Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism

This preliminary study investigated the feasibility of a combined model constructed using radiomic features based on computed tomography (CT) and clinical features to predict adverse clinical outcomes in acute pulmonary embolism (APE). Currently, there is no widely recognized predictive model. Patients with confirmed APE who underwent CT pulmonary angiography were retrospectively categorized into good and poor prognosis groups. Seventy-four patients were randomized into a training (n = 51) or validation (n = 23) cohort. Feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator regression was used to identify the optimal radiomics features and calculate the radiomics scores; subsequently, the radiomics model was developed. A combined predictive model was constructed based on radiomics scores and selected clinical features. The predictive efficacy of the three models (radiomics, clinical and combined) was assessed by plotting receiver operating characteristic curves. Furthermore, the calibration curves were graphed and the decision curve analysis was performed. Four radiomic features were screened to calculate the radiomic score. Right ventricular to left ventricular ratio (RV/LV) ≥ 1.0 and radiomics score were independent risk factors for adverse clinical outcomes. In the training and validation cohorts, the areas under the curve (AUCs) for the RV/LV ≥ 1.0 (clinical) and radiomics score prediction models were 0.778 and 0.833 and 0.907 and 0.817, respectively. The AUCs for the combined model of RV/LV ≥ 1.0 and radiomics score were 0.925 and 0.917, respectively. The combined and radiomics models had high clinical assessment efficacy for predicting adverse clinical outcomes in APE, demonstrating the clinical utility of both models. Calibration curves exhibited a strong level of consistency between the predictive and observed probabilities of poor and good prognoses in the combined model. The combined model of RV/LV ≥ 1.0 and radiomics score based on CT could accurately and non-invasively predict adverse clinical outcomes in patients with APE.

Metabolically unhealthy individuals, either with obesity or not, have a higher risk of critical coronavirus disease 2019 outcomes than metabolically healthy individuals without obesity

BACKGROUND

This study aimed to determine the relative and independent contributions of impaired metabolic health and obesity to critical coronavirus disease 2019 (COVID-19).

METHODS

We analyzed 4069 COVID-19 patients between January and June 2020 in South Korea, classified into four groups according to metabolic health status and body mass index (BMI): metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The primary outcome was a composite of intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), extracorporeal membrane oxygenation (ECMO), and death. Multivariable Cox proportional hazard regression models were used to estimate the hazard ratio (HR) for the outcome.

RESULTS

The incidence rate (per 100 person-months) of critical COVID-19 was the lowest in the MHNW group (0.90), followed by the MHO (1.64), MUNW (3.37), and MUO (3.37) groups. Compared with MHNW, a significantly increased risk of critical COVID-19 was observed in MUNW (HR, 1.41; 95% CI, 1.01-1.98) and MUO (HR, 1.77; 95% CI, 1.39-2.44) but not in MHO (HR, 1.48; 95% CI, 0.98-2.23). The risk of ICU admission or IMV/ECMO was increased only in MUO; however, the risk of death was significantly higher in MUNW and MUO. The risk of critical COVID-19 increased insignificantly by 2% per 1 kg/m² BMI increase but significantly by 13% per 1 metabolically unhealthy component increase, even after mutually adjusting for BMI and metabolic health status.

CONCLUSIONS

Metabolic health is more important to COVID-19 outcomes than obesity itself, suggesting that metabolic health status should be considered for a precise and tailored management of COVID-19 patients.