) demonstrated fewer side effects, good adherence, and fewer treatment changes. The CYP2C19 phenotype was substantially associated with the group membership. Conclusions-We found a positive association between the CYP2C19 activity scores, as inferred from the genotype, and both the efficacy of and tolerability to both Es/Citalopram. LCA enabled valuable insights into the underlying structure of the population; the CYP2C19 phenotype has a predictive value that discriminates between low-adherence, low-drug-tolerance, and low-response patients and high-adherence, high-drug-tolerance, and high-response patients. Personalized medicine based on CYP2C19 genotyping could evolve as a promising new avenue towards mitigating Escitalopram and Citalopram therapy and the associated side effects and enhancing treatment success.

Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data

The potential of precision population health lies in its capacity to utilize robust patient data for customized prevention and care targeted at specific groups. Machine learning has the potential to automatically identify clinically relevant subgroups of individuals, considering heterogeneous data sources. This study aimed to assess whether unsupervised machine learning (UML) techniques could interpret different clinical data to uncover clinically significant subgroups of patients suspected of coronary artery disease and identify different ranges of aorta dimensions in the different identified subgroups. We employed a random forest-based cluster analysis, utilizing 14 variables from 1170 (717 men/453 women) participants. The unsupervised clustering approach successfully identified four distinct subgroups of individuals with specific clinical characteristics, and this allows us to interpret and assess different ranges of aorta dimensions for each cluster. By employing flexible UML algorithms, we can effectively process heterogeneous patient data and gain deeper insights into clinical interpretation and risk assessment.

Differential responses to tax regulation: The case of Schedule UTP

Schedule UTP was instituted by the Internal Revenue Service to obtain more detailed information about firms' uncertain tax benefits (UTBs) that are required to be reported on a firm's financial statements. We document that the reaction of firms was not uniform. We provide evidence that post Schedule UTP, firms with higher political costs lower their UTBs more than average firms but do not increase their cash taxes paid. In addition, we look at firms reporting zero or missing UTBs before Schedule UTP was implemented. We find that these firms paid more in cash taxes after the introduction of Schedule UTP. From a policy standpoint, schedule UTP affects significant subgroups of firms in different manners.

Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.

Risk reclassification of endometrial cancer according to molecular profiling: UK single centre experience.

e17628 Background: Endometrial cancer (EC) ranks second among gynaecological malignancies worldwide. Traditionally, adjuvant treatment recommendation was based on risk stratification based on clinicopathological data. The Cancer Genome Atlas (TCGA) defined four distinct and prognostically significant molecular subgroups. Recent international guidelines recommend integrating molecular classification into risk stratification. The aim of our study was to evaluate the distribution of molecular groups in our population and its impact on treatment decision. Methods: Retrospective cohort study including all newly diagnosed endometrial cancer cases treated at the Royal Marsden Hospital between July 2020 and May 2022. Tumour specimens underwent next generation sequencing (NGS) of a two hundred gene panel including POLD1, POLE; MMR proteins and TP53; concordance for the latter two assessed also by immunohistochemistry (IHC). Risk stratification according to ESMO guidelines and following molecular classification was reviewed for each case and its potential impact on treatment decision. Results: 121 patients were included. POLE mutated tumours comprised 14 cases (11.6%), MSI hypermutated 24 cases (19.8 %), copy number high 36 cases (29.8%), copy number low 45 cases (37.2%) and 2 cases (1.6%) where classified as NOS (not otherwise specified). According to clinicopathological data, 39 tumours were categorised as low risk, 14 tumours as intermediate risk, 15 tumours as high-intermediate risk, 39 tumours as high risk and 14 were advanced/metastatic. According to final prognostic group when molecular profile was integrated 8.3 % (n = 10) of cases were reclassified: 8 cases were down staged (2 high, 3 high-intermediate, 3 intermediate) to low-risk category due to POLE mutations. 2 cases were upstaged from high-intermediate to high-risk category (copy number high tumours). There were no shifts noted in the low risk (based on clinicopathological data) prognostic group. One further case in the high-risk category (stage III, gr3 endometrioid tumour with substantial LVSI) was found to be POLE mutated and may be reclassified as low risk. For the 10 cases that were reclassified, adjuvant treatment recommendations may have been adjusted according to the most recent ESMO guidelines. Conclusions: Molecular classification refines traditional risk classification of EC and may change treatment recommendation in a non-negligible proportion of patients. Efforts should be made to implement molecular analysis in routine clinical practice to guide appropriate treatment.

Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients.

Immune checkpoint inhibitors, especially PD-1/PD-L1 blockade, have revolutionized cancer treatment and brought tremendous benefits to patients who otherwise would have had a limited prognosis. Nonetheless, only a small fraction of patients respond to immunotherapy, and the costs and side effects of immune checkpoint inhibitors cannot be ignored. With the advent of machine and deep learning, clinical and genetic data have been used to stratify patient responses to immunotherapy. Unfortunately, these approaches have typically been "black-box" methods that are unable to explain their predictions, thereby hindering their responsible clinical application. Herein, we developed a "white-box" Bayesian network model that achieves accurate and interpretable predictions of immunotherapy responses against nonsmall cell lung cancer (NSCLC). This tree-augmented naïve Bayes (TAN) model accurately predicted durable clinical benefits and distinguished two clinically significant subgroups with distinct prognoses. Furthermore, our state-of-the-art white-box TAN approach achieved greater accuracy than previous methods. We hope that our model will guide clinicians in selecting NSCLC patients who truly require immunotherapy and expect our approach to be easily applied to other types of cancer.

Abstract 2049: Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC)

Abstract Background: Lung cancer is the leading cause of cancer death worldwide, with LUSC accounting for a third of cases. Stage I and II LUSC is typically treated with resection, and decisions related to adjuvant chemotherapy are related to recurrence risk, which is a function of stage. However, disease stage does not fully capture tumor biology, which may be a more important determinant of recurrence risk. Our objective was to identify biologically significant molecular subgroups of LUSC that more accurately reflect recurrence risk. Methods: Transcriptomic data for resected stage I/II LUSC were obtained from The Cancer Genome Atlas (TCGA). A training set consisting of patients who underwent resection without adjuvant chemotherapy or radiotherapy was used for discovery (N=161). A proprietary machine learning algorithm (HighLifeR™) was used to identify the genes most associated with disease-free survival (DFS). Molecular subgroups were identified by unsupervised clustering of prognostic genes. Functional differences between molecular subgroups were identified by gene set enrichment analysis (GSEA) and Ingenuity Pathway Analysis (IPA). Results: HighLifeR™ identified 60 highly prognostic genes. Two molecular subgroups were identified with significantly different median DFS: a high-risk group with 59.300 months and hazard ratio of 11.551, and a low-risk group that did not reach 50% DFS (log-rank p = 1.96 x 10-5). These subgroups did not differ in age, sex, race, smoking status, or overall stage (p &amp;gt; 0.05). Importantly, univariate and multivariate Cox regression analysis showed that the molecular subgroups outperformed clinical stage in predicting DFS. The molecular subgroups were biologically distinct. On GSEA, the high-risk group was significantly enriched in genes involved in mitotic spindle assembly (NES=1.94, p&amp;lt;0.001); TGFα signaling and PI3K/AKT/mTOR signaling were modestly enriched. IPA pathway analysis demonstrated significant enrichment of numerous pathways linked to neuronal growth and signaling (eg: CREB signaling, S100 signaling, pathways in myelination and synaptogenesis), as well as TGFα signaling and AMPK signaling (all p&amp;lt;0.001). Conclusions: Our prognostic transcriptomic signature identified two biologically distinct molecular subgroups of LUSC. Molecular subgroup classification was more predictive of DFS in stage I and II LUSC than any other clinical or pathological variable. If further validation confirms this, then this biomarker may form the basis of a diagnostic test that helps inform which patients could be considered for adjuvant chemotherapy. Citation Format: Ashar Siddiqui, Cynthia Stretch, Farshad Farshidfar, Oliver F. Bathe. Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2049.

Tentative Epidemiological Cut-Off Values and Distribution of Resistance Genes in Aquatic Pseudomonas Species Isolated from Rainbow Trout

Epidemiological cut-off value (ECV) analysis for commonly used antimicrobials in aquaculture have not been established for many aquatic pathogens, including Pseudomonas. This study was the first to examine the categorization of 92 aquatic Pseudomonas isolates by calculating seven antimicrobials ECVs using two analytical methods: normalized resistance interpretation and ECOFFinder. Pseudomonas spp. isolates had decreased sensitivity to all antimicrobials examined except for doxycycline and ciprofloxacin. The PCR analysis of the 91 isolates of Pseudomonas spp. detected the tetracycline genes are predominant with the count of 41 genes, including tetA, tetC, tetD, tetM, tetS and tetH, following sulfonamide genes are in 21 isolates including sul1 and sul2, floR gene in 15 isolates and ermA gene in three isolates. Our findings provide an understanding of the antimicrobial categorization of Pseudomonas species, which are significant groups, subgroups, and species for aquaculture due to insufficiently defined breakpoints or cut-off values reported in CLSI and/or EUCAST.

The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.

Objectives: Managing multimorbidity as aging stroke patients is complex; standard self-management programs necessitate adaptations. We used visual analytics to examine complex relationships among aging stroke survivors' comorbidities. These findings informed pre-adaptation of a component of the Chronic Disease Self-Management Program. Methods: Secondary analysis of 2013-2014 Medicare claims with stroke as an index condition, hospital readmission within 90days (n = 42,938), and 72 comorbidities. Visual analytics identified patient subgroups and co-occurring comorbidities. Guided by the framework for reporting adaptations and modifications to evidence-based interventions, an interdisciplinary team developed vignettes that highlighted multimorbidity to customize the self-management program. Results: There were five significant subgroups (z = 6.19, p < .001) of comorbidities such as obesity and cancer. We constructed 6 vignettes based on the 5 subgroups. Discussion: Aging stroke patients often face substantial disease-management hurdles. We used visual analytics to inform pre-adaptation of a self-management program to fit the needs of older adult stroke survivors.

Comparative Outcomes and/or Complications Following Ventriculoperitoneal versus Lumboperitoneal Shunts: A Scoping Review

Cerebrospinal fluid (CSF) diversion procedure in the form of ventriculoperitoneal shunt (VPS) has been performed for over a century and is a well-established method for treating hydrocephalus. With the passage of time and the availability of better technology, several other CSF diversion modalities developed with variable risks and benefits. Lumboperitoneal shunt (LPS) is a close and safe contestant for the VP shunt procedure and is regarded as an alternative treatment modality for hydrocephalus. The present scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A literature search for the study was performed on PubMed, COCHRANE, SCOPUS and ScienceDirect (from inception to 7 July 2022). Those studies that were published in the English language with a report on comparison between VPS and LPS for the management of hydrocephalus were included in the review process. Those studies that do not have comparative outcomes, case reports, letters, reviews, preclinical studies, conference reports and non-human studies were excluded from the study. Search results showed 134 records; after removing the duplicates and excluding 12 studies with reasons, five studies were included in the present review. Several complications were described in patients who underwent either VP Shunt or LP Shunt surgery. Although communicating hydrocephalus forms one of the most significant subgroups of patients in neurosurgical practice and VP and LP shunt procedures are also widely performed, the literature review shows limited comparative efficacy. In addition, the reported studies have a broad spectrum of indications, disease conditions and complications with a heterogeneous patient population. To further develop the high level of evidence, there is a need for well-designed prospective protocols to help us develop uniform and practical guidelines. In the present review, we looked into the common complications, including shunt malfunction, shunt infection, subdural haematoma, shunt obstruction and shunt migration between two groups.

Utility of Prognostic Scoring System Based on Histomorphological Parameters in Lowgrade Colorectal Carcinoma

Introduction: Colorectal Carcinoma (CRC) is the third most common cancer worldwide. Percentage of gland formation is the only valid parameter for histologic grading of CRC . Tumour budding and Tumour-infiltrating Lymphocytes (TILs) are emerging prognostic factors in CRC. In recent years high grade CRC has become subject to more precise molecular grading strategies. However low grade cases show in homogenous outcome due to still insufficient categorization. The focus of this study is to determine whether the combination of amount of gland formation, budding, and TILs will allow us to further characterize large in homogenous group of WHO low-grade cases into prognostically significant subgroups. Aim: To estimate the significance of tumour budding and TILs in low-grade CRC and to categorise low-grade CRC into prognostic subgroups taking into account 3 histologic parameters-gland formation, tumour budding and TIL. Materials and Methods: This was a descriptive cross-sectional study done in the Department of Pathology, MES Medical College, Malappuram, Kerala, India. It was an ambispective study (retrospective from January 2015 to December 2021 and prospective from December 2021 to March 2022) which analysed 105 World Health Organization (WHO) low-grade CRC cases. The demographic data of the patients was collected and histopathological assessment of tumour grade, pT, pN, lymphovascular invasion (LVI), Tumour-infiltrating lymphocytes (TIL) and tumour budding (TB) was done on Haematoxylin and Eosin (H&amp;E) stained sections. A morphology-based risk score was developed taking into account 3 parameters- percentage of gland formation, budding, and TIL. For each parameter, 1 to 2 points were given, resulting in a sum score, dividing the CRC cases into a low-, an intermediate-, and a high-risk group. Statistical analysis was performed using SPSS 25. The results were expressed as numbers and percentage. Pearson Chi-square test was used to test the relationship. Results: In the present study degree of budding significantly associated with pT stage (p=0.02), pN stage (p=0.042) and LVI (p=0.038). TIL also differed significantly with pT (p=0.001) pN (p=0.042) and LVI (p=0.004). Applying the prognostic scoring to 105 cases, 33 (31.4%) cases showed high score, 30 (28.6%) cases were of intermediate score and 42 (40%) cases showed low score. The 3 groups differs significantly with pT (0.027), pN (0.035) and LVI (0.015). Conclusion: The present study showed combining different morphological parameters of tumour and tumour environment can help to further subdivide CRC into prognostically significant subgroups.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.

A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information. The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility. In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement. Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

EGS plus: Predicting futility in LVAD patients with emergency surgical disease

BackgroundWhile emergent, non-cardiac surgery can be safely performed in LVAD patients, the inherent perioperative challenges of these rare procedures and the perception that these patients may be poor surgical candidates can contribute to reluctance to perform necessary emergency general surgery (EGS) procedures. We, therefore, sought to identify predictors of inpatient mortality to assist perioperative decision-making. MethodsThe Nationwide Inpatient Sample (2010-2015Q3) was used to identify patients with previously placed LVADs with a subsequent EGS admission diagnosis. Multivariable logistic regression analysis was performed to identify independent predictors of 30-day mortality, and a risk-adjusted probability of death was calculated for significant patient subgroups across age. Additional demographic variables were included in the regression due to clinical relevance. ResultsThere were 1805 (weighted) LVAD-EGS patients with an overall mortality rate of 11%. Independent predictors of mortality were intestinal ischemia and sepsis present on admission. Patients older than 70 with sepsis had an 80% probability of in-hospital mortality (10.6 OR, 1.70–65.5 95% CI) while those over 70 presenting with intestinal ischemia had a 38% probability of death (3.6 OR, 1.50–8.78 95% CI). Mortality risk for younger patients with sepsis was still approximately 50%. ConclusionOlder LVAD patients presenting with either sepsis or intestinal ischemia have a substantial mortality risk while younger patients have a modest risk. These results can be used to guide treatment discussions when emergency surgery is being considered in LVAD patients.

The proteome of clear cell ovarian carcinoma.

Clear cell ovarian carcinoma (CCOC) is the second most common subtype of epithelial ovarian carcinoma. Late-stage CCOC is not responsive to gold-standard chemotherapy and results in suboptimal outcomes for patients. In-depth molecular insight is urgently needed to stratify the disease and drive therapeutic development. We conducted global proteomics for 192 cases of CCOC and compared these with other epithelial ovarian carcinoma subtypes. Our results showed distinct proteomic differences in CCOC compared with other epithelial ovarian cancer subtypes including alterations in lipid and purine metabolism pathways. Furthermore, we report potential clinically significant proteomic subgroups within CCOC, suggesting the biologic plausibility of stratified treatment for this cancer. Taken together, our results provide a comprehensive understanding of the CCOC proteomic landscape to facilitate future understanding and research of this disease. © 2022 The Pathological Society of Great Britain and Ireland.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture.

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

558P Molecular subtyping endometrial carcinoma in Chinese population applying an NGS-based classifier and its association with prognosis

Molecular subtypes of EC is widely recognized and applied in clinics. While, it is still less recognized the distribution and prognostic value of molecular subtypes of endometrial carcinoma in Chinese population. An NGS-based classification strategy with reference to these two classifiers was developed and applied for the first time to explore the prognostic correlation of EC patients in China. Formalin-Fixed Paraffin Embedded (FFPE) tissue of 233 EC patients were retrospectively collected from Qilu Hospital, China. Genomic DNA was extracted and subjected to an all-in-one simplified NGS panel covering POLE, TP53 as well as MSI for subtyping. The information for ProMisE classification were also collected. With the median follow-up was 66 (range 7-122) months, overall survival (OS) and disease-specific survival (DSS) were also been analyzed across the subtypes. Base on NGS panel classification, the fractions of POLE, MSI, CNH and CNL group were 8.15%, 18.88%, 11.59% and 61.37%, respectively. 61patients with ProMisE classification available, the fractions of the former subtypes were11.48%, 36.07%, 14.75%, and 37.70% respectively. The OS and DSS was (P=0.046 for OS, P=0.024 for DSS) significantly in the four subtypes based on NGS testing in 131 followed patients. There was no significant difference in OS (P = 0.91) and DSS (P = 0.95) across the four subtypes by ProMisE in 50 followed patients (Table).Table: 558PSurvival analysis results based on the two classifiersNGS-based classifier（N=131）POLEMSICNHCNLP8 years -OS rate100%80.00%50.00%92.26%0.0468 years -DSS rate100%88.89%66.67%100%0.024ProMisE classifier (N=50)8 years -OS rate83.33%89.47%100%80.00%0.918 years -DSS rate100%94.73%100%92.31%0.95 Open table in a new tab The NGS-based strategy was confirmed to effectively classify four prognostically significant subgroups in endometrial cancer patients. Further analysis showed that the clinicopathological features of each molecular subtype were significantly different. All the results confirmed the feasibility of molecular subtyping of EC in clinical practice by a simplified NGS approach.