Dataset Information

The independent factors associated with oxygen therapy in COVID-19 patients under 65 years old.

ABSTRACT:

Background

The number of hospitalized young coronavirus disease 2019 (COVID-19) patients has increased significantly. However, specific data about COVID-19 patients under 65 years old who are admitted to the hospital are scarce.

Methods

The COVID-19 patients under 65 years old who were admitted to the hospital in Sichuan Province, Renmin Hospital of Wuhan University, and Wuhan Red Cross Hospital were included in this study. Demographic information, laboratory data and clinical treatment courses were extracted from electronic medical records. Risk factors associated with oxygen therapy were explored.

Results

Eight hundred thirty-three COVID-19 patients under 65 years old were included. Of the included patients, 29.4% had one or more comorbidities. Oxygen therapy was required in 63.1% of these patients, and the mortality was 2.9% among the oxygen therapy patients. Fever (odds ratio [OR] 2.072, 95% confidence interval [CI] 1.312-3.271, p = 0.002), dyspnea (OR 2.522, 95% CI 1.213-5.243, p = 0.013), chest distress (OR 2.278, 95% CI 1.160-4.473, p = 0.017), elevated respiratory rate (OR 1.114, 95% CI 1.010-1.228, p = 0.031), and decreased albumin (OR 0.932, 95% CI 0.880-0.987, p = 0.016) and globulin levels (OR 0.929, 95% 0.881-0.980, p = 0.007) were independent factors related to oxygen therapy.

Conclusions

Oxygen therapy is highly required in COVID-19 patients under 65 years old who are admitted to the hospital, but the success rate is high. Respiratory failure-related symptoms, elevated respiratory rate, low albumin and globulin levels, and fever at admission are independent risk factors related to the requirement of oxygen.

SUBMITTER: Ni YN

PROVIDER: S-EPMC7822310 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The independent factors associated with oxygen therapy in COVID-19 patients under 65 years old.

Ni Yue-Nan YN Wang Ting T Liang Bin-Miao BM Liang Zong-An ZA

PloS one 20210122 1

<h4>Background</h4>The number of hospitalized young coronavirus disease 2019 (COVID-19) patients has increased significantly. However, specific data about COVID-19 patients under 65 years old who are admitted to the hospital are scarce.<h4>Methods</h4>The COVID-19 patients under 65 years old who were admitted to the hospital in Sichuan Province, Renmin Hospital of Wuhan University, and Wuhan Red Cross Hospital were included in this study. Demographic information, laboratory data and clinical tre ...[more]

PMID: 33481912

Similar Datasets

Project description:BackgroundPatients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.MethodsWe modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021.ResultsThe mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08-3.06) reduction (P < 0.001) from 7.94% under the standard of care to 5.37% under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic.ConclusionsA personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists' actual flow rate matched the RL decisions.

Project description:BackgroundPatients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.MethodsWe modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1,372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021.ResultsThe mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08-3.06) reduction (P<0.001) from 7.94% under the standard of care to 5.37 % under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic.ConclusionsA personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists' actual flow rate matched the RL decisions.

Dataset Information

The independent factors associated with oxygen therapy in COVID-19 patients under 65 years old.

Background

Methods

Results

Conclusions

Publications

The independent factors associated with oxygen therapy in COVID-19 patients under 65 years old.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure