Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist
Clinicians with COVID-19 ECMO Planning
- URL: http://arxiv.org/abs/2006.01898v1
- Date: Tue, 2 Jun 2020 19:30:29 GMT
- Title: Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist
Clinicians with COVID-19 ECMO Planning
- Authors: Helen Zhou, Cheng Cheng, Zachary C. Lipton, George H. Chen, Jeremy C.
Weiss
- Abstract summary: Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020.
Many cases escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death.
Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life.
- Score: 26.25177784899079
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Respiratory complications due to coronavirus disease COVID-19 have claimed
tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe
Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory
distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO)
is a life-sustaining oxygenation and ventilation therapy that may be used for
patients with severe ARDS when mechanical ventilation is insufficient to
sustain life. While early planning and surgical cannulation for ECMO can
increase survival, clinicians report the lack of a risk score hinders these
efforts. In this work, we leverage machine learning techniques to develop the
PEER score, used to highlight critically ill patients with viral or unspecified
pneumonia at high risk of mortality or decompensation in a subpopulation
eligible for ECMO. The PEER score is validated on two large, publicly available
critical care databases and predicts mortality at least as well as other
existing risk scores. Stratifying our cohorts into low-risk and high-risk
groups, we find that the high-risk group also has a higher proportion of
decompensation indicators such as vasopressor and ventilator use. Finally, the
PEER score is provided in the form of a nomogram for direct calculation of
patient risk, and can be used to highlight at-risk patients among critical care
patients eligible for ECMO.
Related papers
- SurvLatent ODE : A Neural ODE based time-to-event model with competing
risks for longitudinal data improves cancer-associated Deep Vein Thrombosis
(DVT) prediction [68.8204255655161]
We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data.
Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function.
SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
arXiv Detail & Related papers (2022-04-20T17:28:08Z) - Early ICU Mortality Prediction and Survival Analysis for Respiratory
Failure [4.229085609275446]
We propose a dynamic modeling approach for early mortality risk prediction of the respiratory failure patients based on the first 24 hours ICU physiological data.
We achieved a high AUROC performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU admission, compared to the state-of-art prediction models.
arXiv Detail & Related papers (2021-09-06T06:03:23Z) - COVID-Net CXR-S: Deep Convolutional Neural Network for Severity
Assessment of COVID-19 Cases from Chest X-ray Images [74.77272804752306]
We introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest.
We leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment.
The proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients.
arXiv Detail & Related papers (2021-05-01T14:15:12Z) - Machine learning approach to dynamic risk modeling of mortality in
COVID-19: a UK Biobank study [0.0]
The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients.
This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases.
arXiv Detail & Related papers (2021-04-19T11:51:20Z) - Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
We develop Bayesian inference methods to estimate the risk that an individual is infected.
We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic.
Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
arXiv Detail & Related papers (2020-09-20T12:24:45Z) - Real-time Prediction of COVID-19 related Mortality using Electronic
Health Records [30.892335739985526]
COVID-19 Early Warning System (CovEWS) is a clinical risk scoring system for assessing COVID-19 related mortality risk.
CovEWS provides continuous real-time risk scores for individual patients with clinically meaningful predictive performance up to 192 hours (8 days) in advance.
arXiv Detail & Related papers (2020-08-31T08:07:27Z) - Machine Learning and Meta-Analysis Approach to Identify Patient
Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19 [1.221966660783828]
Many individuals who become infected have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk.
We performed a meta-analysis of the published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset.
Results: Our meta-analysis identified chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity.
arXiv Detail & Related papers (2020-08-21T12:31:54Z) - An early warning tool for predicting mortality risk of COVID-19 patients
using machine learning [0.0]
A retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020.
A nomogram was developed for predicting the mortality risk among COVID-19 patients.
An integrated score (LNLCA) was calculated with the corresponding death probability.
arXiv Detail & Related papers (2020-07-29T15:16:09Z) - Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission.
Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
arXiv Detail & Related papers (2020-07-20T19:08:50Z) - Prediction of the onset of cardiovascular diseases from electronic
health records using multi-task gated recurrent units [51.14334174570822]
We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records.
The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
arXiv Detail & Related papers (2020-07-16T17:43:13Z) - Remote health monitoring and diagnosis in the time of COVID-19 [51.01158603315544]
Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance.
arXiv Detail & Related papers (2020-05-18T08:54:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.