Common Clinical Parameters Research Articles

Unsupervised clustering analysis based on multidimensional features reveals distinct clinical characteristics and associated factors of different phenotypes in patients with chronic rhinosinusitis with nasal polyp

Objective: To utilize routinely available clinical parameters to uncover the clinical features of different clusters in patients with chronic rhinosinusitis with nasal polyp (CRSwNP) through unsupervised clustering analysis. Methods: The clinical data from 155 CRSwNP patients undergoing nasal endoscopic surgery at Renmin Hospital of Wuhan University from 2021 to 2023 were prospectively collected, including 112 males and 43 females, aged from 7 to 87 years. Unsupervised clustering analysis was conducted using various clinical parameters, including age, gender, smoking and drinking history, local eosinophil (EOS) and neutrophil (NEU) counts, comorbid allergic rhinitis (AR), comorbid asthma, recurrence status, serum-specific IgE, total IgE, cytokine levels, peripheral blood EOS count and percentage, Lund-Mackay CT score, the ratio of CT scores for the maxillary sinus and ethmoid sinus (E/M ratio), visual analogue scale (VAS) score, Lund-Kennedy endoscopic score, and other common clinical indicators to elucidate the clinical characteristics of each cluster. Statistical analysis was conducted using GraphPad Prism 9.5 software. Results: Hierarchical clustering analysis identified four main clusters (Cluster A1-A4), showcasing distinct characteristics such as mild nasal polyps with higher peripheral blood cytokines levels, nasal polyps accompanied by allergies and asthma, a subtype of nasal polyps with high recurrence rates dominated by neutrophils, and nasal polyps with high eosinophil levels. Further subset clustering revealed two clusters of mild polyps (Cluster B1-B2) featuring high cytokine expression and comorbid AR; and two clusters of severe polyps (Cluster B3-B4) presented with severe symptoms, high Lund-Mackay CT score, and high Lund-Kennedy endoscopic score. Variations between Cluster B3 and B4 included symptom complexity, the degree of eosinophil infiltration, and the probability of comorbid asthma. Further clustering analysis for eosinophilic nasal polyps revealed a cluster characterized by highly neutrophilic infiltration and recurrent nasal polyps. The comprehensive analysis of multi-index correlations demonstrated valuable insights into the relationships between common clinical parameters of nasal polyps, providing valuable information for a deeper understanding of the pathogenesis of CRSwNP. Conclusion: The clustering analysis in this study categorizes CRSwNP patients into different clusters based on clinical features and disease outcomes, providing a new perspective for more precise clinical treatment strategies.

Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species

Coronary artery disease (CAD) often leads to adverse events resulting in significant disease burdens. Underlying risk factors often remain inapparent prior to disease incidence and the cardiovascular (CV) risk is not exclusively explained by traditional risk factors. Platelets inherently promote atheroprogression and enhanced platelet functions and distinct platelet lipid species are associated with disease severity in patients with CAD. Lipidomics data were acquired using mass spectrometry and processed alongside clinical data applying machine learning to model estimates of an increased CV risk in a consecutive CAD cohort (n = 595). By training machine learning models on CV risk measurements, stratification of CAD patients resulted in a phenotyping of risk groups. We found that distinct platelet lipids are associated with an increased CV or bleeding risk and independently predict adverse events. Notably, the addition of platelet lipids to conventional risk factors resulted in an increased diagnostic accuracy of patients with adverse CV events. Thus, patients with aberrant platelet lipid signatures and platelet functions are at elevated risk to develop adverse CV events. Machine learning combining platelet lipidome data and common clinical parameters demonstrated an increased diagnostic value in patients with CAD and might improve early risk discrimination and classification for CV events.

Artificial intelligence–adjudicated spatiotemporal dispersion: A patient-unique fingerprint of persistent atrial fibrillation

BackgroundSpatiotemporal dispersion-guided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied. ObjectivesArtificial intelligence–adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters. MethodsIn a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED). ResultsAI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R2 = 0.006, P = .53; and R2 = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes. ConclusionThe atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients. AI-DED may represent a procedure-implementable fingerprint of the PsAF substrate.

Left Atrial Strain Predicts Cardiovascular and All-Cause Mortality.

Left atrial strain can usefully reflect left atrial function. The follow-up periods in previous studies assessing left atrial strain as a survival predictor have been relatively short, and few studies have examined the ability of left atrial strain to predict mortality in patients with borderline diastolic function. This study sought to investigate the survival predictive value of left atrial strain with a longer follow-up duration. In addition, we also evaluated the survival predictive value of left atrial strain in patients with borderline diastolic function. In total, 652 participants who received routine echocardiography underwent 2-D speckle tracking echocardiography to evaluate left atrial reservoir function by peak atrial longitudinal strain. The study endpoints were all-cause and cardiovascular mortality. The mean left atrial strain was 27.6%, and the median follow-up duration was 92 months. During follow-up, 72 patients died of cardiovascular causes and 181 died of all causes. Univariable Cox regression analysis revealed that lower left atrial strain significantly predicted an increase in all-cause and cardiovascular mortality. After adjusting for common clinical and echocardiographic parameters, lower left atrial strain was still associated with a higher risk of all-cause mortality [hazard ratio (HR) = 0.942, p = 0.011] and cardiovascular mortality (HR = 0.915, p = 0.018) in multivariable Cox-regression analysis. In addition, 293 patients had borderline left ventricular diastolic function. Multivariable analysis still revealed that left atrial strain could predict cardiovascular mortality in this population. Our data showed that left atrial strain could predict all-cause and cardiovascular mortality, even after adjusting for general clinical and echocardiographic parameters.

Agreement of results between dry and wet chemistry system for common clinical chemistry parameters

Agreement of results between dry and wet chemistry system for common clinical chemistry parameters

A machine-learning approach for prediction of hospital mortality in cancer-related sepsis

ObjectiveTo develop a machine learning model to predict hospital mortality and identify risk factors in cancer-related sepsis patients. MethodWe obtained data from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set, which included patients who diagnosed with cancer and fulfilled the definition of sepsis between 2008 and 2019. The data set was randomly split into a training set and a validation set. The dataset was imputed using the K-Nearest Neighbor (KNN) imputation model. An advanced machine learning model called CatBoost was established and then assessed by SHAP value. ResultsA total of 5081 patients were included in the final analysis. The cancer-related sepsis patients had a lower hospital survival (13.8% vs. 25.3%, P < 0.001) than non-cancer-related patients.For cancer-related sepsis patients, ensemble learning algorithms were superior to others with better accuracy and larger AUC, such as CatBoost (AUC: 0.828), LightGBM (AUC: 0.818), and Random Forest Classifier (AUC: 0.803). An evaluation of the performance suggested that the CatBoost model with the most powerful discrimination to predict hospital mortality, outperformed other models with a sensitivity of 76% and a specificity of 74%. The best cutoff was 0.223 for the CatBoost model. In addition, CatBoost also outperformed other severity scores such as SAPS-II (AUC: 0.725) and SOFA (AUC: 0.682). Urine output and the minimum BUN level on admission were the most important features for the hospital mortality prediction of cancer-related sepsis, while the patients’ age and the urine output on admission for non-cancer-related patients. ConclusionFor cancer-related sepsis patients, CatBoost model was a better prediction model. It was easy for clinicians to access by use of common clinical vital signs or laboratory examination parameters, which provides convenience for them to evaluate patient’s condition and make follow-up treatments.

Incidence of relapse and frequently relapsing/steroid-dependent nephrotic syndrome in Chinese children with steroid-sensitive nephrotic syndrome: A cohort study.

This study aimed to investigate the incidence of relapse and FR/SDNS in Chinese children with SSNS and to develop clinical prediction models for relapse and FR/SDNS. This retrospective cohort study involved 339 newly onset SSNS patients between 2006 and 2016. The incidence of relapse and FR/SDNS were estimated using the Kaplan-Meier method. Prediction models were constructed based on Cox proportional-hazards regression. The median follow-up time was 8.7 years. The cumulative incidence of relapse at 1-, 2-, and 5-year was 51.0%, 62.5%, and 66.6%. The cumulative incidence of FR/SDNS at 1-, 2-, and 5-year was 18.4%, 29.0%, and 32.9%. The final prediction model for first relapse included four variables (serum albumin, triglycerides, IgM, and time to first remission). The model's discriminative ability was low (Harrell's C index = 0.62). The final prediction model for FR/SDNS included four variables (serum albumin, lipoprotein(a), time to first remission, and time to first relapse). The discrimination and calibration of the prediction model for FR/SDNS were acceptable (Harrell's C index = 0.73, Brier score at 1- and 2-year were 0.11 and 0.17). The first relapse and FR/SDNS mainly occurred in the first 2 years after initial SSNS onset. The prediction model for relapse developed using common clinical parameters performed poorly, while the prediction model for FR/SDNS might be useful.

Admission D-dimer to lymphocyte counts ratio as a novel biomarker for predicting the in-hospital mortality in patients with acute aortic dissection

BackgroundInflammatory factors are well-established indicators for vascular disease, but the D-dimer to lymphocyte count ratio (DLR) is not measured in routine clinical care. Screening of DLR in individuals may identify individuals at in-hopital mortality of acute aortic dissection (AD).MethodsA retrospective analysis of clinical data from 2013 to 2020 was conducted to identify which factors were related to in-hospital mortality risk of AD. Baseline clinical features, cardiovascular risk factors, and laboratory parameters were obtained from the hospital database. The end point was in-hospital mortality. Forward conditional logistic regression was performed to identify independent risk factors for AA in-hospital death. The cutoff value of the DLR should be ideally calculated by receiver operating characteristic (ROC) analysis.ResultsThe in-hospital mortality rate was 15% (48 of 320 patients). Patients with in-hospital mortality had a higher admission mean DLR level than the alive group (1740 vs. 1010, P < .05). The cutoff point of DLR was 907. The in-hospital mortality rate in the high-level DLR group was significantly higher than that in the low-level DLR group (P < .05). Univariate analysis showed that 8 of 38 factors were associated with in-hospital mortality (P < .05), including admission WBC, neutrophils, lymphocytes, neutrophils/lymphocytes (NLR), prothrombin time (PT), heart rate (HR), D-dimer, and DLR. In multivariate analysis, DLR (odds ratio [OR] 2.127, 95% CI 1.034–4.373, P = 0.040), HR (odds ratio [OR] 1.016, 95% CI 1.002–1.030, P = 0.029) and PT (odds ratio [OR] 1.231, 95% CI 1.018–1.189, P = 0.032) were determined to be independent predictors of in-hospital mortality (P < .05).ConclusionCompared with the common clinical parameters PT and HR, serum DLR level on admission is an uncommon but independent parameter that can be used to assess in-hospital mortality in patients with acute AD.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies.

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Development and External Validation of a Model Predicting New-Onset Chronic Uveitis at Different Disease Durations in Juvenile Idiopathic Arthritis.

To develop and externally validate a prediction model for new-onset chronic uveitis in children with juvenile idiopathic arthritis (JIA) for clinical application. Data from the international Pharmachild registry were used to develop a multivariable Cox proportional hazards model. Predictors were selected by backward selection, and missing values were handled by multiple imputation. The model was subsequently validated and recalibrated in 2 inception cohorts: the UK Childhood Arthritis Prospective Study (CAPS) study and the German Inception Cohort of Newly diagnosed patients with juvenile idiopathic arthritis (ICON) study. Model performance was evaluated by calibration plots and C statistics for the 2-, 4-, and 7-year risk of uveitis. A diagram and digital risk calculator were created for use in clinical practice. A total of 5,393 patients were included for model development, and predictor variables were age at JIA onset (hazard ratio [HR] 0.83 [95% confidence interval (95% CI) 0.77-0.89]), ANA positivity (HR 1.59 [95% CI 1.06-2.38]), and International League of Associations for Rheumatology category of JIA (HR for oligoarthritis, psoriatic arthritis, and undifferentiated arthritis versus rheumatoid factor-negative polyarthritis 1.40 [95% CI 0.91-2.16]). Performance of the recalibrated prediction model in the validation cohorts was acceptable; calibration plots indicated good calibration and C statistics for the 7-year risk of uveitis (0.75 [95% CI 0.72-0.79] for the ICON cohort and 0.70 [95% CI 0.64-0.76] for the CAPS cohort). We present for the first time a validated prognostic tool for easily predicting chronic uveitis risk for individual JIA patients using common clinical parameters. This model could be used by clinicians to inform patients/parents and provide guidance in choice of uveitis screening frequency and arthritis drug therapy.

Gut-microbiome-based predictive model for ST-elevation myocardial infarction in young male patients.

ST-segment elevation myocardial infarction (STEMI) in young male patients accounts for a significant proportion of total heart attack events. Therefore, clinical awareness and screening for acute myocardial infarction (AMI) in asymptomatic patients at a young age is required. The gut microbiome is potentially involved in the pathogenesis of STEMI. The aim of the current study is to develop an early risk prediction model based on the gut microbiome and clinical parameters for this population. A total of 81 young males (age < 44 years) were enrolled in this study. Forty-one young males with STEMI were included in the case group, and the control group included 40 young non-coronary artery disease (CAD) males. To identify the differences in gut microbiome markers between these two groups, 16S rRNA-based gut microbiome sequencing was performed using the Illumina MiSeq platform. Further, a nomogram and corresponding web page were constructed. The diagnostic efficacy and practicability of the model were analyzed using K-fold cross-validation, calibration curves, and decision curve analysis (DCA). Compared to the control group, a significant decrease in tendency regarding α and β diversity was observed in patients in the case group and identified as a significantly altered gut microbiome represented by Streptococcus and Prevotella. Regarding clinical parameters, compared to the control group, the patients in the case group had a higher body mass index (BMI), systolic blood pressure (SBP), triglyceride (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and low blood urea nitrogen (BUN). Additionally, BMI and SBP were significantly (p<0.05) positively correlated with Streptococcus and [Ruminococcus]. Further, BMI and SBP were significantly (p<0.05) negatively correlated with Prevotella and Megasphaera. A significant negative correlation was only observed between Prevotella and AST (p < 0.05). Finally, an early predictive nomogram and corresponding web page were constructed based on the gut microbiome and clinical parameters with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.877 and a C-index of 0.911. For the internal validation, the stratified K-fold cross-validation (K = 3) was as follows: AUC value of 0.934. The calibration curves of the model showed good consistency between the actual and predicted probabilities. The DCA results showed that the model had a high net clinical benefit for use in the clinical setting. In this study, we combined the gut microbiome and common clinical parameters to construct a prediction model. Our analysis shows that the constructed model is a non-invasive tool with potential clinical application in predicting STEMI in the young males.

Evaluation among trace elements, clinical parameters and type 1 diabetes according to sex: A new sight of auxiliary prediction in negative insulin auto-antibodies population

BackgroundType 1 diabetes (T1D) exhibited sex-specific metabolic status including oxidative stress with dynamic change of trace elements, which emphasized the importance of the evaluation of trace elements according to sex. Besides, the most significant characteristic, insulin auto-antibodies, could not be found in all T1D patients, which needed the auxiliary prediction of clinical parameters. And it would benefit the early detection and treatment if some high-risk groups of T1D could predict and prevent the occurrence of disease through common clinical parameters. Hence, there was an urgent need to construct more effective and scientific statistical prediction models to serve clinic better. This study aimed to evaluate the sex-specific levels of trace elements and the relationship between trace elements and clinical parameters in T1D, and construct sex-specific auxiliary prediction model combined with trace elements and clinical parameters. MethodsA total of 105 T1D patients with negative insulin auto-antibodies and 105 age/sex-matched healthy individuals were enrolled in First Hospital of Jilin University. Inductively Coupled Plasma Mass Spectrometry was performed for the measurement of calcium (Ca), magnesium (Mg), zinc (Zn), copper (Cu), iron (Fe), selenium (Se) in the serum, and the data of clinical parameters were received from medical record system. The lambda-mu-sigma method was used to evaluate the relationship between abnormal clinical parameters and trace elements. Training set and validation set were divided for the construction of predictable models in males and females: clinical parameters model, trace element model and the combined model (clinical parameters and trace elements). Goodness fit test, decision curve analysis and other related statistical methods were used to perform data analysis. ResultsLower levels of Mg, Ca, Fe in the serum were found in T1D population in females compared with healthy population, while levels of Fe, Zn and Cu of serum in T1D individuals were higher than those of healthy population in males. Levels of serum Mg, Fe and Cu in T1D group were found with significant sex difference for (P < 0.05), and the levels of Fe and Cu in serum of males were higher than those of females, level of serum Mg in males was lower than those of females. Levels of serum Mg and Zn showed fluctuation trend with increased numbers of abnormal clinical parameters (NACP) in males. Serum Zn in females showed consistent elevated trend with NACP; serum Se increased first and then decreased with NACP in males and females. The auxiliary prediction model (Triglyceride, Total protein, serum Mg) was found with the highest predicted efficiency in males (AUC=0.993), while the model in females (Apolipoprotein A, Creatinine, Fe, Se, Zn/Cu ratio) showed the best predicted efficiency (AUC=0.951). The models had passed the verification in validation set, and Chi-square goodness-of-fit test, DCA results both confirmed their satisfactory clinical applicability. ConclusionSex-specific difference were found in serum Mg, Fe and Cu in T1D. The combination of triglyceride, total protein and serum Mg for males, and apolipoprotein A, creatinine, Fe, Se, Zn/Cu ratio for females could effectively predict T1D in patients with negative anti-bodies, which would provide alarm for the population with high-risk of T1D and serve the T1D prediction in patients with negative anti-bodies.

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

Reference intervals for common clinical chemistry parameters in healthy adults of Northeast Ethiopia.

Clinical chemistry reference intervals are important tools for health evaluation, diagnosis, prognosis and monitoring adverse events. Currently used reference intervals in most African countries including Ethiopia are mainly derived from Western populations, despite studies reporting differences that could lead to incorrect clinical decisions. The aim of this study was to establish reference intervals for commonly used clinical chemistry parameters for healthy adults in Northeast Ethiopia. A community based cross-sectional study was conducted among 328 apparently healthy adults between the ages of 18 and 57 years. Blood samples were collected for clinical chemistry analysis using Dirui CS-T240 auto-analyzer and serological testing to screen the population. Medians and 95% reference intervals were computed using non-parametric method according to the Clinical and Laboratory Standards Institute guideline. The Mann-Whitney U test was used to compare reference values between males and females. Reference intervals established were: ALT 11.2-48.0 U/L, AST 16-60 U/L, ALP 53-342.3 U/L, total protein 5.4-8.9 mg/dL, total bilirubin 0.1-1.23 mg/dL, glucose 65-125 mg/dL, total cholesterol 69-213 mg/dL, triglycerides 46-207 mg/dL, creatinine 0.3-1.2 mg/dL and urea 9.5-46.3 mg/dL. Significant sex-differences were observed for ALT, AST, ALP, total cholesterol, triglycerides, creatinine and urea. We found that the established reference intervals substantially differ from the reference ranges currently in use. Up to 43.1% of apparently healthy adults are considered as having abnormal test values on the bases of the currently in use reference ranges. If the reference values from the United States based intervals were applied to the study population, 81.8% would have been classified as having abnormal laboratory test results. Local population-specific reference intervals were established for commonly used clinical chemistry parameters in adult population of Northeast Ethiopia. Although further study is needed, these reference intervals may have the potential to facility the decision-making process based on laboratory test results in this population.

Spectrum of rare and common genetic variants in arrhythmogenic cardiomyopathy

Abstract Background Arrhythmogenic cardiomyopathy (ACM) is a rare inherited cardiac disorder, whose genetic cause is elusive in about 50% of cases. ACM pathogenic mutations mainly involve desmosomal genes (PKP2, JUP, DSP, DSG2, DSC2). ACM presents a variable disease course, spanning from mild electrocardiographic alterations and/or contractile dysfunctions, to malignant ventricular arrhythmias and heart failure. Purpose In an attempt to broaden etiological diagnosis for ACM patients, this work was aimed at finding potential rare high-impact variants in genes not previously associated with ACM. Secondarily, we meant to assess the impact of genetic polymorphisms on the disease clinical severity. Methods We clinically characterized 82 consecutive ACM probands and performed next-generation sequencing, exploring a panel of 174 genes associated with inherited cardiovascular diseases. We evaluated the pathogenicity of rare variants, according to the American College of Medical Genetics / Association for Molecular Pathology guidelines, to select candidates with potential ACM causative role. Further, a genotype-phenotype correlation analysis between common polymorphisms and clinical variables was performed to find genetic factors associated with specific ACM phenotypes. Finally, we carried out event-free survival analyses, linking specific variants to arrhythmias in 8,6±0,7 years follow-up, to estimate their possible impact on arrhythmic risk. Results We identified 141 rare genetic variants, of which 23 were classified of high impact, including variants in genes never associated with ACM (e.g. ABCC9, APOB, MIB1). These genes deserve future studies as they could participate in ACM pathogenesis. In addition, we found 69 genotype-phenotype significant associations (p≤0.005) between common variants and clinical parameters. Variants associated to arrhythmic phenotypes were found with genes linked to arrhythmias (e.g. KCNQ1, HCN4) and other cardiomyopathies (e.g. MYBPC3, MYL2). Associations involving substrate impairments were found with genes linked to muscle dysfunctions (e.g. LAMA2, SGCD). Arrhythmia-associated polymorphisms, included in survival analyses, were found to be associated with an increased occurrence of arrhythmic events during patients' follow-up. Conclusion The identification of rare high impact variants in novel genes potentially associated with ACM and the observation of genotype-phenotype correlations constitute the starting point to address the current lack of knowledge of the genetic landscape of ACM. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministero Italiano della Salute - Ricerca corrente

Effects of Time Delay in Processing Common Clinical Biochemical Parameters in an Accredited Laboratory

INTRODUCTION: Analyte stability time is challenging to employ in clinical settings. The diversity of processing time may affect the accuracy of results. This study evaluates the maximum time delay permissible before sample centrifugation and analysis. MATERIALS AND METHODS: We evaluated 15 serum electrolytes in 40 samples centrifuged and analysed using an automated analyser. The time was divided into 1, 2, 6, and 24 hours. RESULTS: Most analytes studied remained stable for up to 24 hours before centrifugation. However, delayed processing affected the value of potassium, magnesium, LDH, and calcium after 2 hours up to a maximum of 6 hours at room temperature for total protein, phosphate, and sodium. CONCLUSIONS: Most analytes were unaffected by a delay in centrifugation at room temperatures, but several critical analytes were severely affected.

501P A prognostic model of all-cause mortality at 30 days in patients with cancer and COVID-19

Patients with cancer are at higher risk of dying of COVID-19. Known risk factors for 30-day all-cause mortality (ACM-30) in patients with cancer are older age, sex, smoking status, performance status, obesity, and co-morbidities. We hypothesized that common clinical and laboratory parameters would be predictive of a higher risk of 30-day ACM, and that a machine learning approach (random forest) could produce high accuracy.

Bleeding Risk Assessment in End-Stage Kidney Disease: Validation of Existing Risk Scores and Evaluation of a Machine Learning-Based Approach.

Patients with end-stage kidney disease (ESKD) on hemodialysis (HD) are at increased risk for bleeding. However, despite relevant clinical implications regarding dialysis modalities or anticoagulation, no bleeding risk assessment strategy has been established in this challenging population. Analyses on bleeding risk assessment models were performed in the population-based Vienna InVestigation of Atrial fibrillation and thromboemboLism in patients on hemoDialysIs (VIVALDI) study including 625 patients. In this cohort study, patients were prospectively followed for a median observation period of 3.5 years for the occurrence of major bleeding. First, performances of existing bleeding risk scores (i.e., HAS-BLED, HEMORR2HAGES, ATRIA, and four others) were evaluated in terms of discrimination and calibration. Second, four machine learning-based prediction models that included clinical, dialysis-specific, and laboratory parameters were developed and tested using Monte Carlo cross-validation. Of 625 patients (median age: 66 years, 37% women), 89 (14.2%) developed major bleeding, with a 1-year, 2-year, and 3-year cumulative incidence of 6.1% (95% confidence interval [CI]: 4.2-8.0), 10.3% (95% CI: 8.0-12.8), and 13.5% (95% CI: 10.8-16.2), respectively. C-statistics of the seven contemporary bleeding risk scores ranged between 0.54 and 0.59 indicating poor discriminatory performance. The HAS-BLED score showed the highest C-statistic of 0.59 (95% CI: 0.53-0.66). Similarly, all four machine learning-based predictions models performed poorly in internal validation (C-statistics ranging from 0.49 to 0.55). Existing bleeding risk scores and a machine learning approach including common clinical parameters fail to assist in bleeding risk prediction of patients on HD. Therefore, new approaches, including novel biomarkers, to improve bleeding risk prediction in patients on HD are needed.

90. Decision-making factors leading to fusion vs decompression for one-level degenerative spondylolisthesis: survey results from members of the Lumbar Spine Research Society and Society of Minimally Invasive Spine Surgery

<h3>BACKGROUND CONTEXT</h3> Degenerative spondylolisthesis is one of the most common pathologies spine surgeons treat. While a number of potential factors have been identified, there is no current consensus on which variables most impact the decision to fuse vs decompress alone in this population. <h3>PURPOSE</h3> The aim of this study was to identify radiographic and clinical factors leading to the decision to fuse segments for one-level degenerative spondylolisthesis. <h3>STUDY DESIGN/SETTING</h3> We performed a descriptive cross-sectional study utilizing a survey consisting of questions pertaining to decision-making factors leading to fusion or decompression alone in the setting of degenerative spondylolisthesis. <h3>OUTCOME MEASURES</h3> Radiographic parameters included grade of spondylolisthesis, instability, facet orientation >60°, facet diastasis, laterolisthesis or scoliosis, synovial cysts, vacuum disc, vertical disc space, preserved disc height, concomitant herniated nucleus pulposus, and symptomatic foraminal stenosis. Clinical factors included age >70 years, activity level, patient sex, body mass index >35, osteoporosis, primary complaint of low back pain, primary complaint of neurogenic claudication, smoking, and anxiety/depression. <h3>METHODS</h3> A survey was administered to the Lumbar Spine Research Society and Society of Minimally Invasive Spine Surgery. The primary analysis was limited to completed surveys. Baseline characteristics were summarized. Clinical and radiographic parameters were ranked and compared using chi-square, Fisher's exact test, Kruskal-Wallis, or two-sample t-test as appropriate. <h3>RESULTS</h3> A total of 381 (67.9%) surveys were returned completed. Respondents' mean years in practice was 17.8 ± 9.4 years and 296 (77.7%) had undergone a formal spine fellowship. The majority of respondents were from the US (45.9%) followed by Europe (24.1%), and Asia/Pacific (17.1%). The practice setting included academics (32.5%), private practice (31.0%), hospital-employed (17.1%), or a combination (19.4%). The mean number of degenerative spondylolisthesis cases performed was 53.8 ± 46.7 cases per year with 49.9% of the cohort performing these cases utilizing minimally invasive techniques. With regard to fusion vs decompression: 19.9% fuse all cases, 39.1% fuse >75%, 17.8% fuse 50%-75%, and 23.2% fuse <25%. The most common decompressive technique was a partial laminotomy (51.4%), followed by full laminectomy (28.9%). Among respondents, 82.2% instrument all fusion cases. Instability (93.2%), spondylolisthesis grade (59.8%) and laterolisthesis (37.3%) were the most common radiographic factors impacting the decision to fuse. Instability did not fall below the sixth rank with 75% of surgeons ranking it as the most important parameter. The top parameters also included spondylolisthesis grade, laterolisthesis, facet diastasis, and symptomatic foraminal stenosis. With regard to the clinical factors leading to fusion, mechanical low back pain (83.2%), activity level (58.3%) and neurogenic claudication (42.8%) were the top three clinical parameters. When ranked, the top parameters were mechanical low back pain, activity level, neurogenic claudication, bone mineral density and anxiety/depression. <h3>CONCLUSIONS</h3> There is little consensus on the treatment of degenerative spondylolisthesis, with society members showing substantial variation in treatment patterns. The most common radiographic parameters impacting treatment are instability, spondylolisthesis grade, and laterolisthesis while mechanical low back pain, activity level and neurogenic claudication are the most common clinical parameters. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Spectrum of Rare and Common Genetic Variants in Arrhythmogenic Cardiomyopathy Patients.

Arrhythmogenic cardiomyopathy (ACM) is a rare inherited disorder, whose genetic cause is elusive in about 50–70% of cases. ACM presents a variable disease course which could be influenced by genetics. We performed next-generation sequencing on a panel of 174 genes associated with inherited cardiovascular diseases on 82 ACM probands (i) to describe and classify the pathogenicity of rare variants according to the American College of Medical Genetics and Genomics both for ACM-associated genes and for genes linked to other cardiovascular genetic conditions; (ii) to assess, for the first time, the impact of common variants on the ACM clinical disease severity by genotype-phenotype correlation and survival analysis. We identified 15 (likely) pathogenic variants and 66 variants of uncertain significance in ACM-genes and 4 high-impact variants in genes never associated with ACM (ABCC9, APOB, DPP6, MIB1), which deserve future consideration. In addition, we found 69 significant genotype-phenotype associations between common variants and clinical parameters. Arrhythmia-associated polymorphisms resulted in an increased risk of arrhythmic events during patients’ follow-up. The description of the genetic framework of our population and the observed genotype-phenotype correlation constitutes the starting point to address the current lack of knowledge in the genetics of ACM.