ABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs. MATERIALS AND METHODS: Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values. RESULTS: A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78). CONCLUSION: Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.
ABSTRACT
Background This study looks at the validity of the sequential organ failure assessment score (SOFA) in detecting mortality in patients with Coronavirus disease of 2019 (COVID-19) pneumonia. Also, it is looking to determine the optimal SOFA score that will discriminate between mortality and survival. Methods It is a retrospective chart review of the patients admitted to Henry Ford Hospital from March 2020 to December 2020 with COVID-19 pneumonia who developed severe respiratory distress. We collected the following information; patient demographics (age, sex, body mass index), co-morbidities (history of diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease, coronary artery disease, or cancer), SOFA scores (the ratio of arterial oxygen tension (PaO2) to the fraction of inspired oxygen, Glasgow Coma Scale (GCS) score, mean arterial pressure, serum creatinine level, bilirubin level, and platelet count) as well as inpatient mortality. Results There were 320 patients; out of these, 111 were intubated. The receiver operating characteristic (ROC) curve for SOFA at the moment of inclusion in the study had an area under the curve of 0.883. The optimal point for discrimination between mortality and survival is SOFA of 5. A SOFA score of less than two is associated with 100% survival, while a score of more than 11 is associated with 100% mortality. Conclusions SOFA score in COVID-19 patients with severe respiratory distress strongly correlates with the initial SOFA score. It is a valuable tool for predicting mortality in COVID-19 patients.
ABSTRACT
BACKGROUND: Acute confusional state (ACS) in COVID-19 is shown to be associated with poor clinical outcomes. METHODS: We assessed the impact of ACS - defined as a documented deterioration of mental status from baseline on the alertness and orientation to time, place, and person - on inpatient mortality and the need for intensive care unit (ICU) transfer in inpatient admissions with active COVID-19 infection in a single-center retrospective cohort of inpatient admissions from a designated COVID-19 tertiary care center using an electronic health record system. Furthermore, we developed and validated a neurological history and symptom-based predictive score of developing ACS. RESULTS: Thirty seven out of 245 (15%) patients demonstrated ACS. Nineteen (51%) patients had multifactorial ACS, followed by 11 (30%) patients because of hypoxemia. ACS patients were significantly older (80 [70-85] years vs 50.5 [38-69] years, p < 0.001) and demonstrated more frequent history of dementia (43% vs 9%, p < 0.001) and epilepsy (16% vs 2%, p = 0.001). ACS patients observed significantly higher in-hospital mortality (45.9% vs 1.9%, aOR [adjusted odds ratio]: 15.7, 95% CI = 3.6-68.0, p < 0.001) and need for ICU transfer (64.9% vs 35.1%, aOR: 2.7, 95% CI = 1.2-6.1, p = 0.015). In patients who survived hospitalization, ACS was associated with longer hospital stay (6 [3.5-10.5] days vs 3 [2-7] day, p = 0.012) and numerically longer ICU stay (6 [4-10] days vs 3 [2-6] days, p = 0.078). A score to predict ACS demonstrated 75.68% sensitivity and 81.73% specificity at a cutoff of ≥3. CONCLUSION: A high prevalence of ACS was found in patients with COVID-19 in our study cohort. Patients with ACS demonstrated increased mortality and need for ICU care. An internally validated score to predict ACS demonstrated high sensitivity and specificity in our cohort.