Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data

Abstract

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14th and April 21st2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46–73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79–0.86) for death or intubation within 7 days and 0.82 (0.78–0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural networked-based scoring of chest radiograph severity.

Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow

“…Given the important ongoing discourse [3][4][5][6][7][8] surrounding bias in the clinical setting and bias in artificial intelligence, we believe our analysis of ChatGPT's performance based on the age and gender of patients represents an important touchpoint in both discussions. [21][22][23][24][25] While we did not find that age or gender is a significant predictor of accuracy, we note that our vignettes represent classic presentations of disease, and that atypical presentations may generate different biases.…”

“…Despite its relative infancy, artificial intelligence (AI) is transforming healthcare, with current uses including workflow triage, predictive models of utilization, labeling and interpretation of radiographic images, patient support via interactive chatbots, communication aids for non-English speaking patients, and more. [1][2][3][4][5][6][7][8] Yet, all of these use cases are limited to a specific part of the clinical workflow and do not provide longitudinal patient or clinician support. An under-explored use of AI in medicine is predicting and synthesizing patient diagnoses, treatment plans, and outcomes.…”

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation

“…Moreover, it suffers from manual review and hand editing of automated segmentation, which then limits clinical applicability versus using a fully automated imaging processing pipeline that this study offers. Some studies utilized an end-to-end automated pipeline for processing radiography images and EMR data similar to that used in this study [17,22,24,41]; however, none make direct prediction of intubation and IMV in hospitalized patients. Chung et al [17] and Dayan et al [22] focused on the prediction of oxygen requirement in emergency department patients with limited data availability.…”

“…Duanmu et al [41] focused on predicting the duration on IMV instead, but they are one of the very few using longitudinal data in their pipeline, suggesting that longitudinal data may bring more prognostic value than single-point data. O'Shea et al [24] had one of the highest performance end-to-end automated models, with an AUROC of 0.82 in predicting death or intubation within 7 days. However, those models are limited by the lack of image segmentation that ensure only pulmonary or thoracic features are considered in their models, use of a deep learning model to classify the degree of lung injury but not predict intubation itself, and use of a single point, that is, the first available value for each variable; therefore, they suffer from a lack of robustness that would not account for changes in the radiographs or in the patient's clinical condition.…”

“…Since 2020, many published studies [13][14][15][16][17][18][19][20][21][22][23][24] have tried to use machine learning techniques to predict the need for mechanical ventilation in patients with COVID-19. The majority of these studies used only clinical variables (structured data) [13] and only 15 of them (Figure 1 [13][14][15][16][17][18][19][20][21][22][23][24]) considered chest radiographs (CXRs) as a potential modality combined with clinical variables. Figure 1 is a funnel graph showing the number of similar published studies by criteria of review.…”