METHODS: Computational Fluid Dynamics (CFD) approach is used to simulate the airflow in a neonate, an infant and an adult in sedentary breathing conditions. The healthy CT scans are segmented using MIMICS 21.0 (Materialise, Ann arbor, MI). The patient-specific 3D airway models are analyzed for low Reynolds number flow using ANSYS FLUENT 2020 R2. The applicability of the Grid Convergence Index (GCI) for polyhedral mesh adopted in this work is also verified.
RESULTS: This study shows that the inferior meatus of neonates accounted for only 15% of the total airflow. This was in contrast to the infants and adults who experienced 49 and 31% of airflow at the inferior meatus region. Superior meatus experienced 25% of total flow which is more than normal for the neonate. The highest velocity of 1.8, 2.6 and 3.7 m/s was observed at the nasal valve region for neonates, infants and adults, respectively. The anterior portion of the nasal cavity experienced maximum wall shear stress with average values of 0.48, 0.25 and 0.58 Pa for the neonates, infants and adults.
CONCLUSIONS: The neonates have an underdeveloped nasal cavity which significantly affects their airway distribution. The absence of inferior meatus in the neonates has limited the flow through the inferior regions and resulted in uneven flow distribution.
OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.
METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.
RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.
CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.