Has the Coronavirus Disease 2019 Pandemic Played a Role in the Early Detection of Pulmonary Embolism in Children?

OBJECTIVE: Pulmonary embolism (PE) poses a significant threat to children, and nonspecific symptoms lead to delayed diagnosis. The emergence of coronavirus disease 2019 (COVID-19) has increased the complexity as it is associated with similar symptoms and increased risk of thrombotic complications. This study aimed to assess the risk factors, clinical presentations, and diagnostic features of PE in pediatric patients and to examine the impact of the COVID-19 pandemic on children with PE. MATERIALS AND METHODS: We conducted a retrospective descriptive study examining the clinical and diagnostic data of 44 pediatric patients with radiologically confirmed PE. The study compared and analyzed patients diagnosed before and during the COVID-19 pandemic. RESULTS: In the study, 21 of 44 pediatric patients were diagnosed in the 4 years before the COVID-19 pandemic, and 23 were diagnosed with PE during the COVID-19 pandemic. The mean time to diagnosis was 8 (2 to 14) days before the pandemic and 1 (1 to 2) days during the pandemic (P < 0.001). The most common associated condition in both groups was infection (65.9%). Dyspnea (65.9%) and tachypnea (50.0%) were common symptoms. Except for deep vein thrombosis, there were no significant differences according to associated conditions between the groups (P = 0.001). Pulmonary emboli were anatomically detected using computed tomography angiography, showing bilateral involvement in 45.4% of patients, segmental artery involvement in 38.6%, and main artery involvement in 15.9%. CONCLUSION: The COVID-19 pandemic heightened suspicion of pediatric PE and accelerated diagnosis. Standardized diagnostic guidelines are increasingly necessary to balance accurate diagnosis with avoiding excessive imaging.

Galectin-3 levels in children with cystic fibrosis.

Cystic fibrosis (CF) is a multisystemic disease in which airway obstruction, infection, and inflammation play a critical role in the pathogenesis and progression of CF lung disease. The carbohydrate-binding protein Galectin-3 is increased in several inflammatory and fibrotic diseases and has recently been forwarded as a biomarker in these diseases. We aimed to define the role of serum Galectin-3 in children with CF by comparison with healthy subjects. This is a cross-sectional, case-control study. 143 CF and 30 healthy subjects were enrolled in the study. Peripheral blood and sputum concentrations of Galectins-3, interleukin (IL)-17A, IL-8, and neutrophil elastase (NE) were determined with commercial ELISA kits. There was no significant difference between the groups in age and gender (p = 0.592, p = 0.613, respectively). Serum Galectin-3 and NE concentrations were higher in the patient group than in healthy controls (p = 0.002, p < 0.001, respectively). There were no significant differences between groups according to IL-17A and IL-8 concentrations. Serum Galectin-3 was correlated with age (r = 0.289, p < 0.001) and body mass index (BMI) (r = 0.493, p < 0.001) in children with CF. Sputum Galectin-3 levels are negatively correlated with percent predictive forced expiratory volume in 1 s (FEV1) (r = - 0.297, p = 0.029), FEV1 z-score, (r = - 0.316, p = 0.020), percent predictive forced vital capacity (FVC) (r = - 0.347, p = 0.010), and FVC z-score (r = - 0.373, p = 0.006).   Conclusion: The study shows that serum Galectin-3 levels increased in clinically stable CF patients, and serum Galectin-3 response may depend on age, gender, and BMI. The sputum Galectin-3 was found to be negatively correlated with patients' lung functions. What is known: • Galectin-3 is a key regulator of chronic inflammation in the lung, liver, kidney, and tumor microenvironment. What is new: • Children with cystic fibrosis (CF) have higher serum Galectin-3 concentrations than healthy children. • Serum Galectin-3 expression influenced by age, BMI, and gender in children with CF.

Segmental hair metabolomics analysis in pregnant women with pregnancy complications.

INTRODUCTION: Pregnancy complications, as preeclampsia (PE) and HELLP syndrome, occurring with similar pathophysiological mechanisms, have adverse effects on the health of both mother and fetus during pregnancy and thereafter, they are leading causes of maternal and fetal morbidity and mortality. The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information to be able to assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. OBJECTIVE: The aim of this study was to investigate the hair metabolome profile of pregnant women with PE, HELLP syndrome and healthy women. METHOD: Hair samples of new-borns' mothers (patients and controls) were investigated segmentally relevant to each trimester using a proper sample preparation and gas chromatography-mass spectrometry (GC-MS) to identify robust biomarkers that can be useful for screening, early detection, follow-up and treatment of PE and HELLP syndrome, the etiology of which are still unknown. RESULTS: The results showed a significant change in the metabolome profiles of the patient and control groups regarding the trimesters. A striking decrease was observed in all 100 metabolites investigated in the patient group (p < 0.000). The metabolic pathways associated with significant metabolites have also been investigated, and the most affected pathways were observed to be the urea cycle, glycine, serine, aspartate, methionine and purine metabolism, ammonia cycle, and phosphatidylethanolamine biosynthesis. CONCLUSION: The found metabolites provide us with extensive data on the ability to establish biomarkers for predicting, early detection and monitoring of PE.

Lymphocyte Subgroups and KREC Numbers in Common Variable Immunodeficiency: A Single Center Study.

Common variable immunodeficiency (CVID) results in defective B cell differentiation and impaired antibody production and is the most common symptomatic primary immunodeficiency. Our aim was to evaluate the correlation among B cell subgroups, κ-deleting recombination excision circle (KREC) copy numbers, and clinical and immunological data of the patients with CVID, and evaluate the patients according to classifications currently available to define the role of KREC copy numbers in the diagnosis of CVID. KREC analysis was performed using a quantitative real-time polymerase chain reaction assay, and B cell subgroups were measured by flow cytometry. The median age of the patients (n = 30) was 25 (6-69) years. Parental consanguinity ratio was 33%. The median age at diagnosis was 15 (4-59), and follow-up period was 6 (1-37) years. CD19+ and CD4+ cell counts at the time of diagnosis were low in 66.7% and 46.7% of the patients, respectively. CD19+ cell counts were positively correlated with KREC copy numbers in patients and healthy controls. CD19+ cell counts and KREC copy numbers were significantly reduced in CVID patients compared to healthy controls as expected. KRECs are quantitative markers for B cell defects. We found low CD4+ cell numbers, recent thymic emigrants, and lymphopenia in some of the patients at diagnosis, which reminds the heterogeneity of CVID's etiology. In this study, a positive correlation was shown between CD19+ cell counts and KREC copy numbers. Low KREC copy numbers indicated B cell deficiency; however, high KREC copy numbers were not sufficient to rule out CVID.

Risk assessments of drug-related problems for cardiac surgery patients.

OBJECTIVES: Patients undergoing cardiac surgery are considered at high risk for developing drug-related problems (DRPs) due to comorbidities and complexity of drug treatment. This study aimed to identify DRPs in patients undergoing cardiac surgery and to develop and implement a framework to reduce potential risks associated with drug treatment. STUDY DESIGN: Prospectively designed quasi-experimental study. METHODS: This study consisted of observational (risk assessment and framework development) and interventional (framework implementation) periods and was conducted at a department of cardiovascular surgery in a university hospital. An expert panel evaluated the causes of DRPs. Then a framework was developed in consensus to identify safeguards to be implemented during the interventional period. RESULTS: A total of 200 patients (100 patients per study period) were included. During the observational period, a total of 275 DRPs and 487 causes were identified; 74.5% of DRPs were not solved. For the risk analysis, 487 causes were evaluated and only 32.6% were considered acceptable risk. By implementing the framework in the interventional period, 215 DRPs and 304 causes were identified and 386 interventions were recommended by a clinical pharmacist. A total of 342 (88.6%) interventions were accepted by a health care team, and 128 (59.5%) DRPs were completely solved. For the risk analysis, 304 causes were evaluated and 84.9% were considered acceptable risk ( P < .001 compared with the observational period). CONCLUSIONS: It is possible to reduce risk levels or prevent occurrence of DRPs by implementing a framework for risk management developed by a multidisciplinary care team in areas such as cardiac surgery where time is limited.

The airway microbiota in siblings with primary ciliary dyskinesia: Related factors and correlation with clinical characteristics.

OBJECTIVES-AIM: We aimed to show the composition and structure of and explore affecting factors on airway microbiota in primary ciliary dyskinesia (PCD) patients using culture-independent techniques. METHOD: A cross-sectional observational study was performed. We recruited 14 PCD patients (seven pairs of siblings) and nine parents. Bacterial rDNA was extracted from sputum and nasal samples. Sputum samples were also inoculated on suitable bacteriological media. RESULTS: Thirty-three separate genera were detected in sputum samples of PCD patients, and 41 were in nasal samples of parents. The detected genera were dominated by phyla Proteobacteria in PCD patients and their parents. Culture-dependent analyses could not detect many of the bacterial species detected with culture-independent analyses. There were no significant differences in alpha diversity between the siblings' pairs, and siblings' samples did not cluster together nearly as strongly as nonsiblings' samples. Patients who had no new complaints and no bacterial growth with the culture-dependent method at the time of study and patients who had no Haemophilus influenzae growth in the previous year had a significantly greater diversity (p < .05). Microbiota communities tended to cluster together by age, pulmonary exacerbation status, the existence of at least one H. influenzae growth with culture-dependent analyses in the previous year, and forced expiratory volume in 1 sec z and FEF25-75 z-scores. CONCLUSION: The airway microbiota of patients with PCD have presented more diverse bacterial communities than had been indicated with culture-dependent methods. The study identifies relationships between bacterial airway microbiota composition and the clinical measures of patients. Sibling pairs have no more community similarities than nonsibling PCD patients. Our results may indicate that the patients' clinical characteristics, which determine the disease severity, might affect the PCD microbiome.

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared to univariate analysis. However, there exist serious differences among most studies analyzing the same dataset for the reasons arising from the methods. Therefore, there is a strong need for easily accessible, user-friendly, and interactive tool to perform gene selection for RNA-seq data via machine learning algorithms simultaneously not to miss DEGs. We develop an open-source and freely available web-based tool for gene selection via machine learning algorithms that can deal with high performance computation. This tool includes six machine learning algorithms having different aspects. Moreover, the tool involves classical pre-processing steps; filtering, normalization, transformation, and univariate analysis. It also offers well-arranged graphical approaches; network plot, heatmap, venn diagram, and box-and-whisker plot. Gene ontology analysis is provided for both mRNA and miRNA DEGs. The implementation is carried out on Alzheimer RNA-seq data to demonstrate the use of this web-based tool. Eleven genes are suggested by at least two out of six methods. One of these genes, hsa-miR-148a-3p, might be considered as a new biomarker for Alzheimer's disease diagnosis. Kidney Chromophobe dataset is also analyzed to demonstrate the validity of GeneSelectML web tool on a different dataset. GeneSelectML is distinguished in that it simultaneously uses different machine learning algorithms for gene selection and can perform pre-processing, graphical representation, and gene ontology analyses on the same tool. This tool is freely available at www.softmed.hacettepe.edu.tr/GeneSelectML .

Impact of clinical pharmacist-led intervention for drug-related problems in neonatal intensive care unit a randomized controlled trial.

Introduction: Drug-related problems (DRPs) incidence is higher in neonatal intensive care units (NICUs), compared to other pediatric wards due to aspects like off-label medications, pharmacokinetic/dynamic variability, or organ dysfunction/immaturity. This study aimed to determine whether and to what extent a clinical pharmacist intervention improves medication safety and prevents DRPs [medication errors (MEs), adverse drug reactions (ADRs), drug-drug interactions (DDIs)]. Methods: A prospective, randomized, double blind, controlled study in NICU-admitted neonates was conducted. NICU patients were randomly assigned to the intervention (clinical pharmacist-led) (IG) or control group (standard care such as clinical diagnosis, pharmacotherapy) (CG). The clinical pharmacist was involved in the IG to identify-prevent-intervene MEs, or identify and monitor ADRs and DDIs. The primary outcome was the number of neonates who developed at least one DRP compared with those seen across IG and CG. Secondary outcomes included length of hospital stay, total number of drugs or DRP type. Results: Neonates were randomly assigned to CG (n = 52) or IG (n = 48). In total, 45%, 42%, and 16% of patients had at least 1 MEs, ADRs, and clinically significant DDIs, respectively. The number of patients with at least 1 ME was 28 (53%) and 17 (35%) in the CG and IG (p>0.05). The median (range) number of ME was higher in CG [1 (0-7)] than in IG [0 (0-4)] (p = 0.003). Applying regression analysis, the CG had 2.849 times more MEs than the IG (p<0.001). Furthermore, the number of patients (CG to IG) with at least one detected ADR or clinical DDI was 19 (36%) to 23 (47%) (p>0.05) and 4 (7%) to 12 (25%), respectively (p = 0.028). Conclusion: Clinical pharmacist availability to systematically and standardized identify, prevent and resolve DRPs among NICU patients is effective. Daily detailed clinical pharmacist observations and interventions enables prevention and monitoring of DRPs. Clinical Trial Registration ClinicalTrials.gov, identifier NCT04899960.

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.

Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients. Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms. Setting: A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021. Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2,280 prescriptions over 32,925 patient days were analyzed. At least one physician-related ME and nurse-related ME were found in 174 (42.2%) and 235 (57.0%) of the patients, respectively. The parameters that had the highest correlation with ME occurrence and subsequently included in the model were: total number of drugs, anti-infective drugs, nervous system drugs, 5-min APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related parameters, and weekly working hours of nurses, weekly working hours of physicians, and number of nurses' monthly shifts as care provider-related parameters. The obtained model showed high performance to predict ME (AUC: 0.920; 95% CI: 0.876-0.970) presence and is accessible online (http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/). Conclusion: This is the first developed and validated model to predict the presence of ME using work environment and pharmacotherapy parameters with high-performance ML algorithms in NICU patients. This approach and the current model hold the promise of implementation of targeted/precision screening to prevent MEs in neonates. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT04899960.

Development and validation of machine learning-based clinical decision support tool for identifying malnutrition in NICU patients.

Hospitalized newborns have an increased risk of malnutrition and, especially preterm infants, often experience malnutrition-related extrauterine growth restriction (EUGR). The aim of this study was to predict the discharge weight and the presence of weight gain at discharge with machine learning (ML) algorithms. The demographic and clinical parameters were used to develop the models using fivefold cross-validation in the software-R with a neonatal nutritional screening tool (NNST). A total of 512 NICU patients were prospectively included in the study. Length of hospital stay (LOS), parenteral nutrition treatment (PN), postnatal age (PNA), surgery, and sodium were the most important variables in predicting the presence of weight gain at discharge with a random forest classification (AUROC:0.847). The AUROC of NNST-Plus, which was improved by adding LOS, PN, PNA, surgery, and sodium to NNST, increased by 16.5%. In addition, weight at admission, LOS, gestation-adjusted age at admission (> 40 weeks), sex, gestational age, birth weight, PNA, SGA, complications of labor and delivery, multiple birth, serum creatinine, and PN treatment were the most important variables in predicting discharge weight with an elastic net regression (R2 = 0.748). This is the first study on the early prediction of EUGR with promising clinical performance based on ML algorithms. It is estimated that the incidence of EUGR can be improved with the implementation of this ML-based web tool ( http://www.softmed.hacettepe.edu.tr/NEO-DEER/ ) in clinical practice.

A twenty years' results of the antimicrobial resistance profile and multidrug resistance trend of invasive Streptococcus pneumoniae isolates recovered from adult patients in Turkey: A literature review.

PURPOSE: The aim of this study is to analyze antimicrobial resistance and multidrug (MDR)/extensively (XDR) resistance trend among Streptococcus pneumoniae isolates causing invasive disease in adult patients. METHODS: We analyzed antimicrobial resistance and multidrug resistance trend among invasive S.pneumoniae isolates recovered from adult patients (≥18-years) in a tertiary University Hospital, Turkey between 1996 and 2018. The antibiotic susceptibility pattern was determined by using gradient-test for penicillin and cefotaxime and disk-diffusion method for other antibiotics. RESULTS: A total of 272 isolates (74.3% from the bloodstream) of S. pneumoniae were collected during the study period. The highest non-susceptibility rate was obtained for tetracycline (63.5%), followed by trimethoprim/sulfamethoxazole (48%), penicillin-oral (30.4%), erythromycin (21.7%), clindamycin (15.8%), ciprofloxacin/levofloxacin (5.9%), penicillin-parenteral (5.5%), cefotaxime (2.2%), and rifampisin (1.8%), respectively. No resistance was observed against vancomycin during the years studied. Over the study period, a significant increase in the rate of antimicrobial resistance among invasive pneumococcal isolates was detected with a peak at period 2014-2018. Although there was an increase in the rates of non-susceptibility to penicillin oral, parenteral penicillin, cefotaxime, erythromycin and clindamycin in adult patients, the results were not statistically significant except erythromycin. Prevalence of MDR and XDR S. pneumoniae were 29% and 9.2% respectively. When the serotypes of MDR isolates were examined, it was noted that serotype 19F (35%) and 14 (12.5%) were the most common. CONCLUSIONS: Our study showed an overall increase in non-susceptibility rates of penicillin and erythromycin in invasive S.pneumoniae isolates recovered from Turkish adult patients. Although the prevalence of MDR showed fluctuation between years, the incidence of MDR remained stable. These data indicate the necessity for continuous monitoring and assessment of serotypes and antimicrobial resistance trends in S.pneumoniae in different age groups at both the national and the regional levels as it can be affected by the serotypes dominant in that region, rational use of antibiotics and the vaccination programs.

Novel Method for Early Prediction of Clinically Significant Drug-Drug Interactions with a Machine Learning Algorithm Based on Risk Matrix Analysis in the NICU.

Aims: Evidence for drug-drug interactions (DDIs) that may cause age-dependent differences in the incidence and severity of adverse drug reactions (ADRs) in newborns is sparse. We aimed to develop machine learning (ML) algorithms that predict DDI presence by integrating each DDI, which is objectively evaluated with the scales in a risk matrix (probability + severity). Methods: This double-center, prospective randomized cohort study included neonates admitted to the neonatal intensive care unit in a tertiary referral hospital during the 17-month study period. Drugs were classified by the Anatomical Therapeutic Chemical (ATC) classification and assessed for potential and clinically relevant DDIs to risk analyses with the Drug Interaction Probability Scale (DIPS, causal probability) and the Lexicomp® DDI (severity) database. Results: A total of 412 neonates (median (interquartile range) gestational age of 37 (4) weeks) were included with 32,925 patient days, 131 different medications, and 11,908 medication orders. Overall, at least one potential DDI was observed in 125 (30.4%) of the patients (2.6 potential DDI/patient). A total of 38 of these 125 patients had clinically relevant DDIs causing adverse drug reactions (2.0 clinical DDI/patient). The vast majority of these DDIs (90.66%) were assessed to be at moderate risk. The performance of the ML algorithms that predicts of the presence of relevant DDI was as follows: accuracy 0.944 (95% CI 0.888-0.972), sensitivity 0.892 (95% CI 0.769-0.962), F1 score 0.904, and AUC 0.929 (95% CI 0.874-0.983). Conclusions: In clinical practice, it is expected that optimization in treatment can be achieved with the implementation of this high-performance web tool, created to predict DDIs before they occur with a newborn-centered approach.

An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool.

BACKGROUND: Critically ill neonates are at greater risk for adverse drug reactions (ADRs). The differentiation of ADRs from reactions associated with organ dysfunction/immaturity or genetic variability is difficult. METHODS: In this prospective cohort study, each ADR was assessed using newborn-specific severity and probability scales by the clinical pharmacist. Subsequently, a machine learning-based risk score was designed to predict ADR presence in neonates. RESULTS: In 98/412 (23.8%) of (56.3%; male) neonates included, 187 ADRs (0.42 ADR/patient) were determined related to 49 different drugs (37.12%). Drugs identified as high risk were enoxaparin, dexmedetomidine, vinblastine, dornase alfa, etoposide/carboplatin and prednisolone. The independent variables included in the risk score to predict ADR presence, according to the random forest importance criterion, were: systemic hormones (2 points), cardiovascular drugs (3 points), diseases of the circulatory system (1 point), nervous system drugs (1 point), and parenteral nutrition treatment (1 point), (cut-off value: 3 points). This risk score correctly classified 91.1% of the observations in the test set (c-index: 0.914). CONCLUSIONS: Using the high-performing risk score specific to neonates, it is expected that high-risk neonatal ADRs can be determined and prevented before they occur. Moreover, the awareness of clinicians of these drugs can be improved with this web-tool, and mitigation strategies (change of drug, dose, treatment duration, etc.) can be considered, based on a benefit-harm relationship for suspected drugs with a newborn-centered approach.

The impact of the Adenotonsillectomy on cardiac functions and oxidative stress.

OBJECTIVE: Obstructive sleep apnea syndrome (OSAS) causes cardiovascular comorbidities and increased oxidative stress. Adenotonsillectomy is the first treatment option for OSAS secondary to adenotonsillar hypertrophy (ATH). This study evaluated the presence of cardiovascular changes, hypertension and oxidative stress before and after adenotonsillectomy in patients with OSAS secondary to ATH. METHODS: Patients with ATH diagnosed with OSAS by polysomnography (PSG) were included. All participants received an Echocardiography (ECHO) and 24-h ambulatory blood pressure measurement (ABPM). Serum malonyldialdehyde (MDA) and total oxidant activity (TOS) levels of oxidant parameters; total antioxidant activity (TAS), catalase (CAT), superoxide dismutase (SOD) and glutathione (GSH) levels of antioxidant parameters were measured. All patients received an adenotonsillectomy. Postoperative evaluation was performed at the 6th month. In the postoperative period, PSG, ECHO, ABPM and the oxidant-antioxidant parameter levels in the serum was repeated. RESULTS: Twenty-eight patients (13 males, 15 females; mean age 8.2 ± 2.06 years) were included in the study. In the preoperative period, concentric remodeling was observed in 14,8% of the patients, although they had no cardiovascular system complaints. The apnea-hypopnea index (AHI) scores were classified as mild in 39.3% (n = 11), moderate in 21.4% (n = 6) and severe in 39.3% (n = 11) preoperatively. In the postoperative period, 22 patients were evaluated. It was observed that the severity of OSAS decreased, ventricular functions improved, oxidant parameters decreased and antioxidant parameters increased postoperatively. CONCLUSION: Adenotonsillectomy provides a positive change in cardiovascular system parameters and an antioxidant change in the oxidative balance in patients with OSAS.

Relationship between Abdominal Aortic Calcification, Abdominal Adiposity, and Liver Density.

OBJECTIVE: To identify any relationship among visceral adipose tissue area (visceral FA), liver density (liver HU), psoas muscle area (psoas MA), waist circumference (WC) and the presence and severity of abdominal aortic calcific atherosclerosis (AAC). STUDY DESIGN: Cross-sectional descriptive study. PLACE AND DURATION OF STUDY: Ankara Kecioren Training and Research Hospital, Ankara, Turkey, from January to February 2019. METHODOLOGY: This study included 316 patients, who had CT performed for urolithiasis investigation. For all patients, the presence and grade of AAC was recorded. Then, liver HU, spleen density (spleen HU), psoas MA, visceral FA, total abdominal fat area (total FA), subcutaneous fat area (subcutaneous FA), WC and hip circumference (HC) were measured on a workstation. RESULTS: AAC was present in 127 patients (40.2%). The age, visceral FA, total FA, visceral FA/total FA ratio, WC and WC/HC ratio of patients with AAC were significantly higher than for patients without AAC (p <0.05). Psoas MA was significantly lower in patients with AAC (p <0.05).  The cut-off value of visceral FA for the prediction of AAC was 131 cm2. The risk for AAC was 4.5 times higher in the group with visceral FA >131 cm2 (p <0.001). There were significant correlations between AAC grade and liver HU and spleen HU (p = 0.002 and p = 0.001, respectively). However, there was no significant correlation between AAC grade and liver HU/spleen HU ratio (p = 0.741). CONCLUSION: Psoas muscle area, visceral adiposity and waist circumference can be used to predict abdominal aortic calcification. Key Words: Visceral adipose tissue, Subcutaneous adipose tissue, Fatty liver.