Predict PaO2/FiO2 from non-laboratory data

The goal of this repository is to predict the PaO2/FiO2 ratio from non-laboratory data

Review of bilan2015comparison

Primary application was predicting pao2/fio2 from spo2/fio2 in ards

All patients had to have a previous PaO2/FiO2 ratio <= 300 before enrollment.

data collection:

based on various studies
24 hours BEFORE ENROLLMENT:
- ventilator parameters, study hospital, volume of fluid administered in the 24 hours before enrollment
AT ENROLLMENT:
- Age, sex, BMI, mean arterial pressure, and use of vasopressors at enrollment
  - any dose of norepinephrine, epinephrine, dopamine, phenylephrine, or vasopressin
DAY OF ENROLLMENT:
- ABG closest to 8 am on the day of enrollment
  - serum bilirubin, FIO 2 and SpO 2 at time of ABG

data preprocessing

excluded patients who did not have FIO2, PaO2, and SpO2 recorded from an ABG
adjusted PaO2/FIO2 ratios at the Denver and Utah sites (altitude ~1500m) by the ratio of local to sea level barometric pressure (0.836 in Denver, 0.845 in Utah).

equations used

non-linear imputation based on the Severinghaus equation
- Oxygen Saturation = (23,400 * (po^3 + 150 * po^2)-1 + 1)-1
- inverting this equation is fun I promise
log-linear imputation based on the Pandharipande equation
- Log(PF) = 0.48 + 0.78 x Log(SF)
linear imputation based on the Rice equation
- S/F = 64 + 0.84 * (P/F)

data analysis

correlation between PaFi measured, PaFi imputed
- once for all patients
- once for patients w/ SpO2 <= 96
RMSE of measured/imputed PaFi
Built a regression with Imputed PaO2 + other features, outcome measured PaO2
For the PaO2/FIO2 thresholds that were used to define mortality strata in the Berlin ARDS definition, we calculated the imputed PaO2/FIO2 that was associated with the same mortality as the measured PaO2/FIO2 threshold

results

PaO2/FiO2 - all patients (N=1184)
- correlations
  - 0.84 - non-linear
  - 0.73 - log-linear
  - 0.73 - linear
- RMSE
  - p=0.92 non-linear vs log-linear
  - p<0.001 non-linear vs linear
PaO2 - all patients
- correlations
  - 0.72 for non-linear imputation
  - 0.30 for log-linear
  - 0.13 for linear
- RMSE
  - p<0.001 non-linear vs log-linear
  - p<0.001 non-linear vs linear
Patients with SpO2 <= 96%
- PaO2/FiO2 - correlations
  - 0.90 - non-linear
  - 0.88 - log-linear
  - 0.88 - linear
- RMSE - PaO2/FiO2
  - 51.7 - non-linear
  - 52.0 - log-linear
  - 66.4 - linear
- RMSE - PaO2
  - 28.6 - non-linear
  - 32.2 - log-linear
  - 46.4 - linear
- All RMSE p-values < 0.0001
PaO2 - patients with SpO2 <= 96% (N=707)
- correlations
  - 0.72 for non-linear imputation
  - 0.13 for linear
  - 0.30 for log-linear
- RMSE
  - p<0.001 non-linear vs linear
  - p<0.001 non-linear vs log-linear

Confusion matrix for PaO2/FiO2 > 200 (0 = lower, 1 = higher i.e. severe ARDS)

 | 0   | 1 (Imputed)

--- | --- | --- 0 | 764 (65%) | 101 (9%) 1 (true) | 70 (6%) | 249 (20%)