Dataset Information

The relationship between delivery and the PaO₂ /FiO₂ ratio in COVID-19: a cohort study.

ABSTRACT:

Objective

To study the effect of delivery on the pO₂ /FiO₂ ratio (P/F ratio) in patients with COVID-19-related acute respiratory distress syndrome (ARDS) and to compare characteristics between delivered and undelivered pregnant patients with COVID-19.

Design

Retrospective cohort.

Setting

Four hospitals in Houston, Texas.

Population

Pregnant patients admitted to the hospital for COVID-19.

Methods

Among patients with ARDS who were delivered during their hospitalisation for COVID-19, linear mixed models were used to investigate time trends before and after delivery of the P/F ratio. Patient characteristics were compared between patients delivered during their hospitalisation for COVID-19 and those discharged undelivered.

Main outcome measures

The P/F ratio, age, gestational age, length of stay and severity of illness, RESULTS: Between 4 May 2020 and 26 July 2020, a total of 61 pregnant patients were admitted for COVID-19. Baseline characteristics were similar between the study groups. Delivery occurred in 21 (34%) of patients during their hospitalisation for COVID-19. Delivered patients had more severe disease and were admitted at a later gestational age than patients not delivered. Ten of these 21 patients (48%) were delivered preterm; of these, six were delivered due to complications of COVID-19 and four were delivered for obstetric indications. In patients with ARDS who were delivered (n = 17), the P/F ratio had a negative slope that improved after delivery.

Conclusions

COVID-19-related ARDS in pregnancy requires multidisciplinary care and individualised decision-making, but delivery slows the deterioration of the P/F ratio in these patients.

Tweetable abstract

Delivery improves the P/F ratio in COVID-19-related ARDS, though individualised delivery management is needed.

SUBMITTER: Pineles BL

PROVIDER: S-EPMC8441853 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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

The relationship between delivery and the PaO₂ /FiO₂ ratio in COVID-19: a cohort study.

Objective

Design

Setting

Population

Methods

Main outcome measures

Conclusions

Tweetable abstract

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OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

The relationship between delivery and the PaO2 /FiO2 ratio in COVID-19: a cohort study.

Objective

Design

Setting

Population

Methods

Main outcome measures

Conclusions

Tweetable abstract

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

The relationship between delivery and the PaO₂ /FiO₂ ratio in COVID-19: a cohort study.