Prediction of anemia with thoracic computed tomography findings.

BACKGROUND: This study explored the potential of non-contrast thoracic computed tomography (CT) to predict anemia by correlating CT parameters with hemoglobin (Hb) levels in patients who underwent non-contrast thoracic CT for various indications. METHODS: This retrospective study included 150 patients who underwent non-contrast thoracic CT scans and complete blood counts within 24 hours at our center between January and June 2023. Exclusion criteria included acute bleeding, iron accumulation disorders, recent transfusions, pregnancy, and certain thoracic CT artifacts. Hounsfield Unit (HU) measurements were obtained from the ascending aorta, left ventricular cavity, and descending aorta, and compared with Hb and hematocrit (Htc) values. Anemia indicators such as the 'Aortic Ring Sign (ARS)' and the 'Hyperdense Septum Sign (HSS)' were also evaluated. RESULTS: Anemic patients (48%) exhibited significantly lower HU measurements at all three CT scan locations and higher instances of ARS and HSS compared to non-anemic patients. Notably, the presence of HSS and ARS was strongly associated with anemia. Thresholds for HU measurements corresponding to anemia were determined using receiver operating characteristic curve analysis, which also revealed strong positive correlations between HU measurements and Hb/Htc levels. CONCLUSION: The study concludes that non-contrast thoracic CT parameters, particularly HU measurements and the presence of ARS and HSS, are significantly associated with anemia. These CT indicators could serve as reliable, non-invasive markers for predicting anemia in patients, potentially aiding in the early diagnosis and management of the condition.

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels.

The demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discerning test strategies. The aim of this study is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in cases of erythrocytosis. Test results were sourced from samples sent to a tertiary hospital's genetic laboratory between 2017 and 2023, meeting 2016 World Health Organization criteria for JAK2V617F mutation testing. The JAK2 Somatic Mutation Screening Kit was used for genetic testing. Machine learning models were trained and tested using Python programming language. Out of 458 cases, JAK2V617F mutation was identified in 13.3%. There were significant differences in complete blood count parameters between mutation carriers and non-carriers. Various models were trained with data, with the random forest (RF) model demonstrating superior precision, recall, F1-score, accuracy, and area under the receiver operating characteristic, all reaching 100%. Gradient boosting (GB) model also showed high scores. When compared with existing algorithms, the RF and GB models displayed superior performance. The RF and GB models outperformed other methods in accurately identifying and classifying erythrocytosis cases, offering potential reductions in unnecessary testing and costs.

Anemia and Iron Deficiency Predict All-Cause Mortality in Patients with Heart Failure and Preserved Ejection Fraction: 6-Year Follow-Up Study.

AIMS: The aim of this study was to assess the prevalence of anemia and iron deficiency in patients with heart failure with preserved ejection fraction (HFpEF) and its impact on clinical outcomes. METHODS: We retrospectively analyzed 212 patients with HFpEF and identified anemia as a serum hemoglobin level of less than 13 g/dL in men and less than 12 g/dL in women. Additionally, ID was defined as a serum ferritin concentration < 100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%. Patients were followed up for an average of 66.2 ± 12.1 months, with the endpoint being all-cause mortality among patients with HFpEF, both with and without anemia and iron deficiency. Furthermore, we explored other predictors of all-cause mortality. RESULTS: The average age of the entire group was 70.6 ± 10.5 years, with females comprising 55% of the patients. Anemia was present in 81 (38.2%) patients, while 108 (50.9%) had iron deficiency. At the end of the follow-up period, 60 (28.3%) of the patients had passed away. Patients with anemia displayed more heart failure (HF) symptoms, diastolic dysfunction, higher NT-pro-BNP levels, and worse baseline functional capacity than those without. Similarly, patients with iron deficiency showed more pronounced HF symptoms and worse functional capacity than those without. The results from the multivariable analyses revealed that anemia (hazard ratio [HR]: 5.401, 95% confidence interval [CI]: 4.303-6.209, log-rank p = 0.001), advanced age, iron deficiency (HR: 3.502, 95% CI: 2.204-6.701, log-rank p = 0.015), decreased left ventricular ejection fraction, chronic kidney disease, and paroxysmal nocturnal dyspnea were all independently associated with all-cause mortality. CONCLUSIONS: It is essential to consider anemia and iron deficiency as common comorbidities in managing and prognosis HFpEF, as they significantly increase mortality risk.

Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis.

BACKGROUND: This study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features. METHODS: Retrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023. The ML model was trained and tested using MATLAB, with 80% of the data allocated for training/validation, and 20% for testing. Performance was evaluated based on validation and testing accuracy, and receiver operating characteristic curves with comparing trained models and existing classification rules. RESULTS: The study analyzed 992 LNs, with 42.3% malignancy prevalence. Malignant LNs showed characteristic features such as larger size and distinct margins. The fine tuned models achieved testing accuracies of 95.9% and 96.4% for fine Gaussian SVM and KNN, respectively. Corresponding AUROC's were 0.955 and 0.963, outperforming other similar studies and conventional analyses. CONCLUSION: Fine tuned ML applications like SVM and KNN, can significantly enhance the analysis of EBUS images, improving diagnostic accuracy.

Acquired partial lipodystrophy is associated with increased risk for developing metabolic abnormalities.

OBJECTIVE: Acquired partial lipodystrophy (APL) is a rare disorder characterized by progressive selective fat loss. In previous studies, metabolic abnormalities were reported to be relatively rare in APL, whilst they were quite common in other types of lipodystrophy syndromes. METHODS: In this nationwide cohort study, we evaluated 21 Turkish patients with APL who were enrolled in a prospective follow-up protocol. Subjects were investigated for metabolic abnormalities. Fat distribution was assessed by whole body MRI. Hepatic steatosis was evaluated by ultrasound, MRI and MR spectroscopy. Patients with diabetes underwent a mix meal stimulated C-peptide/insulin test to investigate pancreatic beta cell functions. Leptin and adiponectin levels were measured. RESULTS: Fifteen individuals (71.4%) had at least one metabolic abnormality. Six patients (28.6%) had diabetes, 12 (57.1%) hypertrigylceridemia, 10 (47.6%) low HDL cholesterol, and 11 (52.4%) hepatic steatosis. Steatohepatitis was further confirmed in 2 patients with liver biopsy. Anti-GAD was negative in all APL patients with diabetes. APL patients with diabetes had lower leptin and adiponectin levels compared to patients with type 2 diabetes and healthy controls. However, contrary to what we observed in patients with congenital generalized lipodystrophy (CGL), we did not detect consistently very low leptin levels in APL patients. The mix meal test suggested that APL patients with diabetes had a significant amount of functional pancreatic beta cells, and their diabetes was apparently associated with insulin resistance. CONCLUSIONS: Our results show that APL is associated with increased risk for developing metabolic abnormalities. We suggest that close long-term follow-up is required to identify and manage metabolic abnormalities in APL.