Advanced Modeling to Predict Pneumonia in Combat Trauma Patients

Abstract

Background Tools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia. Methods This was a retrospective study of 73 primarily blast‐injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave‐one‐out‐cross‐validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone. Results Incidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF‐basic, IL‐2R, and IL‐6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87—both higher than LR algorithms. Conclusion Advanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast‐injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

Extrathoracic multiple trauma dysregulates neutrophil function and exacerbates pneumonia-induced lung injury

“…[8][9][10][11][12] Although mechanistic studies have provided heterogeneous results, clinical data remain consistent, with trauma associated with an increased risk of infection including with low-virulence opportunistic pathogens, such as Pseudomonas aeruginosa (PA). [13][14][15][16] While it is known that polytrauma, defined as simultaneous injury to two or more body systems, causes an inflammatory response that contributes to acute lung injury (ALI), the mechanism of this and its contribution to the development of secondary infections, such as pneumonia, are not well understood.…”

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

“…Machine learning classifiers have been applied to combat injuries for prediction of pneumonia 19 , infection 48 , closure timing 29 , venous thromboembolism 18 , and heterotopic ossification 49 . The utility of clinical variables for predicting outcomes was demonstrated in these studies, thus the current study employed a microbe-centric focus.…”

Association of a Network of Immunologic Response and Clinical Features With the Functional Recovery From Crotalinae Snakebite Envenoming

“…In 2019, Gelbard et al developed models using clinical data, cytokines, chemokines and growth factors that predicted severe sepsis and organ space infections following laparotomy for abdominal trauma (58). This modeling approach has expanded to combat trauma, where a predictive model composed of clinical features and immunologic biomarkers can accurately predict pneumonia in a predominantly blast-injured cohort of patients (59). By expanding this technique to SBE, we have built on the prior work evaluating both the clinical features predicting severity and studies of the association of soluble biomarkers with severity (47,48,65,66).…”

“…developed models using clinical data, cytokines, chemokines and growth factors that predicted severe sepsis and organ space infections following laparotomy for abdominal trauma ( 58 ). This modeling approach has expanded to combat trauma, where a predictive model composed of clinical features and immunologic biomarkers can accurately predict pneumonia in a predominantly blast-injured cohort of patients ( 59 ).…”

“…Clinical features alone have not shown adequate prognostic performance in SBE, despite having some predictive value (37,38,40,55,56). Combining clinical features with immunologic markers has been used to prognosticate outcomes in other disease states (57)(58)(59)(60). An improved understanding of the immunologic response combined with available clinical features in SBE could inform a prognostic model to predict severity and recovery in SBE.…”