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ROC curves for predicting left ventricular geometry using the CTR value on the chest radiograph. (a) CR: concentric remodeling of the left ventricular. (b) CH: concentric hypertrophy of the left ventricular. (c) EH: eccentric hypertrophy of the left ventricular.
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The aim of the study was to verify the usefulness of the radiological cardiothoracic ratio as a potential marker of left ventricular hypertrophy assessed by echocardiography. The study included 96 patients (mean age: 49.52 ± 9.64 years). Chest radiograph in the PA projection and echocardiography were performed. In CR the measurement of the cardioth...
Citations
... It may also predispose to stroke [2] . One of the main causes of death in Nigeria and other West African nations is cardiac disease [5] . Prompt diagnosis and treatment of heart disorders may be aided by early detection of cardiomegaly [7] . ...
... The normal range of CTR is between 0.42 and 0.50. Values that are more than 0.5 or less than 0.42 indicate pathologic situations like cardiomegaly [5] . Cardiothoracic ratio is affected by different factors such as age, gender, race, body posture and physique [11] . ...
... Cardiothoracic ratio is affected by different factors such as age, gender, race, body posture and physique [11] . Previous research has found racial differences in the cardiothoracic ratio of different populations, with Caucasians having an upper limit of normal at 50% and people of African descent having an upper limit of normal at 55% [5,13,18] . Gender and age-group differences in CTR have been reported in previous studies [2,13,18] . ...
Cardiothoracic ratio (CTR) evaluation is a useful screening method used to detect cardiomegaly. It varies in different populations due to genetic, geographical and environmental factors that influence body morphology. This study aimed at assessing the CTR and determining its association with gender and age among adult Nigerians. This retrospective cross-sectional study was conducted in the Radiology Department of a Teaching Hospital in Delta State after obtaining ethical clearance. Postero-anterior chest radiographs of 200 adults (108 males and 92 females) were used to measure the transverse cardiac diameter (TCD) and transverse thoracic diameter (TTD) in centimeters (cm). These were used to compute the CTR (TCD/TTD*100). Statistical Package for Social Sciences version 22.0 was used to analyze the data. Independent t-test and analysis of variance (ANOVA) were used to determine the differences in the measurements with regards to gender and age-groups respectively. The correlation that exists between the variables and their association with age were assessed using Pearson’s correlation. P value was set at < 0.05. The TTD and TCD were significantly larger in males than in females (p=0.001 each) while the CTR was significantly larger in females than in males (p=0.016). The TTD and CTR showed significant differences in the various age groups (p=0.002, 0.031) (p=0.195). The two diameters showed a significant positive correlation with age (0<r<0.5). The study provides the normal mean CTR values for the studied population based on age and gender which will help clinicians in the screening for heart conditions. This will enhance early diagnosis and intervention. Keywords: cardiomegaly, thoracic diameter, cardiac diameter, chest radiographs, diagnosis, cardiothoracic ratio
... Patients with CHF generally have a higher CTR than healthy individuals [22], and a negative correlation between CTR and ejection fraction has been reported [23]. A higher CTR (>0.49) may be a marker of left ventricular hypertrophy (LVH), with a high accuracy of 84.4% [24]. Previous studies have reported that cardiomegaly, assessed by the CTR, is correlated with all-cause mortality and cardiovascular mortality [25,26]. ...
A high ultrafiltration rate (UFR) is associated with increased mortality in hemodialysis patients. However, whether a high UFR itself or heart failure with fluid overload followed by a high UFR causes mortality remains unknown. In this study, 2615 incident hemodialysis patients were categorized according to their initial cardiothoracic ratios (CTRs) to assess whether UFR was associated with mortality in patients with high or low CTRs. In total, 1317 patients (50.4%) were women and 1261 (48.2%) were diabetic. During 2246 (1087–3596) days of follow-up, 1247 (47.7%) cases of all-cause mortality were noted. UFR quintiles 4 and 5 were associated with higher risks of all-cause mortality than UFR quintile 2 in fully adjusted Cox regression analysis. As the UFR increased by 1 mL/kg/h, the risk of all-cause mortality increased 1.6%. Subgroup analysis revealed that in UFR quintile 5, hazard ratios (HRs) for all-cause mortality were 1.91, 1.48, 1.22, and 1.10 for CTRs of >55%, 50–55%, 45–50%, and <45%, respectively. HRs for all-cause mortality were higher in women and patients with high body weight. Thus, high UFRs may be associated with increased all-cause mortality in incident hemodialysis patients with a high CTR, but not in those with a low CTR.
Background: Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs (DLSPCR) in patients with heart failure is currently unclear.
Objective: To develop and validate a deep learning survival prediction model using chest radiographs (DLSPCR) in patients with HF.
Methods: The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest radiographs (CR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n=247) and validation (n=106) datasets. Univariate and multivariate Cox analysis were applied to the training dataset to develop clinical and imaging survival prediction models. The DLSPCR algorithm was trained and the selected clinical parameters were incorporated into DLSPCR to establish a new model called DLSPinteg. Discrimination performance was evaluated by using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-year survival. Delong’s test was employed for the comparison of difference between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by Kaplan-Meier curve.
Results: In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (SPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III-IV and cardiothoracic ratio (CTR) ≥ 0.62 in CR were independent predictors of poor prognosis in patients with HF. As the receiver operating characteristic (ROC) curve analysis showed, DLSPCR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs 0.561, P=0.01). DLSPinteg as the optimal model outperforms clinical Cox model (AUC: 0.826 vs 0.633, P=0.03), imaging Cox model (AUC: 0.826 vs 0.555, P<0.001), and DLSPCR (AUC: 0.826 vs 0.767, P=0.06).
Conclusions: Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.
