Nomogram-Based Prediction of Post-Traumatic Occult Shock: Development and Evaluation of Novel Predictive Scores

Abstract Background Shock is a common severe complication of trauma and can result in multiple organ dysfunction and even death. To improve prognosis, it is crucial that clinicians intervene and treat occult shock, which shows no obvious changes in vital signs. The purpose of this study was to develop a new, nomogram-based shock prediction score model that is independent of the trauma injury severity score, abbreviated injury scale, and Glasgow coma scale. Our shock prediction score should help clinicians diagnose post-traumatic occult shock (PTOS) early and intervene promptly to improve patient outcomes. Patients and methods This study recruited 1005 injured patients (431 with shock and 574 without shock) from the emergency department of the Affiliated Hospital of Guizhou Medical University. Based on least absolute shrinkage and selection operator technology, 31 commonly used indicators were screened for influence on traumatic shock. We used multivariate logistic regression analysis to screen each independent influencing factor (p < 0.05), construct a nomogram-based prediction model, and formulate a PTOS score. We also estimated the predictive power of the model using C-index and the area under the receiver operating characteristic curve (AUC) of PTOS. The stability and net benefit range of this model were evaluated using a calibration diagram, internal grouping data verification, and decision curve analysis. Results Six individual influencing factors were selected using multivariate logistic regression to construct the nomogram-based model (systolic pressure, diastolic pressure, white blood cell count, absolute basophil count, red blood cell count, and hematocrit). The incidence of traumatic shock increased with an increase in the PTOS score. The C-index of the PTOS score in the training dataset was 0.92 (95% confidence interval [CI]: 0.90– 0.94), and the AUC was 0.92. The C-index of the validation dataset was 0.90 (95% CI: 0.86–0.94), and the AUC value was 0.90. The calibration chart showed that our model had excellent prediction. Decision curve analysis showed that the shock nomogram predictive score was clinically useful when the intervention was decided at a shock probability threshold more significant than 2%. Conclusion Using clinical electronic medical record system data, we developed and validated a new PTOS score with high accuracy, stability, and calibration to predict the risk of shock in patients with trauma.