Prediction of massive transfusion in trauma patients in the surgical intensive care units (THAI-SICU study)

Akaraborworn, Osaree; Chaiwat, Onuma; Chatmongkolchart, Sunisa; Kitsiripant, Chanatthee; Chittawatanarat, Kaweesak; Morakul, Sunthiti; Thawitsri, Thammasak; Wacharasint, Petch; Poopipatpab, Sujaree; Chau-In, Waraporn; Kusumaphanyo, Chaiyapruk

doi:10.1016/j.cjtee.2019.04.004

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…In the present study, the incidence of MBT was 3.9%, concordant with prior studies that have been reported in a range from 1.8% to 5.0%. [ 1 - 3 ] MBT can occur unexpectedly as an event, which is important to consider because such an event significantly increased mortality in the present study. This finding is similar to what was previously shown in a study by O’Keeffe et al ., which demonstrated that MBT was significantly associated with 30-day mortality.…”

Section: Discussionmentioning

confidence: 77%

“…Massive blood transfusion (MBT) occurs in several situations including for severely injured trauma victims, [ 1 ] uncontrolled blood loss, [ 2 , 3 ] and intraoperative incidental injury to major vessels. [ 4 ] The incidence of MBT ranges from 1.8% to 5.0%, [ 1 - 3 ] and the common situations that lead to MBT include trauma, cardiac surgery, liver transplantation, ruptured abdominal aortic aneurysm, gastrointestinal hemorrhage, and postpartum hemorrhage. [ 2 , 3 ] However, the association between MBT and mortality remains inconclusive.…”

Section: Introductionmentioning

confidence: 99%

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Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations

Sungkaro

Taweesomboonyat

Kaewborisutsakul

2022

JNRP

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Objectives: A massive blood transfusion (MBT) is an unexpected event that may impact mortality. Neurosurgical operations are a major operation involving the vital structures and risk to bleeding. The aims of the present research were (1) to develop a nomogram to predict MBT and (2) to estimate the association between MBT and mortality in neurosurgical operations. Material and Method: We conducted a retrospective cohort study including 3660 patients who had undergone neurosurgical operations. Univariate and multivariate logistic regression analyses were used to test the association between clinical factors, pre-operative hematological laboratories, and MBT. A nomogram was developed based on the independent predictors. Results: The predictive model comprised five predictors as follows: Age group, traumatic brain injury, craniectomy operation, pre-operative hematocrit, and pre-operative international normalized ratio and the good calibration were observed in the predictive model. The concordance statistic index was 0.703. Therefore, the optimism-corrected c-index values of cross-validation and bootstrapping were 0.703 and 0.703, respectively. Conclusion: MBT is an unexpectedly fatal event that should be considered for appropriate preparation blood components. Further, this nomogram can be implemented for allocation in limited-resource situations in the future.

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Section: Discussionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations

Sungkaro

Taweesomboonyat

Kaewborisutsakul

2022

JNRP

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“…28 Several scoring systems have been developed to predict the need for MBT, including the ABC score, which takes into account the penetrating mechanism, ED SBP, ED HR, and a positive Focused Assessment with Sonography for Trauma (FAST) exam, as well as the MBTT score, which is calculated based on age (>60 years), BE (<−10 mEq/L), lactate (>4 mmol/L), and HR (>105/min) or SOFA score (>9). [29][30][31] Other indicators, such as the shock index at ED arrival, can indicate hypovolemic shock and the likelihood of requiring transfusion. 17 In the study by Koch et al, shock index was shown to be a good predictor, where a shock index >0.9-1.1 could predict massive blood transfusion with an RR of 1.61 (95% CI: 1.13-2.31) but could not predict ICU admission and mortality.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Trauma on Scene Triage Screening Tool (Shock Index, Reverse Shock Index Glasgow Coma Scale and National Early Warning Score) to Predict the Severity of Emergency Department Triage: A Retrospective Cross-Sectional Study

Yuksen¹,

Angkoontassaneeyarat²,

Thananupappaisal³

et al. 2023

OAEM

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Introduction Prehospital trauma care includes on-scene assessments, essential treatment, and facilitating transfer to an appropriate trauma center to deliver optimal care for trauma patients. While the Simple Triage and Rapid Treatment (START), Revised Triage Sieve (rTS), and National Early Warning Score (NEWS) tools are user-friendly in a prehospital setting, there is currently no standardized on-scene triage protocol in Thailand Emergency Medical Service (EMS). Therefore, this study aims to evaluate the precision of these tools (SI, rSIG, and NEWS) in predicting the severity of trauma patients who are transferred to the emergency department (ED). Methods This study was a retrospective cross-sectional and diagnostic research conducted on trauma patients transferred by EMS to the ED of Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand, from January 2015 to September 2022. We compared the on-scene triage tool (SI, rSIG, and NEWS) and ED triage tool (Emergency Severity Index) parameters, massive transfusion protocol (MTP), and intensive care unit (ICU) admission with the area under ROC (univariable analysis) and diagnostic odds ratio (multivariable logistic regression analysis). The optimal cut-off threshold for the best parameter was determined by selecting the value that produced the highest area under the ROC curve. Results A total of 218 patients were traumatic patients transported by EMS to the ED, out of which 161 were classified as ESI levels 1–2, while the remaining 57 patients were categorized as levels 3–5 on the ESI triage scale. We found that NEWS was a more accurate triage tool to discriminate the severity of trauma patients than rSIG and SI. The area under the ROC was 0.74 (95% CI 0.70–0.79) (OR 18.98, 95% CI 1.06–337.25), 0.65 (95% CI 0.59–0.70) (OR 1.74, 95% CI 0.17–18.09) and 0.58 (95% CI 0.52–0.65) (OR 0.28, 95% CI 0.04–1.62), respectively (P-value <0.001). The cut point of NEWS to discriminate ESI levels 1–2 and levels 3–5 was >6 points. Conclusion NEWS is the best on-scene triage screening tool to predict the severity at the emergency department, massive transfusion protocol (MTP), and intensive care unit (ICU) admission compared with other triage tools SI and rSIG.

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“…Potential mechanisms may include the fact that SOFA scores are more likely to indicate liver failure or cardiovascular failure. Those organ failures have a high tendency to bleed, and subsequently leading to coagulopathy (26).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach for the Prediction of Traumatic Brain Injury Induced Coagulopathy

Yang

Peng

et al. 2021

Front. Med.

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Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

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Prediction of massive transfusion in trauma patients in the surgical intensive care units (THAI-SICU study)

Cited by 7 publications

References 8 publications

Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations

Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations

Accuracy of Trauma on Scene Triage Screening Tool (Shock Index, Reverse Shock Index Glasgow Coma Scale and National Early Warning Score) to Predict the Severity of Emergency Department Triage: A Retrospective Cross-Sectional Study

A Machine Learning Approach for the Prediction of Traumatic Brain Injury Induced Coagulopathy

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