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Pathophysiology of Advanced Heart Failure
پاتوفیزیولوژی نارسایی پیشرفته قلب-2021 The pathophysiologyof advanced heart failure (HF) can be characterized asa complex interplay ofdysregulated mechanisms comprising impaired hemodynamics, neurohormonal and proinflammatory
activation, dysfunctional cardiorespiratory reflex control, and inadequate energy handling, all of which
ultimately lead to multiorgan dysfunction; at the later stage of HF, numerous comorbidities, whose underlying pathophysiologiesoftenamplifyHFprogression,tendtodominatetheclinicalpicture and therapeutic approach, and some of these mechanisms have been identified as therapeutic targets in HF.
Blockade of the renin-angiotensin-aldosterone system (preferably with an angiotensin receptorneprilysin inhibitor, but alternatively with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers together with mineralocorticoid receptor antagonist) and sympathetic nervous system (with b-blockers) is now considered a fundamental element of pharmacologic therapy for all patients with advanced HF and reduced ejection fraction. Autonomic modulation (vagal nerve stimulation or baroreflex stimulation) in advanced HF tends to benefit functional variables (qualityof life,NewYork HeartAssociation class, 6-minutewalking distance), whereas improvement in the outcomes (total mortality, HF hospitalizations) still remains uncertain. Fluid overload with central and/or peripheral congestion characterize the clinical picture of advanced HF and is the main reason for hospital admission in these patients; distinction of different clinical patterns of congestion with different underlying mechanisms may improve the management of fluid overload in advanced HF. Recent clinicaltrials have shown that the following novel therapiestargeting impairedpathophysiologic pathways in advanced HF seem to improve patients’ outcomes: (1) vericiguat, a soluble guanylate cyclase stimulator; (2) omecamtiv mecarbil, a selective cardiac myosin activator; (3) sodium-glucose cotransporter 2 inhibitors; (4) ferric carboxymaltose, for patients with concomitant iron deficiency. Better understanding of the pathophysiology underlying HF progression may allow characterization of novel mechanisms that can be targeted in order to revert to a natural pathway of HF development and progression. |
مقاله انگلیسی |
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Self-management on heart failure: A meta-analysis
خود مدیریتی در نارسایی قلبی: متاآنالیز-2021 Background and aims: Heart failure (HF) is a severe public health problem all over the World. Selfmanagement is an effective method to progress self-care ability. However, the role of selfmanagement in heart failure has not been thoroughly elucidated.
Methods: The research articles related to heart failure were searched by the PubMed, Embase, Cochrane databases, and China National Knowledge Database on articles published through March 2020. The average 95% of confidence intervals (CIs) were used to calculate using random-effects or fixed-effects. Review Manager (version 5.2) was adopted for meta-analysis, sensitivity analysis, and bias analysis. Results: Eight (8) eligible studies with 1707 patients with HF were included in this analysis. In the Metaanalysis showed significant differences for Self-management (SM) groups in Dutch Heart Failure Knowledge Scale (DHFK) (MD ¼ 1.36, 95%CI [-0.03, 2.75], P ¼ 0.04; I2 ¼ 83%), in Self-Care of Heart Failure Index (SCHFI) (MD ¼ 5.51, 95%CI [0.62, 10.40], P ¼ 0.03; I2 ¼ 70%), and in Self-Efficacy for Managing Chronic Disease Scale (SEMCDI) (I2 ¼ 47%, Z ¼ 5.43, P of over effect < 0.0001) than control groups. One bias is detected as attrition bias, and another one is reporting bias. Sensitivity analysis satisfied the stability of the results. Conclusion: Self-management was associated with significant outcomes in patients with HF through knowledge, attitude, and practice (KAP). keywords: نارسایی قلبی | خود مدیریت | می شود | متاآنالیز | Heart failure | Self-management | KAP | Meta-analysis |
مقاله انگلیسی |
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Effects of self-management interventions on heart failure: Systematic review and meta-analysis of randomized controlled trials - Reprint
اثرات مداخلات خودمراقبتی بر نارسایی قلبی: بررسی سیستماتیک و متاآنالیز آزمایش های کنترل شده تصادفی - چاپ مجدد-2021 Background: Self-management intervention is an important component of disease management in patients
with heart failure. It can improve heart failure knowledge, quality of life, and heart failure-related hospitalizations of heart failure patients. However, studies on the effect of two self-management interventions
tasks have reported conflicting results.
Objective: This study conducted an up-to-date systematic review of the literature to evaluate the effects of self-management interventions on heart failure knowledge, quality of life, and heart failure-related hospitalizations in patients with heart failure. Design: Systematic review and meta-analysis. Data sources: We searched PubMed, Embase, Web of Science, Cochrane Library, and the references of articles in 14th December 2019. Methods: The study characteristics included: authors, year, country, sample size, mean age of patients with heart failure, duration of intervention, recruitment and intervention delivery, interventions based on self-efficacy theory, cognitive behavioral therapy, disease management, self-care education. The risk of bias for each study was assessed independently by two investigators based on the Cochrane Handbook. This study used Revman to analyze different research outcomes. The fixed-effect model was used in the absence of significant heterogeneity or low heterogeneity, and if the heterogeneity was high, the random effect model was used. Results: A total of 4977 publications were retrieved in this study. After eliminating duplicates and screening for titles and abstracts, 209 articles were retrieved for full-text evaluation. Finally, a total sample size analyzed across 15 randomized controlled trials was 2630 participants. This study showed that selfmanagement interventions significantly improved heart failure knowledge (0.61, 95% confidence interval (CI) 0.27–0.95, p = 0.0004), quality of life (0.20, 95% CI 0.02–0.38, p = 0.03), and heart failure-related hospitalization (OR 0.40, 95% CI 0.29 to 0.55, p<0.00001) in patients with heart failure. Conclusions: This study reveals the beneficial effects of self-management interventions on heart failure knowledge, quality of life, and heart failure-related hospitalization in patients with heart failure. Therefore, high quality randomized controlled designs are needed to explore the optimal self-management interventions for patients with heart failure. keywords: نارسایی قلبی | خود مدیریت | دانش نارسایی قلب | کیفیت زندگی | بستری شدن از نارسایی های قلب | Heart failure | Self-management | Heart failure knowledge | Quality of life | Heart failure-related hospitalization |
مقاله انگلیسی |
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Multiple Random Empirical Kernel Learning with Margin Reinforcement for imbalance problems
یادگیری چند هسته ای تجربی تصادفی با تقویت حاشیه برای و مسائل عدم تعادل-2020 Imbalance problems arise in real-world applications when the number of negative samples far exceeds the
number of positive samples, such as medical data. When solving the classification of imbalance problems, the
samples located near the decision hyperplane contribute more to the decision hyperplane, and the samples
far from the decision hyperplane contribute less to the decision hyperplane. So we can consider giving higher
weights to the samples near the decision hyperplane, but they are sensitive to noise, and too much emphasis
on them may lead to unstable performance. This paper proposes a Margin Reinforcement (MR) method to
overcome the above dilemma. Because the imbalance problem is a cost-sensitive problem, MR gives positive
samples a uniform high weight to improve the misclassification cost of the positive sample. For negative
samples, according to their entropy, samples away from the decision surface and noise samples mixed in the
positive samples are given a smaller weight, in order to improve the efficiency and robustness of the algorithm.
Therefore, MR can emphasize the importance of samples located in overlapping regions of positive and negative
classes and ignore the effects of noise samples to produce superior performance. Multiple Random Empirical
Kernel Learning (MREKL) has proven to be effective and efficient in dealing with balance problems. In order
to improve the performance of MREKL on imbalanced datasets, MR is introduced into MREKL to propose
a novel Multiple Random Empirical Kernel Learning with Margin Reinforcement (MREKL-MR). MREKL-MR
efficiently map the samples into low-dimensional feature spaces, then utilizes the MR approach to reenforce the
importance of margin samples and decrease the effects of noise samples. Experimental results on 28 imbalanced
datasets indicate that MREKL-MR is superior to comparison algorithms. Finally, the effectiveness of MREKL-MR
in dealing with imbalance problems is verified on the Heart Failure dataset. Keywords: Classification | Imbalanced problems | Empirical kernel mapping | Multiple kernel learning | Margin reinforcement |
مقاله انگلیسی |
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Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions
استفاده از هوش مصنوعی برای پیش بینی عفونت محل جراحی بعد از عمل: یک گروه گذشته نگر از 4046 اتصالات خلفی ستون فقرات-2020 Objectives: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly
applied to model outcomes and complications within medicine. In spinal surgery, post-operative surgical site
infections (SSIs) are a rare, yet morbid complication. This paper applied AI to predict SSIs after posterior spinal
fusions.
Patients and Methods: 4046 posterior spinal fusions were identified at a single academic center. A Deep Neural
Network DNN classification model was trained using 35 unique input variables The model was trained and tested
using cross-validation, in which the data were randomly partitioned into training n=3034 and testing n=1012
datasets. Stepwise multivariate regression was further used to identify actual model weights based on predictions
from our trained model.
Results: The overall rate of infection was 1.5 %. The mean area under the curve (AUC), representing the accuracy
of the model, across all 300 iterations was 0.775 (95 % CI [0.767,0.782]) with a median AUC of 0.787. The
positive predictive value (PPV), representing how well the model predicted SSI when a patient had SSI, over all
predictions was 92.56 % with a negative predictive value (NPV), representing how well the model predicted
absence of SSI when a patient did not have SSI, of 98.45 %. In analyzing relative model weights, the five highest
weighted variables were Congestive Heart Failure, Chronic Pulmonary Failure, Hemiplegia/Paraplegia,
Multilevel Fusion and Cerebrovascular Disease respectively. Notable factors that were protective against infection
were ICU Admission, Increasing Charlson Comorbidity Score, Race (White), and being male. Minimally
invasive surgery (MIS) was also determined to be mildly protective.
Conclusion: Machine learning and artificial intelligence are relevant and impressive tools that should be employed
in the clinical decision making for patients. The variables with the largest model weights were primarily
comorbidity related with the exception of multilevel fusion. Further study is needed, however, in order to draw
any definitive conclusions. Keywords: Artificial intelligence | Spine surgery | Surgical site infection |
مقاله انگلیسی |
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Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention
تکنیک های یادگیری ماشین برای پیش بینی پیش بینی بیمار پس از مداخله کرونر در رحم-2019 OBJECTIVES This study sought to determine whether machine learning can be used to better identify patients at risk
for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
BACKGROUND Contemporary risk models for event prediction after PCI have limited predictive ability. Machine
learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of
models.
METHODS We evaluated 11,709 distinct patients who underwent 14,349 PCIs between January 2004 and December
2013 in the Mayo Clinic PCI registry. Fifty-two demographic and clinical parameters known at the time of admission were
used to predict in-hospital mortality and 358 additional variables available at discharge were examined to identify patients
at risk for CHF readmission. For each event, we trained a random forest regression model (i.e., machine learning) to
estimate the time-to-event. Eight-fold cross-validation was used to estimate model performance. We used the predicted
time-to-event as a score, generated a receiver-operating characteristic curve, and calculated the area under the curve
(AUC). Model performance was then compared with a logistic regression model using pairwise comparisons of AUCs and
calculation of net reclassification indices.
RESULTS The predictive algorithm identified a high-risk cohort representing 2% of all patients who had an in-hospital
mortality of 45.5% (95% confidence interval: 43.5% to 47.5%) compared with a risk of 2.1% for the general population
(AUC: 0.925; 95% confidence interval: 0.92 to 0.93). Advancing age, CHF, and shock on presentation were the
leading predictors for the outcome. A high-risk group representing 1% of all patients was identified with 30-day
CHF rehospitalization of 8.1% (95% confidence interval: 6.3% to 10.2%). Random forest regression outperformed
logistic regression for predicting 30-day CHF readmission (AUC: 0.90 vs. 0.85; p ¼ 0.003; net reclassification
improvement: 5.14%) and 180-day cardiovascular death (AUC: 0.88 vs. 0.81; p ¼ 0.02; net reclassification
improvement: 0.02%).
CONCLUSIONS Random forest regression models (machine learning) were more predictive and discriminative than
standard regression methods at identifying patients at risk for 180-day cardiovascular mortality and 30-day CHF rehospitalization,
but not in-hospital mortality. Machine learning was effective at identifying subgroups at high risk for postprocedure
mortality and readmission. (J Am Coll Cardiol Intv 2019;12:1304–11) © 2019 Published by Elsevier on behalf of
the American College of Cardiology Foundation. |
مقاله انگلیسی |
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Deep Representation Learning for Individualized Treatment Effect Estimation using Electronic Health Records
یادگیری بازنمایی عمیق برای ارزیابی اثر درمانی شخصی با استفاده از سوابق الکترونیکی بهداشت-2019 Utilizing clinical observational data to estimate individualized treatment effects (ITE)
is a challenging task, as confounding inevitably exists in clinical data. Most of the existing
models for ITE estimation tackle this problem by creating unbiased estimators of the
treatment effects. Although valuable, learning a balanced representation is sometimes
directly opposed to the objective of learning an effective and discriminative model for
ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and
K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly
adopts multi-task deep learning to extract both outcome-predictive and treatment-specific
latent representations from Electronic Health Records (EHR), by jointly performing the
outcome prediction and treatment category classification. Thereafter, we estimate
counterfactual outcomes by KNN based on the learned hidden representations. We
validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a
real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance
of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the
estimation of heterogeneous effect (PEHE) and average treatment effect (ATE),
respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score
respectively, on HF dataset. The results demonstrate that the proposed model achieves
competitive performance over state-of-the-art models. In addition, the results reveal
several findings which are consistent with existing medical domain knowledge, and
discover certain suggestive hypotheses that could be validated through further
investigations in the clinical domain. Keywords: Individualized Treatment Effect Estimation | Counterfactual Inference | Deep Representation Learning | Multi-task Learning | K-Nearest Neighbors |
مقاله انگلیسی |
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Automatic staging model of heart failure based on deep learning
مدل مرحله بندی خودکار نارسایی قلبی مبتنی بر یادگیری عمیق-2019 Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learningyielded new techniques to train deep neural networks, which resulted in highly successful applica-tions in many pattern recognition tasks such as object detection and speech recognition. To improve thediagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based modelson combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-timeand dynamically. We employed the data segmentation and data augmentation pre-processing datasetto make the classification performance of the proposed architecture better. Specifically, this paper useconvolutional neural network (CNN) as a feature extractor instead of training the entire network toextract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine theabove feature set with other clinical features, feed the combined features to RNN for classification, andfinally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paperachieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% fortwo seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%,96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid tohelp clinicians confirm their diagnosis. Keywords:Heart failure | Staging model | Deep learning | Deep CNN-RNN model |
مقاله انگلیسی |
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Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures
یادگیری ماشین به طور دقیق نتایج کوتاه مدت را به دنبال کاهش باز و رفع داخلی شکستگی های مچ پا پیش بینی می کند-2019 Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and
internal fixation (ORIF). Several patient-related risk factors may contribute to poor short-term outcomes, and machine
learning may be a valuable tool for predicting outcomes. The objective of this study was to evaluate machine-learning
algorithms for accurately predicting short-term outcomes after ORIF for ankle fractures. The Nationwide Inpatient Sample
and Nationwide Readmissions Database were queried for adult patients ≥18 years old who underwent ORIF of an
ankle fracture during 2013 or 2014. Morbidity and mortality, length of stay >3 days, and 30-day all-cause readmission
were the outcomes of interest. Twomachine-learning models were created to identify patient and hospital characteristics
associated with the 3 outcomes. The machine learning models were evaluated using confusion matrices and
receiver operating characteristic area under the curve values. A total of 16,501 cases were drawn from the Nationwide
Inpatient Sample and used to assessmorbidity and mortality and length of stay >3 days, and 33,504 cases were drawn
from the Nationwide Readmissions Database to assess 30-day readmission. Older age, Medicaid, Medicare, deficiency
anemia, congestive heart failure, chronic lung disease, diabetes, hypertension, and renal failure were the variables associated
with a statistically significant increased risk of developing all 3 adverse events. Logistic regression and gradient
boosting had similar area under the curve values for each outcome, but gradient boosting was more accurate and more
specific for predicting each outcome. Our results suggest that several comorbidities may be associated with adverse
short-term outcomes after ORIF of ankle fractures, and thatmachine learning can accurately predict these outcomes. Keywords: ankle fracture | ankle ORIF | gradient boosting | logistic regression | machine learning | readmissions |
مقاله انگلیسی |
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ارزیابی کارایی تکنیک های طبقه بندی داده کاوی برای پیش بینی بیماری قلبی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 12 بیماری قلبی ممکن است یکی از دلایل اصلی مرگ باشد. به علت فقدان دانش و تجربیات متخصصان درمورد علائم نارسایی قلب برای پیش بینی اولیه این بیماری، کار آسان برای تشخیص بیماری نیست. در نتیجه، پیش بینی مبتنی بر رایانه؛ مبتلایان به بیماری قلبی می تواند نقش مهمی را در تشخیص پیش از مرحله برای انجام اقدامات مناسب با توجه به بهبودی بیماران بازی کند. با این حال، انتخاب روش طبقه بندی مناسب داده کاوی می تواند به طور موثر پیش بینی مرحله اولیه بیماری را برای بازگشت از آن به همراه داشته باشد. در این مقاله، سه تکنیک طبقه بندی استفاده شده غالب از قبیل ماشین بردار پشتیبانی (SVM)، نزدیکترین همسایۀ k (KNN) و شبکه عصبی مصنوعی (ANN) را مورد بررسی قرار می دهیم، با توجه به ارزیابی آنها برای پیش بینی بیماری های قلبی با استفاده از مجموعه داده های بیماری کلیوی استاندارد مورد مطالعه قرار گرفته است.. نتایج تجربی نشان می دهد که دقت طبقه بندی با استفاده از SVM (85.1852٪) بهتر از استفاده از KNN (82663٪) و ANN (73.3333٪) است.
لغات کلیدی: داده کاوی | ماشین بردار پشتیبانی | نزدیکترین همسایۀ k | شبکه عصبی مصنوعی | پیش بینی بیماری قلبی | تکنیک های طبقه بندی |
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