Development and Validation of a Nomogram for Predicting the Risk of Coronavirus-Associated Acute Respiratory Distress Syndrome: A Retrospective Cohort Study

Abstract

Background: Since the outbreak of coronavirus disease in December 2019 in Wuhan, it has spread rapidly worldwide. We aimed to establish and validate a nomogram that predicts the probability of coronavirus-associated acute respiratory distress syndrome (CARDS). Methods: In this single-centre, retrospective study, 261 patients with COVID-19 were recruited using positive reverse transcriptionpolymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 in Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China). These patients were randomly distributed into the training cohort (75%) and the validation cohort (25%). The factors included in the nomogram were determined using univariate and multivariate logistic regression analyses based on the training cohort. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in the training and validation cohorts. Results: Independent predictive factors, including fasting plasma glucose, platelet, D-dimer, and cTnI, were determined using the nomogram. In the training cohort, the AUC and concordance index were 0.93. Similarly, in the validation cohort, the nomogram still showed great distinction (AUC: 0.92) and better calibration. The calibration plot also showed a high degree of agreement between the predicted and actual probabilities of CARDS. In addition, the DCA proved that the nomogram was clinically beneficial. Conclusion: Based on the results of laboratory tests, we established a predictive model for acute respiratory distress syndrome in patients with COVID-19. This model shows good performance and can be used clinically to identify CARDS early.

A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study

“…Therefore, early identification of patients with sepsis who are at high risk of developing ARDS is very important. Previous research has found that some prediction model for predicting the ARDS risk were developed and validated in traumatic brain injury (TBI) patients [19], non-emergency department hospitalized patients [10], patients undergoing cardiac surgery [20], and patients with coronavirus disease (COVID-19) [21]. However, these prediction models were not focused on patients with sepsis so far.…”

The risk factors of postoperative hypoxemia in patients with Stanford type A acute aortic dissection

“…Multiple studies on COVID-19 patients have found that high cTnI levels are positively associated with the occurrence of ARDS and poor patient prognosis. [ 41 , 42 ] Sheng et al [ 43 ] reported that elevated cTnI was one of the significant prognostic risk factors for acute lung injury after pediatric living-donor liver transplantation in children with biliary atresia. This study suggests that subtle damage to the myocardium mirrored by elevated cTnI levels may also adversely affect postoperative lung function, even though patients with severe perioperative cardiac insufficiency have already been excluded.…”

A prediction model for acute respiratory distress syndrome in immunocompetent adults with adenovirus-associated Pneumonia: a multicenter retrospective analysis

“…Previous investigators developed multiple prediction models that contained different risk factors to identify patients at risk for ARDS in a setting of sepsis [ 21 ], COVID-19 [ 47 ] and severe acute pancreatitis [ 20 ], reflecting that variations in models can exist within the various ARDS populations. Our study focused on patients with HAdV-associated pneumonia, which is more specific and makes the HAdV-related ARDS prediction model more targeted.…”