Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure

Mohammadi, Tanya; Tofighi, Said; Mohammadi, Babak; Halimi, Shadi; Gharebakhshi, Farshad

doi:10.1007/s40292-023-00598-x

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…The Boruta algorithm excels in feature selection due to its ability to capture non‐linear relationships among variables, providing a more nuanced understanding of complex data structures. It effectively handles class imbalance by considering shadow attributes, ensuring a balanced representation during the feature selection process 22 . In addition, Boruta is robust against multicollinearity and outliers, increasing the reliability of selected features.…”

Section: Discussionmentioning

confidence: 99%

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Post‐load glucose is a stronger predictor of adverse pregnancy outcomes than first‐trimester HbA1c in women without gestational diabetes

Rajabi,

Toghraee,

Nejatipour

2024

J of Diabetes Invest

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Aims/IntroductionA debate exists on the relation of adverse pregnancy outcomes with glycemic levels in early pregnancy. We aimed to investigate the association of maternal characteristics including post‐load glucose and first‐trimester HbA1c test results with adverse pregnancy outcomes in women without gestational diabetes mellitus.Materials and MethodsA dataset (January 2011 and September 2017) from a hospital prenatal clinic was explored to find the important predictors of adverse pregnancy outcomes using maternal characteristics and glucose assessments in mothers without gestational diabetes. We used two machine learning algorithms to capture nonlinearity in selecting important maternal characteristics and developed predictive models for each outcome. In total, 1,618 pregnant women were included in the analytic dataset with a mean (SD) age of 26.8 (3.5) years and gravida of 1.7 (0.9).ResultsImportant associations were detected between maternal features and primary cesarean section, fetal distress, premature rupture of membranes, macrosomia, small or large for gestational age, APGAR <7 at 1 or 5 min, hyperbilirubinemia, and poly‐ or oligo‐hydramnios. Overall, the predictive models showed good performance and large areas under the curves (0.732, 0.765, 0.646, 0.651, 0.730, 0.646, 0.684, 0.716, and 0.678, respectively). Specifically, they had high positive likelihood ratios.ConclusionsHigh glucose levels were associated with adverse pregnancy outcomes. Post‐load glucose was the most reliable test for predicting the outcomes. Overall, fasting blood sugar was of more predictive value than HbA1c. Our study showed that further research should account for the nonlinearity and interactions inherent in the data.

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Section: Discussionmentioning

confidence: 99%

“…We used a machine learning algorithm known as Boruta to evaluate the importance of risk factors for predicting adverse pregnancy outcomes. The Boruta algorithm is a wrapper around a random forest machine learning model 22 . It is used to identify the most important features in a data set without making parametric assumptions.…”

Section: Methodsmentioning

confidence: 99%

Post‐load glucose is a stronger predictor of adverse pregnancy outcomes than first‐trimester HbA1c in women without gestational diabetes

Rajabi,

Toghraee,

Nejatipour

2024

J of Diabetes Invest

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“….3.3 | The effects of AIM2 inflammasomes in heart failure(HF) HF occurs as a result of damage to the myocardium arising from myocardial infarction, cardiomyopathy, haemodynamic overload or inflammation, leading to alterations in myocardial structure and function and ultimately compromised ventricular pumping or filling(Mohammadi et al, 2023;Weymon et al, 2023).Assessment of monocytes/macrophages from failing hearts showed that the expression of AIM2 inflammasomes and NLRC4 inflammasomes was increased, while that of NLRP1 inflammasomes and NLRP3 inflammasomes was not(On odi et al, 2021). In T2D mellitus, defective mitophagy after infarction releases mitochondrial mtDNA and overactivates AIM2 inflammasomes and NLRC4 inflammasomes in macrophages, which accelerates HF by cell death and IL-18 secretion(Durga Devi et al, 2017).…”

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confidence: 99%

The cytoplasmic sensor, the AIM2 inflammasome: A precise therapeutic target in vascular and metabolic diseases

Lin,

Wang,

Fang

et al. 2024

British J Pharmacology

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Cardio‐cerebrovascular diseases encompass pathological changes in the heart, brain and vascular system, which pose a great threat to health and well‐being worldwide. Moreover, metabolic diseases contribute to and exacerbate the impact of vascular diseases. Inflammation is a complex process that protects against noxious stimuli but is also dysregulated in numerous so‐called inflammatory diseases, one of which is atherosclerosis. Inflammation involves multiple organ systems and a complex cascade of molecular and cellular events. Numerous studies have shown that inflammation plays a vital role in cardio‐cerebrovascular diseases and metabolic diseases. The absent in melanoma 2 (AIM2) inflammasome detects and is subsequently activated by double‐stranded DNA in damaged cells and pathogens. With the assistance of the mature effector molecule caspase‐1, the AIM2 inflammasome performs crucial biological functions that underpin its involvement in cardio‐cerebrovascular diseases and related metabolic diseases: The production of interleukin‐1 beta (IL-1β), interleukin‐18 (IL-18) and N‐terminal pore‐forming Gasdermin D fragment (GSDMD‐N) mediates a series of inflammatory responses and programmed cell death (pyroptosis and PANoptosis). Currently, several agents have been reported to inhibit the activity of the AIM2 inflammasome and have the potential to be evaluated for use in clinical settings. In this review, we systemically elucidate the assembly, biological functions, regulation and mechanisms of the AIM2 inflammasome in cardio‐cerebrovascular diseases and related metabolic diseases and outline the inhibitory agents of the AIM2 inflammasome as potential therapeutic drugs.

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Predictors of pain intensity in carpal tunnel syndrome: Development and validation of a model

Rezaee,

Roshandel,

Rahimibarghani

et al. 2024

Clinical Neurology and Neurosurgery

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Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure

Cited by 3 publications

References 40 publications

Post‐load glucose is a stronger predictor of adverse pregnancy outcomes than first‐trimester HbA1c in women without gestational diabetes

Post‐load glucose is a stronger predictor of adverse pregnancy outcomes than first‐trimester HbA1c in women without gestational diabetes

The cytoplasmic sensor, the AIM2 inflammasome: A precise therapeutic target in vascular and metabolic diseases

Predictors of pain intensity in carpal tunnel syndrome: Development and validation of a model

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