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Public Health Public Health

Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders.

In Medical science monitor : international medical journal of experimental and clinical research

BACKGROUND Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years. MATERIAL AND METHODS This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters. RESULTS Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty. CONCLUSIONS Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.

Majnarić Ljiljana Trtica, Bekić Sanja, Babič František, Pusztová Ľudmila, Paralič Ján

2020-Sep-15

General General

Untargeted metabolomics yields insight into ALS disease mechanisms.

In Journal of neurology, neurosurgery, and psychiatry

OBJECTIVE : To identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics.

METHODS : Untargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status.

RESULTS : There were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. 'Benzoate metabolism', 'ceramides', 'creatine metabolism', 'fatty acid metabolism (acyl carnitine, polyunsaturated)' and 'hexosylceramides' sub-pathways were enriched by all methods, and 'sphingomyelins' by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%.

CONCLUSION : In our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.

Goutman Stephen A, Boss Jonathan, Guo Kai, Alakwaa Fadhl M, Patterson Adam, Kim Sehee, Savelieff Masha Georges, Hur Junguk, Feldman Eva L

2020-Sep-14

Ophthalmology Ophthalmology

Artificial Intelligence in Global Ophthalmology: Using Machine Learning to Improve Cataract Surgery Outcomes at Ethiopian Outreaches.

In Journal of cataract and refractive surgery

Differences between target and implanted intraocular lens (IOL) power in Ethiopian cataract outreach campaigns were evaluated and machine learning (ML) applied to optimize IOL inventory and minimize avoidable refractive error. Patients from Ethiopian cataract campaigns with available target and implanted IOL records were identified and the diopter difference between the two measured. A gradient descent (an ML algorithm) was used to generate an optimal IOL inventory and measured the model's performance across varying surplus levels.Only 45.6% of patients received their target IOL power and 23.6% received underpowered IOLs with current inventory (50% surplus). The ML-generated IOL inventory ensured that >99.5% of patients received their target IOL when using only 39% IOL surplus.In Ethiopian cataract campaigns, the majority of patients have avoidable postoperative refractive error secondary to suboptimal IOL inventory. Optimizing IOL inventory using our ML model might eliminate refractive error from insufficient inventory and reduce costs.

Brant Arthur R, Hinkle John, Shi Siyu, Hess Olivia, Zubair Talhah, Pershing Suzann, Tabin Geoffrey C

2020-Sep-03

Surgery Surgery

Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper.

In Annals of surgery ; h5-index 104.0

OBJECTIVES : Sepsis and sterile both release "danger signals' that induce the systemic inflammatory response syndrome (SIRS). So differentiating infection from SIRS can be challenging. Precision diagnostic assays could limit unnecessary antibiotic use, improving outcomes.

METHODS : After surveying human leukocyte cytokine production responses to sterile damage-associated molecular patterns (DAMPs), bacterial pathogen-associated molecular patterns, and bacteria we created a multiplex assay for 31 cytokines. We then studied plasma from patients with bacteremia, septic shock, "severe sepsis," or trauma (ISS ≥15 with circulating DAMPs) as well as controls. Infections were adjudicated based on post-hospitalization review. Plasma was studied in infection and injury using univariate and multivariate means to determine how such multiplex assays could best distinguish infective from noninfective SIRS.

RESULTS : Infected patients had high plasma interleukin (IL)-6, IL-1α, and triggering receptor expressed on myeloid cells-1 (TREM-1) compared to controls [false discovery rates (FDR) <0.01, <0.01, <0.0001]. Conversely, injury suppressed many mediators including MDC (FDR <0.0001), TREM-1 (FDR <0.001), IP-10 (FDR <0.01), MCP-3 (FDR <0.05), FLT3L (FDR <0.05), Tweak, (FDR <0.05), GRO-α (FDR <0.05), and ENA-78 (FDR <0.05). In univariate studies, analyte overlap between clinical groups prevented clinical relevance. Multivariate models discriminated injury and infection much better, with the 2-group random-forest model classifying 11/11 injury and 28/29 infection patients correctly in out-of-bag validation.

CONCLUSIONS : Circulating cytokines in traumatic SIRS differ markedly from those in health or sepsis. Variability limits the accuracy of single-mediator assays but machine learning based on multiplexed plasma assays revealed distinct patterns in sepsis- and injury-related SIRS. Defining biomarker release patterns that distinguish specific SIRS populations might allow decreased antibiotic use in those clinical situations. Large prospective studies are needed to validate and operationalize this approach.

Cahill Laura A, Joughin Brian A, Kwon Woon Yong, Itagaki Kiyoshi, Kirk Charlotte H, Shapiro Nathan I, Otterbein Leo E, Yaffe Michael B, Lederer James A, Hauser Carl J

2020-Oct

Cardiology Cardiology

Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

In Physiological measurement ; h5-index 36.0

OBJECTIVE : In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.

APPROACH : The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n=3,088 patients and p=26,913 hours of continuous single-channel electrocardiogram raw data were used. Three of the databases (n=125, p=2,513) were used for training a machine learning model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n=2,963, p=24,400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist's visual inspection of individuals suspected of having AF (n=118), a total of 70 patients were diagnosed with prominent AF in SHHS1.

MAIN RESULTS : Model prediction on SHHS1 showed an overall Se=0.97,Sp=0.99,NPV=0.99 and PPV=0.67 in classifying individuals with or without prominent AF. PPV was non-inferior (p=0.03) for individuals with an apnea-hypopnea index (AHI) ≥15 versus AHI < 15. Over 22% of correctly identified prominent AF rhythm cases were not previously documented as AF in SHHS1.

SIGNIFICANCE : Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe OSA. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

Chocron Armand, Efraim Roi, Mandel Franck, Rueschman Michael, Palmius Niclas, Penzel Thomas, Elbaz Meyer, Behar Joachim

2020-Sep-15

atrial fibrillation, digital biomarkers, machine learning, medicine during sleep, obstructive sleep apnea

Cardiology Cardiology

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database.

METHODS : Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers.

RESULTS : We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively.

CONCLUSION : Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.

Yildirim Ozal, Talo Muhammed, Ciaccio Edward J, Tan Ru San, Acharya U Rajendra

2020-Sep-08

12-lead ECG, Arrhythmia detection, Deep neural networks, Ecg signals