Dataset Information

Early, awake proning in emergency department patients with COVID-19.

ABSTRACT:

Objective

Proning has been shown to improve oxygenation and mortality in certain populations of intubated patients with acute respiratory distress syndrome. Small observational analyses of COVID-19 patients suggest awake proning may lead to clinical improvement. Data on safety and efficacy is lacking. We sought to describe the effect of proning on oxygenation in nonintubated COVID-19 patients. We also evaluated feasibility, safety, and other physiological and clinical outcomes associated with this intervention.

Methods

We conducted a prospective, observational cohort study of nonintubated patients with COVID-19 who underwent proning per an Emergency Department (ED) clinical protocol. Patients with mild to moderate respiratory distress were included. We calculated change in oxygenation by comparing the oxygen saturation to fraction of inspired oxygen ratio (SpO2:FiO2) during the five minutes prior to proning and first 30 min of proning. We also captured data on respiratory rate, duration of proning, need for intubation, intensive care unit admission, survival to discharge.

Results

Fifty-two patients were enrolled. Thirty were excluded for not meeting protocol inclusion criteria or missing baseline oxygenation data, leaving 22 for analysis. The SpO2:FiO2 ratio increased by a median of 5 (IQR: 0-15) in the post-proning period compared to the pre-proning period (median: 298 (IQR: 263-352) vs 295 (IQR: 276-350), p = 0.01). Respiratory rate did not change significantly between time periods. No immediate adverse events occurred during proning. Five patients (23%) were intubated within 48 h of admission.

Conclusion

Early, awake proning may be feasible in select COVID-19 patients and was associated with improved oxygenation.

SUBMITTER: Dubosh NM

PROVIDER: S-EPMC7713606 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:To assess the knowledge, attitude, and practice of the patients who underwent prone positioning during their COVID-19 illness in the hospital.DesignCross-sectional study.SettingAga Khan University Hospital, Karachi, Pakistan.ParticipantsCOVID-19-recovered patients.InterventionsNone.Main outcomes and measuresKnowledge was assessed by five questions with a dichotomous response (yes/no). An yes response was awarded 1 score. Since there were five questions, five would be the highest score. Patients' perceptions of the therapy were assessed by four questions, each scored on a Likert scale of 5 (5 = strongly agree, 4 = agree, 3 = neither agree nor disagree, 2 = disagree, and 1 = strongly disagree). As it had four questions, the maximum score achieved could therefore be 20. Proning practices were assessed using seven questions: four were used to calculate practice scores, whereas three were open-ended regarding the patient's experience. Each question carries a different score, and the maximum score was 8.ResultsThe study included 98 patients. The mean age of the patients was 57 years (sd, 12.2 yr). Majority of the patients were males (70.4%). 69.1% of males and 76.6% of females fell in the poor knowledge group. The poor knowledge cohort had an educational level of 48% in high school, 45.5% in graduates, and 6% in postgraduates. Compared with 56.5% of males, 73% of females preferred prone positioning. Sixty percent of those with a high school education viewed prone positioning favorably, whereas 46% of those with graduate and postgraduate did. Seventy-nine percent of males and 63% of females scored positively on the practice questionnaire. Eighty percent of graduates and postgraduates preferred proning practices to 67% of participants with high school education levels.Conclusions and relevanceOur study population had a little understanding of awake proning, found it unpleasant, and practiced for a shorter period than advised. Despite the overall negative attitude toward the position, our patients highly recommend this position to future patients.

Project description:Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.