Identify Trauma Patients Research Articles

Machine Learning Differentiates Extracorporeal Membrane Oxygenation Mortality Risk Profiles Among Trauma Patients.

Extracorporeal membrane oxygenation (ECMO) is resource intensive with high mortality. Identifying trauma patients most likely to derive a survival benefit remains elusive despite current ECMO guidelines. Our objective was to identify unique patient risk profiles using the largest database of trauma patients available. ECMO patients ≥16years were identified using Trauma Quality Improvement Program data (2010-2019). Machine learning K-median clustering (ML) utilized 101 variables including injury severity, demographics, comorbidities, and hospital stay information to generate unique patient risk profiles. Mortality and patient and center characteristics were evaluated across profiles. A total of 1037 patients were included with 33% overall mortality, mean age 32years, and median ISS = 26. The ML identified 3 unique patient risk profile groups. Although mortality rates were equivalent across the 3 groups, groups were distinguished by (Group 1) young (median 25years), severely injured (ISS = 34) patients with thoracic and head injuries (99%) via blunt mechanism (93%), and a high prevalence of ARDS (77%); (Group 2) relatively young (median 30years) and moderately injured (ISS = 22) patients with exposure-related injuries (11%); and (Group 3) older (median 46years) patients with a high proportion of comorbidities (69%) and extremity injuries (100%). There were no differences based on center ECMO volume, teaching status, or ACS-Level across all 3 groups. Machine learning compliments traditional analyses by identifying unique mortality risk profiles for trauma patients receiving ECMO. These details can further inform treatment guidelines, clinical decision making, and institutional criteria for ECMO usage.

Use of Reverse Shock Index Multiplied by Simplified Motor Score in a Five-Level Triage System: Identifying Trauma in Adult Patients at a High Risk of Mortality.

Background and Objectives: The Taiwan Triage and Acuity Scale (TTAS) is reliable for triaging patients in emergency departments in Taiwan; however, most triage decisions are still based on chief complaints. The reverse-shock index (SI) multiplied by the simplified motor score (rSI-sMS) is a more comprehensive approach to triage that combines the SI and a modified consciousness assessment. We investigated the combination of the TTAS and rSI-sMS for triage compared with either parameter alone as well as the SI and modified SI. Materials and Methods: We analyzed 13,144 patients with trauma from the Taipei Tzu Chi Trauma Database. We investigated the prioritization performance of the TTAS, rSI-sMS, and their combination. A subgroup analysis was performed to evaluate the trends in all clinical outcomes for different rSI-sMS values. The sensitivity and specificity of rSI-sMS were investigated at a cutoff value of 4 (based on previous study and the highest score of the Youden Index) in predicting injury severity clinical outcomes under the TTAS system were also investigated. Results: Compared with patients in triage level III, those in triage levels I and II had higher odds ratios for major injury (as indicated by revised trauma score < 7 and injury severity score [ISS] ≥ 16), intensive care unit (ICU) admission, prolonged ICU stay (≥14 days), prolonged hospital stay (≥30 days), and mortality. In all three triage levels, the rSI-sMS < 4 group had severe injury and worse outcomes than the rSI-sMS ≥ 4 group. The TTAS and rSI-sMS had higher area under the receiver operating characteristic curves (AUROCs) for mortality, ICU admission, prolonged ICU stay, and prolonged hospital stay than the SI and modified SI. The combination of the TTAS and rSI-sMS had the highest AUROC for all clinical outcomes. The prediction performance of rSI-sMS < 4 for major injury (ISS ≥ 16) exhibited 81.49% specificity in triage levels I and II and 87.6% specificity in triage level III. The specificity for mortality was 79.2% in triage levels I and II and 87.4% in triage level III. Conclusions: The combination of rSI-sMS and the TTAS yielded superior prioritization performance to TTAS alone. The integration of rSI-sMS and TTAS effectively enhances the efficiency and accuracy of identifying trauma patients at a high risk of mortality.

Angioembolization performed by trauma surgeons for trauma patients: is it feasible in Korea? A retrospective study.

Recent advancements in interventional radiology have made angioembolization an invaluable modality in trauma care. Angioembolization is typically performed by interventional radiologists. In this study, we aimed to investigate the safety and efficacy of emergency angioembolization performed by trauma surgeons. We identified trauma patients who underwent emergency angiography due to significant trauma-related hemorrhage between January 2020 and June 2023 at Jeju Regional Trauma Center. Until May 2022, two dedicated interventional radiologists performed emergency angiography at our center. However, since June 2022, a trauma surgeon with a background and experience in vascular surgery has performed emergency angiography for trauma-related bleeding. The indications for trauma surgeon-performed angiography included significant hemorrhage from liver injury, pelvic injury, splenic injury, or kidney injury. We assessed the angiography results according to the operator of the initial angiographic procedure. The term "failure of the first angioembolization" was defined as rebleeding from any cause, encompassing patients who underwent either re-embolization due to rebleeding or surgery due to rebleeding. No significant differences were found between the interventional radiologists and the trauma surgeon in terms of re-embolization due to rebleeding, surgery due to rebleeding, or the overall failure rate of the first angioembolization. Mortality and morbidity rates were also similar between the two groups. In a multivariable logistic regression analysis evaluating failure after the first angioembolization, pelvic embolization emerged as the sole significant risk factor (adjusted odds ratio, 3.29; 95% confidence interval, 1.05-10.33; P=0.041). Trauma surgeon-performed angioembolization was not deemed a significant risk factor in the multivariable logistic regression model. Trauma surgeons, when equipped with the necessary endovascular skills and experience, can safely perform angioembolization. To further improve quality control, an enhanced training curriculum for trauma surgeons is warranted.

Assessing Reverse Shock Index as a Survival Predictor for Trauma Patients in Emergency Settings: A Retrospective Observational Study

ntroduction: Identifying trauma patients with shock is crucial, as early intervention and prompt treatment improve patient prognosis and survival. To address this, the ratio of Systolic Blood Pressure (SBP) and Heart Rate (HR), known as the Reverse Shock Index (RSI), is measured. Aim: To evaluate the effectiveness of RSI calculation in assessing prognosis. Materials and Methods: This was a retrospective observational study in which data were retrospectively collected on trauma patients treated in the Emergency Room (ER) at Dhiraj Hospital, Sumandeep Vidyapeeth Deemed to be University, Vadodara, Gujarat, India, from January 2021 to December 2022. Patients involved in road traffic accidents, fall from a height of ≥6 m, assault, and machinery injuries were included. Upon arrival, vital signs such as HR, SBP, Respiratory Rate (RR), Glasgow Coma Scale (GCS), associated injuries, and in-hospital mortality were documented. Any resuscitative procedures required, such as Cardiopulmonary Resuscitation (CPR), intubation, oxygen therapy, chest tube insertion, and blood transfusion, were also recorded. The RSI was calculated for all trauma patients and divided into two groups (RSI &lt;1 and RSI ≥1). The t-test was performed with a 95% Confidence Interval (CI). Results: Out of 363 patients, data from 320 patients were included. Among them, 55 patients (17.2%) had RSI &lt;1, and 265 patients (82.8%) had RSI ≥1. Patients with RSI &lt;1 exhibited lower GCS scores, tachypnoea (RR &gt;29), or bradypnoea (RR &lt;10), along with higher mortality rates. These patients also required resuscitative interventions. Those with RSI &lt;1 experienced more head injuries, thoracic trauma, and maxillofacial injuries (p&lt;0.001). Conclusion: The RSI &lt;1 in trauma patients demonstrated significantly higher predictive accuracy for adverse outcomes, serving as a primary tool for early intervention and aggressive care in the ER.

Non-compressible truncal and junctional hemorrhage: A retrospective analysis quantifying potential indications for advanced bleeding control in Dutch trauma centers

BackgroundTruncal and junctional hemorrhage is the leading cause of potentially preventable deaths in trauma patients. To reduce this mortality, the application of advanced bleeding control techniques, such as resuscitative endovascular balloon occlusion of the aorta (REBOA), junctional tourniquets, Foley catheters, or hemostatic agents should be optimized. This study aimed to identify trauma patients with non-compressible truncal and junctional hemorrhage (NCTJH) who might benefit from advanced bleeding control techniques during initial trauma care. We hypothesized that there is a substantial cohort of Dutch trauma patients that can possibly benefit from advanced bleeding control techniques. MethodsAdult trauma patients with an Abbreviated Injury Scale ≥3 in the torso, neck, axilla, or groin region, who were presented between January 1st, 2014 and December 31st, 2018 to two Dutch level-1 trauma centers, were identified from the Dutch Trauma Registry. Potential indications for advanced bleeding control in patients with NCTJH were assessed by an expert panel of three trauma surgeons based on injury characteristics, vital signs, response to resuscitation, and received treatment. ResultsIn total, 1719 patients were identified of whom 249 (14.5 %) suffered from NCTJH. In 153 patients (60.6 %), hemorrhagic shock could have been mitigated or prevented with advanced bleeding control techniques. This group was younger and more heavily injured: median age of 40 versus 48 years and median ISS 33 versus 22 as compared to the entire cohort. The mortality rate in these patients was 31.8 %. On average, each of the included level-1 trauma centers treated an NCTJH patient every 24 days in whom a form of advanced bleeding control could have been beneficial. ConclusionsMore than half of included Dutch trauma patients with NCTJH may benefit from in-hospital application of advanced bleeding control techniques, such as REBOA, during initial trauma care. Widespread implementation of these techniques in the Dutch trauma system may contribute to reduction of mortality and morbidity from non-compressible truncal and junctional hemorrhage.

Identifying Trauma Patients in Need for Emergency Surgery in the Prehospital Setting: The Prehospital Prediction of In-Hospital Emergency Treatment (PROPHET) Study.

Prehospital field triage often fails to accurately identify the need for emergent surgical or non-surgical procedures, resulting in inefficient resource utilization and increased costs. This study aimed to analyze prehospital factors associated with the need for emergent procedures (such as surgery or interventional angiography) within 6 h of hospital admission. Additionally, our goal was to develop a prehospital triage tool capable of estimating the likelihood of requiring an emergent procedure following hospital admission. We conducted a retrospective observational study, analyzing both prehospital and in-hospital data obtained from the Lombardy Trauma Registry. We conducted a multivariable logistic regression analysis to identify independent predictors of emergency procedures within the first 6 h from admission. Subsequently, we developed and internally validated a triage score composed of factors associated with the probability of requiring an emergency procedure. The study included a total of 3985 patients, among whom 295 (7.4%) required an emergent procedure within 6 h. Age, penetrating injury, downfall, cardiac arrest, poor neurological status, endotracheal intubation, systolic pressure, diastolic pressure, shock index, respiratory rate and tachycardia were identified as predictors of requiring an emergency procedure. A triage score generated from these predictors showed a good predictive power (AUC of the ROC curve: 0.81) to identify patients requiring an emergent surgical or non-surgical procedure within 6 h from hospital admission. The proposed triage score might contribute to predicting the need for immediate resource availability in trauma patients.

Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model.

Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality. The critical administration threshold (CAT, 3 or more units of red blood cells in a 60-minute period) indicates severe hemorrhage and predicts mortality, whereas early identification of such patients improves survival. Patients from the PRospective, Observational, Multicenter, Major Trauma Transfusion and Pragmatic, Randomized Optimal Platelet, and Plasma Ratio studies were identified as either CAT+ or CAT-. Candidate variables were separated into 4 tiers based on the anticipated time of availability during the patient's assessment. ML models were created with the stepwise addition of variables and compared with the baseline performance of the assessment of blood consumption (ABC) score for CAT+ prediction using a cross-validated training set and a hold-out validation test set. Of 1245 PRospective, Observational, Multicenter, Major Trauma Transfusion and 680 Pragmatic, Randomized Optimal Platelet and Plasma Ratio study patients, 1312 were included in this analysis, including 862 CAT+ and 450 CAT-. A CatBoost gradient-boosted decision tree model performed best. Using only variables available prehospital or on initial assessment (Tier 1), the ML model performed superior to the ABC score in predicting CAT+ patients [area under the receiver-operator curve (AUC = 0.71 vs 0.62)]. Model discrimination increased with the addition of Tier 2 (AUC = 0.75), Tier 3 (AUC = 0.77), and Tier 4 (AUC = 0.81) variables. A dynamic ML model reliably identified CAT+ trauma patients with data available within minutes of trauma center arrival, and the quality of the prediction improved as more patient-level data became available. Such an approach can optimize the accuracy and timeliness of massive transfusion protocol activation.

Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study

To develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route. We developed a prediction model using the classical cross-validation method from the Pan-Asia Trauma Outcomes Study (PATOS) database from 1 January 2015 to 31 December 2020. Eligible patients aged ≥18 years were transported to the hospital by the EMS. The primary outcome (EMS-witnessed TCA) was defined based on changes in vital signs measured on the scene or en route. We included variables that were immediately measurable as potential predictors when EMTs arrived. An integer point value system was built using multivariable logistic regression. The area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration in the derivation and validation cohorts. In total, 74,844 patients were eligible for database review. The model comprised five prehospital predictors: age <40 years, systolic blood pressure <100mmHg, respiration rate >20/minute, pulse oximetry <94%, and levels of consciousness to pain or unresponsiveness. The AUROC in the derivation and validation cohorts was 0.767 and 0.782, respectively. The HL test revealed good calibration of the model (p=0.906). We established a prediction model using variables from the PATOS database and measured them immediately after EMS personnel arrived to predict EMS-witnessed TCA. The model allows prehospital medical personnel to focus on high-risk patients and promptly administer optimal treatment.

APPRAISE-HRI: AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR TRIAGE OF HEMORRHAGE CASUALTIES.

Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to assess the ability of an artificial intelligence triage algorithm to automatically analyze vital-sign data and stratify hemorrhage risk in trauma patients. Methods: Here, we developed the APPRAISE-Hemorrhage Risk Index (HRI) algorithm, which uses three routinely measured vital signs (heart rate and diastolic and systolic blood pressures) to identify trauma patients at greatest risk of hemorrhage. The algorithm preprocesses the vital signs to discard unreliable data, analyzes reliable data using an artificial intelligence-based linear regression model, and stratifies hemorrhage risk into low (HRI:I), average (HRI:II), and high (HRI:III). Results: To train and test the algorithm, we used 540 h of continuous vital-sign data collected from 1,659 trauma patients in prehospital and hospital (i.e., emergency department) settings. We defined hemorrhage cases (n = 198) as those patients who received ≥1 unit of packed red blood cells within 24 h of hospital admission and had documented hemorrhagic injuries. The APPRAISE-HRI stratification yielded a hemorrhage likelihood ratio (95% confidence interval) of 0.28 (0.13-0.43) for HRI:I, 1.00 (0.85-1.15) for HRI:II, and 5.75 (3.57-7.93) for HRI:III, suggesting that patients categorized in the low-risk (high-risk) category were at least 3-fold less (more) likely to have hemorrhage than those in the average trauma population. We obtained similar results in a cross-validation analysis. Conclusions: The APPRAISE-HRI algorithm provides a new capability to evaluate routine vital signs and alert medics to specific casualties who have the highest risk of hemorrhage, to optimize decision-making for triage, treatment, and evacuation.

Predictive influence of artificial intelligence‐based body composition analysis in trauma patients with pelvic injuries

AbstractBackgroundHigh‐energy trauma patients represent the ultimate challenge to trauma care and are at risk of injury‐related mortality and morbidity—a common cause of loss of productivity. The aim of this study was to implement computed tomography (CT)‐derived, artificial intelligence (AI)‐based body composition analysis (BCA) to identify predictors of morbidity.MethodsRetrospectively, we enrolled 104 patients (38 females and 66 males) who underwent CT imaging for assessment of injuries caused by high‐energy trauma (motor vehicle accidents, falls from significant height, or blast injury). We sought to identify risk factors for prolonged length of stay in hospital and intensive care unit (ICU) and fractures requiring pelvic surgery. Cox and logistic regression analysis were performed using BCA parameters as covariates besides conventional risk factors. Additionally, the effects of pre‐existing conditions, obesity, and sarcopenia were analysed.ResultsIncreased subcutaneous adipose tissue (SAT), determined by BCA, at hospital admittance is a predictor of prolonged hospital stay (P = 0.02) independent of treatment regime and occurrence of related complications, whereas muscle mass does not influence the length of stay. Individuals with sarcopenia and a decreased psoas muscle index (PMI) sustaining high‐energy trauma are at risk of pelvic injuries requiring surgical treatment.ConclusionBCA parameters are easily available from routine CT and significantly predict outcomes in trauma patients with pelvic injuries. Patients with reduced muscle mass are at risk for injuries requiring pelvic surgery, and increased SAT is a risk factor for longer hospital stays. These findings underline the potential of BCA, which may be valuable in identifying trauma patients who require specific support to optimize their physiological reserves and clinical outcome.

Performance of a provincial prehospital trauma triage protocol: A retrospective audit

Objective To assess the accuracy of a five-step prehospital trauma triage protocol ( Échelle québécoise de triage préhospitalier en traumatologie (EQTPT)) to identify patients requiring urgent and specialized in-hospital trauma care in the Capitale-Nationale region – Québec. Methods The medical records of trauma patients transported by ambulance to one of the five participating emergency departments (EDs) between November 2016 and March 2017 were reviewed. Our primary outcome was the need for one of the following urgent and specialized trauma care: endotracheal intubation in the ED, administration of ≥ 2 blood products in the ED, angioembolization or surgery (excluding single limb surgery) &lt; 24 h and admission to the intensive care unit (ICU) or in-hospital trauma-related death. Results A total of 902 patients were included. The median age was 63 (interquartile range (IQR) 51) and 494 (54.8%) were female. The main trauma mechanism was falls (n = 592), followed by motor vehicle accidents (n = 201). Eighty-two (9.1%) patients required at least one urgent and specialized trauma care. Of those, 44 (53.6%) were identified as requiring transport to a level one trauma centre (steps 1–3), 16 were identified as requiring transport to a centre with a lower level of trauma designation (steps 4–5) while 22 (26.8%) did not meet any of the EQTPT criteria. For steps 1 to 3, the sensitivity was 53.7% (95% confidence interval (CI) 42.9–64.4) and the specificity was 81.7% (95% CI 79.1–84.4) in identifying patients requiring specialized trauma care. Conclusion The EQTPT lacked sensitivity and was poorly specific to identify trauma patients who need specialized in-hospital trauma care.

Development and Validation of a Renal Replacement after Trauma Scoring Tool.

Stress on the healthcare system requires careful allocation of resources such as renal replacement therapy (RRT). The COVID-19 pandemic generated difficulty securing access to RRT for trauma patients. We sought to develop a renal replacement after trauma (RAT) scoring tool to help identify trauma patients who may require RRT during their hospitalization. The 2017 to 2020 TQIP database was divided into a derivation (2017 to 2018 data) and validation (2019 to 2020 data) set. A 3-step methodology was used. Adult trauma patients admitted from the emergency department to the operating room or ICU were included. Patients with chronic kidney disease, transfers from another hospital, and emergency department death were excluded. Multiple logistic regression models were created to determine the risk for RRT in trauma patients. The weighted average and relative impact of each independent predictor was used to derive a RAT score, which was validated using area under receiver operating characteristic curve (AUROC). From 398,873 patients in the derivation and 409,037 patients in the validation set, 11 independent predictors of RRT were included in the RAT score derived with scores ranging from 0 to 11. The AUROC for the derivation set was 0.85. The rate of RRT increased to 1.1%, 3.3%, and 20% at scores of 6, 8, and 10, respectively. The validation set AUROC was 0.83. RAT is a novel and validated scoring tool to help predict the need for RRT in trauma patients. With future improvements including baseline renal function and other variables, the RAT tool may help prepare for the allocation of RRT machines/staff during times of limited resources.

Shock Index for Early Detection of Low Plasma Fibrinogen in Trauma: A Prospective Observational Cohort Pilot Study.

Shock index (a ratio between heart rate and systolic blood pressure) predicts transfusion requirements and the need for haemostatic resuscitation in severe trauma patients. In the present study, we aimed to determine whether prehospital and on-admission shock index values can be used to predict low plasma fibrinogen in trauma patients. Between January 2016 and February 2017, trauma patients admitted from the helicopter emergency medical service into two large trauma centres in the Czech Republic were prospectively assessed for demographic, laboratory and trauma-associated variables and shock index at scene, during transport and at admission to the emergency department. Hypofibrinogenemia defined as fibrinogen plasma level of 1.5 g·L-l was deemed as a cut-off for further analysis. Three hundred and twenty-two patients were screened for eligibility. Of these, 264 (83%) were included for further analysis. The hypofibrinogenemia was predicted by the worst prehospital shock index with the area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI 0.64-0.91) and by the admission shock index with AUROC of 0.79 (95% CI 0.66-0.91). For predicting hypofibrinogenemia, the prehospital shock index ≥ 1 has 0.5 sensitivity (95% CI 0.19-0.81), 0.88 specificity (95% CI 0.83-0.92) and a negative predictive value of 0.98 (0.96-0.99). The shock index may help to identify trauma patients at risk of hypofibrinogenemia early in the prehospital course.

Cardiovascular parameters on computed tomography are independently associated with in-hospital complications and outcomes in level-1 trauma patients

BackgroundIn-hospital complications after trauma may result in prolonged stays, higher costs, and adverse functional outcomes. Among reported risk factors for complications are pre-existing cardiopulmonary comorbidities. Objective and quick evaluation of cardiovascular risk would be beneficial for risk assessment in trauma patients. Studies in non-trauma patients suggested an independent association between cardiovascular abnormalities visible on routine computed tomography (CT) imaging and outcomes. However, whether this applies to trauma patients is unknown.PurposeTo assess the association between cardiopulmonary abnormalities visible on routine CT images and the development of in-hospital complications in patients in a level-1 trauma center.MethodsAll trauma patients aged 16 years or older with CT imaging of the abdomen, thorax, or spine and admitted to the UMC Utrecht in 2017 were included. Patients with an active infection upon admission or severe neurological trauma were excluded. Routine trauma CT images were analyzed for visible abnormalities: pulmonary emphysema, coronary artery calcifications, and abdominal aorta calcification severity. Drug-treated complications were scored. The discharge condition was measured on the Glasgow Outcome Scale.ResultsIn total, 433 patients (median age 50 years, 67% male, 89% ASA 1–2) were analyzed. Median Injury Severity Score and Glasgow Coma Scale score were 9 and 15, respectively. Seventy-six patients suffered from at least one complication, mostly pneumonia (n = 39, 9%) or delirium (n = 19, 4%). Left main coronary artery calcification was independently associated with the development of any complication (OR 3.9, 95% CI 1.7–8.9). An increasing number of calcified coronary arteries showed a trend toward an association with complications (p = 0.07) and was significantly associated with an adverse discharge condition (p = 0.02). Pulmonary emphysema and aortic calcifications were not associated with complications.ConclusionCoronary artery calcification, visible on routine CT imaging, is independently associated with in-hospital complications and an adverse discharge condition in level-1 trauma patients. The findings of this study may help to identify trauma patients quickly and objectively at risk for complications in an early stage without performing additional diagnostics or interventions.

Minimally Invasive Surgery for Genitourinary Trauma: A Nationwide Outcomes-Based Analysis

IntroductionBladder and ureteral injuries are uncommon in trauma patients but are associated with increased morbidity and mortality. Patients presenting with such injuries may undergo either open surgical repair or laparoscopic repair. We aimed to compare outcomes of open surgical approach and laparoscopy in trauma patients with isolated bladder and ureteral injury. We hypothesized that laparoscopy is associated with improved outcomes. MethodsWe performed a 2017 review of American College of Surgeons Trauma Quality Improvement Program and identified trauma patients with bladder and ureteral injury who underwent open surgical repair or laparoscopy. A 1:1 propensity score matching was performed adjusting for demographics, emergency department vitals (systolic blood pressure, heart rate, Glasgow Coma Scale), mechanism of injury, Injury Severity Score, each body region Abbreviated Injury Scale score, and transfusion units. Outcomes were rates of in-hospital major complications and mortality. ResultsOf the 1,004,440 trauma patients, 384 patients (open: 192 and laparoscopy: 192) were matched and included. The mean age was 36 ± 15 y, Injury Severity Score was 27 [27-48], 77% were males, and 56% of patients had a blunt mechanism of injury, and 44% had penetrating injuries. Overall mortality was 7.3%. On univariate analysis, mortality was lower in the open group as compared to the laparoscopy group (10.4% versus 4.2%, P = 0.019) and survivor-only hospital length of stay was longer in the open group (8 [8-9] versus 7 [5-11], P = 0.008). There was no difference in overall major complications (23% versus 21%, P = 0.621). On multivariate analysis, open surgical repair was independently associated with lower odds of mortality (adjusted odds ratio: 0.405, 95% confidence interval: [0.17-0.95], P-value = 0.038) ConclusionsIn our analysis open surgical repair of bladder and ureteral injuries was associated with lower mortality with other outcomes being similar when compared to laparoscopy. Laparoscopic surgical repair may not have an advantage over the open surgical repair for bladder and ureteral injuries. Further prospective studies are needed to delineate the ideal surgical approach for these injuries.

Incidence of Acute Kidney Injury in Polytrauma Patients and Predictive Performance of TIMP2 × IGFBP7 Biomarkers for Early Identification of Acute Kidney Injury.

Background: Acute kidney injury (AKI) is a common cause of organ failure in trauma patients who survive their initial injuries. It is independently associated with increased morbidity and mortality and prolongs the length of hospital stays. The objectives of this study were to describe the incidence of early AKI and influence of risk factors in polytrauma patients and evaluate the predictive potential of TIMP2 × IGFBP7 biomarkers in this patient cohort. Methods: We conducted a retrospective cohort study of severely injured adult patients who were consecutively admitted to a multidisciplinary ICU from May 2017 to May 2019. Detailed patient data was retrieved from ICU medical records. Fluid balance, urinary output, and sCr values up to 72 h were collected. Urine samples for measuring TIMP2 × IGFBP7 concentrations were obtained and analyzed from ICU admission to 72 h. Results: Among the 153 patients eligible for analysis, 45 were included in the AKI, and 108 in the no AKI cohorts. The incidence of AKI within 72 h, based on KDIGO criteria, was 28.8%. There were no differences in ISS, type and mechanism of injury, heart rate, and systolic BP at admission between groups. Patients with early AKI were older (68 vs. 49 years, p < 0.001), obese (BMI 26.2 vs. 24.7, p < 0.048), and more likely to have previous cardiac disease (27% vs. 5.6%, p < 0.001). TIMP2 × IGFBP7 values on ICU admission were associated with subsequent AKI in patients without evidence of AKI at the time of ICU admission. They were also higher in the AKI cohort and significantly correlated with renal replacement therapy (RRT) and episodes of hypotension. Multivariable analysis confirmed age, previous cardiac disease, and NephroCheck as the variables mostly associated with AKI, with AUC 0.792. Conclusions: TIMP2 × IGFBP7 may help identify trauma patients with tubular damage that may evolve into a clinically manifested syndrome. Prospective studies of TIMP2 × IGFBP7, as a trigger for early AKI bundle care, are warranted.

Multidimensional machine learning models predicting outcomes after trauma

BackgroundAn 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. MethodsThis 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. ResultsA 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. ConclusionClinical, 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.

Early Identification of Post-Traumatic Stress Disorder in Trauma Patients: Development of a Multivariable Risk Prediction Model.

The purpose of this study was to build a risk prediction model to identify trauma patients at the time of injury who are at high risk for post-traumatic stress disorder (PTSD) 1year later. Patients 18+ with operative orthopedic trauma injuries were enrolled in prospective social determinants of health cohort. Data were collected through initial surveys, medical records at time of injury, and 1-year follow-up phone screenings. Univariate analysis examined associations between factors and PTSD at 1 year. The best fit multivariable logistic regression model led to a novel PTSD risk prediction tool based on weights assigned similar to the Charlson index methods. Of 329 enrolled patients, 87 (26%) completed follow-up surveys; 58% screened positive for chronic PTSD. The best fit model predicting PTSD included age, insurance, violent mechanism, and 2 acute stress screening questions (AUC .89). Using these parameters, the maximum possible TIPPS index was 19. Those with PTSD at 1year had a mean TIPPS index of 12.9 ± 4.0, compared to 5.9 ± 4.2 for those who did not (P < .001). Traumatic injury often leads to PTSD, which can be predicted by a novel risk score incorporating age, insurance status, violent injury mechanism, and acute stress reaction symptoms. Stability in life and relationships with primary care physicians may be protective of PTSD. Diagnostic level II.

Keeping it simple: the value of mortality prediction after trauma with basic indices like the Reverse Shock Index multiplied by Glasgow Coma Scale

BackgroundIdentification of trauma patients at significant risk of death in the prehospital setting is challenging. The prediction probability of basic indices like vital signs, Shock Index (SI), SI multiplied by...

Utility of Anterior Atlantodens Interval Widening on Cervical Spine CT for Assessing Transverse Atlantal Ligament Injury.

Retrospective, cross-sectional. To identify trauma patients with confirmed tears of the transverse atlantal ligament on cervical MRI and measure several parameters of atlanto-axial alignment on cervical CT, including the anterior atlantodens interval, to determine which method is most sensitive in predicting transverse atlantal ligament injury. Adult trauma patients who suffered a transverse atlantal ligament tear on cervical MRI were identified retrospectively. The cervical CT and MRI exams for these patients were reviewed for the following: anterior and lateral atlantodens interval widening, lateral C1 mass offset, C1-C2 rotatory subluxation, and transverse atlantal ligament injuries on cervical MRI. Twenty-six patients were identified with a tear of the transverse atlantal ligament on cervical MRI. Twelve percent of these patients demonstrated an anterior dens interval measuring greater than 2mm, 26% of patients demonstrated lateral mass offset of C1 on C2 (average offset of 2.4mm), 18% of patients demonstrated an asymmetry greater than 1mm between the left and right lateral atlantodens interval, and one patient demonstrated atlanto-axial rotation measuring greater than 20%. Ten patients had an accompanying C1 burst fracture and eight patients had a C2 fracture. One patient demonstrated widening of the atlanto-occipital joint space greater than 2mm indicative of craniocervical dissociation injury. An anterior atlantodens interval measuring greater than 2mm is an unreliable methodology to screen trauma patients for transverse altantal ligament injuries and atlanto-axial instability. Moreover, C1 lateral mass offset, lateral atlantodens asymmetry, and atlanto-axial rotation were all poor predictors of transverse atlantal ligament tears.