Grouping Of Patients Research Articles

Phenotypes of patients with lupus-associated pulmonary hypertension in the Chinese Han population: a cluster analysis.

Pulmonary hypertension (PH) is a severe complication of systemic lupus erythematosus (SLE). This study investigated the clinical features of patients with SLE-PH in China based on disease manifestations and organ involvement to define precise treatment of SLE and early prevention of complications. In total, 205 SLE-PH patients were included in this study. A cluster analysis was applied according to six clinical and serological variables to define different subgroups of patients. The survival rates of SLE-PH patients and risk factors that influenced prognosis were also compared and a clinical prediction model was developed for the diagnosis of associated lupus nephritis (LN). Patients were clustered in five groups. Patients in cluster 1 had severe renal damage (all patients had LN and had the highest creatinine and urea nitrogen and the lowest eGFR levels). Patients in cluster 2 had mild renal damage (more than half of the patients had associated LN and 87.5% had increased urinary protein levels but presented a lower degree of renal damage than those in the first group. Patients in cluster 3 had low-grade proteinuria (all patients had 24h urinary protein < 0.5g). Patients in cluster 4 had hematuria or urinary tubular damage (26% of patients had associated LN, but none of the patients had proteinuria. In cluster 5, patients barely had any major organ involvement. The clinical prediction model for a diagnosis of SLE-PH-LN was anti-dsDNA antibodies > 364.64IU/mL and neutrophil-to-leukocyte ratio (NLR) > 5.55. Our findings provide evidence indicating that SLE-PH presents varying clinical phenotypes and the treatment varies accordingly, suggesting the need for individualized treatment. Key Points • Clustered grouping of patients with SLE-PH is associated with renal injury. • This may be because PH and LN share a common pathogenesis. • The clinical prediction model for a diagnosis of SLE-PH-LN was anti-dsDNA antibodies >364.64 IU/mL and NLR >5.55.

Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis.

Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine-the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g.,trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.

Assessing the Metabolic Variations of Invasive and Noninvasive Unilateral Retinoblastoma Patients.

Retinoblastoma (Rb) is a pediatric eye cancer which if diagnosed at later stages can lead to Rb invasion into the choroid, optic nerve, sclera, or beyond, with the potential of undergoing metastasis. Cancer cells, including Rb cells, reprogram their metabolic circuits for their own survival and progression, which provides a great opportunity to monitor the extent of Rb progression based on metabolic differences. Henceforth, the present study aims to map the metabolic variations in patients with invasive (primarily enucleated eyes with high-risk histopathological features) and noninvasive (eyes salvaged with treatment) unilateral retinoblastoma (Rb) using nuclear magnetic resonance (NMR) based serum metabolomics. Quantification of differential metabolites in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb along with 6 controls (no retinal pathology) was carried out using 1H NMR spectroscopy. A total of 71 metabolites, such as organic acids, amino acids, carbohydrates, and others, were identified in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb. Partial least-squares discriminant analysis (PLS-DA) models depicted distinct grouping of invasive and noninvasive Rb patients and controls. Differential metabolic fingerprints were observed for invasive and noninvasive Rb patients based on their biostatistical analyses with respect to controls. Remarkable perturbation was observed among various metabolites such as 4-aminobutyrate, 2-phosphoglycerate, O-phosphocholine, proline, Sn-glycero-3-phosphocholine (Sn-GPC), and O-phosphoethanolamine in noninvasive and invasive Rb patients with most of the effects being heightened in the latter group. Metabolic changes unique to invasive and noninvasive Rb patients were also observed. Multivariate receiver operating characteristics (ROC) analysis unveiled the highest accuracy and potency of ROC models 2 and 5 to distinguish the noninvasive and invasive Rb from controls, respectively. Metabolites identified in the serum of patients with invasive and noninvasive Rb may aid in advancing our knowledge about Rb tumor biology. Differential aberrant metabolic variations in patients with invasive Rb compared to those with noninvasive Rb may guide the decision of enucleation versus globe salvage.

Molecular mechanisms and clinical manifestations of hereditary hemorrhagic telangiectasia

IntroductionHereditary Hemorrhagic Telangiectasia (HHT) is charactered by telangiectasia and arteriovenous malformations (AVMs). Recurrent visceral and mucocutaneous bleeding is frequently reported among HHT patients, while data on the prevalence of thrombosis remains limited. This study aims to describe the clinical manifestations and molecular biological characteristics of HHT patients. MethodsWe conducted a retrospective study at Ruijin Hospital, affiliated with Shanghai Jiao Tong University School of Medicine. A total of 24 HHT patients, observed between January 2019 and December 2023, were included. We recorded the biological, clinical, and therapeutic events, with particular attention to bleeding and thrombotic events. Gene mutation analysis and blood constituent measurements were performed. ResultsThe prevalence of bleeding among all HHT patients was 100 %, while thrombotic events were noted in 41.70 % of cases. Hepatic arteriovenous malformations (HAVMs) were identified in six patients, pulmonary arteriovenous malformations (PAVMs) in five patients, and cerebral arteriovenous malformations (CAVMs) in one patient. For patients with thrombosis, the discontinuation rates were 23.08 % for antiplatelet therapy and 33.33 % for anticoagulant therapy due to the increased risk of bleeding. Genetic mutations related to HHT were present in 16 patients, with ACVRL1 (activin A receptor-like type 1) mutations being the most frequent at 41.67 %, followed by ENG (endoglin) mutations at 20.83 %, and GDF2 (growth differentiation factor 2) mutations at 4.17 %. The incidence of PAVMs was 75.00 % in HHT1 patients with ENG mutations and 20 % in HHT2 patients with ACVRL1 mutations, while HAVMs occurred in 0 % and 40.00 % of these groups, respectively. Patients were divided into non-AVMs and AVMs groups. Compared to normal controls, von Willebrand factor (vWF) activity was significantly increased in all HHT patients (149.10 % vs. 90.65 %, P < 0.001). In the non-AVMs group, the median level of stromal cell-derived factor-1 (SDF-1) was significantly elevated (124.31 pg/mL vs. 2413.57 pg/mL, P < 0.05), while vWF antigen levels were markedly higher in the AVMs group (165.30 % vs. 130.60 %, P = 0.021). Further grouping of HHT patients based on bleeding and thrombosis phenotypes revealed that those with thrombosis had significantly higher median percentages of schistocytes (3.50 % vs. 0 %, P = 0.002), ferritin concentrations (318.50 μg/L vs. 115.50 μg/L, P = 0.001), and lactate dehydrogenase (LDH) levels (437 U/L vs. 105 U/L, P < 0.001). There were no significant differences in the activity of vWF, protein C (PC), protein S (PS), and factor VIII (FVIII) between the two groups. ConclusionThis study highlighted the complex relationship between arteriovenous malformations and genetic mutations in HHT patients. A comprehensive assessment of bleeding and thrombosis risks should be conducted for each HHT patient, additionally, further clinical studies are needed to explore the risk factors for thrombosis and anticoagulant-related bleeding in HHT.

ANA-associated arthritis: clinical and biomarker characterization of a population for basket trials.

ANA-associated rheumatic and musculoskeletal (MSK) diseases (RMDs) [SLE, primary SS (pSS), scleroderma, inflammatory myositis, MCTD and UCTD] make up a disease spectrum with overlapping clinical and immunological features. MSK inflammation is common and impactful across ANA-associated RMDs. The objectives of this study were to evaluate MSK inflammation (ANA-associated arthritis) prevalence in a multidisease ANA-associated RMD study, assess its clinical impact across ANA-associated RMD diagnoses, propose new basket groupings of patients, and evaluate immunological profiles in legacy and new basket contexts. An observational study enrolled patients with ANA-associated RMDs. Demographic variables, comorbidities, therapies, disease activity instruments [BILAG, SLEDAI, the EULAR SS disease activity index (ESSDAI), physician visual analogue scale (VAS)], patient-reported outcomes [SF36, FACIT-Fatigue, EQ5D, ICECAP-A, Work Productivity and Activity impairment (WPAI), patient VAS] and the biomarker profile (six-gene expression scores, flow cytometry, autoantibody profile) were analysed. Reclustering utilized Gaussian mixture modelling (GMM). The clinical and immune features of new and legacy clusters were compared. Inflammatory MSK symptoms were prevalent across ANA-associated RMDs, in 213/294 patients. In ANA-associated arthritis patients, most variables did not differ between diagnoses, with the exception of the EQ5D-5L index and mobility domains (lower in MCTD/pSS, both P < 0.05). FM and OA prevalence were similar across diagnoses. Therapy use differed significantly, the use of biologics being greatest in SLE (P < 0.05). GMM yielded two multidisease clusters: High MSK disease activity (n = 89) and low MSK disease activity (n = 124). The high MSK disease activity cluster included all patients with active joint swelling, and they had significantly higher prednisolone usage, physician global assessment (PGA), Sm/RNP/SmRNP/chromatin positivity, Tetherin mean fluorescence intensity (MFI), and IFN Score-A activity, along with numerically lower FM and OA prevalence. We defined ANA-associated arthritis, a more clinically and immunologically homogeneous population than existing RMD populations for trials, and a more prevalent population for therapies in the clinic.

#1259 Relationship between renal dysfunction, non-obstructive ACS and in-hospital prognosis: findings from the CCC-ACS project

Abstract Background and Aims Acute coronary syndrome (ACS) is an acute and critical condition of coronary heart disease (CHD) with high mortality and poor prognosis. The prevalence of non-obstructive ACS is about 5% ∼ 15%. Among patients with non-obstructive ACS, which patients are more prone to poor prognosis is a concern. Chronic kidney disease (CKD) is an important risk factor for cardiovascular disease, and due to its special pathophysiological mechanism, non-obstructive ACS may not be uncommon in CKD patients. Therefore, the aim of this study was to investigate the relationship between renal function, the occurrence and prognosis of non-obstructive ACS. Method Improving Care for Cardiovascular Disease in China - Acute Coronary Syndrome (CCC-ACS), a hospital-based medical quality improvement project, is a hospital-based medical quality improvement project to improve the compliance of ACS guidelines in China and improve the prognosis of patients. It was jointly sponsored by the American Heart Association (AHA) and the Chinese Society of Cardiology (CSC) in 2014, with 240 hospitals participating, representing different levels of ACS management in Chinese hospitals. We analyzed patients who underwent coronary angiography from November 2014 to July 2019 in CCC-ACS. Patients were divided into eGFR &amp;lt;60 ml/min·1.73 m² and eGFR≥60 ml/min·1.73 m² groups according to estimated glomerular filtration rate (eGFR) calculated by the EPI formula. According to the results of coronary angiography, the patients were divided into obstructive ACS group and non-obstructive ACS group (obstructive ACS group was defined as at least one coronary artery stenosis ≥ 50%; the others were non-obstructive ACS group). Outcome was defined as in-hospital all-cause mortality and in-hospital MACEs. Results A total of 77 586 patients were included, including 2 292 patients (3.0%) with non-obstructive ACS and 75 294 patients (97.0%) with obstructive ACS. After adjusting for confounding factors using a multivariate logistic regression model, patients with eGFR&amp;lt;60 ml/min·1.73 m² had an approximately 20% increased risk of non-obstructive ACS compared with patients with eGFR≥60 ml/min·1.73 m². The comparison of in-hospital outcomes between the patients with non-obstructive ACS and obstructive ACS is shown in Fig. 1. In-hospital outcomes showed that although the obstructive ACS group had a worse in-hospital prognosis than the non-obstructive ACS group, and the eGFR&amp;lt;60 ml/min·1.73 m² group had a worse in-hospital prognosis than the eGFR≥60 ml/min·1.73 m² group, further grouping of patients showed that the non-obstructive ACS group with eGFR&amp;lt;60 ml/min·1.73 m² had a worse prognosis than the obstructive ACS group with eGFR≥60 ml/min·1.73 m². Confounding factors adjusted for non-obstructive ACS showed that lower eGFR was associated with a higher risk of in-hospital MACEs. After analyzing the in-hospital medication and discharge medication of patients, it was found that the standardized cardiovascular drug therapy was inadequate in patients with non-obstructive ACS. Conclusion Patients with non-obstructive ACS have worse renal function than patients with obstructive ACS, and the proportion of patients with eGFR&amp;lt;60 ml/min·1.73 m² is higher. In patients with non-obstructive ACS, the risk of in-hospital MACEs significantly increased as eGFR decreased. This suggests that we may consider giving standardized cardiovascular drug therapy to patients with non-obstructive ACS, especially those with non-obstructive ACS combined with abnormal renal function, in order to improve the prognosis of patients. This needs to be confirmed by more randomized controlled trial results.

Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients.

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer.

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect. The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research. The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

The diversity of the microbiome impacts chronic lymphocytic leukemia development in mice and humans.

The gut microbiota plays a critical role in maintaining a healthy human body and its dysregulation is associated with various diseases. In this study, we investigated the influence of gut microbiome diversity on the development of chronic lymphocytic leukemia (CLL). Analysis of stool samples from 59 CLL patients revealed individual and heterogeneous microbiome compositions, but allowed for grouping of patients according to their microbiome diversity. Interestingly, CLL patients with lower microbiome diversity and an enrichment of bacteria linked to poor health suffered from a more advanced or aggressive form of CLL. In the Eµ-TCL1 mouse model of CLL, we observed a faster course of disease when mice were housed in high hygiene conditions. Shotgun DNA sequencing of fecal samples showed that this was associated with a lower microbiome diversity which was dominated by Mucispirillum and Parabacteroides genera in comparison to mice kept under lower hygiene conditions. In conclusion, we applied taxonomic microbiome analyses to demonstrate a link between gut microbiome diversity and the clinical course of CLL in humans, as well as the development of CLL in mice. Our novel data serve as a basis for further investigations to decipher the pathological and mechanistic role of intestinal microbiota in CLL development.

Cost-effectiveness of diabetic retinopathy screening for newly diagnosed type 2 diabetic patients: A nationwide population-based propensity score-matched cohort study

AimsThis study investigated the association between the frequency of screening for diabetic retinopathy (DR) versus the development of DR and corresponding medical expenses among patients newly diagnosed with type 2 diabetes mellitus (T2DM). MethodsThis longitudinal, population-based study used the Taiwan National Health Insurance Research Database (2004 to 2020) as a data source. Propensity score matching (PSM) (sex, age, comorbidities and concurrent medication use) was employed in the grouping of T2DM patients according to different frequency of DR screening. Outcome measures included the proportion of patients who developed DR, who received DR treatment, and the associated medical expenses and hospitalizations. ResultsThe 17-year cohort included 337,046 patients. After PSM, three groups each containing 35,739 patients were assembled and analyzed. Compared to low-frequency screening, high-frequency screening was more effective in detecting patients requiring treatment; however, the net cost for treatment was significantly lower. Standard-frequency screening appears to provide the best balance in terms of DR detection, diagnosis interval, the risk of DR-related hospitalization, and DR treatment costs. ConclusionsIn this real-world cohort study covering all levels of the healthcare system, infrequent screening was associated with delayed diagnosis and elevated treatment costs, while a fundus screening interval of 1–2 years proved optimal in terms of detection and medical expenditures.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach

ObjectivesTo investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. MethodsWe conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. ResultsA real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. ConclusionsHM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

Precise capture of circulating endometrial cells in endometriosis.

Endometriosis (EM) is a complex benign gynecological disease, but it has malignant biological behavior and can invade any part of the body. Clinical manifestations include pelvic pain, dysmenorrhea, infertility, pelvic nodules, and masses. Our previous study successfully detected circulating endometrial cells (CECs) in the peripheral blood of patients with EM. The purpose of this study is to overcome the limitation of cell size in the previous microfluidic chip method, to further accurately capture CECs, understand the characteristics of these cells, and explore the relationship between CECs and the clinical course characteristics of patients with EM. Human peripheral venous blood used to detect CECs and circulating vascular endothelial cells (CVECs) was taken from EM patients ( n = 34) hospitalized in the Peking University People's Hospital. We used the subtraction enrichment and immunostaining fluorescence in situ hybridization (SE-iFISH) method to exclude the interference of red blood cells, white blood cells, and CVECs, so as to accurately capture the CECs in the peripheral blood of patients with EM. Then we clarified the size and ploidy number of chromosome 8 of CECs, and a second grouping of patients was performed based on clinical characteristics to determine the relationship between CECs and clinical course characteristics. The peripheral blood of 34 EM patients and 12 non-EM patients was evaluated by SE-iFISH. Overall, 34 eligible EM patients were enrolled. The results showed that the detection rates of CECs were 58.8% in EM patients and 16.7% in the control group. However, after classification according to clinical characteristics, more CECs could be detected in the peripheral blood of patients with rapidly progressive EM, with a detection rate of 94.4% (17/18). In total, 63.5% (40/63) of these cells were small cells with diameters below 5 μm, and 44.4% (28/63) were aneuploid cells. No significant association was found between the number of CECs and EM stage. The number and characteristics of CECs are related to the clinical course characteristics of patients with EM, such as pain and changes in lesion size, and may be used as biomarkers for personalized treatment and management of EM in the future.

Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study.

Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy. Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach. In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation.

Patients with the target gene mutation frequently derive significant clinical benefits from target therapy. However, differences in the abundance level of mutations among patients resulted in varying survival benefits, even among patients with the same target gene mutations. Currently, there is a lack of rational and interpretable models to assess the risk of treatment failure. In this study, we investigated the underlying coupled factors contributing to variations in medication sensitivity and established a statistically interpretable framework, named SAFE-MIL, for risk estimation. We first constructed an effectiveness label for each patient from the perspective of exploring the optimal grouping of patients' positive judgment values and sampled patients into 600 and 1,000 groups, respectively, based on multi-instance learning (MIL). A novel and interpretable loss function was further designed based on the Hosmer-Lemeshow test for this framework. By integrating multi-instance learning with the Hosmer-Lemeshow test, SAFE-MIL is capable of accurately estimating the risk of drug treatment failure across diverse patient cohorts and providing the optimal threshold for assessing the risk stratification simultaneously. We conducted a comprehensive case study involving 457 non-small cell lung cancer patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Results demonstrate that SAFE-MIL outperforms traditional regression methods with higher accuracy and can accurately assess patients' risk stratification. This underscores its ability to accurately capture inter-patient variability in risk while providing statistical interpretability. SAFE-MIL is able to effectively guide clinical decision-making regarding the use of drugs in targeted therapy and provides an interpretable computational framework for other patient stratification problems. The SAFE-MIL framework has proven its effectiveness in capturing inter-patient variability in risk and providing statistical interpretability. It outperforms traditional regression methods and can effectively guide clinical decision-making in the use of drugs for targeted therapy. SAFE-MIL offers a valuable interpretable computational framework that can be applied to other patient stratification problems, enhancing the precision of risk assessment in personalized medicine. The source code for SAFE-MIL is available for further exploration and application at https://github.com/Nevermore233/SAFE-MIL.

Impact of pre-bypass ultrafiltration on prime values and clinical outcomes in neonatal and infant cardiopulmonary bypass

Background: A standard blood prime for cardiopulmonary bypass (CPB) in congenital cardiac surgery may possess non-physiologic values for electrolytes, glucose, and lactate. Pre-bypass Ultrafiltration (PBUF) can make these values more physiologic and standardized prior to bypass initiation. We aimed to determine if using PBUF on blood primes including packed red blood cells and thawed plasma would make prime values more predictable and physiologic. Additionally, we aimed to evaluate whether the addition of PBUF had an impact on outcome measures. Methods: Retrospective review of consecutive patients ≤ 1 year of age undergoing an index cardiac operation on CPB between 8/2017 and 9/2021. As PBUF was performed at the perfusionists’ discretion, a natural grouping of patients that received PBUF vs. those that did not occur. Differences in electrolytes, glucose, and lactate were compared at specific time points using Fisher’s exact test for categorical variables and the Wilcoxon rank sum test for continuous variables. Clinical outcomes were also assessed. Results: In both cohorts, the median age at surgery was 3 months and 47% of patients were female; 308/704 (44%) of the PBUF group and 163/414 (39%) of the standard prime group had at least one preoperative risk factor. The proportion of PBUF circuits which demonstrated more physiologic values for glucose (318 [45%]), sodium (434, [62%]), potassium (688 [98%]), lactate (612 [87%]) and osmolality (595 [92%]) was significantly higher when compared to standard prime circuit levels for glucose (8 [2%]), sodium (13 [3%], potassium (150 [36%]), lactate (56 [13%]) and osmolality (23 [6%]) prior to CPB initiation. There were no differences in clinical outcomes or rates of major adverse events between the two cohorts. Conclusions: PBUF creates standardized and more physiologic values for electrolytes, glucose, and lactate before the initiation of bypass without significant impacts on in-hospital outcomes.

Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma.

Background: With a poor prognosis for affected individuals, pancreatic adenocarcinoma (PAAD) is known as a complicated and diverse illness. Immunocytes have become essential elements in the development of PAAD. Notably, sphingolipid metabolism has a dual function in the development of tumors and the invasion of the immune system. Despite these implications, research on the predictive ability of sphingolipid variables for PAAD prognosis is strikingly lacking, and it is yet unclear how they can affect PAAD immunotherapy and targeted pharmacotherapy. Methods: The investigation process included SPG detection while also being pertinent to the prognosis for PAAD. Both the analytical capability of CIBERSORT and the prognostic capability of the pRRophetic R package were used to evaluate the immunological environments of the various HCC subtypes. In addition, CCK-8 experiments on PAAD cell lines were carried out to confirm the accuracy of drug sensitivity estimates. The results of these trials, which also evaluated cell survival and migratory patterns, confirmed the usefulness of sphingolipid-associated genes (SPGs). Results: As a result of this thorough investigation, 32 SPGs were identified, each of which had a measurable influence on the dynamics of overall survival. This collection of genes served as the conceptual framework for the development of a prognostic model, which was carefully assembled from 10 chosen genes. It should be noted that this grouping of patients into cohorts with high and low risk was a sign of different immune profiles and therapy responses. The increased abundance of SPGs was identified as a possible sign of inadequate responses to immune-based treatment approaches. The careful CCK-8 testing carried out on PAAD cell lines was of the highest importance for providing clear confirmation of drug sensitivity estimates. Conclusion: The significance of Sphingolipid metabolism in the complex web of PAAD development is brought home by this study. The novel risk model, built on the complexity of sphingolipid-associated genes, advances our understanding of PAAD and offers doctors a powerful tool for developing personalised treatment plans that are specifically suited to the unique characteristics of each patient.

Evaluating Physiological MRI Parameters in Patients with Brain Metastases Undergoing Stereotactic Radiosurgery-A Preliminary Analysis and Case Report.

Brain metastases occur in ten to thirty percent of the adult cancer population. Treatment consists of different (palliative) options, including stereotactic radiosurgery (SRS). Sensitive MRI biomarkers are needed to better understand radiotherapy-related effects on cerebral physiology and the subsequent effects on neurocognitive functioning. In the current study, we used physiological imaging techniques to assess cerebral blood flow (CBF), oxygen extraction fraction (OEF), cerebral metabolic rate of oxygen (CMRO2) and cerebrovascular reactivity (CVR) before and three months after SRS in nine patients with brain metastases. The results showed improvement in OEF, CBF and CMRO2 within brain tissue that recovered from edema (all p ≤ 0.04), while CVR remained impacted. We observed a global post-radiotherapy increase in CBF in healthy-appearing brain tissue (p = 0.02). A repeated measures correlation analysis showed larger reductions within regions exposed to higher radiotherapy doses in CBF (rrm = -0.286, p < 0.001), CMRO2 (rrm = -0.254, p < 0.001), and CVR (rrm = -0.346, p < 0.001), but not in OEF (rrm = -0.004, p = 0.954). Case analyses illustrated the impact of brain metastases progression on the post-radiotherapy changes in both physiological MRI measures and cognitive performance. Our preliminary findings suggest no radiotherapy effects on physiological parameters occurred in healthy-appearing brain tissue within 3-months post-radiotherapy. Nevertheless, as radiotherapy can have late side effects, larger patient samples allowing meaningful grouping of patients and longer follow-ups are needed.

Study of Echocardiogram Parameters from PPG Signal Using Self-Organized Operational Map-based Network.

Two crucial echocardiography parameters -left ventricular ejection fraction (LVEF) and myocardial performance index (MPI) are referred by clinicians to diagnose heart health. Here, an attempt was made to study the possibility of predicting values of LVEF and MPI from Photoplethysmography (PPG) signals. The classification of patients based on the LVEF and MPI values was also evaluated. After PPG signal feature extraction, the Dual Attention-Self Organised Operational Map-LSTM-Conv Network (DASLCN) was used to find the necessary results. Self-organized operational maps (SOOM) helped map the features before sending them to BiLSTM and 1D CNN layers. The results obtained were regression=0.86 with error% of 5.32±8.9 for MPI and accuracy=0.90 & sensitivity=0.89 for LVEF. This technique might help diagnose heart conditions from PPG signals without routine echocardiography.Clinical relevance- PPG is an easy cost-effective portable technique. Whereas, clinical echocardiography is possible only in specialized hospitals. Thus, exploring PPG signals to predict LVEF and MPI values were tried here. This study has been made on whether the grouping of patients based on the range of LVEF and MPI values was possible or not. Newly designed DASLCN helped to perform regression and classification in the same network.

MSR37 Identifying Clinical Subtypes of Long COVID: An Unsupervised Machine Learning Approach

The symptoms of patients with post-acute COVID-19 syndrome are heterogenous, impact multiple systems, and are often non-specific. To better understand the symptomatic profile of this population, this study used real-world data and unsupervised machine learning techniques to identify distinct groupings of long COVID patients. Children/adolescents (age 0-17) and adults (age 18-64 and ≥65) with ≥2 primary diagnoses for U09.9 “Post COVID-19 condition” from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum’s de-identified Clinformatics® Data Mart Database, with the first diagnosis deemed index. Included patients had ≥1 diagnosis for COVID-19 at least 4 weeks before index and continuous enrollment during the 12 months prior to index. Diagnoses recorded ±2 weeks from index that were not present prior to the initial COVID-19 diagnosis were captured and used as patient features for k-means clustering. Final cluster assignments were selected based on silhouette coefficient and clinical relevancy of groupings. 3,587 patients met eligibility criteria, yielding three clusters. Concurrent symptom domains surrounding index included breathing, fatigue, pain, cognitive, and cardiovascular diagnoses. The first cluster (N=2,578, 71.8%) was characterized by patients with only a single symptom domain (33% breathing, 33% cardiovascular, 20% fatigue, 11% cognitive). The second cluster (N=651, 18.1%) all presented with breathing symptoms accompanied by one additional domain (cardiovascular 40%, fatigue 28%, pain 18%). The final cluster (N=358, 9.9%) experienced breathing symptoms accompanied by two additional domains (fatigue and cardiovascular 34%, cardiovascular and cognitive 34%). Cluster 3 was slightly older than clusters 1 or 2 (mean age 66 vs. 58 years, respectively). Unsupervised machine learning identified distinct groups of long COVID patients, which may help inform multidisciplinary care needs. Our analysis suggests that many patients with long COVID may experience symptoms from only a single domain, and multi-system illness may generally include breathing complications accompanied by fatigue and/or cardiovascular complications.

Construction of a Novel RNA Prognostic-Predicting Model for Rectum Adenocarcinoma by Bioinformatics Analysis

We aimed to construct and validate a prognostic-predicting model of rectum adenocarcinoma (READ) based on RNA-binding protein-related genes (RBPGs) by bioinformatics and statistical analysis. We obtained the expression matrix containing 1542 RBPGs from the RBPDB database through the R package. Then, 126 differentially expressed RBPGs (DE-RBPGs) were obtained by differential expression analysis between groups, among which 63 down-regulated genes and 63 up-regulated genes. Next, Ribonucleoprotein complex biosynthesis and assembly were the primary biological processes (BP) identified by the Gene Ontology (GO) enrichment study, cytoplasmic translation, ncRNA processing, ncRNA and rRNA metabolic process. The functions of cellular components (CC) were closely related to organellar and mitochondrial ribosomes and their subunits, spliceosomal complex, mitochondrial matrix and ribonucleoprotein granule. Then, we put 126 DE-RBPGs into the protein–protein interaction (PPI) network, showing the mutual regulation between each DE-RBPGs. In addition, eight prognostics DE-RBPGs (PDE-RBPGs) were identified by Cox regression analysis, among which DIS3L, EFTUD2, FAM98B, IREB2, NOP58, PDCD7 and STRBP were low-risk PDE-RBPGs (HR less than 1), while GTF3A was a high-risk PDE-RBPG (HR greater than 1). A prognosis model consisting of two PDE-RBPGs (EFTUD2 and FAM98B) was finally optimized. The results of the study of the Receiver Operating Characteristics (ROC) curve and the survival analysis revealed that the prognostic-predicting model constructed by us could accurately predict the grouping and prognosis of READ patients. The above results further elucidated the important molecular functions, key biological pathways and gene (protein) interactions of DE-RBPGs. The prognostic-predicting model constructed by us can accurately predict the patients with READ, which is very valuable as a guide for READ early clinical evaluation and therapy.