Clinical Test Data Research Articles

Prevalence of Molecular Diagnoses for Usher Syndrome and the Need for Coordinated Care.

Usher syndrome (USH) is a rare, autosomal-recessive genetic disorder and a leading cause of early onset deaf-blindness. A clinical diagnosis is made by the presence of retinitis pigmentosa (RP) with sensorineural hearing loss (SNHL). Subtype (USH1, USH2, USH3) is determined by severity and age of onset. Molecular testing is able to further distinguish USH subtypes by causative gene. As gene therapy strategies continue to be explored for USH, it is important to know the underlying genetic cause and to coordinate care among an interdisciplinary team. We reviewed charts of 198 individuals presenting to the RP clinic at Bascom Palmer Eye Institute (BPEI) for suspected USH. Demographic information, USH clinical diagnosis, molecular testing, molecular diagnosis, and audiological data were collected. Of the 198 patients reviewed, 190 (96%) met clinical criteria for USH and received a clinical diagnosis. There were 67 (35%) that had a genetic test with a pathogenic molecular diagnosis. The average ages at molecular diagnosis were USH1B, 20 years old; USH2A, 37 years old; USH2C, 50 years old. Of the 67 with a molecular diagnosis, 23 (34%) established ophthalmic care and 8 of these (11%) established audiological care. Molecular testing and diagnosis should be part of the routine care of USH individuals to facilitate earlier interventions and coordinated care between ophthalmology and audiology. 4 Laryngoscope, 2024.

Clinical and genetic characteristics of hypoparathyroidism in children: a multicenter experience in China.

This study was aimed to analyze the clinical and genetic characteristics of hypoparathyroidism in children. We performed a retrospective analysis of 74 patients diagnosed with pediatric hypoparathyroidism from 2014 to 2023, recruited in five medical centers across China. Data of basic information and clinical tests were extracted from patients' records. Whole-exome sequencing (WES), multiplex ligation-dependent probe amplification (MLPA), and chromosomal microarray analysis (CMA) were utilized to identify the genetic causes. The results indicated a median onset age of 6.07 ± 4.82 years and a median diagnosis age of 6.91 ± 4.88 years. Of the 46 patients who underwent genetic tests, 35 were found to carry pathogenic variants related to hypoparathyroidism. Specifically, 19 cases (19/46, 41.30%) had 22q11.2 microdeletion, while other variations included AIRE (8/46, 17.39%), GATA3 (3/46, 6.52%), CaSR (2/46, 4.34%), and the rest 3 patients with mutations of TBCE, PTH and mitochondrial gene deletion respectively. Convulsions were the most common initial presentation, observed in 43 cases. The non-DGS group exhibited the lowest serum PTH levels compared to DiGeorge syndrome and gene-negative group. Among the 66 patients who underwent cranial CT or MR, 26 (26/66, 39.99%) presented with intracranial calcification. We reported the largest cohort of childhood hypoparathyroidism with genetic diagnoses, reinforcing the view that genetic disorders account for the majority of pediatric hypoparathyroidism, with the 22q11.2 microdeletion being the most prevalent. Identifying the genetic causes of hypoparathyroidism is crucial for predicting patient outcomes, managing comorbidities, and, importantly, informing decisions regarding the potential use of emerging recombinant human PTH therapy.

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

The Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score Predicts the Prognosis of Adults with Status Epilepticus: A Retrospective Study.

The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been indicated to be a novel indicator that reflects systemic inflammation and is associated with the functional outcome in various neurological diseases. Since inflammation is closely involved in the pathogenesis of status epilepticus (SE), this study aimed to explore the predictive value of HALP for the prognosis of SE patients. In the retrospective study, we investigated and validated the predictive value of HALP for the prognosis of adult SE patients. We collected patient's clinical characteristics and laboratory test data. Information of 30-day mortality after SE onset was obtained, and modified Rankin scale (mRS) scores were used to evaluate the clinical outcomes. Multivariable logistic regression models were applied to explore the association between HALP and SE prognosis. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff of HALP to predict 30-day mortality in SE patients. We first analyzed the discovery cohort of 116 patients with SE. We observed that 19.0% (22/116) of the patients died within 30 days after SE onset, and 26.3% (20/76) had poor outcomes during the 6-month follow-up. Multivariate logistic regression analysis indicated that lower HALP on admission was an independent predictor of 30-day mortality (odds ratio: 0.980; 95% confidence interval, 0.962-0.999; P = 0.044). In ROC analysis, a HALP of 20.32 was identified as the optimal cutoff value to predict 30-day mortality in SE patients. Furthermore, multivariate logistic regression analysis showed that HALP < 20.32 was related to an increased risk of poor SE outcomes (odds ratio: 27.051; 95% confidence interval, 3.589-203.909; P = 0.001), which was validated in the external cohort. Lower HALP is independently associated with an increased risk of 30-day mortality and poor functional outcomes in patients with SE. Our findings suggest that HALP may be a promising biomarker for predicting SE prognosis.

Monocyte-to-Albumin Ratio Is Associated With Hematoma Expansion in Spontaneous Intracerebral Hemorrhage.

Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a severe complication that independently predicts poor prognosis. In this study, we aimed to investigate whether monocyte-to-albumin ratio (MAR), a novel marker of systemic inflammation, could predict HE in patients with ICH. We retrospectively assessed the data of patients with ICH. The clinical, imaging, and laboratory test data including, the MAR on admission, were analyzed. A multivariate logistic regression analysis was carried out to explore the relationship between MAR and hematoma growth. The receiver operating characteristic (ROC) curve was employed to investigate the predictive value of MAR for HE after ICH. A total of 246 patients were included in the present study. Multivariate logistic regression analysis demonstrated that the MAR was associated with HE (odds ratio [OR]=1.179; 95% confidence interval, 1.093-1.272; p=0.000). ROC curve analysis showed that MAR could predict HE, with an area under the curve of 0.802 (95% CI: 0.744-0.859, p<0.001). The optimal predictive cutoff value of MAR for HE was 10.01 (sensitivity: 72.43%, specificity: 77.05%). Our results suggested that a high MAR on admission was associated with an increased risk of HE in ICH patients, and MAR can become an independent predictor of HE in ICH patients.

Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures.Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons.We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score.Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

SP5.13 - Assessing the use of electronic QLIK app in Mortality and Morbidity meetings compared with traditional manual hand collected data in colorectal surgery

Abstract Aims To validate and compare the effectiveness of the QLIK app as an electronic method of data collection compared to traditional manual methods for mortality and morbidity meetings (M&amp;Ms). Methods All data from the theatre operating management system was linked in the Trust Data Warehouse to clinical test and vital statistics data and displayed using a QLIK App. A retrospective study was conducted comparing the manual collection of data of elective colorectal M&amp;M with the QLIK app data for the years 2021 and 2022. Data collected included: total operations, number of deaths, readmissions, reoperations and post-operative complications. The combined data was then analysed in three different rounds of validation. Results There was a high level of concordance between the manual data and the QLIK app, with both correctly identifying the same number of deaths. In 2021 the app recorded 569 total operations compared to 584 in the manual data, and 127 post operative complications to 126 respectively. In 2022, the app recorded 607 operations to 489 in the manual data, and 120 complications compared to 149. Of the complications recorded in the manual data but not the app, 96.8% of these were Clavien-Dindo Grade 1 and 2 complications. Manual data collection of the M&amp;M data was estimated to take approximately 250 hours per year from collection to analysis, whilst the app displayed the data in near real time. Conclusions Use of data visualization tools and coded electronic hospital data for M&amp;Ms can minimise errors and allow for greater efficiency. This approach gives real time surgical performance data.

The Detection of Xylazine in Tijuana, Mexico: Triangulating Drug Checking and Clinical Urine Testing Data.

Xylazine is a veterinary anesthetic increasingly present alongside illicit fentanyl in the US and Canada, presenting novel health risks. Although xylazine remains less common in the Western US, Mexican border cities serve as key trafficking hubs and may have higher prevalence of novel substances, but surveillance has been limited. We examined deidentified records from the Prevencasa harm reduction clinic in Tijuana, describing urine and paraphernalia testing from patients reporting using illicit opioids within 24 hr. Xylazine (two types), fentanyl, opiate, methamphetamine, amphetamine, benzodiazepine, and nitazene test strips were used to test urine and paraphernalia samples. Paraphernalia samples were also analyzed with mass spectrometry. The study consisted of 23 participants that provided both urine and paraphernalia samples. Of the participants studied, 100 %, 91.3 %, and 69.6 % reported using China White/fentanyl, methamphetamine, and tar heroin, respectively. The mean age was 41.7 years, 95.7 % were male, 65.2 % were unhoused, and 30.4 % had skin wounds at the time of sample collection.Xylazine positivity in urine, for the two types used, was 82.6 % and 65.2 %. For paraphernalia testing, the xylazine positivity was 65.2 % and 47.8 %. Confirmatory testing of paraphernalia samples by mass spectrometry indicated a 52.2 % xylazine positivity. This testing also revealed positivity rates for fentanyl (73.9 %), fluorofentanyl (30.4 %), tramadol (30.4 %), and lidocaine (30.4 %).The mass spectrometry results suggest lidocaine triggered n = 3 and n = 0 false positives among the xylazine test strip types. A total of n = 0 and n = 1 false negatives were also observed. Xylazine is present on the U.S.-Mexico border, requiring public health intervention. High lidocaine positivity complicates the clinical detection of xylazine via testing strips. Xylazine was found to be more prevalent in urine than in paraphernalia samples. Confirmatory urine studies are needed to better understand possible complications of using test strips for toxicological testing.

Impacts of extreme temperature conditions on acute health outcomes using clinical test and laboratory data in Minnesota, USA

Impacts of extreme temperature conditions on acute health outcomes using clinical test and laboratory data in Minnesota, USA

Predictors of the efficacy of vedolizumab in patients with ulcerative colitis.

Vedolizumab is a treatment option for ulcerative colitis but data on predictors of treatment response remain insufficient to establish personalized treatment strategies. We aimed to investigate the real-world effectiveness of vedolizumab in adult patients with ulcerative colitis and explore factors involved in predicting treatment response. This single-center, single-arm, prospective observational study included 26 patients with clinically active ulcerative colitis patients' characteristics at baseline, epidemiological information, existing treatment, clinical activity index score, endoscopic score, and blood test data were collected. Serum levels of tumor necrosis factors alpha, interferon gamma, interleukin-4, interleukin-6, interleukin-10, interleukin-17, soluble mucosal addressin cell adhesion molecule 1, and soluble vascular cell adhesion molecule 1 were measured. Patient characteristics in the remission and non-remission groups were compared based on these parameters. Clinical remission at 6 weeks of treatment occurred in 9 (35%) of the 26 patients. At 14 weeks, clinical remission was observed in 11 patients (42%). There were no significant differences pertaining to age, sex, duration of disease, extent of disease, steroid resistance, or prior treatment with biological agents among the two groups after 14 weeks of treatment. Hemoglobin ≥ 11.5 g/dL (odds ratio, 15.0; 95% confidence interval, 1.50-149; P=0.014) and soluble mucosal addressin cell adhesion molecule 1 ≥ 765 pg/mL (odds ratio, 17.3; 95% confidence interval, 2.36-127; P=0.004) were significant factors. In conclusion, hemoglobin and serum soluble mucosal addressin cell adhesion molecule 1 levels are factors correlated with the therapeutic efficacy of vedolizumab.

Long-read sequencing and optical genome mapping identify causative gene disruptions in noncoding sequence in two patients with neurologic disease and known chromosome abnormalities.

Despite advances in next generation sequencing (NGS), genetic diagnoses remain elusive for many patients with neurologic syndromes. Long-read sequencing (LRS) and optical genome mapping (OGM) technologies improve upon existing capabilities in the detection and interpretation of structural variation in repetitive DNA, on a single haplotype, while also providing enhanced breakpoint resolution. We performed LRS and OGM on two patients with known chromosomal rearrangements and inconclusive Sanger or NGS. The first patient, who had epilepsy and developmental delay, had a complex translocation between two chromosomes that included insertion and inversion events. The second patient, who had a movement disorder, had an inversion on a single chromosome disrupted by multiple smaller inversions and insertions. Sequence level resolution of the rearrangements identified pathogenic breaks in noncoding sequence in or near known disease-causing genes with relevant neurologic phenotypes (MBD5, NKX2-1). These specific variants have not been reported previously, but expected molecular consequences are consistent with previously reported cases. As the use of LRS and OGM technologies for clinical testing increases and data analyses become more standardized, these methods along with multiomic data to validate noncoding variation effects will improve diagnostic yield and increase the proportion of probands with detectable pathogenic variants for known genes implicated in neurogenetic disease.

Multi-factor normalisation of viral counts from wastewater improves the detection accuracy of viral disease in the community

The detection of viruses (e.g. SARS-CoV-2, norovirus) in wastewater represents an effective way to monitor the prevalence of these pathogens circulating within the community. However, accurate quantification of viral concentrations in wastewater, proportional to human input, is constrained by a range of uncertainties, including (i) dilution within the sewer network, (ii) degradation of viral RNA during wastewater transit, (iii) catchment population and facility use, (iv) efficiency of viral concentration and extraction from wastewater, and (v) inhibition of amplification during the RT-qPCR step. Here, we address these uncertainties by investigating several potential normalisation factors including the concentration of ammonium and orthophosphate. A faecal indicator virus (crAssphage), and the recovery of the process-control viruses (murine norovirus and bacteriophage Phi6), used for quality control during the RT-qPCR step, were also considered. We found that multi-factor normalisation of SARS-CoV-2 RT-qPCR data was optimal using a combination of crAssphage, process-control virus recovery, and concentration efficiency to improve prediction accuracy relative to clinical test data. Using multi-normalised SARS-CoV-2 RT-qPCR data, we found a lasso regression model with random forest modelled residuals lowers the prediction error of positives by 46 %, compared to a single linear regression using raw data. This multi-normalised approach enables more accurate wastewater-based predictions of clinical cases up to five days in advance of clinical data, identifying trends in disease prevalence before clinical testing, and demonstrates the potential to improve viral pathogen detection for a range of currently monitored and emerging diseases.

Analysis of factors that indicated surgery in 400 patients submitted to a complete diagnostic workup for obstructed defecation syndrome and rectal prolapse using a supervised machine learning algorithm.

Patient selection is extremely important in obstructed defecation syndrome (ODS) and rectal prolapse (RP) surgery. This study assessed factors that guided the indications for ODS and RP surgery and their specific role in our decision-making process using a machine learning approach. This is a retrospective analysis of a long-term prospective observational study on female patients reporting symptoms of ODS who underwent a complete diagnostic workup from January 2010 to December 2021 at an academic tertiary referral center. Clinical, defecographic, and other functional tests data were assessed. A supervised machine learning algorithm using a classification tree model was performed and tested. A total of 400 patients were included. The factors associated with a significantly higher probability of undergoing surgery were follows: as symptoms, perineal splinting, anal or vaginal self-digitations, sensation of external RP, episodes of fecal incontinence and soiling; as physical examination features, evidence of internal and external RP, rectocele, enterocele, or anterior/middle pelvic organs prolapse; as defecographic findings, intra-anal and external RP, rectocele, incomplete rectocele emptying, enterocele, cystocele, and colpo-hysterocele. Surgery was less indicated in patients with dyssynergia, severe anxiety and depression. All these factors were included in a supervised machine learning algorithm. The model showed high accuracy on the test dataset (79%, p < 0.001). Symptoms assessment and physical examination proved to be fundamental, but other functional tests should also be considered. By adopting a machine learning model in further ODS and RP centers, indications for surgery could be more easily and reliably identified and shared.

Development of a grading diagnostic model for schistosomiasis-induced liver fibrosis based on radiomics and clinical laboratory indicators

To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People's Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients'radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients'radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = -5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

Diagnoses of muscular dystrophy in a veterans health system.

Early diagnosis of a chronic neuromuscular disease such as muscular dystrophy (MD) generally excludes an individual from active-duty military service. However, it is not known whether veterans are sometimes diagnosed with milder forms of MD at a later timepoint. We aimed to determine the prevalence of MD in a veterans health system. We abstracted clinical and genetic test data on patients who received care for a diagnosis of MD at the North Florida/South Georgia Veterans Health System between 2008 and 2021. We then determined which of these individuals would meet criteria for a definite diagnosis of MD, based on electrodiagnostic testing, muscle biopsy, and genetic testing of the individual or an affected first degree relative. We identified 12 patients with definite MD and 36 with possible or probable MD. The definite cases included myotonic dystrophy type 1 (4), myotonic dystrophy type 2 (3), oculopharyngeal MD (2), Becker MD (1), distal MD (1), and facioscapulohumeral MD (1). At least five of the cases classified as definite developed symptoms after discharge from active duty. Clinicians who care for veterans should be knowledgeable about, and have access to, diagnostic testing and treatment options for MD. When conducting MD surveillance, it is important to include veterans health systems as a data source. Mild cases of MD and those of later onset appear to be compatible in some cases with successful completion of military service.

#2075 Impact of chronic obstructive pulmonary disease on the outcomes of hemodialysis patients

Abstract Background and Aims Chronic obstructive pulmonary disease, COPD, is a common cause of morbidity and mortality. Comorbidities are frequent among COPD patients, some of which are explained by shared risk factors of smoking and increased age. Chronic kidney disease, leading to end-stage renal disease requiring hemodialysis (HD) is not uncommon in patients with COPD, and vice-versa. The prevalence and the impact of coexistence of COPD among HD patients was not extensively described. We aimed to measure the prevalence of COPD among a cohort of HD patients, and study how it relates to significant outcomes of mortality, morbidity, including unplanned hospital admissions, and kidney transplantation (KT). Method A retrospective study of subjects who initiated HD during 2015-2018. Participants were categorized into one of two groups, with or without COPD. Clinical, laboratory and pulmonary function tests data were extracted from medical records, as were outcomes of mortality, morbidity, and KT (until December 31st, 2021, or until death). Results Forty-three of 234 were diagnosed with COPD (18.4%). Their average FEV1%pred was 60%. Thirty-eight subjects in the COPD group (88.4%) were hospitalized for severe COPD exacerbations during a mean follow-up of 36.1 months, averaging 3.6 severe exacerbations per patient. Despite this, maintenance inhaler therapy was underutilized. All-cause mortality rates were higher for HD patients with COPD (72.1% vs. 47.1%, p=0.003), as were rates of acute coronary syndrome (39.5% vs. 23.6%, p=0.026). Conclusion COPD is common among HD patients and is associated with significant morbidity and increased mortality.

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation.

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

Immune-inflammatory biomarkers for the occurrence of MACE in patients with myocardial infarction with non-obstructive coronary arteries.

Neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), platelet-to-high-density lipoprotein cholesterol ratio (PHR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) have been identified as immune-inflammatory biomarkers associated with the prognosis of cardiovascular diseases. However, the relationship of these biomarkers with the prognosis of myocardial infarction with non-obstructive coronary arteries (MINOCA) remains unclear. Patients with MINOCA who underwent coronary angiography at the 920th Hospital of Joint Logistics Support Force were included in our study. Clinical baseline characteristics and laboratory testing data were collected from the hospital record system. The patients were divided into two groups on the basis of major adverse cardiovascular events (MACE) occurrence. Multiple logistic regression analysis was conducted to assess the relationship between NHR, MHR, LHR, PHR, SII, SIRI, AISI, and MACE. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive value of NHR, MHR, LHR, PHR, SII, SIRI, and AISI for MACE in patients with MINOCA. The accuracy of the prediction was indicated by the area under the curve (AUC) value. The study included 335 patients with MINOCA. (81 in the MACE group and 254 in the No-MACE group). The MACE group had higher levels of NHR, MHR, LHR, PHR, SII, SIRI, and AISI than the No-MACE group. Multiple logistic regression analysis adjusted for confounding factors indicated that the higher levels of NHR, MHR, PHR, SII, SIRI, and AISI were associated with the occurrence of MACE in patients with MINOCA (P < 0.001). The AUC values for NHR, MHR, PHR, SII, SIRI, and AISI were 0.695, 0.747, 0.674, 0.673, 0.688, and 0.676, respectively. The combination of NHR, MHR, PHR, SII, SIRI, and AISI improved the accuracy of predicting MACE in patients with MINOCA (AUC = 0.804). Higher levels of NHR, MHR, PHR, SII, SIRI, and AISI were associated with the occurrence of MACE, and the combination of NHR, MHR, PHR, SII, SIRI, and AISI improved the accuracy for predicting the incidence of MACE events in patients with MINOCA.

Factors influencing early postsurgical mobilization following Vascularized Iliac Crest Flap for jaw defect reconstruction

Objectives/hypothesisEarly mobility following vascularized iliac flap (VIF) may be influenced by several factors. This study evaluated early mobility and factors influencing patient mobilization following VIF. Study designProspective observational study MethodsWe recruited 115 inpatients who underwent VIF surgery for jaw defect reconstruction at our hospital. Sociodemographic, clinical, and Tinetti test data were analyzed using univariate and multiple logistic regression. Receiver operating characteristics (ROC) curves were used to analyze factors hindering early mobilization. ResultsThe proportion of patients who could sit up on POD2 and walk on POD3 was 61.74% and 56.52%, respectively. The mean Tinetti score at discharge was 9.56 ± 4.27 (range, 3–22). Logistic regression analysis revealed five significant indicators associated with delayed mobilization: pain (odds ratio [OR] = 0.396, p = 0.010), soft tissue flap area (OR = 0.818, p = 0.012), length of bone harvested (OR = 0.443, p = 0.009), and fatigue (OR = 0.449, p = 0.030). The area under the ROC curve for the five indicators was 0.964, with a cutoff value of 22.108, sensitivity of 0.940, specificity of 0.846, and Youden's index of 0.786. The length of bone harvested (B = −0.696, p < 0.001), soft tissue flap area (B = −0.103, p = 0.039), and fatigue (B = −0.628, p = 0.009) influenced the Tinetti test at discharge. ConclusionsMore than half of the patients could be mobilized early. Pain significantly influenced mobilization progress, followed by soft tissue flap area, length of bone harvested, and fatigue.