Dataset Information

PaO₂/FiO₂ ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

ABSTRACT: We studied the predictive value of the PaO₂/FiO₂ ratio for classifying COVID-19-positive patients who will develop severe clinical outcomes. One hundred fifty patients were recruited and categorized into two distinct populations ("A" and "B"), according to the indications given by the World Health Organization. Patients belonging the population "A" presented with mild disease not requiring oxygen support, whereas population "B" presented with a severe disease needing oxygen support. The AUC curve of PaO₂/FiO₂ in the discovery cohort was 0.838 (95% CI 0.771-0.908). The optimal cut-off value for distinguishing population "A" from the "B" one, calculated by Youden's index, with sensitivity of 71.79% and specificity 85.25%, LR+4.866, LR-0.339, was < 274 mmHg. The AUC in the validation cohort of 170 patients overlapped the previous one, i.e., 0.826 (95% CI 0.760-0.891). PaO₂/FiO₂ ratio < 274 mmHg was a good predictive index test to forecast the development of a severe respiratory failure in SARS-CoV-2-infected patients. Moreover, our work highlights that PaO₂/FiO₂ ratio, compared to inflammatory scores (hs-CRP, NLR, PLR and LDH) indicated to be useful in clinical managements, results to be the most reliable parameter to identify patients who require closer respiratory monitoring and more aggressive supportive therapies. Clinical trial registration: Prognostic Score in COVID-19, prot. NCT04780373 https://clinicaltrials.gov/ct2/show/NCT04780373 (retrospectively registered).

SUBMITTER: Sinatti G

PROVIDER: S-EPMC8505469 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PaO2/FiO2 ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

Sinatti Gaia G Santini Silvano Junior SJ Tarantino Giovanni G Picchi Giovanna G Cosimini Benedetta B Ranfone Francesca F Casano Nicolò N Zingaropoli Maria Antonella MA Iapadre Nerio N Bianconi Simone S Armiento Antonietta A Carducci Paolo P Ciardi Maria Rosa MR Mastroianni Claudio Maria CM Grimaldi Alessandro A Balsano Clara C

Internal and emergency medicine 20211012 3

We studied the predictive value of the PaO2/FiO2 ratio for classifying COVID-19-positive patients who will develop severe clinical outcomes. One hundred fifty patients were recruited and categorized into two distinct populations ("A" and "B"), according to the indications given by the World Health Organization. Patients belonging the population "A" presented with mild disease not requiring oxygen support, whereas population "B" presented with a severe disease needing oxygen ...[more]

PMID: 34637082

Similar Datasets

Project description:BackgroundThe current Berlin definition of acute respiratory distress syndrome (ARDS) uses the PaO2/FiO2 (P/F) ratio to classify severity. However, for the same P/F ratio, a patient on a higher positive end-expiratory pressure (PEEP) may have more severe lung injury than one on a lower PEEP.ObjectivesWe designed a new formula, the P/FP ratio, incorporating PEEP into the P/F ratio and multiplying with a correction factor of 10 [(PaO2*10)/(FiO2*PEEP)], to evaluate if it better predicts hospital mortality compared to the P/F ratio post-intubation and to assess the resultant changes in severity classification of ARDS.MethodsWe categorized patients from a dataset of seven ARDS network trials using the thresholds of ≤ 100 (severe), 101-200 (moderate), and 201-300 (mild) for both P/F (mmHg) and P/FP (mmHg/cmH2O) ratios and evaluated hospital mortality using areas under the receiver operating characteristic curves (AUC).ResultsOut of 3,442 patients, 1,057 (30.7%) died. The AUC for mortality was higher for the P/FP ratio than the P/F ratio for PEEP levels > 5 cmH2O: 0.710 (95% CI 0.691-0.730) versus 0.659 (95% CI 0.637-0.681), P < 0.001. Improved AUC was seen with increasing PEEP levels; for PEEP ≥ 18 cmH2O: 0.963 (95% CI 0.947-0.978) versus 0.828 (95% CI 0.765-0.891), P < 0.001. When the P/FP ratio was used instead of the P/F ratio, 12.5% and 15% of patients with moderate and mild ARDS, respectively, were moved to more severe categories, while 13.9% and 33.6% of patients with severe and moderate ARDS, respectively, were moved to milder categories. The median PEEP and FiO2 were 14 cmH2O and 0.70 for patients reclassified to severe ARDS, and 5 cmH2O and 0.40 for patients reclassified to mild ARDS.ConclusionsThe multifactorial P/FP ratio has a greater predictive validity for hospital mortality in ARDS than the P/F ratio. Changes in severity classification with the P/FP ratio reflect both true illness severity and the applied PEEP strategy.Trial registrationClinialTrials.gov-NCT03946150.

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

PaO₂/FiO₂ ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

Publications

PaO<sub>2</sub>/FiO<sub>2</sub> ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets