Dataset Information

Combining blood glucose and SpO₂/FiO₂ ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

ABSTRACT: The increasing requirement of mechanical ventilation (MV) due to the novel coronavirus disease (COVID-19) is still a global threat. The aim of this study is to identify markers that can easily stratify the impending use of MV in the emergency room (ER). A total of 106 patients with COVID-19 requiring oxygen support were enrolled. Fifty-nine patients were provided MV 0.5 h (interquartile range: 0.3 to 1.4) post-admission. Clinical and laboratory data before intubation were collected. Using a multivariate logistic regression model, we identified four markers associated with the impending use of MV, including the ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO₂/FiO₂ ratio), alanine aminotransferase, blood glucose (BG), and lymphocyte counts. Among these markers, SpO₂/FiO₂ ratio and BG, which can be measured easily and immediately, showed higher accuracy (AUC: 0.88) than SpO₂/FiO₂ ratio alone (AUC: 0.84), despite no significant difference (DeLong test: P = 0.591). Moreover, even in patients without severe respiratory failure (SpO₂/FiO₂ ratio > 300), BG (> 138 mg/dL) was predictive of MV use. Measuring BG and SpO₂/FiO₂ ratio may be a simple and versatile new strategy to accurately identify ER patients with COVID-19 at high risk for the imminent need of MV.

SUBMITTER: Sakai K

PROVIDER: S-EPMC10733355 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining blood glucose and SpO<sub>2</sub>/FiO<sub>2</sub> ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

Sakai Kazuya K Okoda Kai K Nishii Mototsugu M Saji Ryo R Ogawa Fumihiro F Abe Takeru T Takeuchi Ichiro I

Scientific reports 20231220 1

The increasing requirement of mechanical ventilation (MV) due to the novel coronavirus disease (COVID-19) is still a global threat. The aim of this study is to identify markers that can easily stratify the impending use of MV in the emergency room (ER). A total of 106 patients with COVID-19 requiring oxygen support were enrolled. Fifty-nine patients were provided MV 0.5 h (interquartile range: 0.3 to 1.4) post-admission. Clinical and laboratory data before intubation were collected. Using a mult ...[more]

PMID: 38123659

Similar Datasets

Project description:BackgroundChanges in oxygen saturation (SpO2) exposure have been shown to have a marked impact on neonatal outcomes and therefore careful titration of inspired oxygen is essential. In routine use, however, the frequency of SpO2 alarms not requiring intervention results in alarm fatigue and its corresponding risk. SpO2 control systems that automate oxygen adjustments (Auto-FiO2) have been shown to be safe and effective. We speculated that when using Auto-FiO2, alarm settings could be refined to reduce unnecessary alarms, without compromising safety.MethodsAn unblinded randomized crossover study was conducted in a single NICU among infants routinely managed with Auto-FiO2. During the first 6 days of respiratory support a tight and a loose alarm strategy were switched each 24 h. A balanced block randomization was used. The tight strategy set the alarms at the prescribed SpO2 target range, with a 30-s delay. The loose strategy set the alarms 2 wider, with a 90-s delay. The effectiveness outcome was the frequency of SpO2 alarms, and the safety outcomes were time at SpO2 extremes (< 80, > 98%). We hypothesized that the loose strategy would result in a marked decrease in the frequency of SpO2 alarms, and no increases at SpO2 extremes with 20 subjects. Within subject differences between alarm strategies for the primary outcomes were evaluated with Wilcoxon signed-rank test.ResultsDuring a 13-month period 26 neonates were randomized. The analysis included 21 subjects with 49 days of both tight and loose intervention. The loose alarm strategy resulted in a reduction in the median rate of SpO2 alarms from 5.2 to 1.6 per hour (p < 0.001, 95%-CI difference 1.6-3.7). The incidence of hypoxemia and hyperoxemia were very low (less than 0.1%-time) with no difference associated with the alarm strategy (95%-CI difference less than 0.0-0.2%).ConclusionsIn this group of infants we found a marked advantage of the looser alarm strategy. We conclude that the paradigms of alarm strategies used for manual titration of oxygen need to be reconsidered when using Auto-FiO2. We speculate that with optimal settings false positive SpO2 alarms can be minimized, with better vigilance of clinically relevant alarms.Trial registrationRetrospectively registered 15 May 2018 at ISRCTN ( 49239883 ).

Project description:PurposePaO2 to FiO2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO2/FiO2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO2/FiO2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F.MethodsWe extracted data from the eICU Collaborative Research Database. The features age, gender, SaO2, PaO2, FIO2, admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n = 26690) and 20% to testing (n = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values.ResultsThe XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200.ConclusionS/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.

Dataset Information

Combining blood glucose and SpO₂/FiO₂ ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

Publications

Combining blood glucose and SpO<sub>2</sub>/FiO<sub>2</sub> ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Combining blood glucose and SpO2/FiO2 ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

Publications

Combining blood glucose and SpO<sub>2</sub>/FiO<sub>2</sub> ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Combining blood glucose and SpO₂/FiO₂ ratio facilitates prediction of imminent ventilatory needs in emergency room COVID-19 patients.