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

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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

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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.

Project description:BackgroundMonitoring safety outcomes following COVID-19 vaccination is critical for understanding vaccine safety especially when used in key populations such as elderly persons age 65 years and older who can benefit greatly from vaccination. We present new findings from a nationally representative early warning system that may expand the safety knowledge base to further public trust and inform decision making on vaccine safety by government agencies, healthcare providers, interested stakeholders, and the public.MethodsWe evaluated 14 outcomes of interest following COVID-19 vaccination using the US Centers for Medicare & Medicaid Services (CMS) data covering 30,712,101 elderly persons. The CMS data from December 11, 2020 through Jan 15, 2022 included 17,411,342 COVID-19 vaccinees who received a total of 34,639,937 doses. We conducted weekly sequential testing and generated rate ratios (RR) of observed outcome rates compared to historical (or expected) rates prior to COVID-19 vaccination.FindingsFour outcomes met the threshold for a statistical signal following BNT162b2 vaccination including pulmonary embolism (PE; RR = 1.54), acute myocardial infarction (AMI; RR = 1.42), disseminated intravascular coagulation (DIC; RR = 1.91), and immune thrombocytopenia (ITP; RR = 1.44). After further evaluation, only the RR for PE still met the statistical threshold for a signal; however, the RRs for AMI, DIC, and ITP no longer did. No statistical signals were identified following vaccination with either the mRNA-1273 or Ad26 COV2.S vaccines.InterpretationThis early warning system is the first to identify temporal associations for PE, AMI, DIC, and ITP following BNT162b2 vaccination in the elderly. Because an early warning system does not prove that the vaccines cause these outcomes, more robust epidemiologic studies with adjustment for confounding, including age and nursing home residency, are underway to further evaluate these signals. FDA strongly believes the potential benefits of COVID-19 vaccination outweigh the potential risks of COVID-19 infection.

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.

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