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

Ni, Yue Nan; Wang, Ting; Liang, Bin; Liang, Zong An

doi:10.1371/journal.pone.0245690

Cited by 14 publications

(24 citation statements)

References 33 publications

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“…They also found that compared to patients without this symptom on admission, the degree of ending oxygen therapy was 29.5% lower in cases with shortness of breath. Ni et.al [40] concluded that dyspnea is among the related factors to oxygen therapy for COVID-19 patients under 65 years and can increase their need for oxygen. They found that 59.5% of patients with dry cough received oxygen therapy.…”

Section: Discussionmentioning

confidence: 99%

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Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Saadatmand

Salimifard

Mohammadi

et al. 2022

Med Biol Eng Comput

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Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient's background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.

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Section: Discussionmentioning

confidence: 99%

“…In our ML models, fever was an important feature in the prediction of oxygen requirement that can be considered as an early sign on admission. [40] found the relationship between fever and oxygen therapy. Among COVID-19 patients, 70.9% of those with fever symptom received oxygen therapy.…”

Section: Discussionmentioning

confidence: 99%

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Saadatmand

Salimifard

Mohammadi

et al. 2022

Med Biol Eng Comput

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“…As a first relevant transition after hospitalisation, we focused on oxygen support among patients with COVID-19 aged less than 70 years. In this group of subjects, dyspnea, chest distress and respiratory rate were found to be highly associated with oxygen therapy, suggesting strict monitoring for parameters that can be highly associated with clinical deterioration and adverse outcomes [ 38 ]. In addition to saturation < 95%, we also found levels of LDH ≥ 237 U/L and lymphocytes< 1.2 × 10 3 /μL as adequate targets for oxygen therapy.…”

Section: Discussionmentioning

confidence: 99%

Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score

Polilli

Frattari

Esposito

et al. 2022

BMC Health Serv Res

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Background The hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department. Methods We carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March–June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020–January 2021) and Delta/Omicron (January–March 2022). Results Several clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21–7.72) and diabetes (OR = 3.04; 1.09–9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75–41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40–15.39) and lymphocytes< 1.2 × 103/μL (OR = 4.49; 1.84–11.53); for intensive care, end stage renal disease (OR = 59.42; 2.43–2230.60), lactate dehydrogenase≥334 U/L (OR = 5.59; 2.46–13.84), D-dimer≥2.37 mg/L (OR = 5.18; 1.14–26.36), monocyte distribution width ≥ 25 (OR = 3.32; 1.39–8.50); for death, procalcitonin≥0.2 ng/mL (HR = 2.86; 1.95–4.19) and saturation < 96% (HR = 2.74; 1.76–4.28). Risk scores derived from predictive models using optimal thresholds achieved values of the area under the curve between 81 and 91%. Validation of the scoring algorithm for the evolving virus achieved accuracy between 65 and 84%. Conclusions A set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.

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“…Mortality among patients admitted to hospital due to this disease was found to be nearly three times higher when compared to hospitalized cases with influenza [ 2 ]. Additionally, more than half of young patients with Coronavirus Disease 2019 (COVID-19) required oxygen therapy during hospitalization [ 3 ]. The COVID-19 pandemic also resulted in the increased incidence of mental health disorders, such as anxiety, depression, and insomnia [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Neurological Prognostic Factors in Hospitalized Patients with COVID-19

et al. 2022

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We aimed to search whether neurological symptoms or signs (NSS) and the MEWS (Modified Early Warning Score) score were associated with in-hospital mortality or oxygen requirement during the first 14 days of hospitalization in COVID-19 patients recruited at the University Hospital in Krakow, Poland. The detailed clinical questionnaires on twenty NSS were either filled out by patients prospectively or retrospectively assessed by neurologists based on daily medical records. NSS were considered high or low-risk if they were associated with increased or decreased mortality in the univariable analysis. This cohort study included 349 patients with COVID-19 (median age 64, interquartile range (51–77), women 54.72%). The presence of high-risk NSS (decreased level of consciousness, delirium, seizures, and symptoms of stroke or transient ischemic attack) or its combination with the absence of low-risk NSS (headache, dizziness, decreased mood, and fatigue) increased the risk of in-hospital mortality in SARS-CoV-2 infection 3.13 and 7.67-fold, respectively. The presence of low-risk NSS decreased the risk of in-hospital mortality in COVID-19 patients more than 6-fold. Death in patients with SARS-CoV-2 infection, apart from NSS, was predicted by older age, neoplasm, and higher MEWS scores on admission. High-risk NSS or their combination with the absence of low-risk NSS increased the risk of oxygen requirement during hospitalization in COVID-19 patients 4.48 and 1.86-fold, respectively. Independent predictors of oxygen therapy during hospitalization in patients with SARS-CoV-2 infection were also older age, male sex, neoplasm, and higher MEWS score on admission.

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

Cited by 14 publications

References 33 publications

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score

Neurological Prognostic Factors in Hospitalized Patients with COVID-19

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