A random forest model using flow cytometry data identifies pulmonary infection after thoracic injury.

BACKGROUND: Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma. METHODS: We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out. RESULTS: Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4 + central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85. CONCLUSION: Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level IV.

Multidimensional machine learning models predicting outcomes after trauma.

BACKGROUND: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.

Cellular microRNAs correlate with clinical parameters in multiple injury patients.

INTRODUCTION: The pathophysiology of the inflammatory response after major trauma is complex, and the magnitude correlates with severity of tissue injury and outcomes. Study of infection-mediated immune pathways has demonstrated that cellular microRNAs may modulate the inflammatory response. The authors hypothesize that the expression of microRNAs would correlate to complicated recoveries in polytrauma patients (PtPs). METHODS: Polytrauma patients enrolled in the prospective observational Tissue and Data Acquisition Protocol with Injury Severity Score of >15 were selected for this study. Polytrauma patients were divided into complicated recoveries and uncomplicated recovery groups. Polytrauma patients' blood samples were obtained at the time of admission (T0). Established biomarkers of systemic inflammation, including cytokines and chemokines, were measured using multiplexed Luminex-based methods, and novel microRNAs were measured in plasma samples using multiplex RNA hybridization. RESULTS: Polytrauma patients (n = 180) had high Injury Severity Score (26 [20-34]) and complicated recovery rate of 33%. MicroRNAs were lower in PtPs at T0 compared with healthy controls, and bivariate analysis demonstrated that variations of microRNAs correlated with age, race, comorbidities, venous thromboembolism, pulmonary complications, complicated recovery, and mortality. Positive correlations were noted between microRNAs and interleukin 10, vascular endothelial growth factor, Acute Physiology and Chronic Health Evaluation, and Sequential Organ Failure Assessment scores. Multivariable Lasso regression analysis of predictors of complicated recovery based on microRNAs, cytokines, and chemokines revealed that miR-21-3p and monocyte chemoattractant protein-1 were predictive of complicated recovery with an area under the curve of 0.78. CONCLUSION: Systemic microRNAs were associated with poor outcomes in PtPs, and results are consistent with previously described trends in critically ill patients. These early biomarkers of inflammation might provide predictive utility in early complicated recovery diagnosis and prognosis. Because of their potential to regulate immune responses, microRNAs may provide therapeutic targets for immunomodulation. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level II.

Viral Micro-RNAs Are Detected in the Early Systemic Response to Injury and Are Associated With Outcomes in Polytrauma Patients.

OBJECTIVES: To evaluate early activation of latent viruses in polytrauma patients and consider prognostic value of viral micro-RNAs in these patients. DESIGN: This was a subset analysis from a prospectively collected multicenter trauma database. Blood samples were obtained upon admission to the trauma bay (T0), and trauma metrics and recovery data were collected. SETTING: Two civilian Level 1 Trauma Centers and one Military Treatment Facility. PATIENTS: Adult polytrauma patients with Injury Severity Scores greater than or equal to 16 and available T0 plasma samples were included in this study. Patients with ICU admission greater than 14 days, mechanical ventilation greater than 7 days, or mortality within 28 days were considered to have a complicated recovery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Polytrauma patients (n = 180) were identified, and complicated recovery was noted in 33%. Plasma samples from T0 underwent reverse transcriptase-quantitative polymerase chain reaction analysis for Kaposi's sarcoma-associated herpesvirus micro-RNAs (miR-K12_10b and miRK-12-12) and Epstein-Barr virus-associated micro-RNA (miR-BHRF-1), as well as Luminex multiplex array analysis for established mediators of inflammation. Ninety-eight percent of polytrauma patients were found to have detectable Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus micro-RNAs at T0, whereas healthy controls demonstrated 0% and 100% detection rate for Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus, respectively. Univariate analysis revealed associations between viral micro-RNAs and polytrauma patients' age, race, and postinjury complications. Multivariate least absolute shrinkage and selection operator analysis of clinical variables and systemic biomarkers at T0 revealed that interleukin-10 was the strongest predictor of all viral micro-RNAs. Multivariate least absolute shrinkage and selection operator analysis of systemic biomarkers as predictors of complicated recovery at T0 demonstrated that miR-BHRF-1, miR-K12-12, monocyte chemoattractant protein-1, and hepatocyte growth factor were independent predictors of complicated recovery with a model complicated recovery prediction area under the curve of 0.81. CONCLUSIONS: Viral micro-RNAs were detected within hours of injury and correlated with poor outcomes in polytrauma patients. Our findings suggest that transcription of viral micro-RNAs occurs early in the response to trauma and may be associated with the biological processes involved in polytrauma-induced complicated recovery.

Predicting the need for massive transfusion: Prospective validation of a smartphone-based clinical decision support tool.

BACKGROUND: Improper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision. METHODS: We prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon's initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon's final decision on activation. RESULTS: Three hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit -4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of "high" or "moderate" probability. Of these, 4 (15%) patients received <10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having "moderate" or "high" likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with "high" probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93-0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86. CONCLUSION: A smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment.

An integrative model using flow cytometry identifies nosocomial infection after trauma.

BACKGROUND: Flow cytometry (FCM) is a rapid diagnostic tool for monitoring immune cell function. We sought to determine if assessment of cell phenotypes using standardized FCM could be used to identify nosocomial infection after trauma. METHODS: Prospective study of trauma patients at a Level I center from 2014 to 2018. Clinical and FCM data were collected within 24 hours of admission. Random forest (RF) models were developed to estimate the risk of severe sepsis (SS), organ space infection (OSI), and ventilator-associated pneumonia (VAP). Variables were selected using backward elimination and models were validated with leave-one-out. RESULTS: One hundred and thirty-eight patients were included (median age, 30 years [23-44 years]; median Injury Severity Score, 20 (14-29); 76% (105/138) Black; 60% (83/138) gunshots). The incidence of SS was 8.7% (12/138), OSI 16.7% (23/138), and VAP 18% (25/138). The final RF SS model resulted in five variables (RBCs transfused in first 24 hours; absolute counts of CD56- CD16+ lymphocytes, CD4+ T cells, and CD56 bright natural killer [NK] cells; percentage of CD16+ CD56+ NK cells) that identified SS with an AUC of 0.89, sensitivity of 0.98, and specificity of 0.78. The final RF OSI model resulted in four variables (RBC in first 24 hours, shock index, absolute CD16+ CD56+ NK cell counts, percentage of CD56 bright NK cells) that identified OSI with an AUC of 0.76, sensitivity of 0.68, and specificity of 0.82. The RF VAP model resulted in six variables (Sequential [Sepsis-related] Organ Failure Assessment score: Injury Severity Score; CD4- CD8- T cell counts; percentages of CD16- CD56- NK cells, CD16- CD56+ NK cells, and CD19+ B lymphocytes) that identified VAP with AUC of 0.86, sensitivity of 0.86, and specificity of 0.83. CONCLUSIONS: Combined clinical and FCM data can assist with early identification of posttraumatic infections. The presence of NK cells supports the innate immune response that occurs during acute inflammation. Further research is needed to determine the functional role of these innate cell phenotypes and their value in predictive models immediately after injury. LEVEL OF EVIDENCE: Prognostic, level III.

Correction to: Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

In the original article, the units indicated on the y-axes of Fig. 3 are incorrectly labelled. The correct label is pg/mL. Following is the corrected Fig. 3.

Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

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.

Random forest modeling can predict infectious complications following trauma laparotomy.

BACKGROUND: Identifying clinical and biomarker profiles of trauma patients may facilitate the creation of models that predict postoperative complications. We sought to determine the utility of modeling for predicting severe sepsis (SS) and organ space infections (OSI) following laparotomy for abdominal trauma. METHODS: Clinical and molecular biomarker data were collected prospectively from patients undergoing exploratory laparotomy for abdominal trauma at a Level I trauma center between 2014 and 2017. Machine learning algorithms were used to develop models predicting SS and OSI. Random forest (RF) was performed, and features were selected using backward elimination. The SS model was trained on 117 records and validated using the leave-one-out method on the remaining 15 records. The OSI model was trained on 113 records and validated on the remaining 19. Models were assessed using areas under the curve. RESULTS: One hundred thirty-two patients were included (median age, 30 years [23-42 years], 68.9% penetrating injury, median Injury Severity Score of 18 [10-27]). Of these, 10.6% (14 of 132) developed SS and 13.6% (18 of 132) developed OSI. The final RF model resulted in five variables for SS (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, interleukin-6, and eotaxin) and four variables for OSI (Penetrating Abdominal Trauma Index, serum epidermal growth factor, monocyte chemoattractant protein-1, and interleukin-8). The RF models predicted SS and OSI with areas under the curve of 0.798 and 0.774, respectively. CONCLUSION: Random forests with RFE can help identify clinical and biomarker profiles predictive of SS and OSI after trauma laparotomy. Once validated, these models could be used as clinical decision support tools for earlier detection and treatment of infectious complications following injury. LEVEL OF EVIDENCE: Prognostic, level III.