Early Detection Of Patients Research Articles

Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study.

The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.

Massive transfusion protocol.

The management of severe traumatic bleeding includes damage control resuscitation procedures including, in addition to surgical bleeding control, the application of the massive transfusion protocol. The aim of this paper is to present the massive transfusion protocol and selected scoring systems for an early detection of patients with severe post-traumatic bleeding. The use of a standardized protocol to activate the massive transfusion protocol reduces lethality due to severe traumatic bleeding and the consumption of blood products in trauma centers.

Echocardiography Can Improve the Selection of Mechanical Support Strategies for Patients With Severe COVID-19 Respiratory Distress Syndrome.

To the Editor: I read with interest the study by Beyls et al.1 which compared the different clinical characteristics and outcomes of patients with COVID-19–related respiratory distress syndrome (CARDS) requiring veno-venous extracorporeal membrane oxygenation (VV-ECMO) during the three epidemic waves of COVID-19 in France. Despite the increasing knowledge on the particularities of severe COVID-19 and the growing experience in the management of severe CARDS, that study revealed a massive increase in the 90 day mortality of patients undergoing VV-ECMO during the second epidemic wave. One plausible explanation for that important observation could be the fact that over the course of the pandemic, new variants of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have continuously emerged. Because the different waves of infection were mainly associated with one or more SARS-CoV-2 “variants of concern” (VOC) with different pathogenicity, it appears obvious that the pathogenicity of the dominant VOC in an infection wave can have important impacts on the outcomes of patients with severe COVID-19. Indeed, the Delta/B.1.617.2 variant, which was dominant during the second wave, is known to be associated with higher risk of death than both the previously dominant VOCs (i.e., Alpha/B.1.1.7, Beta/B.1.351 and Gamma/P.1) and the Omicron/B1.1.529 variant which became the dominant VOC during third wave.2,3 The excessively high mortality rate (i.e., 85.7%1) of patients supported by ECMO during the second wave deserves particular attention because a renewed appearance of more pathogenic VOCs cannot be ruled out. The high incidence of extensive pulmonary thrombotic microangiopathy associated with severe ventilation-perfusion mismatch, increased resistance in the pulmonary circulation and frequent afterload-induced right ventricular (RV) failure, which were identified as distinct features of severe CARDS (especially in infections caused by the Delta/B.1.617.2 variant) were often the leading cause of death attributed to COVID-19.4,5 Early detection of patients with increasing RV hemodynamic overloading can facilitate the selection of the most appropriate management (including the requirement of ECMO or a combined respiratory and circulatory support), which can be decisive for the patients’ survival.4 In severe RV afterload mismatch, the RV output decreases substantially, and RV dilation-related tricuspid regurgitation can rapidly increase. In such situations, attempting to treat the underlying etiology of impaired gas exchange using VV-ECMO alone may become insufficient.4 Considering the high incidence and the deleterious prognostic impact of right heart involvement in severe COVID-19 it seems likely that this was also the major cause of the high mortality rate documented by Beyls et al.1 for their ECMO-supported patients (92% with VV-ECMO) during the second wave. Thus, close monitoring of the right heart in patients with CARDS associated with hemodynamic instability, already before overt RV failure develops, could substantially improve therapeutic decision-making. It is therefore difficult to understand the still limited use of routine echocardiography for the surveillance of hospitalized patients with COVID-19. Echocardiography can improve the identification of patients with CARDS requiring ECMO therapy, the selection of the most appropriate support strategy (VV-ECMO, veno-arterial-ECMO, or VV-ECMO plus RV or left ventricle temporary percutaneous mechanical support) and also the weaning decision-making after cardiopulmonary amelioration during the support.

External validation of the ORBI risk score for cardiogenic shock prediction in patients with ST-segment elevation myocardial infarction

Abstract Funding Acknowledgements Type of funding sources: None. Background Cardiogenic shock (CS) complicates 5 to 15 % of ST-segment elevation myocardial infarction (STEMI) cases. With a high-short term fatality rate estimated between 40 to 50 %, it is considered as the first cause of mortality in myocardial infarction. Early detection of patients in the state of "pre-CS" may improve prognosis as it makes it possible to adapt the monitoring and to establish an appropriate treatment at an earlier stage. The ORBI risk score (ORS) based on 11 variables was created in order to identify patients at high-risk of in-hospital STEMI-related CS but external validation is lacking. Aim The primary objective of this study was to evaluate the external validity of the ORBI Risk Score in a Tertiary Hospital Centre in southern France. Methods Consecutive STEMI patients managed by primary percutaneous coronary intervention (&amp;lt; 24 h after chest-pain onset) at our tertiary hospital in 2019 were retrospectively included. Patients with CS at admission, with previous fibrinolysis or without attempted p-PCI were excluded. Data necessary for the calculation of the ORBI risk score were collected (age, prior stroke/transient ischaemic attack, cardiac arrest upon admission, type of STEMI, first medical contact-to-pPCI delay, initial clinical presentation (Killip class, heart rate, blood pressure, glycaemia, culprit lesion, and post-pPCI TIMI-flow grade and the occurrence of in-hospital CS). The score's calibration was assessed by a graphical method and its discriminatory performance by calculating the C-statistic (ROC curve). Results 287 patients were included (mean age 64 y.o, 81.2% male, anterior STEMI 40.8%, LAD culprit lesion 47%, median first medical contact-to-PCI delay 112 min). In-hospital CS occurred in 18 patients (6.3%) with all-cause in-hospital mortality of 4.2%. The observed in-hospital CS rates were respectively 1.8%, 2.9%, 41.7%, and 50 % in the four risk categories, ranging from low ( ORS 0–7) to low- to-intermediate (ORS 8–10), and from intermediate-to-high (ORS 11–12) to high (ORS≥13). The score showed satisfactory discrimination (c-statistic of 0.90 (CI 95% 0.83–0.97), p&amp;lt;0.001) with 93 % correctly classified patients at a cut-off ≥ 10. However, in term of calibration, the score tends to under-estimate the occurrence of CS, mostly in high-risk patients. Conclusion The ORBI risk score demonstrated good discrimination, but it lacked calibration in the case of this validation cohort. Notwithstanding this drawback, it may facilitate the identification of STEMI patients at high risk of CS and thus increase their enrolment in CS dedicated clinical trials. Further validation studies are needed before scaling up its routinely clinical use.

SmartLab 2.0 in cardiovascular prevention of atherogenic dyslipidemia

IntroductionSmartLab 2.0 is an innovative concept of multidisciplinary collaboration between the clinical laboratory and the diabetes day unit that was born with the aim of identifying patients at high cardiovascular risk who require priority attention, such as patients with atherogenic dyslipidemia, in order to create a cardiovascular prevention strategy. ObjectiveImplementation in the Laboratory Information System (LIS) of an automated biochemical algorithm for the identification of patients with atherogenic dyslipidemia in routine analyses and priority referral to the diabetes day unit. Material and methodsThe algorithm designed in the SIL was: HBA1c>9.3+TG>150mg/dl+HDLc<40mg/dl+LDL/ApoB<1.3. A comment was inserted alerting the requesting physician of the diagnosis of atherogenic dyslipidemia and priority referral was made from the laboratory to the diabetes day unit in the necessary cases. ResultsIn the 1-year period, a total of 899 patients with HBA1c>7 and atherogenic dyslipidemia criteria were identified. Of these, 203 patients from primary care with HbA1c>9.3 were referred to the diabetes day hospital. ConclusionsReinforcement of cardiovascular prevention is necessary at all levels. The clinical laboratory should play a fundamental role in the diagnosis of dyslipidemias. Early detection of patients at high cardiovascular risk is essential and collaboration between the different clinical units is fundamental to guarantee patient safety.

Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model

The tremendous growth of the Covid19 epidemic in recent months is devastatingly affecting human civilization. Many different biomarkers are being studied to monitor the patient's health. This might mask the symptoms of various diseases, making it more challenging for a doctor to make a correct diagnosis or prognosis. Therefore, this study aimed to create several classes of prediction methods that can handle situations of varying severity (severe, moderate, and mild). Using machine learning, a Lasso-logistic regression model is developed. To create the Covid-19 clinical dataset, researchers enlisted the help of 78 patients from the Azizia main hospital sector, the Wasit Health Directorate, and the Ministry of Health. The results show that the proposed method is generally accurate to 85.9%. Deaths have been reduced thanks to the established prediction method that enables early detection of patients across three severity levels.

Altered Distribution of Unesterified Cholesterol among Lipoprotein Subfractions of Patients with Diabetes Mellitus Type 2.

Biomarkers are important tools to improve the early detection of patients at high risk for developing diabetes as well as the stratification of diabetic patients towards risks of complications. In addition to clinical variables, we analyzed 155 metabolic parameters in plasma samples of 51 healthy volunteers and 66 patients with diabetes using nuclear magnetic resonance (NMR) spectrometry. Upon elastic net analysis with lasso regression, we confirmed the independent associations of diabetes with branched-chain amino acids and lactate (both positive) as well as linoleic acid in plasma and HDL diameter (both inverse). In addition, we found the presence of diabetes independently associated with lower concentrations of free cholesterol in plasma but higher concentrations of free cholesterol in small HDL. Compared to plasmas of non-diabetic controls, plasmas of diabetic subjects contained lower absolute and relative concentrations of free cholesterol in all LDL and HDL subclasses except small HDL but higher absolute and relative concentrations of free cholesterol in all VLDL subclasses (except very small VLDL). These disbalances may reflect disturbances in the transfer of free cholesterol from VLDL to HDL during lipolysis and in the transfer of cell-derived cholesterol from small HDL via larger HDL to LDL.

Identifying Diagnostic Biomarkers of Breast Cancer Based on Gene Expression Data and Ensemble Feature Selection

Background: In recent years, the identification of biomarkers or signatures based on gene expression profiling data has attracted much attention in bioinformatics. The successful discovery of breast cancer (BRCA) biomarkers will be beneficial in reducing the risk of BRCA among patients for early detection. Methods: This paper proposes an Ensemble Feature Selection method to screen biomarkers (abbreviat-ed as EFSmarker) for BRCA from publically available gene expression data. Firstly, we employ twelve filter feature selection methods, namely median, variance, Chi-square, Relief, Pearson and Spearman correlation, mutual information, minimal-redundancy-maximal-relevance criterion, ridge regression, decision tree and random forest with Gini index and accuracy index, to calculate the importance (weights or coefficients) of all features on the training dataset. Secondly, we apply the logistic regres-sion classifier on the test dataset to calculate the classification AUC value of each feature subset indi-vidually selected by twelve methods. Thirdly, we provide an ensemble feature selection method by ag-gregating feature importance with classification AUC value. In particular, we establish a feature im-portance score (FIS) to evaluate the importance of each feature underlying all feature selection methods. Finally, the features with higher FIS are taken as identified biomarkers. Results: With the direction of the FIS index induced by the EFSmarker method, 12 genes (COL10A1, COL11A1, MMP11, LOC728264, FIGF, GJB2, INHBA, CD300LG, IGFBP6, PAMR1, CXCL2 and FXYD1) are regarded as diagnostic biomarkers for BRCA. Especially, COL10A1, ranked first with a FIS value of 0.663, is identified as the most credible biomarker. The findings justified via gene and protein expression validation, functional enrichment analysis, literature checking and independent dataset validation verify the effectiveness and efficiency of these selected biomarkers. Conclusion: Our proposed biomarker discovery strategy not only utilizes the feature contribution but also considers the prediction accuracy simultaneously, which may also serve as a model for identifying unknown biomarkers for other diseases from high-throughput gene expression data. The source code and data are available at https://github.com/zpliulab/EFSmarker.

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

BackgroundThe many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important.ObjectivesTo assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease.DesignThis is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups.SummaryThis trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines.Trial registrationClinicalTrials.Gov Identifier: NCT05377320

Assessment of Calcium Score Cutoff Point for Clinically Significant Aortic Stenosis on Lung Cancer Screening Program Low-Dose Computed Tomography-A Cross-Sectional Analysis.

Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected in lung cancer screenees. Therefore, initial aortic valve calcification evaluation should be additionally performed on LDCT. The aim of this study was to estimate a calcium score (CS) cutoff point for clinically significant AS diagnosis based on LDCT, confirmed by echocardiographic examination. The study included 6631 heavy smokers who participated in a lung cancer screening program (MOLTEST BIS). LDCTs were performed on all individuals and were additionally assessed for aortic valve calcification with the use of CS according to the Agatston method. Patients with CS ≥ 900 were referred for echocardiography to confirm the diagnosis of AS and to evaluate its severity. Of 6631 individuals, 54 met the inclusion criteria and underwent echocardiography for confirmation and assessment of AS. Based on that data, receiver operating characteristic (ROC) curves of CS were plotted, and cutoff points for clinically significant AS diagnosis were established: A CS of 1758 for at least moderate AS had 85.71% (CI 65.36-95.02%) sensitivity and 75.76% (CI 58.98-87.17%) specificity; a CS of 2665 for severe AS had 87.5% (CI 73.89-94.54%) sensitivity and 76.92% (CI 49.74-91.82%) specificity. This is the first study to assess possible CS cutoff points for diagnosing clinically significant AS detected by LDCT in lung cancer screening participants. LDCT with CS assessment could enable early detection of patients with clinically significant AS and therefore identify patients who require appropriate treatment.

Early reocclusion after successful mechanical thrombectomy for large artery occlusion-related stroke.

Despite initial successful recanalization after mechanical thrombectomy (MT), some patients with large artery occlusion (LAO)-related stroke will experience an early reocclusion of the injured vessel which may worsen their prognosis. We aimed to investigate the prevalence, associated factors and prognosis of early reocclusion after successful MT in a large prospective cohort of stroke patients with LAO. We included patients from the Lille reperfusion registry with LAO-related stroke involving M1 segment, internal carotid artery terminus or tandem ICA-M1 occlusion, with successful recanalization after MT and available 24 h imaging follow-up. Early reocclusion was defined as internal carotid artery terminus or M1 occlusion on 24 h magnetic resonance imaging (MRI) or computed tomography (CT) vascular imaging. Multivariable logistic regression models were used to investigate factors associated with early reocclusion and its impact on outcomes. Between 2015 and 2020, 62 of 1015 included patients experienced an early reocclusion (6.1%). Age (odds ratio (OR) per 15 years decrease: 1.38 (1.05-1.81)) antiplatelet use (OR: 0.41 (0.19-0.89)), several device passes (OR: 2.13 (1.18-3.83)), atherosclerosis cause (OR: 2.38 (1.19-4.78)), and early clinical worsening (OR: 2.45 (1.18-5.07)) were independently associated with early reocclusion. Early reocclusion was independently associated with poor prognosis (OR: 7.15 (3.49-14.65)) and mortality (OR: 2.05 (1.07-3.91)) at 3 months. Six percent of patients with LAO-related stroke and initial successful recanalization experienced early reocclusion. This event is associated with a 7-fold increased risk of poor functional outcome and a 2-fold increased risk of mortality. Further efforts are warranted to refine early detection of patients at risk of reocclusion and to improve their management.

Study of Gene Expression Signatures for the Diagnosis of Pediatric Acute Lymphoblastic Leukemia (ALL) Through Gene Expression Array Analyses

Background: Acute lymphoblastic leukemia (ALL) as the most common malignancy in children is associated with high mortality and significant relapse. Currently, the non-invasive diagnosis of pediatric ALL is a main challenge in the early detection of patients. In the present study, a systems biology approach was used through network-based analysis to identify the key candidate genes related to ALL development and relapse.&#x0D; Materials and methods: In this systems biology (experimental) study, main and validating datasets were retrieved from a gene expression omnibus (GEO). Gene expression analyses were done using a bioinformatics array research tool (BART) and ExAtlas. Gene ontology and pathway enrichment analysis were also performed via Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the Search Tool for the Retrieval of Interacting Genes (STRING) and cytoscape V.3.9.1 were used to network construction and analysis. The MCODE and NCMine Plugin of cytoscape were applied to find clusters and a functional module in the network. The Kaplan Myer curve was applied in order to survival analysis of the validated candidate genes. A P-value of &lt; 0.05 was considered as significant.&#x0D; Results: A total of 671 differentially expressed genes (DEGs) mainly involved in transporter/channel activity functions, cell communication/signaling processes and fatty acid transport/PPAR signaling/eicosanoid metabolism pathways were identified (P-value &lt; 0.05). The main cellular compartments were plasma membrane, cell periphery and cell surface (P-value &lt;0.05). The network analysis revealed 68 hub genes, 29 of which were candidate genes. Five candidate genes were also validated in two independent experiments. These genes were considered as key candidate genes, and three of them (BCL2L11, IGF1, PDE5A) were predictors of pediatric ALL patients survival (P-value &lt; 0.05). &#x0D; Conclusion: BCL2L11, IGF1 and PDE5A genes, as key candidate genes, are potentially good diagnostic biomarkers and therapeutic targets for pediatric ALL.

Analysis of Factor Influencing Delayed Referral of Undescended Testis

BACKGROUND: The rate of delay in the referral of patients with undescended testis (UDT) to health facilities in Indonesia is still relatively high due to delays in the handling of urological surgery. Hence, it requires rapid and appropriate identification and early detection of patients with UDT in the community. AIM: The study aims to analyze the factors of delay in referral in cases of UDT management. METHODS: This study used a cohort design with a retrospective review approach to analyze various factors that cause delays in referring patients with UDT. Collecting identification was done by tracing UDT case-patient documents from January 2016 to November 2021. Data analysis used the Mann–Whitney and Chi-square comparison test, with significance achieved if the p &lt; 0.05. RESULTS: Delay by doctors/paramedics who were not followed up, ignorance of the risk of delaying surgery, and undiagnosed UDT by parents/health-care providers showed a significant difference where the percentage of patients who were delayed was greater on these factors. In addition, the reference source is also known to have a significant difference with a significant value (p &lt; 0.005). CONCLUSIONS: Factors that played a significant role in the delay in operative procedures for treating UDT were delays in referral from doctors (p &lt; 0.025) (primary care physicians and pediatricians) and undiagnosed UDT conditions in patients (&lt; 0.005).

Pooling Strategy for Detection of SARS-CoV-2 RNA by Real-Time RT-PCR: Comparison of Pooling 5 and 10 Samples.

We evaluated whether it was appropriate to screen SARS-CoV-2 in sample pools of 5 and 10. This study was aimed to evaluate whether the pooling strategy would be an appropriate strategy for SARS-CoV-2 screening. In the study, 5 and 10 sample pools were formed using 720 nasopharyngeal swab samples, of which 72 were positive, and 648 were negative. The samples were analyzed in three groups according to their Ct values as high, medium, and low viral load. SARS-CoV-2 RNA in nasopharyngeal swab samples was detected by the real-time PCR method on the Bio-Rad platform. The sensitivity of 5-sample pooling was 77.8%, and the sensitivity of 10-sample pooling was 75%. The false-negative rate was 22.2% in 5 sample poolings and 25% in 10 sample poolings. Out of the samples with medium and high viral loads, none of the positive samples were lost in either pool. In pools containing both 5 samples and 10 samples, the individual mean Ct values of the samples detected as false-negative were significantly higher (low viral load) than those of the other samples (p < 0.001). In this study, 5 and 10 pooling seems useful in detecting patients with medium and high viral loads. Pooling strategies that allow mass screening of SARS-CoV-2 can contribute to early detection of patients at high risk of SARS-CoV-2 transmission in low prevalence areas, as well as timely public health interventions.

Особенности течения идиопатического сколиоза у лиц казахского этноса

Idiopathic scoliosis is one of the most common diseases in the structure of orthopedic pathology. This article presents the preliminary results of the authors' study, which is aimed at a comprehensive study of the features of idiopathic scoliosis in individuals of the Kazakh ethnic group.The purpose of the study - to study the main characteristics of the course of idiopathic scoliosis in individuals of the Kazakh ethnic group.Methods. During the study, the following examination methods were used: history taking, examination, questioning and standing spondylography in two projections. When the diagnosis was confirmed, the patients were divided into the main group, in the absence of deformity - into the control group. Biological material was taken from the subjects of the main group for further study.Results. The study involved 400 patients of the Kazakh ethnic group, who were divided into two groups. The average age of patients in the main group is 24.5+-8.2 years. The prevalence by gender in patients with identified deformity showed that female patients (153 patients - 76.5%) are more likely to develop idiopathic scoliosis than male patients (47 patients – 23.5%) are.Conclusions. Now, there is a need for a detailed study of the etiology and prevalence of idiopathic scoliosis. The expected result of the genetic decoding of the collected biomaterial can contribute to the early detection of patients with this pathology, a possible further prediction of the course of the disease.Key words: idiopathic scoliosis, deformity, genetic predisposition.

Development conceptual of pharmacoeconomic model of technology for early diagnosis and pharmacotherapy of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a rare, progressive disease with a poor prognosis in the absence of timely diagnosis and appropriate therapy. Despite significant advances in the treatment of PAH, many patients remain undiagnosed years after the onset of the disease. Evaluation of the implementation of early diagnostic technologies for PAH is increasingly being used to develop economic evidence for health care at the early stages of diagnosis. The development of models of early diagnosis technology for LAH is highly relevant and can be used in the development and management of new medical technologies, as well as to reduce the perceived risks during the implementation of screening programs for LAH diagnosis.&#x0D; The objective of the paper: Conceptual development of a pharmacoeconomic model of the technology of early diagnosis and pharmacotherapy of PAH.&#x0D; Our pharmacoeconomic analysis is based on the «cost–effectiveness» model, which reflects the effectiveness and associated costs of implementing diagnostic screening for early detection of patients with PAH. The analysis of input data of the existing health care system was conducted using systematic review, documentary, informational and graphical research methods.&#x0D; The presented pharmacoeconomic model describes not only the structure of the incidence of PAH in the studied risk group or at the level of the entire population, but also takes into account the pharmacoeconomic evaluation of the complex technology of timely medical care for patients with PAH based on diagnostic screening.&#x0D; A pharmacoeconomic model of the technology of early diagnosis and pharmacotherapy of pulmonary arterial hypertension was developed, which covered a horizon of 10 years and was conducted from the point of view of the existing health care system, showed that for patients with delayed diagnosis of PAH, the modeling determined the average life expectancy at the level of 4.13 years and 2.08 QALYs.&#x0D; It has been determined that a diagnostic screening strategy for PAH would be cost–effective compared to no screening at different thresholds of diagnostic cost. However, other factors such as public awareness and acceptance of the screening programme and availability of human resources should be considered.

The Splicing Factor PTBP1 Represses TP63 γ Isoform Production in Squamous Cell Carcinoma.

Quantifying TP63γ isoforms in patients' tumors could allow for the early detection of patients with HNSCC with an early loss in desmosomal gene expression and poor prognostic. The identification of PTBP1 as a transacting factor controlling TP63γ production may allow to control TP63γ expression.

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

ObjectiveEarly identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis.MethodsThis study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach.ResultsA total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively.ConclusionAn ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

569 IMPACT OF FRAILTY ON VERY LONG-TERM OUTCOME IN PATIENTS UNDERGOING PERCUTANEOUS TRANSCATHETER AORTIC VALVE IMPLANTATION (TAVI) IN A HIGH-FLOW REFERRAL CENTER: A PROSPECTIVE STUDY

Abstract Background Transcatheter strategies for aortic stenosis (AS) are a therapeutic option in older patients not amenable for open heart surgery. Guidelines recommend surgical scores like the Society of Thoracic Surgeons (STS) for risk stratification. However, the long-term predictive power of these scores in older patients is limited. Purpose To assess the impact of frailty status on the very long-term mortality in patients with severe AS evaluated for transcatheter aortic valve implantation (TAVI) in a high-flow and high-volume tertiary care center. Methods Consecutive patients &amp;gt;80 years referred to TAVI from January to December 2019 at our tertiary care institution were prospectively screened for frailty through a comprehensive geriatric assessment (CGA) based on physical function and the Multidimensional Prognostic Index (MPI). The MPI is a three-level score used to stratify frailty and risk of mortality (low, intermediate, or high risk) based on eight key domains for frailty assessment (functional and cognitive status, nutrition, mobility, and risk of pressure sores, multimorbidity, polypharmacy and co-habitation). Frailty was defined by an intermediate-high MPI. Patients were also analyzed by STS score (&amp;lt;4%: low risk; 4-8%: intermediate risk; &amp;gt;8%: high risk). All patients prospectively called at 1, 2 and 3 years from TAVI. Results Overall, 134 patients were referred for TAVI (mean age: 84±4 years; &amp;gt;90 years: 12%, women 67%). Average STS score was 4.6 ± 3.0 (low risk: 49%; intermediate: 39%, high risk: 12%). Mean SPPB was 6.3±3.7 (SPPB &amp;lt;6: 32%). Ninety-five (71%) patients belonged to the MPI-low risk group, 30 (22%) to the MPI intermediate risk group and nine (7%) to the MPI high risk group. After 3 years, 17 (12.7%) patients died, and 40 (29.9%) were hospitalized: 21 (15.7%) for cardiovascular complications (N=11 for acute heart failure, N=6 for major bleeding, N=4 for stroke). Four patients (2.9%) implanted a pacemaker. One patient (0.7%) required a de-novo intervention. At Cox multivariable analysis, only frailty (MPI intermediate-high) was associated with mortality (HR 5.42, 95% C.I. 1.88-12.11, p=0.002) while a high STS score showed a trend towards higher mortality risk (HR 1.63, 95% C.I. 0.91-2.91, p=0.101). Overall, at Kaplan Meier survival analysis, patients considered fit (non-frail) were characterized by a better prognosis irrespective of STS score (&amp;lt;8 vs &amp;gt;8). Conclusions In a prospectively enrolled cohort of old TAVI candidates, frailty status was able to identify patients at higher risk of mortality irrespective of STS risk. Frailty assessment could be a useful tool, coupled with other scores, for early detection of patients at risk of disability, and potentially, for preventing the futility of the TAVI procedure.

Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer

BackgroundColorectal cancer (CRC) has been regarded as one of the most frequently diagnosed malignancies among the leading causes of cancer-related morbidity and mortality globally. Diagnosis of CRC at the early-stages of tumour might improve the survival rate of patients. The current study sought to determine the performance of fecal Fusobacterium nucleatum (F. nucleatum) and Streptococcus bovis (S. bovis) for timely predicting CRC.MethodsThrough a case–control study, the fecal sample information of 83 individuals (38 females, 45 males) referring to a hospital in Tehran, Iran was used. All patients underwent a complete colonoscopy, regarded as a gold standard test. Bacterial species including S. bovis and F. nucleatum were measured by absolute quantitative real-time PCR. The Bayesian univariate and bivariate latent class models (LCMs) were applied to estimate the ability of the candidate bacterial markers in order to early detection of patients with CRC.ResultsBayesian univariate LCMs demonstrated that the sensitivities of S. bovis and F. nucleatum were estimated to be 86% [95% credible interval (CrI) 0.82–0.91] and 82% (95% CrI 0.75–0.88); while specificities were 84% (95% CrI 0.78–0.89) and 80% (95% CrI 0.73–0.87), respectively. Moreover, the area under the receiver operating characteristic curves (AUCs) were 0.88 (95% CrI 0.83–0.94) and 0.80 (95% CrI 0.73–0.85) respectively for S. bovis and F. nucleatum. Based on the Bayesian bivariate LCMs, the sensitivities of S. bovis and F. nucleatum were calculated as 93% (95% CrI 0.84–0.98) and 90% (95% CrI 0.85–0.97), the specificities were 88% (95% CrI 0.78–0.93) and 87% (95% CrI 0.79–0.94); and the AUCs were 0.91 (95% CrI 0.83–0.99) and 0.88(95% CrI 0.81–0.96), respectively.ConclusionsOur data has identified that according to the Bayesian bivariate LCM, S. bovis and F. nucleatum had a more significant predictive accuracy compared with the univariate model. In summary, these intestinal bacteria have been highlighted as novel tools for early-stage CRC diagnosis.