Use of Logistic Regression to Determine Parameter Ability for Recovery Prediction in Experimental Rat Hemorrhagic Shock Model
Authors: Lucas Alfredo , Williams Alexander Thomas , Cabrales Pedro 
Published: June 2021

IntroductionHemorrhagic shock (HS) severely decreases oxygen (O2) delivery and induces cardiovascular collapse. It is of interest for clinicians to estimate the potential of recovery during the early onset of a shock event as that would determine whether resources should be expended in environments of material shortage such as warzones and low resource communities. Multiple parameters, such as mean arterial pressure (MAP) and heart rate, are traditionally monitored during shock as a means of estimating the potential for recovery. In this study we used a logistic regression model to determine the ability of systemic hemodynamics, cardiac function, and blood gas parameters in determining the ability of successful HS recovery.MethodsPacked red blood cells (pRBCs) stored in AS‐3 after leukorfiltration were created from donor Sprague‐Dawley rats. pRBC units were randomly stored under either 1) conventional; 2) anaerobic; or 3) anaerobic/hypercapnic conditions. Rats (150–200g) were hemorrhaged to 50% of blood volume, held in hypovolemia for 30 minutes, and resuscitated to recover blood pressure to 90% pre‐hemorrhage with pRBC stored for either 1 or 3 weeks. Systemic hemodynamic, cardiac function, and blood gas parameters were monitored during shock and resuscitation. The percent resuscitation (%Res) corresponding to the percentage of the animal's original blood volume infused at the end of recovery, was used to create two classes of subjects with %Res=50% (equivalent to approximately 5 blood units) set as the threshold. A binary classification, logistic regression model was created with all measured parameters as features, and trained on random instances. The weights with an above threshold contribution assigned to each parameter after an L2 norm cost function minimization were extracted and matched with the corresponding features creating a reduced feature vector. A Monte Carlo approach was applied for n=100 trials of the previously described procedure to get a distribution of the most commonly selected features. The performance of the new feature vector was assessed by another logistic regression model with a 5‐ fold cross‐validation.ResultsThe top 3 features chosen by the model in order of increasing number of occurrences were: lactate 30 minutes into shock, mean arterial pressure 30 minutes into shock and contractility (dp/dtmax) at baseline. The model trained using these three features had a mean cross‐validation accuracy of 90.2%, significantly above the 66.9% accuracy (p<0.001) for a baseline classifier trained with random features. A final test set accuracy of 88.5% was obtained.ConclusionsIt has been shown that a logistic regression model can be effectively used for the determination of important parameters for the prediction of successful recovery after shock in an experimental rat hemorrhagic shock model.Support or Funding InformationNIH: R01 HL126945, NIH: T32 HL105373This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.