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ORIGINAL ARTICLE |
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| Ahead of print
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BIG score and its comparison with different scoring systems for mortality prediction in children with severe traumatic brain injury admitted in pediatric intensive care unit
Arsheen Zeeshan1, Muhammad Jawwad1, Mujtaba Moazzam2, Muhammad T Yousafzai1, Qalab Abbas1
1 Department of Pediatrics and Child Health, Medical College, Aga Khan University, Karachi, Pakistan
2 Medical College, Aga Khan University, Karachi, Pakistan
| Date of Submission | 24-Jan-2022 |
| Date of Acceptance | 01-May-2022 |
| Date of Web Publication | 30-Jan-2023 |
Correspondence Address:
Qalab Abbas,
Department of Pediatrics and Child Health, Aga Khan University, Stadium Road, 74800 Karachi
Pakistan
 Source of Support: None, Conflict of Interest: None DOI: 10.4103/jpn.JPN_16_22
 Abstract | | |
Objective: We aimed to determine the association of BIG score with mortality in pediatric traumatic brain injury (TBI) and its comparison with other trauma scores. Materials and Methods: A retrospective review of medical records of all children, aged 1–18 years, who were admitted in our pediatric intensive care unit within 24 h of trauma (from January 2015 to December 2018), was carried out. Data were collected on a structured case report form (CRF). They were analyzed using STATA version 15. Results: Ninety-four patients were identified. Data were collected on a structured CRF. Median BIG, Injury Severity Scoring (ISS), Pediatric Trauma Score (PTS), and New Injury Severity Score (NISS) were 15 (9.3–18), 13 (9–19), 4 (2–6), and 13 (9–22), respectively. Cutoffs for all scores were calculated using the Youden index, which was 19 for BIG score, 13.5 for ISS, 13.5 for NISS, and 3.5 for PTS. On the univariate analysis, younger age, tachycardia in the first 24 h, hypotension on arrival, base deficit, deranged international normalization ratio, low Glasgow Coma Scale and higher BIG score, higher ISS, and higher NISS scores were associated with mortality. Using the multivariate regression analysis, BIG score was associated with mortality. The BIG score had an area under the curve (AUC) of 0.802 (0.650–0.956) with the highest specificity of 90.5% (82.1–95.8%) and ISS had an AUC of 0.756 (0.702–0.810) with the highest sensitivity of 100% (69.2–100%). Conclusion: BIG and ISS are specific and sensitive scores, respectively, to predict mortality in pediatric TBI.
Keywords: BIG score, injury severity score, outcome, pediatric, pediatric intensive care unit developing country, traumatic brain injury
How to cite this URL:
Zeeshan A, Jawwad M, Moazzam M, Yousafzai MT, Abbas Q. BIG score and its comparison with different scoring systems for mortality prediction in children with severe traumatic brain injury admitted in pediatric intensive care unit. J Pediatr Neurosci [Epub ahead of print] [cited 2023 Oct 3]. Available from: https://www.pediatricneurosciences.com/preprintarticle.asp?id=368801 |
Level of evidence: II
| Background | |  |
Pediatric traumatic brain injury (pTBI) is one of the leading causes of mortality and morbidity in children all over the world.[1],[2],[3] The situation is similar in low-middle income countries (LMIC), where TBI was observed in more than 75% of fall-related injuries and 50% of road traffic accidents in emergency department visits.[4],[5] Pakistan is an LMIC with a population of 200 million and a high rate of TBI.[6] The true burden and outcomes of pTBI in Pakistan are unknown. However, a surveillance study showed that 46% of the patients suffering from TBI were less than 25 years of age.[7] This study showed that there is a severe lack of good emergency medical services for patients with TBI.
Prognostic (scoring) models that are simple and can be obtained early in the course of TBI can be very helpful to understand the impact of variation of clinical care as well as to predict which patients will benefit from novel interventions.
There are many trauma scoring systems in use; a few of the most used are Injury Severity Scoring (ISS), New Injury Severity Score (NISS), and Pediatric Trauma Score (PTS).[8],[9],[10] All of these are also being used in pediatrics with variable sensitivity and specificity.[11],[12],[13],[14],[15] All of these scores involve complex calculations and complicated trauma details (e.g., fractures, areas involved, and physiologic variables such as conscious status and blood pressure). The BIG score (Base deficit [B], International normalized ratio [I], Glasgow Coma Scale [G]) is a novel score being validated in pediatric trauma for mortality prediction.[16],[17] It is a unique scoring system, which can be calculated as (Admission BD) + (2.5 ×) + (15 − GCS).[14] It takes into account the physiological parameters that get deranged commonly in trauma: coagulopathy, acidosis due to shock, and Glasgow Coma Scale (GCS).[18],[19] Pediatric trauma BIG score has been evaluated as a tool for mortality prediction in different kinds of pediatric trauma and has shown good predictive ability for mortality.[16],[20],[21],[22],[23] The data on the usefulness of BIG in pTBI, especially from LMIC, are almost non-existent, and due to lack of multiple resources (emergency care, skilled human resource, medical records, etc.) in this setting, such a score can be very useful at emergency triage for healthcare providers to decide the best care pathways for these patients and for prediction of patients’ course.
The primary objective of this study is to determine the association of BIG score with mortality in children admitted with TBI in our pediatric intensive care unit (PICU) and to compare its performance with other trauma scoring systems.
| Materials and Methods | | |
Study design, setting, and study population
Our hospital is a 700-bedded tertiary care hospital which caters to Karachi, the largest cosmopolitan city of Pakistan, and the surrounding cities. Its PICU is a 12-bed closed multidisciplinary unit with state-of-the-art facilities for the treatment of pediatric population with trauma and other severe illnesses. All children with severe TBI (GCS ≤8) and those with moderate TBI who require some surgical intervention for TBI or other reasons (polytrauma) are admitted in PICU. Every patient visiting outpatient or inpatient at the hospital for the first time is assigned a unique medical record number (MRN), and all the subsequent visits including the laboratory reports are archived electronically using that MRN. The hospital follows the International Classification of Disease (ICD) system for coding the diagnosis of patients.
We conducted a retrospective review of medical records of all children (aged 1 month to 18 years) who are admitted to our PICU from emergency department or step up from the ward, between January 2015 and December 2018 with TBI as a primary manifestation of blunt trauma presenting within 24 h. Initially, the ICD-9 coding was used for the classification of the diseases and since 2019, the records have been successfully transferred to the latest ICD-10 coding system. Trained transcriptionists were involved in coding all the diagnoses at discharge for inpatients and for those in the outpatient department. In this study, data were retrieved using the ICD-9 coding. Patients diagnosed with penetrating trauma, burn, electric shock, patients with chronic diseases such as chronic renal failure, hepatic, hematologic, or neurologic diseases were excluded from the study. Patients who were transferred to the ward or those who expired in the emergency room (ER) were also excluded.
Data collection
We developed a structured case report form (CRF) with sociodemographic variables (age/date of birth, gender), anthropometric variables (weight in kilogram and height in cm), clinical variables at the time of presentation in ER including heart rate, systolic blood pressure, GCS, duration of the event, trauma profile such as pre-hospital intubation, presence of hypotension, tachycardia, airway condition, central nervous system status, and mechanism of injury, and laboratory variables (prothrombin time [PT]/international normalization ratio [INR], pH, and base deficit/excess). The CRF also contained variables for secondary outcomes such as the duration of PICU stay, duration of mechanical ventilation (MV), requirement of transfusion, and mortality and the cause of death. The medical record department of the hospital retrieved the required medical records from the PICU admissions from 2015 to 2018, identifying the MRN, age or date of birth, date of hospital admission, and ICD-9 code, which were then double-checked with PICU log of admissions. A trained physician (PICU fellow) with the help of fourth-year medical students visited each record to retrieve the required information using the structured CRF developed for this study under supervision of the faculty. The BIG score was calculated using the initial values on patients’ presentation to the ER.
In order to ensure quality control of the retrieved information, one of the investigators (faculty) randomly picked 5% of the filled CRFs and validated the recorded information against the electronic medical record system. Any discrepancies, errors, or missing information were corrected.
Ethical approval was obtained from the Ethics Review Committee of the hospital (No. 2019-1215-3225).
Statistical analysis
Data were analyzed using STATA version 15. We checked the data for any missing information, illegible responses, and any other inconsistencies before the analysis. Quantitative data such as age, weight for age z-score, heart rate, blood pressure, length of stay in hospital, GCS, BIG score, ISS, PTS, base deficit, and NISS scores were checked for normal distribution, and means with standard deviations and/or medians with inter-quartile ranges were calculated accordingly. The receiver operating characteristic (ROC) curve was generated to obtain the values of AUC with 95% confidence interval (CI), and sensitivity and specificity for BIG score, ISS, NISS, PTS, INR, GCS, and BD as predictors of survival/mortality. To determine the locally appropriate cutoff point for the BIG score, the Youden index (sensitivity + specificity − 1) was calculated and the corresponding cutoff value for the highest Youden index was considered as the optimal cutoff value.[24]
The same procedure was applied for identifying the suitable cutoffs for other scores as well. We also performed univariate and multivariable logistic regression analyses to identify the associated factors for survival (yes/no) and need for neurosurgical intervention (yes/no). Variables significant at the 20% level of significance or clinical significance were carried forward into the multivariable logistic regression. Purposeful selection was used to develop the parsimonious multivariable logistic regressions. The final model obtained was based on statistical significance and clinical importance of variables. Adjusted odds ratios with 95% CIs were calculated.
| Results | | |
Demographic and outcome details of study population
A total of 94 children were enrolled. The median age of the study population was 8 years (interquartile range [IQR] 3–13 years), and 80% were male. Mechanisms of injury were: falls from height, 33 (35%); motor vehicle accidents, 39 (41%); and pedestrian injury, 13 (14%). Other features are presented in [Table 1]. The median length of PICU and hospital stay was 3.5 days (IQR 2–6) and 11 days (IQR 6–16), respectively. During the study period, 10 (10.63%) patients expired [Table 1]. Of the 10 non-survivors, 4 (40%) had a fall from height, 4 (40%) were involved in a motor vehicle incident, and the remaining suffered from other types of blunt trauma. The median ISS was 13 (IQR 9–19), BIG score 15 (IQR 9–18), PTS 4 (IQR 2–6), and NISS 13 (IQR 9–22), respectively. | Table 1: Basic demographic and clinical data of study population (n = 94)
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Inferential statistics: association with outcomes
On the univariate analysis, younger age, tachycardia in the first 24 h, hypotension on arrival, base deficit, deranged INR, low GCS and higher BIG score, and higher ISS and higher NISS scores were associated with mortality [Table 1]. Higher BIG was also significantly associated with mortality (median BIG scores were 13 and 22 in survivors and non-survivors, respectively). On the multivariate regression analysis, only BIG scores were associated with mortality [Table 2].
Our study results showed that 26 (27.7%) patients had pure TBI, whereas 68 (72.3%) patients had polytrauma. We did not find any significant difference in BIG score between the two [Table 3]. Further, the time duration of the incidence between survivors and non-survivors is not significantly different [Table 4].  | Table 4: Association of pre-hospital time with in-hospital trauma mortality
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We also looked for the association of different factors with requirement for neurosurgical intervention, and using the multivariate regression, lower weight and being comatose on presentation were associated with the need for neurosurgical intervention [Supplementary Table 1]. These children also required a prolonged stay in the hospital. | Supplementary Table 1: Univariate logistic regression: Need of surgical intervention (No = 0, Yes = 1)
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Different scores and their association with outcomes
We calculated the cutoff for BIG, ISS, NISS, and PTS associated significantly with mortality by the Youden index [Table 5]. The optimal BIG score cutoff was 19. At a cutoff of 19, 8/84 (10%) children among survivors and 7/10 (70%) children among non-survivors had a BIG score >19 (P = 0.00) [Table 5]. A BIG score of ≥ 19 demonstrated a sensitivity of 0.70 (95% CI: 0.34–0.93) and a specificity of 0.90 (95% CI: 0.82–0.95) to identify mortality. The cutoff values for other scores are given in [Table 6]. | Table 5: Association of mortality (or survival) with different scores’ cutoff calculated by the Youden index
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Sensitivity results and area under the curve
ROC for the BIG score vs. the ISS and PTS with respect to mortality was also generated [Figure 1]. The area under the BIG score ROC curve was 0.802 (sensitivity of 70% and specificity of 90.5%) when compared with an area under the ISS and PTS ROC curves of 0.674, respectively [Table 6]. | Figure 1: Receiver operating characteristics curve mortality vs. score (cut points)
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Sensitivity and specificity to identify mortality were also calculated for the individual components of the BIG score. The three individual components of the BIG score demonstrated sensitivities varying between 0.60 and 0.90, whereas the specificities varied between 0.79 and 0.92 [Table 6].
GCS <7 also had an AUC of 0.850 (0.742–0.950), with sensitivity and specificity of 90% (55.5–99.7%) and 79.8% (69.6–87.7%), respectively [Table 6].
We found a weak correlation of BIG, ISS, NISS, and PTS scores with the length of hospital stay [Table 7].
| Discussion | | |
We studied the performance of a novel BIG score for mortality prediction in children with TBI and compared its performance with other commonly used trauma scores. A total of 94 children with TBI were included, and BIG score >19 had the greatest AUC (0.802 [0.650–0.956]) among all other scores; 96% of all patients with <19 BIG score survived. BIG score was lower in survivors[10] when compared with those who expired.[19] At this cutoff of 19, the BIG score had a specificity of 90% and sensitivity of 70%, whereas ISS had the highest sensitivity that reached 100%. Our patients’ population differed in many aspects from that of previous studies; the on-arrival need for resuscitation was high, although many of them received some form of resuscitation before presentation, majority (two-thirds) of our patients required MV, fluid resuscitation as well as inotropes on arrival in ED. Half of the patients also required surgical intervention. All these show that majority of the patients had severe injury and required multiple interventions in PICU.
The median age of our study population was 8 years,[3],[4],[5],[6],[7],[8],[9],[10],[25],[26],[27] and the major mechanisms of injury were motor vehicle accident (41%) and fall from height (35%), similar to a study by Davis et al.[21],[22]
Our study shows that BIG and all the individual components of BIG (base deficit, INR, and GCS) were significantly associated with mortality along with all other scores (ISS, NISS, and PTS) on the univariate analysis, but only BIG was associated with mortality on the multivariate regression. These findings are similar to that shown by an earlier study by Davies et al.[21] However, the AUC for BIG in this study was 0.95 when compared with AUC for ISS of 0.81. While in our study the AUC for BIG was 0.80 compared with 0.67 for ISS, it is important to note that GCS had the highest AUC of 0.85. This contrasts to another study by Haung et al., in which ISS had the highest AUC (0.97) for mortality in their trauma patients, but GCS also had a good AUC of 0.86. The AUC for GCS from another study by Borgialli et al.[28] for TBI in children was 0.81. BIG was originally developed and validated on patients with blast and penetrating trauma.[16] It has also been validated in children with TBI and has shown to correspond with neurological outcomes at discharge as well as mortality.[22]
The optimal cutoff for BIG in our study was calculated to be 19, where the specificity reached 90.5% (82.1–95.8%). Patients with a BIG score of >19 had a mortality of 70% compared with those with <19 in whom the mortality was 10%. This value is higher than the previous studies which have shown the cutoffs of 16, 16.8, and 12.5.[17],[21],[29] These differences might be because of a different population subset and study setting; our patients primarily had severe TBI and were all admitted to the PICU. Our center is a referral center and many patients receive some form of resuscitation, including blood products, which can affect the BIG score. The cutoffs in the previous studies, in contrast, were obtained using a subset of patients with blunt trauma only. It has previously been argued that BIG score in TBI can be different compared with other forms of injuries because major contribution in BIG score will come from GCS compared with BD and INR, which are more deranged in patients with other types of traumas and hemorrhagic shock.[30]
We calculated other trauma scores and also calculated their AUC, cutoff, sensitivity, and specificity. The scoring system that has almost equal prediction for mortality in our population was ISS. However, ISS calculation is complex. It is more anatomic, requires time and training for its calculation, and the chances of missing some data are higher compared with the BIG score. ISS cutoff for this study calculated by the Youden index was 13.5, which was 100% sensitive (all patients [100%] who expired had ISS >13.5) but it was not specific with a specificity of 51.2% (49% of the patients who survived had ISS >13.5).
We also looked for the prediction of need for neurosurgical intervention. Lower weight for age and comatose patients were more likely to undergo surgery (such as decompressive craniectomy, evacuation of bleed, external ventricular drain placement, etc.), while it was also associated with the need for MV and longer length of stay. None of the scores accurately predicted the need for neurosurgical intervention.
The BIG score can be successfully used not only for clinical use but also for trauma center/trauma care bench marking and trauma trials in pediatrics, especially those involving TBI. Further studies can test its utility to predict the need for PICU admission, need for massive transfusion, and surgical intervention, since all these require alerting the system in a timely manner.
Strengths and limitation
To the best of our knowledge, this is the first study which tested the performance of BIG in children with TBI in an LMIC.
We did not look at the functional and neurological outcomes at discharge or follow-up, as our primary objective was mortality prediction in the PICU. Our sample size is also limited, and our study has the inherent limitations of being a retrospective study which we tried to overcome by data quality checks and through hospitals electronic records.
| Conclusion | | |
BIG and ISS are specific and sensitive scores, respectively, to predict mortality in pediatric TBI. BIG score’s rapid calculation can provide benefit to the emergency department, where appropriately selected patients could be directed to pediatric intensive care for successful execution of treatment. The policymakers should take necessary steps for the implementation of precise patient evaluation and build trauma preventive measure programs to improve service and quality of life.
Financial support and sponsorship
Not applicable.
Conflicts of interest
There are no conflicts of interest.
Authors’ contribution
AZ: contributed to literature search, study design, data collection supervision, writing manuscript.
MTY: contributed to study design and methodology, analysis plan, writing results, critical review, and finalization of manuscript.
MM: contributed to data collection and entry, writing manuscript, and final review.
MJ: contributed to analysis plan and formal analysis, methods and results writing, and final review of the manuscript.
QA: contributed to study idea, design, supervision of data collection and analysis, writing manuscript, and critical revisions.
Ethics statement
The study was approved by the Ethical Review Committee of the Hospital as exemption for consent from the patients. Since it was an observation, retrospective study which did not involve any interaction or interventions with patients, consents from participants were not obtained.
| Supplementary Material | | |
We calculated the pediatric trauma BIG score as follows:
Pediatric BIG score equation = (Admission base deficit) + (2.5 × INR) + (15−GCS)
PTS was calculated by the summation of these variables [Table A].
 | Supplementary Table 2: Association of need of surgical intervention with different scores’ cutoff calculated by Youden index
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 | Supplementary Table 3: Multivariate logistic regression: Need of surgical intervention (No = 0, Yes = 1)
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[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11]
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