Systemic Corticosteroid as an Adjunct for Acute Respiratory Distress Syndrome in Non-Fatal Fresh Water Drowning: An Evidence-based Case Report.

BACKGROUND: Acute lung injury or acute respiratory distress syndrome (ARDS) is one of the most common complications of non-fatal drowning. Although respiratory societies' guidelines endorse the role of systemic corticosteroids in ARDS, the evidence for systemic corticosteroid use in ARDS due to non-fatal drowning is limited. METHODS: A search was conducted on Pubmed, OVID, and EuropePMC, assessing the clinical question using inclusion and exclusion criteria. The selected studies were critically appraised, and the results were summarized. RESULTS: A total of six retrospective studies were selected and assessed, all studies showed poor validity and a high risk of bias. Out of six studies, only four informed us of steroid administration's effect on outcomes. In two studies, mortality associated with corticosteroid administration seemed to be higher. On the contrary, one study found no mortality in the corticosteroid group, but 100% mortality was observed in the control group. In another study, steroid therapy seemed to not affect hospital length of stay or mechanical ventilation rates. CONCLUSION: Corticosteroid administration for non-fatal drowning and its impact on clinical outcomes remains equivocal. Routine administration of corticosteroids is not indicated and should be done on a case-by-case basis.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study.

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study.

BACKGROUND: The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. OBJECTIVE: The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. METHODS: We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. RESULTS: The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). CONCLUSIONS: The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.