Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.

Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average. Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.

Prospective predictive performance comparison between clinical gestalt and validated COVID-19 mortality scores.

Most COVID-19 mortality scores were developed at the beginning of the pandemic and clinicians now have more experience and evidence-based interventions. Therefore, we hypothesized that the predictive performance of COVID-19 mortality scores is now lower than originally reported. We aimed to prospectively evaluate the current predictive accuracy of six COVID-19 scores and compared it with the accuracy of clinical gestalt predictions. 200 patients with COVID-19 were enrolled in a tertiary hospital in Mexico City between September and December 2020. The area under the curve (AUC) of the LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV, and NEWS2 scores and the AUC of clinical gestalt predictions of death (as a percentage) were determined. In total, 166 patients (106 men and 60 women aged 56±9 years) with confirmed COVID-19 were included in the analysis. The AUC of all scores was significantly lower than originally reported: LOW-HARM 0.76 (95% CI 0.69 to 0.84) vs 0.96 (95% CI 0.94 to 0.98), qSOFA 0.61 (95% CI 0.53 to 0.69) vs 0.74 (95% CI 0.65 to 0.81), MSL-COVID-19 0.64 (95% CI 0.55 to 0.73) vs 0.72 (95% CI 0.69 to 0.75), NUTRI-CoV 0.60 (95% CI 0.51 to 0.69) vs 0.79 (95% CI 0.76 to 0.82), NEWS2 0.65 (95% CI 0.56 to 0.75) vs 0.84 (95% CI 0.79 to 0.90), and neutrophil to lymphocyte ratio 0.65 (95% CI 0.57 to 0.73) vs 0.74 (95% CI 0.62 to 0.85). Clinical gestalt predictions were non-inferior to mortality scores, with an AUC of 0.68 (95% CI 0.59 to 0.77). Adjusting scores with locally derived likelihood ratios did not improve their performance; however, some scores outperformed clinical gestalt predictions when clinicians' confidence of prediction was <80%. Despite its subjective nature, clinical gestalt has relevant advantages in predicting COVID-19 clinical outcomes. The need and performance of most COVID-19 mortality scores need to be evaluated regularly.

The low-harm score for predicting mortality in patients diagnosed with COVID-19: A multicentric validation study.

Objective: We sought to determine the accuracy of the LOW-HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury) for predicting death from coronavirus disease 2019) COVID-19. Methods: We derived the score as a concatenated Fagan's nomogram for Bayes theorem using data from published cohorts of patients with COVID-19. We validated the score on 400 consecutive COVID-19 hospital admissions (200 deaths and 200 survivors) from 12 hospitals in Mexico. We determined the sensitivity, specificity, and predictive values of LOW-HARM for predicting hospital death. Results: LOW-HARM scores and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 5 (SD: 14) versus 70 (SD: 28). The overall area under the curve for the LOW-HARM score was 0.96, (95% confidence interval: 0.94-0.98). A cutoff > 65 points had a specificity of 97.5% and a positive predictive value of 96%. Conclusions: The LOW-HARM score measured at hospital admission is highly specific and clinically useful for predicting mortality in patients with COVID-19.