We have located links that may give you full text access.
Risk Prediction Models for Invasive Mechanical Ventilation in Patients with Autoimmune Encephalitis: A Retrospective Cohort Study.
METHODS: A multivariate predictive nomogram model was developed using the risk factors identified by LASSO regression and assessed by receiver operator characteristics (ROC) curve, calibration curve, and decision curve analysis.
RESULTS: The risk factors predictive of severe respiratory failure were male gender, impaired hepatic function, elevated intracranial pressure, and higher neuron-specific enolase. The final nomogram achieved an AUC of 0.770. After validation by bootstrapping, a concordance index of 0.748 was achieved.
CONCLUSIONS: Our nomogram accurately predicted the risk of developing respiratory failure needing IMV in AE patients and provide clinicians with a simple and effective tool to guide treatment interventions in the AE patients.
RESULTS: The risk factors predictive of severe respiratory failure were male gender, impaired hepatic function, elevated intracranial pressure, and higher neuron-specific enolase. The final nomogram achieved an AUC of 0.770. After validation by bootstrapping, a concordance index of 0.748 was achieved.
CONCLUSIONS: Our nomogram accurately predicted the risk of developing respiratory failure needing IMV in AE patients and provide clinicians with a simple and effective tool to guide treatment interventions in the AE patients.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app