Prognostic value of the fibrinogen-to-albumin ratio (FAR) in patients with chronic heart failure across the different ejection fraction spectrum.

The ratio of fibrinogen to albumin (FAR) is considered a new inflammatory biomarker and a predictor of cardiovascular disease risk. However, its prognostic value for patients with chronic heart failure (CHF) with different ejection fractions (EFs) remains unclear. A total of 916 hospitalized patients with CHF from January 2017 to October 2021 in the First Affiliated Hospital of Kunming Medical University were included in the study. Death occurred in 417 (45.5%) patients out of 916 patients during a median follow-up time of 750 days. Among these patients, 381 patients suffered from HFrEF (LVEF <40%) and 535 patients suffered from HFpEF or HFmrEF (HFpEF plus HFmrEF, LVEF ≥ 40%). Patients were categorized into high-level FAR (FAR-H) and low-level FAR (FAR-L) groups based on the optimal cut-off value of FAR (9.06) obtained from receiver operating characteristic (ROC) curve analysis. Upon analysing the Kaplan - Meier plots, the incidence of death was significantly higher in all patients with FAR-H and patients in both HF subgroups (p < 0.001). The multivariate Cox proportional hazard analyses indicated that the FAR was an independent predictor of all-cause mortality, regardless of heart failure subtype. (HR 1.115, 95% CI 1.089-1.142, p < 0.001; HFpEF plus HFmrEF, HR 1.109, 95% CI 1.074-1.146, p < 0.0001; HFrEF, HR 1.138, 95% CI 1.094-1.183, p < 0.0001) The optimal cut-off value of FAR in predicting all-cause mortality was 9.06 with an area under the curve value of 0.720 (95% CI: 0.687-0.753, p < 0.001), a sensitivity of 68.8% and a specificity of 65.6%. After adjusting for the traditional indicators (LVEF, Lg BNP, etc.), the new model with the FAR had better prediction ability in patients with CHF. Elevated FAR is an independent predictor of death in CHF and is not related to the HF subtype.

Dynamic changes in chest CT findings of patients with coronavirus disease 2019 (COVID-19) in different disease stages: a multicenter study.

BACKGROUND: To investigate the dynamic changes in high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) patients with different severities in different disease stages. METHODS: We retrospectively collected the clinical and imaging data of 96 patients in Yunnan Province, China, who were diagnosed with COVID-19 between January 22 and March 15, 2020. Based on disease severity, the COVID-19 patients were classified into four types: mild (n=15), moderate (n=59), severe (n=19), and critical (n=3). Based on hospital stay and number of computed tomography (CT) scans, the clinical/disease course was divided into four stages, including stage 1 (days 0-4), stage 2 (days 5-9), stage 3 (days 10-14), and stage 4 (days 15-19). The HRCT findings, CT value, and lesion volume were analyzed for each stage and compared among the four stages of COVID-19 patients. RESULTS: CT findings were negative over the four stages for all mild COVID-19 patients. More lesions were found in the peripheral lung fields than in peripheral + central fields (P<0.05), and the number of negative patients in stage 4 were more than those in stages 1-3 (P<0.05). The left and right lower lobe were the most frequently affected lobes (P<0.05). In moderate patients, round ground glass opacities (GGOs) decreased from stage 1 to stage 4; partial consolidation peaked in stage 2 and then decreased in stages 3-4; fibrous stripes and subpleural lines increased from stage 1 and peaked in stage 4. Partial consolidation and consolidation were more common in severe patients than in moderate patients over the disease course (P<0.05). Critical patients showed significant partial consolidation and consolidation; The CT value, lesion volume and lesion volume percentage significantly decreased from stages 1-2 to stage 4 (all P<0.05). CONCLUSIONS: The dynamic changes in lung HRCT images are clinically related to the disease course of COVID-19.

Dynamic analysis of pulmonary computed tomography (CT) characteristics in cured coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: To retrospectively analyze the pulmonary computed tomography (CT) characteristics and dynamic changes in the lungs of cured coronavirus disease 2019 (COVID-19) patients at discharge and reexamination. METHODS: A total of 155 cured COVID-19 patients admitted to designated hospitals in Yunnan Province, China, from February 1, 2020, to March 20, 2020, were included. All patients underwent pulmonary CT at discharge and at 2 weeks after discharge (during reexamination at hospital). A retrospective analysis was performed using these two pulmonary CT scans of the cured patients to observe changes in the number, distribution, morphology, and density of lesions. RESULTS: At discharge, the lung CT images of 15 cured patients showed no obvious lesions, while those of the remaining 140 patients showed different degrees of residual lesions. Patients with moderate disease mostly had multiple pulmonary lesions, mainly in the lower lobes of both lungs. At reexamination, the lung lesions in the patients with moderate disease had significantly improved (P<0.05), and the lung lesions in the patients with severe disease had partially improved, especially in patients with multi-lobe involvement (χ 2 =3.956, P<0.05). At reexamination, the lung lesions of patients with severe disease did not show significant changes (P>0.05). CONCLUSIONS: The pulmonary CT manifestations of cured COVID-19 patients had certain characteristics and variation patterns, providing a reference for the clinical evaluation of treatment efficacy and prognosis of patients.

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

BACKGROUND: This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images. METHODS: A total of 186 patients' CT images were used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n = 223) and validation set (n = 75) with the ratio of 3:1. Multiple random forest models were trained applying hyperparameters grid search with 10-fold cross-validation using precision or recall as evaluation standard. Then a decision threshold was searched on the selected model. The final model was evaluated through ROC curve and prediction accuracy. RESULTS: From those segmented images of 186 patients, 1218 features were obtained via feature extraction. The preferred model was selected with recall as evaluation standard and the optimal decision threshold was set 0.56. The model had a prediction accuracy of 89.33% and the AUC score was 0.9296. CONCLUSION: A hyperparameters tuning random forest classifier had greater performance in predicting the survival status of non-small cell lung cancer patients, which could be taken for an automated classifier promising to stratify patients.