Performance of the MOST Score for Severe Traumatic Brain Injury: First External Validation in Resource-Limited Neurosurgical Systems

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Perez-Chadid, Umaima Khan, Muhammad Sohaib Khan, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8312895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Prognostic models can assist neurosurgeons in making early decisions for patients with severe traumatic brain injury (TBI), yet most have been developed in high-resource trauma systems. The MOrtality Score for TBI (MOST) simplifies prediction using readily available clinical variables. Its performance in resource-limited settings remains unknown. Our objective was to externally validate the MOST score in a resource-limited neurosurgical environment and to compare its performance with the CRASH and IMPACT models. Methods: We performed a multicentric retrospective cohort study of adults with severe TBI (Glasgow Coma Scale [GCS] < 9) admitted to two neurosurgical referral centers. Demographics, injury characteristics, incident-to-presentation time (minutes), imaging, and corrected MOST/CRASH/IMPACT scores were collected. Primary outcome was in-hospital mortality; secondary outcomes included Glasgow Outcome Scale (GOS) at discharge. Discrimination was assessed with ROC AUCs and pairwise DeLong tests; calibration with Brier scores. Multivariable logistic regression identified independent predictors of mortality and poor functional outcome (GOS ≤ 3). Results: Among 1,105 patients, overall mortality was 12.1% and 94.8% had GOS ≤ 3 at discharge. Median incident-to-presentation time was 360 minutes (IQR 180–540); rural patients arrived later than urban patients (480 vs 180 minutes, p < 0.0001). In adjusted models, higher MOST scores were associated with increased mortality (OR 1.60, 95% CI 1.37–1.86), while higher GCS was protective (OR 0.59, 95% CI 0.42–0.83); IMPACT showed a modest inverse association (OR 0.87, 95% CI 0.78–0.97). MOST showed the best discrimination for mortality (AUC 0.85) versus CRASH (0.79) and IMPACT (0.82) (all pairwise p ≤ 0.026) and the lowest Brier score (0.187). Conclusions: The MOST maintained robust performance under the system constraints typical of low-resource neurosurgical care, confirming its generalizability beyond high-income trauma networks. These findings highlight the potential role of validated prognostic models in guiding triage and communication in severe TBI. Traumatic Brain Injury Prognosis Mortality Glasgow Coma Scale Neurosurgery Figures Figure 1 Figure 2 Figure 3 Introduction Traumatic Brain Injury (TBI) represents a major global health challenge, with an estimated annual incidence of 346 per 100,000 people and affecting up to 69 million individuals worldwide 1 , 2 . Trauma mortality attributable to TBI is also disproportionately high, as it accounts for almost 40% of trauma-related deaths 3 , 4 . Notably, the incidence and mortality rates of TBI are up to four times higher in low and middle-income countries (LMICs) 5−,7 . In settings where resources are scarce, early and accurate prognostication guides triage, operative decision-making, and communication with families. Determining which patients are most likely to benefit from surgical intervention or intensive care measures requires tools that are both reliable and practical across diverse clinical environments 7 – 9 . Over the past two decades prognostic modelling tools have emerged to aid neurosurgeons in decision making. The Corticosteroid Randomisation After Significant Head Injury (CRASH) model and the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) model are among the most extensively validated tools for severe TBI 10 , 11 . They integrate demographic, clinical, and radiologic variables to estimate mortality and functional outcome. However, both models have shown variable calibration and reduced accuracy when applied outside their original derivation cohorts 8 , 12 . In addition, they rely on multiple predictors including advanced imaging and laboratory data that may not be consistently available in time-critical or resource-constrained environments 10 , 11 . The recently published MOrtality Score for Traumatic Brain Injury (MOST) was developed using data from the American College of Surgeons Trauma Quality Program (ACS-TQP), which includes more than 250 000 patients treated at U.S. Level I and II trauma centers 13 . MOST simplifies outcome prediction by using only five easily obtained clinical variables: age, motor and verbal components of the Glasgow Coma Scale, eye response, and pupillary reactivity. In its derivation study, MOST achieved high discrimination and outperformed both CRASH-Basic and IMPACT-Core for predicting in-hospital mortality 13 . Although the MOST score demonstrated excellent performance in its original development cohort, its generalizability to different clinical environments remains uncertain since it was tested in a high-resource system where timely transport, imaging, and critical care are available. These conditions differ from those in many LMICs, where delayed presentation, limited infrastructure, and constrained intensive care capacity are common 4 , 6 , 14 . Evaluating the accuracy of the MOST score in such contexts is essential to determine its broader applicability. Because it can be calculated entirely from bedside variables without requiring imaging or laboratory data, MOST may provide a practical and scalable approach to early risk stratification in settings with limited resources. Our study performs the first external validation of the MOST score and compares its predictive performance to the CRASH and IMPACT models in forecasting in-hospital TBI mortality to guide outcome prediction and neurosurgical decision-making across diverse care environments. Methods Study Design and Setting We conducted a multicentric retrospective cohort study of adult patients with severe traumatic brain injury (TBI) presenting to two major referral centers in Khyber Pakhtunkhwa (KP) province, Pakistan: Northwest General Hospital (NWGH) and Lady Reading Hospital (LRH). The study protocol was approved by the Institutional Review Board & Ethical Committee of Alliance Healthcare (Pvt) Ltd., Northwest General Hospital (Ref: IRB&EC-2025-GH-0203). These hospitals serve as primary neurosurgical facilities for both urban and rural populations in KP and act as major trauma referral hubs, receiving patients from throughout the province and neighboring regions, including cross-border transfers from Afghanistan. The study period spanned January 2024 to December 2024. This study adheres to the STROBE reporting guideline. Study Population Eligible patients were adults aged 18 years or older with severe TBI, defined as Glasgow Coma Scale (GCS) score < 9 at admission and documented time of injury. Patients were excluded if they had GCS ≥ 9, missing key timing variables (incident time, arrival time, or treatment time), or missing outcome data. Data Collection Data were extracted from electronic medical records (EMRs) and trauma logs at NWGH and LRH using a standardized data-collection form. Variables collected included sociodemographics (age, sex, occupation for socioeconomic classification, and district of residence); incident details (mechanism of injury such as road traffic accident, fall, firearm injury, or assault; incident site; estimated distance from injury location to hospital in kilometers; and incident-to-presentation time in minutes); clinical status at admission (Glasgow Coma Scale [GCS], pupil reactivity, intubation status, vital signs, hemoglobin, and CT findings); treatment timing (time from arrival to CT scan in minutes and time from CT to treatment initiation in minutes); outcomes (in-hospital mortality, discharge GCS, and discharge Glasgow Outcome Scale [GOS]); and prognostic scores (corrected MOST, CRASH, and IMPACT scores). A comparison between scores and included variables is available in Table 1 . Incident districts within Pakistan were categorized as either urban or rural based on the Pakistan Bureau of Statistics (PBS) administrative classification. Table 1 Characteristics of Prognostic Models for Severe Traumatic Brain Injury Characteristic CRASH IMPACT MOST Derivation dataset MRC CRASH trial (n = 10 008; 239 centers, 49 countries) Pooled data from 8 RCTs + 3 observational cohorts (n = 8 509; mainly Europe/North America) ACS Trauma Quality Program registry (n = 259 404; U.S. Level I–II trauma centers) Primary outcome 14-day mortality and 6-month unfavorable outcome (GOS) 6-month mortality and unfavorable outcome (GOS-E) In-hospital mortality (functional outcome tested secondarily) Strengths Large, international dataset; validated long-term outcomes Advanced modeling and broad validation Simple, fast, and feasible for bedside use Limitations Requires imaging and income-group adjustment Data-intensive; less practical for emergencies Derived only in U.S. high-resource systems; needs external validation Resource suitability High and middle income High income Potentially universal; ideal for low-resource systems Variables Required by Common Prognostic Models for Severe Traumatic Brain Injury Category CRASH IMPACT MOST Demographic Age Age Age Clinical (Prehospital / Admission) Glasgow Coma Scale (GCS) total score Motor component of GCS Eye, verbal, and motor components of GCS Pupillary reactivity (none / one / both reactive) Pupillary reactivity (bilateral / unilateral / none) Pupillary reactivity (bilateral / unilateral / none) Major extracranial injury (yes / no) Hypotension (systolic BP < 90 mm Hg) — — Hypoxia (PaO₂ <8 kPa or O₂ saturation 5 mm Traumatic subarachnoid hemorrhage — Non-evacuated mass lesion Compressed/absent basal cisterns — Laboratory / Physiologic — Hemoglobin concentration — — Blood glucose level — Outcomes and Key Predictors The primary outcome was in-hospital mortality. Secondary outcomes included discharge GOS (analyzed both as ordinal and binary ≤ 3 vs. >3) and discharge GCS. Key predictors included mechanism of injury, incident-to-presentation delay (minutes), travel distance to hospital (km), admission GCS, pupil reactivity, intubation status, CT findings, and calculated prognostic scores (MOST, CRASH, IMPACT). Statistical Analysis Sample characteristics were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) for continuous variables, and as counts with percentages for categorical variables. Sample characteristics and outcomes were compared between urban vs. rural areas, patients who survived vs. died (for in-hospital mortality), and between patients with good (GOS > 3) vs. poor (GOS ≤ 3) functional outcomes, using t-tests, Mann–Whitney U tests, or chi-square tests, as appropriate. Boxplots were generated to visualize the distribution of incident-to-presentation times across urban and rural groups. For visualization only, we truncated the top 5% of incident-to-presentation times in the box plot; all statistical analyses used the full, untrimmed dataset. Multivariable logistic regression was used to identify correlates of in-hospital mortality and poor functional outcome (defined as GOS ≤ 3). Missing data were assessed for each variable of interest. Cases with missing primary outcome data (mortality status or discharge GOS) or critical timing variables (incident time, arrival time, or treatment time) were excluded from analysis, as detailed in the Study Population section. For remaining variables, we conducted complete case analysis; missing data were not imputed. While most variables had < 2% missingness, pupil reactivity had substantially higher missingness (~ 43%) and was excluded from logistic regression. A summary of missingness by variable is included in Supplementary Table 1. The discriminative performance of the MOST 13 , CRASH 10 and IMPACT 11 prognostic models was assessed using receiver operating characteristic (ROC) statistics with bootstrapped 95% confidence intervals. Pairwise comparisons of ROCs were conducted using DeLong’s test. Calibration was evaluated with decile-based calibration plots and quantified using Brier scores. All analyses were performed in Python (pandas, numpy, scikit-learn, statsmodels, MLstatkit), with statistical significance defined as p < 0.05. AI disclosure: AI-assisted language tools (ChatGPT) were used for grammar refinement and organizational editing during manuscript preparation. All scientific content, data interpretation, and conclusions were developed, verified, and approved by the authors. Results Patient Cohort Characteristics A total of 1105 patients were included in the cohort (Table 2 ). The median age was 43 years (IQR 31–58), with a mean of 44.8 ± 16.4 years. The majority of patients were male (80.2%). Table 2 Summary of Variables by Area Type Category Total Urban Rural Continuous Variables: Median [IQR] Age (years) 43 [31–58] 44 [32–57] 43 [30–58] Incident-to-presentation time (min) 360 [180–540] 180 [60–360] 480 [300–600] Distance to hospital (km) 70 [7–210] 6 [5–40] 180 [70–240] GCS at admission 6 [5–7] 6 [5–7] 6 [5–7] Arrival-to-CT time (min) 20 [14–26] 19 [14–25] 20 [15–26] CT-to-treatment time (min) 23 [19–27] 22 [18–26] 24 [19–27] Discharge GCS 13 [10–13] 13 [10–13] 13 [10–13] Discharge GOS 3 [2–3] 3 [2–3] 3 [2–3] Categorical Variables: n (%) Sex Male 886 (80.2%) 405 (80.4%) 481 (80.0%) Female 219 (19.8%) 99 (19.6%) 120 (20.0%) Residence type Urban 504 (45.6%) 504 (100.0%) 0 (0.0%) Rural 601 (54.4%) 0 (0.0%) 601 (100.0%) Mechanism of Injury RTA 515 (46.6%) 221 (43.8%) 294 (48.9%) Fall from Height (FOH) 491 (44.4%) 241 (47.8%) 250 (41.6%) Firearm Injury (FAI) 58 (5.2%) 26 (5.2%) 32 (5.3%) Physical Assault 41 (3.7%) 16 (3.2%) 25 (4.2%) Intubation status Yes 254 (23.0%) 121 (24.0%) 133 (22.1%) No 851 (77.0%) 383 (76.0%) 468 (77.9%) CT Findings Traumatic SAH 348 (31.5%) 162 (32.1%) 186 (30.9%) Contusion 334 (30.2%) 150 (29.8%) 184 (30.6%) Extradural Hematoma (EDH) 298 (27.0%) 138 (27.4%) 160 (26.6%) Acute Subdural Hematoma (ASDH) 109 (9.9%) 47 (9.3%) 62 (10.3%) Mortality Survived 972 (88.0%) 441 (87.5%) 531 (88.4%) Died 133 (12.0%) 63 (12.5%) 70 (11.6%) Occupation Retail & Skilled Trades 365 (33.0%) 180 (35.7%) 185 (30.8%) Housewife 151 (13.7%) 66 (13.1%) 85 (14.1%) Service & Labor 133 (12.0%) 58 (11.5%) 75 (12.5%) Unemployed/Retired 86 (7.8%) 34 (6.7%) 52 (8.7%) Education sector 76 (6.9%) 33 (6.5%) 43 (7.2%) Student 74 (6.7%) 31 (6.2%) 43 (7.2%) Professional/Office 70 (6.3%) 41 (8.1%) 29 (4.8%) Security & Police 66 (6.0%) 18 (3.6%) 48 (8.0%) Healthcare worker 50 (4.5%) 29 (5.8%) 21 (3.5%) Unknown/Other 34 (3.1%) 14 (2.8%) 20 (3.3%) Regarding mechanism of injury, road traffic accidents (RTA) accounted for 46.6% of cases, falls from height (HOF) for 44.4%, firearm injuries (FAI) for 5.2%, and physical assault for 3.7%. Intubation was performed for 23.0% of patients. Clinical Presentation and Hospital Course The median incident-to-presentation time was 360 minutes (IQR 180–540), with a mean of 425.3 ± 639.6 minutes, reflecting substantial variability in time to care. The median distance to hospital was 70 km (IQR 7–210), with a mean of 111.2 ± 114.9 km. The median Glasgow Coma Scale (GCS) at admission was 6 [5–7]. Median times for arrival-to-CT and CT-to-treatment were 20 [14–26] and 23 [19–27] minutes, respectively, with mean values of 22.8 ± 27.6 and 27.8 ± 59.4 minutes. Arrival-to-CT and CT-to-treatment times did not differ significantly by mortality or functional outcome. Median discharge GCS was 13 [10–13], and the median discharge Glasgow Outcome Scale (GOS) score was 3 [2–3]. Overall, 94.8% had poor functional outcomes (GOS ≤ 3) at discharge, underscoring the severe nature of injuries in this cohort. Overall, 12.1% of patients died prior to discharge. Geographic Distribution and Access to Care Our two study sites—NWGH and LRH—are major neurosurgical referral centers in Khyber Pakhtunkhwa (KP), Pakistan. They receive severe TBI patients from throughout the province and neighboring regions, including Afghanistan. This broad catchment area contributes to substantial variability in prehospital transport distances and incident-to-presentation times. To explore geographic disparities in access to care, patients’ districts of injury were mapped to urban or rural administrative classifications. Figure 1 maps the distribution of reported incident districts, highlighting the wide geographic catchment area served by these urban referral centers. For visualization only, the box plot of incident-to-presentation time excluded the top 5% of values for visual clarity. There was a difference in median presentation time between urban (median 180.0 minutes [IQR 60.0–360.0]) and rural (480.0 minutes [IQR 300.0–600.0]) areas (p < 0.0001) using a Mann–Whitney U test. Mean times were 235.0 minutes (urban) and 585.0 minutes (rural). Multivariable Logistic Regression Multivariable logistic regression identified several correlates of mortality and poor functional outcome (GOS ≤ 3) (Table 3 ). Table 3 Multivariable Logistic Regression Predicting Mortality and Poor Functional Outcome (GOS ≤ 3) Variable Mortality OR 95% CI p-value Poor Functional Outcome OR 95% CI p-value Intubation = Yes 0.66 0.37–1.19 0.167 1.54 0.72–3.32 0.267 Age (years) 0.99 0.98–1.01 0.382 1.01 1.00–1.03 0.159 Sex = Female 1.38 0.80–2.40 0.249 1.38 0.60–3.17 0.452 Distance (km) 1.00 1.00–1.00 0.867 1.00 1.00–1.00 0.200 Incident-to-Presentation Time 1.00 1.00–1.00 0.128 1.00 1.00–1.00 0.131 GCS at Admission 0.59 0.42–0.83 0.002 1.25 0.93–1.67 0.139 Corrected MOST Score 1.60 1.37–1.86 < 0.001 1.18 1.02–1.36 0.027 Corrected CRASH Score 0.99 0.95–1.03 0.617 0.99 0.95–1.03 0.647 Corrected IMPACT Score 0.87 0.78–0.97 0.010 1.09 0.97–1.22 0.156 For mortality, higher GCS at admission was protective (OR = 0.59, 95% CI 0.42–0.83, p = 0.002). The corrected MOST score was strongly associated with higher mortality (OR = 1.60, 95% CI 1.37–1.86, p < 0.001), while the corrected IMPACT score showed a modest inverse association (OR = 0.87, 95% CI 0.78–0.97, p = 0.010). CRASH, intubation, age, sex, distance, and incident-to-presentation time (per minute) were not significant (all p ≥ 0.12). For poor functional outcome, only the corrected MOST score remained significant (OR = 1.18, 95% CI 1.02–1.36, p = 0.027). Estimates for GCS at admission, intubation, age, sex, distance, incident-to-presentation time, CRASH, and IMPACT had confidence intervals crossing 1. Prognostic Model Performance We evaluated the discriminative ability, calibration, and overall predictive performance of the MOST, CRASH, and IMPACT prognostic scores for in-hospital mortality (Fig. 3 , Table 4 ). Table 4 Predictive Performance of Prognostic Scores for Mortality (GOS ≤ 3) Model AUC (95% CI) Brier Score MOST 0.851 0.1873 CRASH 0.792 0.2262 IMPACT 0.823 0.2418 DeLong Test (pairwise comparisons) : MOST vs. CRASH z = -4.899 p < 0.0001 MOST vs. IMPACT z = -3.320 p = 0.0009 CRASH vs. IMPACT z = -2.230 p = 0.0257 STROBE Statement—Checklist of items that should be included in reports of cohort studies For in-hospital mortality, the MOST score demonstrated the highest discriminative performance with an AUC of 0.85 (95% CI 0.81–0.89), outperforming both CRASH (AUC = 0.79, 95% CI 0.75–0.84) and IMPACT (AUC = 0.82, 95% CI 0.78–0.86). Pairwise DeLong tests confirmed these differences were statistically significant (MOST vs. CRASH, p < 0.0001; MOST vs. IMPACT, p = 0.0009; CRASH vs. IMPACT, p = 0.0257). MOST also achieved the lowest Brier score for mortality prediction (0.1873), indicating better overall probabilistic accuracy compared to CRASH (0.2262) and IMPACT (0.2418). Discussion Severe traumatic brain injury remains one of the leading causes of preventable death and long-term disability worldwide, and its impact is greatest in LMICs, where access to timely neurotrauma care is often limited 1 , 15 . In these settings, neurosurgeons and other health professionals must make rapid decisions about operative intervention and transfer priority, often before imaging or laboratory assessments can be obtained 4 , 7 . Prognostic tools that are both accurate and readily available at the bedside have the potential to support early decision-making and guide the use of scarce critical care resources 9 , 16 . In this study, we provide the first external validation of the MOST score for predicting mortality in severe TBI and the first application of this tool in a LMIC population 13 . MOST showed strong discrimination (AUC 0.85) in predicting in hospital mortality and performed favorably compared with established prognostic models. These findings indicate that a practical, examination based score can retain accuracy outside the high resource trauma systems in which it was developed. The ability to apply a validated tool at the bedside from the moment of hospital arrival is particularly valuable where delays to imaging and neurosurgical intervention remain common 4 , 6 , 7 . External validation is an essential step in determining whether a prognostic model is suitable for clinical use, because good performance in the setting where a model is developed does not guarantee accuracy in other health systems 17 . The experience of CRASH and IMPACT strongly illustrates this point 10 , 11 . Although both models performed well in their derivation studies, their calibration and accuracy frequently declined when applied in different regions. International validation studies have reported they underestimated mortality and unfavorable outcomes despite acceptable discrimination (AUC generally above 0.80), while in urban United States settings, there were persistent calibration concerns 5 , 8 , 18 – 22 . These discrepancies reflect differences in prehospital delays, availability of imaging, and critical care capacity across trauma systems 4 , 6 . External validation in LMICs is particularly important since most TBI occurs in these settings, while prognostic tools are generally developed based on high-income populations. Demonstrating that MOST performs reliably in a resource-constrained environment, therefore supports the equitable extension of evidence-based neurosurgical care to the populations that bear the greatest burden of TBI. Our study also documented significant geographic barriers to neurotrauma care. The median incident-to-presentation time exceeded 360 minutes, and rural patients experienced delays of approximately 480 minutes, nearly 300 minutes longer than those from urban areas. These delays were far greater than those reported in other LMIC studies 4 , 7 , 23 . The prolonged transport times observed in this cohort reflect the wide geographic catchment of the two tertiary referral hospitals and limited prehospital coordination in Khyber Pakhtunkhwa. In hospital mortality was 12.1 percent and almost 95 percent of patients had poor functional status at discharge, which reflects the severity of injury, high rate of delayed presentation, and limited access to rehabilitation resources. Most patients were working age adults, which highlights the substantial societal and economic burden of TBI. Predictors of mortality included higher corrected MOST score, while intubation, age, sex, distance, and per minute delay were not independently significant in adjusted models. MOST was also the only independent predictor of poor functional outcomes. Limitations As a retrospective study based on hospital records, our data are subject to missingness and misclassification bias. Although most variables used had low missingness, pupil reactivity, an important clinical indicator, was missing in over 40% of cases and was excluded from logistic regression analysis. For all other variables, we used complete-case analysis without imputation. While this may introduce bias if data were not missing at random, the proportion of missingness was low across most fields used in multivariable modeling. Referral bias is also inherent in our setting: both study hospitals are major neurosurgical centers receiving the most severe cases from distant districts, likely inflating observed delays and mortality relative to less-severe injuries managed locally. Conclusions This study provides the first external validation of the MOST score for predicting mortality in severe traumatic brain injury in a low resource setting. MOST showed strong discrimination and outperformed CRASH and IMPACT while requiring only bedside neurologic variables. These findings support the utility of MOST as a practical tool for early risk stratification where rapid imaging and neurosurgical resources may be limited. Marked delays in presentation, particularly among rural patients, and the very high rate of poor functional status at discharge highlight the ongoing challenges to timely and effective neurotrauma care. Addressing these disparities and improving prehospital and referral systems remain essential priorities for reducing the burden of TBI in similar settings. Future work should include longer term outcome assessment and prospective evaluation of whether implementation of MOST improves clinical decision making and patient outcomes. Validated, simple tools such as MOST can help advance equitable access to evidence based neurotrauma care worldwide. Declarations 1. Compliance With Instructions to Authors We confirm that this manuscript complies fully with all Instructions for Authors for Neurocritical Care , including formatting, ethical requirements, reference style, figure and table specifications, and submission of the appropriate reporting guideline (STROBE). 2. Author Contributions (ICMJE-Compliant) Aafreen Azmi, BS – Study design, data analysis, statistical modeling, manuscript drafting, figure preparation, revisions, final approval. Daniela A. Perez-Chadid, MD – Study supervision, data interpretation, critical revisions, final approval. Umaima Khan – Data curation, quality control, manuscript review, final approval. Muhammad Sohaib Khan, MBBS – Data acquisition (LRH), patient identification, accuracy verification, manuscript review, final approval. Adnan Khan, MBBS – Data acquisition (LRH), medical record validation, manuscript review, final approval. Syed Shayan Shah, MBBS – Data acquisition (LRH), trauma log review, manuscript review, final approval. Almas Khattak, MBBS, MPH – Data acquisition (NWGH), data cleaning, manuscript review, final approval. Syeda Shamal, BDS, MHPE – Hospital coordination, clinical variable validation, manuscript review, final approval. Waseem Daad Khan, MBBS – Data curation, data accuracy checks, manuscript review, final approval. Rose Calixte, PhD, PStat® – Statistical consultation, model performance review, manuscript revisions, final approval. Ernest J. Barthélemy, MD, MPH, MA – Conceptual guidance, interpretation, manuscript revisions, final approval. Anil Nanda, MD, MPH – Study oversight, conceptual development, manuscript revisions, final approval. Tariq Khan, MBBS – Site coordination, data verification, manuscript review, final approval. Muhammad Nawaz Khan, MBBS – Senior author, study conception, data oversight, manuscript editing, final approval. All authors meet all four ICMJE criteria for authorship. 3. Confirmation of Authorship Requirements All listed authors meet ICMJE authorship criteria, have approved the final submitted version, and agree to be accountable for all aspects of the work. 4. Originality and Previous Publication This manuscript is original, has not been published previously, and is not under consideration by any other journal. 5. Ethical Approval and Human Subjects Protection The study was approved by the Institutional Review Board & Ethical Committee of Alliance Healthcare (Pvt) Ltd., Northwest General Hospital (Ref: IRB&EC-2025-GH-0203). This was a retrospective study using de-identified data; informed consent requirements were waived by the ethics board. 6. Conflicts of Interest All authors declare no conflicts of interest relevant to this work. 7. Reporting Guideline Statement This study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guideline. The completed STROBE checklist is included at the end of the manuscript and uploaded as a separate file. 8. Funding Statement No external funding was received for this study. The authors conducted all work independently without financial support from industry, governmental bodies, or private organizations. 9. AI-Use Disclosure AI-assisted language tools (ChatGPT) were used for grammar refinement and organizational editing. AI was not used for data analysis, statistical modeling, scientific interpretation, figure generation, or literature synthesis. All content was reviewed, verified, and approved by the authors, and no AI-generated text or images were used without human editing. References Dewan MC, Rattani A, Gupta S, et al. Estimating the global incidence of traumatic brain injury. 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Neurocrit Care. 2024;41(3):730–8. 10.1007/s12028-024-02040-z . GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(1):56–87. 10.1016/S1474-4422(18)30415-0 . Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inf Decis Mak. 2006;6:38. 10.1186/1472-6947-6-38 . Steckler A, McLeroy KR. The importance of external validity. Am J Public Health. 2008;98(1):9–10. 10.2105/AJPH.2007.126847 . Mulla A, Bavishi D, Khajanchi M, Gerdin Wärnberg M. External validation of CRASH prognostic model in an urban tertiary care public university hospital. J Surg Res. 2025;309:224–32. 10.1016/j.jss.2025.03.040 . Zarei H, Vazirizadeh-Mahabadi M, Adel Ramawad H, Sarveazad A, Yousefifard M. Prognostic value of CRASH and IMPACT models for predicting mortality and unfavorable outcome in traumatic brain injury: a systematic review and meta-analysis. Arch Acad Emerg Med. 2023;11(1):e27. 10.22037/aaem.v11i1.1885 . Han J, King NKK, Neilson SJ, Gandhi MP, Ng I. External validation of the CRASH and IMPACT prognostic models in severe traumatic brain injury. J Neurotrauma. 2014;31(13):1146–52. 10.1089/neu.2013.3003 . Castaño-Leon AM, Lora D, Munarriz PM, et al. Predicting outcomes after severe and moderate traumatic brain injury: an external validation of IMPACT and CRASH prognostic models in a large Spanish cohort. J Neurotrauma. 2016;33(17):1598–606. 10.1089/neu.2015.4182 . Maeda Y, Ichikawa R, Misawa J, et al. External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan. PLoS ONE. 2019;14(8):e0221791. 10.1371/journal.pone.0221791 . Rivera-Lara L, Videtta W, Calvillo E, et al. Reducing the incidence and mortality of traumatic brain injury in Latin America. Eur J Trauma Emerg Surg. 2023;49(6):2381–8. 10.1007/s00068-022-02214-4 . Supplementary Files NCCSTROBE.docx Supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8312895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573575900,"identity":"9d233e50-08eb-427e-be32-26cd3dac9d16","order_by":0,"name":"Aafreen Azmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACxgcwRiOIxcNHWAuzAUxLM4jFw0aCFgY2CTBJSAN/+2G2Dx8q7sjLTzvcVvk1x06GjYH54aMbeLRInElmnjnjzDPDxtmJbbdltyUDHcZmbJyDz5oD+YeZedsOMzZLA7VIbmMGauFhk8anRf78Y2bmv/8O27cBtRRLbqsnrMXgRjIzM2PD4cQeoBbGj9sOE9ZieOMxM2PPscPJM6QTm6UZtx3nYWMm4Be588nMDD9qDtvOn53+8OPPbdX2/OzNDx/j9T4yYOYBk8QqBwHGH6SoHgWjYBSMghEDACKxRjl0jndYAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-4248-2122","institution":"Rutgers Robert Wood Johnson Medical School","correspondingAuthor":true,"prefix":"","firstName":"Aafreen","middleName":"","lastName":"Azmi","suffix":""},{"id":573575901,"identity":"78fb3651-11fc-4347-821a-e00399adf04e","order_by":1,"name":"Daniela A. 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13:03:17","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144165,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/fd2cf8487d11931caa0f28a8.html"},{"id":100407205,"identity":"4a73ffb8-5dbb-4051-856a-bd390cfe0b2f","added_by":"auto","created_at":"2026-01-16 13:04:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62576,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial distribution of traumatic brain injury (TBI) cases across districts in Khyber Pakhtunkhwa province, Pakistan, with case counts visualized by intensity on the map (left). The accompanying table (right) lists reported incident sites and corresponding case counts. Darker shading on the map indicates higher numbers of TBI cases. Abbreviations: ADM2, administrative level 2.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/834545c02624d5e3a1ad3378.png"},{"id":100406588,"identity":"8ec3a033-f703-4e59-85c6-906b13ad3d12","added_by":"auto","created_at":"2026-01-16 13:03:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15650,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of incident-to-presentation times among patients with traumatic brain injury, stratified by area type (rural vs urban). Boxes represent interquartile ranges, with horizontal lines denoting medians and whiskers indicating range. Patients from rural areas demonstrated longer presentation delays compared with those from urban areas.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/34c0c67bcb5d108c1f344be3.png"},{"id":100407067,"identity":"5a33c039-221e-4e19-a4cd-990200d586ae","added_by":"auto","created_at":"2026-01-16 13:03:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28826,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves comparing the prognostic performance of the MOST, CRASH, and IMPACT models for in-hospital mortality. The MOST model demonstrated the highest discriminative ability (AUC = 0.85, 95% CI 0.81–0.89), followed by the IMPACT model (AUC = 0.82, 95% CI 0.78–0.86) and the CRASH model (AUC = 0.79, 95% CI 0.75–0.84).\u003c/p\u003e","description":"","filename":"OnlineFIgure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/05d39767c770c298a9f88aea.png"},{"id":101752437,"identity":"c155fc04-b03c-4e3c-b61f-75e9f31ab945","added_by":"auto","created_at":"2026-02-03 10:27:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/039246c6-cde9-469b-bd64-622c69834f0d.pdf"},{"id":100406435,"identity":"d209e2f9-d110-4e27-bb2d-e02935ab2bc6","added_by":"auto","created_at":"2026-01-16 13:01:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21447,"visible":true,"origin":"","legend":"","description":"","filename":"NCCSTROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/19aeb8fcbef03f8fb7d3f25d.docx"},{"id":100407209,"identity":"e1b3e43b-28fb-4e6b-bdb9-e907ca8941c9","added_by":"auto","created_at":"2026-01-16 13:04:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16086,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8312895/v1/540c19da4701ab20eb338271.docx"}],"financialInterests":"","formattedTitle":"Performance of the MOST Score for Severe Traumatic Brain Injury: First External Validation in Resource-Limited Neurosurgical Systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic Brain Injury (TBI) represents a major global health challenge, with an estimated annual incidence of 346 per 100,000 people and affecting up to 69\u0026nbsp;million individuals worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Trauma mortality attributable to TBI is also disproportionately high, as it accounts for almost 40% of trauma-related deaths\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Notably, the incidence and mortality rates of TBI are up to four times higher in low and middle-income countries (LMICs)\u003csup\u003e5\u0026minus;,7\u003c/sup\u003e. In settings where resources are scarce, early and accurate prognostication guides triage, operative decision-making, and communication with families. Determining which patients are most likely to benefit from surgical intervention or intensive care measures requires tools that are both reliable and practical across diverse clinical environments\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the past two decades prognostic modelling tools have emerged to aid neurosurgeons in decision making. The Corticosteroid Randomisation After Significant Head Injury (CRASH) model and the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) model are among the most extensively validated tools for severe TBI\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. They integrate demographic, clinical, and radiologic variables to estimate mortality and functional outcome. However, both models have shown variable calibration and reduced accuracy when applied outside their original derivation cohorts\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In addition, they rely on multiple predictors including advanced imaging and laboratory data that may not be consistently available in time-critical or resource-constrained environments\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe recently published MOrtality Score for Traumatic Brain Injury (MOST) was developed using data from the American College of Surgeons Trauma Quality Program (ACS-TQP), which includes more than 250 000 patients treated at U.S. Level I and II trauma centers\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. MOST simplifies outcome prediction by using only five easily obtained clinical variables: age, motor and verbal components of the Glasgow Coma Scale, eye response, and pupillary reactivity. In its derivation study, MOST achieved high discrimination and outperformed both CRASH-Basic and IMPACT-Core for predicting in-hospital mortality\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e Although the MOST score demonstrated excellent performance in its original development cohort, its generalizability to different clinical environments remains uncertain since it was tested in a high-resource system where timely transport, imaging, and critical care are available. These conditions differ from those in many LMICs, where delayed presentation, limited infrastructure, and constrained intensive care capacity are common\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Evaluating the accuracy of the MOST score in such contexts is essential to determine its broader applicability. Because it can be calculated entirely from bedside variables without requiring imaging or laboratory data, MOST may provide a practical and scalable approach to early risk stratification in settings with limited resources. Our study performs the first external validation of the MOST score and compares its predictive performance to the CRASH and IMPACT models in forecasting in-hospital TBI mortality to guide outcome prediction and neurosurgical decision-making across diverse care environments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eWe conducted a multicentric retrospective cohort study of adult patients with severe traumatic brain injury (TBI) presenting to two major referral centers in Khyber Pakhtunkhwa (KP) province, Pakistan: Northwest General Hospital (NWGH) and Lady Reading Hospital (LRH). The study protocol was approved by the Institutional Review Board \u0026amp; Ethical Committee of Alliance Healthcare (Pvt) Ltd., Northwest General Hospital (Ref: IRB\u0026amp;EC-2025-GH-0203). These hospitals serve as primary neurosurgical facilities for both urban and rural populations in KP and act as major trauma referral hubs, receiving patients from throughout the province and neighboring regions, including cross-border transfers from Afghanistan. The study period spanned January 2024 to December 2024. This study adheres to the STROBE reporting guideline.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eEligible patients were adults aged 18 years or older with severe TBI, defined as Glasgow Coma Scale (GCS) score\u0026thinsp;\u0026lt;\u0026thinsp;9 at admission and documented time of injury. Patients were excluded if they had GCS\u0026thinsp;\u0026ge;\u0026thinsp;9, missing key timing variables (incident time, arrival time, or treatment time), or missing outcome data.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were extracted from electronic medical records (EMRs) and trauma logs at NWGH and LRH using a standardized data-collection form. Variables collected included sociodemographics (age, sex, occupation for socioeconomic classification, and district of residence); incident details (mechanism of injury such as road traffic accident, fall, firearm injury, or assault; incident site; estimated distance from injury location to hospital in kilometers; and incident-to-presentation time in minutes); clinical status at admission (Glasgow Coma Scale [GCS], pupil reactivity, intubation status, vital signs, hemoglobin, and CT findings); treatment timing (time from arrival to CT scan in minutes and time from CT to treatment initiation in minutes); outcomes (in-hospital mortality, discharge GCS, and discharge Glasgow Outcome Scale [GOS]); and prognostic scores (corrected MOST, CRASH, and IMPACT scores). A comparison between scores and included variables is available in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Incident districts within Pakistan were categorized as either urban or rural based on the Pakistan Bureau of Statistics (PBS) administrative classification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Prognostic Models for Severe Traumatic Brain Injury\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRASH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIMPACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMOST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDerivation dataset\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRC CRASH trial (n\u0026thinsp;=\u0026thinsp;10 008; 239 centers, 49 countries)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled data from 8 RCTs\u0026thinsp;+\u0026thinsp;3 observational cohorts (n\u0026thinsp;=\u0026thinsp;8 509; mainly Europe/North America)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACS Trauma Quality Program registry (n\u0026thinsp;=\u0026thinsp;259 404; U.S. Level I\u0026ndash;II trauma centers)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14-day mortality and 6-month unfavorable outcome (GOS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-month mortality and unfavorable outcome (GOS-E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIn-hospital mortality (functional outcome tested secondarily)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStrengths\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge, international dataset; validated long-term outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdvanced modeling and broad validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple, fast, and feasible for bedside use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRequires imaging and income-group adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData-intensive; less practical for emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived only in U.S. high-resource systems; needs external validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResource suitability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh and middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentially universal; ideal for low-resource systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables Required by Common Prognostic Models for Severe Traumatic Brain Injury\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCRASH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIMPACT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMOST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical (Prehospital / Admission)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlasgow Coma Scale (GCS) total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMotor component of GCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEye, verbal, and motor components of GCS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePupillary reactivity (none / one / both reactive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePupillary reactivity (bilateral / unilateral / none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePupillary reactivity (bilateral / unilateral / none)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMajor extracranial injury (yes / no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypotension (systolic BP\u0026thinsp;\u0026lt;\u0026thinsp;90 mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia (PaO₂ \u0026lt;8 kPa or O₂ saturation\u0026thinsp;\u0026lt;\u0026thinsp;90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiologic (CT findings)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence of petechial hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresence of traumatic SAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObliteration of third ventricle or basal cisterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEpidural hematoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidline shift\u0026thinsp;\u0026gt;\u0026thinsp;5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraumatic subarachnoid hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-evacuated mass lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompressed/absent basal cisterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory / Physiologic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemoglobin concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood glucose level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eOutcomes and Key Predictors\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was in-hospital mortality. Secondary outcomes included discharge GOS (analyzed both as ordinal and binary\u0026thinsp;\u0026le;\u0026thinsp;3 vs. \u0026gt;3) and discharge GCS.\u003c/p\u003e \u003cp\u003eKey predictors included mechanism of injury, incident-to-presentation delay (minutes), travel distance to hospital (km), admission GCS, pupil reactivity, intubation status, CT findings, and calculated prognostic scores (MOST, CRASH, IMPACT).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSample characteristics were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) for continuous variables, and as counts with percentages for categorical variables. Sample characteristics and outcomes were compared between urban vs. rural areas, patients who survived vs. died (for in-hospital mortality), and between patients with good (GOS\u0026thinsp;\u0026gt;\u0026thinsp;3) vs. poor (GOS\u0026thinsp;\u0026le;\u0026thinsp;3) functional outcomes, using t-tests, Mann\u0026ndash;Whitney U tests, or chi-square tests, as appropriate. Boxplots were generated to visualize the distribution of incident-to-presentation times across urban and rural groups. For visualization only, we truncated the top 5% of incident-to-presentation times in the box plot; all statistical analyses used the full, untrimmed dataset. Multivariable logistic regression was used to identify correlates of in-hospital mortality and poor functional outcome (defined as GOS\u0026thinsp;\u0026le;\u0026thinsp;3).\u003c/p\u003e \u003cp\u003eMissing data were assessed for each variable of interest. Cases with missing primary outcome data (mortality status or discharge GOS) or critical timing variables (incident time, arrival time, or treatment time) were excluded from analysis, as detailed in the Study Population section. For remaining variables, we conducted complete case analysis; missing data were not imputed. While most variables had\u0026thinsp;\u0026lt;\u0026thinsp;2% missingness, pupil reactivity had substantially higher missingness (~\u0026thinsp;43%) and was excluded from logistic regression. A summary of missingness by variable is included in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe discriminative performance of the MOST\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, CRASH\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and IMPACT\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e prognostic models was assessed using receiver operating characteristic (ROC) statistics with bootstrapped 95% confidence intervals. Pairwise comparisons of ROCs were conducted using DeLong\u0026rsquo;s test. Calibration was evaluated with decile-based calibration plots and quantified using Brier scores.\u003c/p\u003e \u003cp\u003eAll analyses were performed in Python (pandas, numpy, scikit-learn, statsmodels, MLstatkit), with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAI disclosure: AI-assisted language tools (ChatGPT) were used for grammar refinement and organizational editing during manuscript preparation. All scientific content, data interpretation, and conclusions were developed, verified, and approved by the authors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Cohort Characteristics\u003c/h2\u003e \u003cp\u003eA total of 1105 patients were included in the cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median age was 43 years (IQR 31\u0026ndash;58), with a mean of 44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4 years. The majority of patients were male (80.2%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Variables by Area Type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eContinuous Variables: Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 [31\u0026ndash;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 [32\u0026ndash;57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 [30\u0026ndash;58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncident-to-presentation time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 [180\u0026ndash;540]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 [60\u0026ndash;360]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e480 [300\u0026ndash;600]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to hospital (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 [7\u0026ndash;210]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [5\u0026ndash;40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 [70\u0026ndash;240]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS at admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [5\u0026ndash;7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [5\u0026ndash;7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 [5\u0026ndash;7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArrival-to-CT time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 [14\u0026ndash;26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 [14\u0026ndash;25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 [15\u0026ndash;26]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-to-treatment time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 [19\u0026ndash;27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 [18\u0026ndash;26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 [19\u0026ndash;27]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarge GCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 [10\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 [10\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 [10\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarge GOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [2\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [2\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategorical Variables: n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e886 (80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e481 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e601 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanism of Injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e294 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall from Height (FOH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e491 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirearm Injury (FAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Assault\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntubation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e851 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383 (76.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraumatic SAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtradural Hematoma (EDH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Subdural Hematoma (ASDH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvived\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e972 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e531 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetail \u0026amp; Skilled Trades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService \u0026amp; Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/Office\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity \u0026amp; Police\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding mechanism of injury, road traffic accidents (RTA) accounted for 46.6% of cases, falls from height (HOF) for 44.4%, firearm injuries (FAI) for 5.2%, and physical assault for 3.7%. Intubation was performed for 23.0% of patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Presentation and Hospital Course\u003c/h3\u003e\n\u003cp\u003eThe median incident-to-presentation time was 360 minutes (IQR 180\u0026ndash;540), with a mean of 425.3\u0026thinsp;\u0026plusmn;\u0026thinsp;639.6 minutes, reflecting substantial variability in time to care. The median distance to hospital was 70 km (IQR 7\u0026ndash;210), with a mean of 111.2\u0026thinsp;\u0026plusmn;\u0026thinsp;114.9 km. The median Glasgow Coma Scale (GCS) at admission was 6 [5\u0026ndash;7].\u003c/p\u003e \u003cp\u003eMedian times for arrival-to-CT and CT-to-treatment were 20 [14\u0026ndash;26] and 23 [19\u0026ndash;27] minutes, respectively, with mean values of 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6 and 27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;59.4 minutes. Arrival-to-CT and CT-to-treatment times did not differ significantly by mortality or functional outcome.\u003c/p\u003e \u003cp\u003eMedian discharge GCS was 13 [10\u0026ndash;13], and the median discharge Glasgow Outcome Scale (GOS) score was 3 [2\u0026ndash;3]. Overall, 94.8% had poor functional outcomes (GOS\u0026thinsp;\u0026le;\u0026thinsp;3) at discharge, underscoring the severe nature of injuries in this cohort.\u003c/p\u003e \u003cp\u003eOverall, 12.1% of patients died prior to discharge.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographic Distribution and Access to Care\u003c/h2\u003e \u003cp\u003eOur two study sites\u0026mdash;NWGH and LRH\u0026mdash;are major neurosurgical referral centers in Khyber Pakhtunkhwa (KP), Pakistan. They receive severe TBI patients from throughout the province and neighboring regions, including Afghanistan. This broad catchment area contributes to substantial variability in prehospital transport distances and incident-to-presentation times.\u003c/p\u003e \u003cp\u003eTo explore geographic disparities in access to care, patients\u0026rsquo; districts of injury were mapped to urban or rural administrative classifications. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e maps the distribution of reported incident districts, highlighting the wide geographic catchment area served by these urban referral centers. For visualization only, the box plot of incident-to-presentation time excluded the top 5% of values for visual clarity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was a difference in median presentation time between urban (median 180.0 minutes [IQR 60.0\u0026ndash;360.0]) and rural (480.0 minutes [IQR 300.0\u0026ndash;600.0]) areas (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) using a Mann\u0026ndash;Whitney U test. Mean times were 235.0 minutes (urban) and 585.0 minutes (rural).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Logistic Regression\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression identified several correlates of mortality and poor functional outcome (GOS\u0026thinsp;\u0026le;\u0026thinsp;3) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Predicting Mortality and Poor Functional Outcome (GOS\u0026thinsp;\u0026le;\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMortality OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePoor Functional Outcome OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntubation\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u0026ndash;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u0026ndash;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncident-to-Presentation Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS at Admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected MOST Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected CRASH Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected IMPACT Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u0026ndash;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor mortality, higher GCS at admission was protective (OR\u0026thinsp;=\u0026thinsp;0.59, 95% CI 0.42\u0026ndash;0.83, p\u0026thinsp;=\u0026thinsp;0.002). The corrected MOST score was strongly associated with higher mortality (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI 1.37\u0026ndash;1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the corrected IMPACT score showed a modest inverse association (OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI 0.78\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.010). CRASH, intubation, age, sex, distance, and incident-to-presentation time (per minute) were not significant (all p\u0026thinsp;\u0026ge;\u0026thinsp;0.12).\u003c/p\u003e \u003cp\u003eFor poor functional outcome, only the corrected MOST score remained significant (OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI 1.02\u0026ndash;1.36, p\u0026thinsp;=\u0026thinsp;0.027). Estimates for GCS at admission, intubation, age, sex, distance, incident-to-presentation time, CRASH, and IMPACT had confidence intervals crossing 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Model Performance\u003c/h2\u003e \u003cp\u003eWe evaluated the discriminative ability, calibration, and overall predictive performance of the MOST, CRASH, and IMPACT prognostic scores for in-hospital mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive Performance of Prognostic Scores for Mortality (GOS\u0026thinsp;\u0026le;\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrier Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMPACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeLong Test (pairwise comparisons)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOST vs. CRASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ez = -4.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOST vs. IMPACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ez = -3.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRASH vs. IMPACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ez = -2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.0257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSTROBE Statement\u0026mdash;Checklist of items that should be included in reports of \u003cb\u003ecohort studies\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor in-hospital mortality, the MOST score demonstrated the highest discriminative performance with an AUC of 0.85 (95% CI 0.81\u0026ndash;0.89), outperforming both CRASH (AUC\u0026thinsp;=\u0026thinsp;0.79, 95% CI 0.75\u0026ndash;0.84) and IMPACT (AUC\u0026thinsp;=\u0026thinsp;0.82, 95% CI 0.78\u0026ndash;0.86). Pairwise DeLong tests confirmed these differences were statistically significant (MOST vs. CRASH, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; MOST vs. IMPACT, p\u0026thinsp;=\u0026thinsp;0.0009; CRASH vs. IMPACT, p\u0026thinsp;=\u0026thinsp;0.0257). MOST also achieved the lowest Brier score for mortality prediction (0.1873), indicating better overall probabilistic accuracy compared to CRASH (0.2262) and IMPACT (0.2418).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSevere traumatic brain injury remains one of the leading causes of preventable death and long-term disability worldwide, and its impact is greatest in LMICs, where access to timely neurotrauma care is often limited\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In these settings, neurosurgeons and other health professionals must make rapid decisions about operative intervention and transfer priority, often before imaging or laboratory assessments can be obtained\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Prognostic tools that are both accurate and readily available at the bedside have the potential to support early decision-making and guide the use of scarce critical care resources\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we provide the first external validation of the MOST score for predicting mortality in severe TBI and the first application of this tool in a LMIC population\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. MOST showed strong discrimination (AUC 0.85) in predicting in hospital mortality and performed favorably compared with established prognostic models. These findings indicate that a practical, examination based score can retain accuracy outside the high resource trauma systems in which it was developed. The ability to apply a validated tool at the bedside from the moment of hospital arrival is particularly valuable where delays to imaging and neurosurgical intervention remain common\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExternal validation is an essential step in determining whether a prognostic model is suitable for clinical use, because good performance in the setting where a model is developed does not guarantee accuracy in other health systems\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The experience of CRASH and IMPACT strongly illustrates this point\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although both models performed well in their derivation studies, their calibration and accuracy frequently declined when applied in different regions. International validation studies have reported they underestimated mortality and unfavorable outcomes despite acceptable discrimination (AUC generally above 0.80), while in urban United States settings, there were persistent calibration concerns\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These discrepancies reflect differences in prehospital delays, availability of imaging, and critical care capacity across trauma systems\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. External validation in LMICs is particularly important since most TBI occurs in these settings, while prognostic tools are generally developed based on high-income populations. Demonstrating that MOST performs reliably in a resource-constrained environment, therefore supports the equitable extension of evidence-based neurosurgical care to the populations that bear the greatest burden of TBI.\u003c/p\u003e \u003cp\u003eOur study also documented significant geographic barriers to neurotrauma care. The median incident-to-presentation time exceeded 360 minutes, and rural patients experienced delays of approximately 480 minutes, nearly 300 minutes longer than those from urban areas. These delays were far greater than those reported in other LMIC studies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The prolonged transport times observed in this cohort reflect the wide geographic catchment of the two tertiary referral hospitals and limited prehospital coordination in Khyber Pakhtunkhwa.\u003c/p\u003e \u003cp\u003eIn hospital mortality was 12.1 percent and almost 95 percent of patients had poor functional status at discharge, which reflects the severity of injury, high rate of delayed presentation, and limited access to rehabilitation resources. Most patients were working age adults, which highlights the substantial societal and economic burden of TBI.\u003c/p\u003e \u003cp\u003ePredictors of mortality included higher corrected MOST score, while intubation, age, sex, distance, and per minute delay were not independently significant in adjusted models. MOST was also the only independent predictor of poor functional outcomes.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAs a retrospective study based on hospital records, our data are subject to missingness and misclassification bias. Although most variables used had low missingness, pupil reactivity, an important clinical indicator, was missing in over 40% of cases and was excluded from logistic regression analysis.\u003c/p\u003e \u003cp\u003eFor all other variables, we used complete-case analysis without imputation. While this may introduce bias if data were not missing at random, the proportion of missingness was low across most fields used in multivariable modeling.\u003c/p\u003e \u003cp\u003e Referral bias is also inherent in our setting: both study hospitals are major neurosurgical centers receiving the most severe cases from distant districts, likely inflating observed delays and mortality relative to less-severe injuries managed locally.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first external validation of the MOST score for predicting mortality in severe traumatic brain injury in a low resource setting. MOST showed strong discrimination and outperformed CRASH and IMPACT while requiring only bedside neurologic variables. These findings support the utility of MOST as a practical tool for early risk stratification where rapid imaging and neurosurgical resources may be limited.\u003c/p\u003e \u003cp\u003eMarked delays in presentation, particularly among rural patients, and the very high rate of poor functional status at discharge highlight the ongoing challenges to timely and effective neurotrauma care. Addressing these disparities and improving prehospital and referral systems remain essential priorities for reducing the burden of TBI in similar settings.\u003c/p\u003e \u003cp\u003eFuture work should include longer term outcome assessment and prospective evaluation of whether implementation of MOST improves clinical decision making and patient outcomes. Validated, simple tools such as MOST can help advance equitable access to evidence based neurotrauma care worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Compliance With Instructions to Authors\u003c/strong\u003e\u003cbr\u003eWe confirm that this manuscript complies fully with all Instructions for Authors for \u003cem\u003eNeurocritical Care\u003c/em\u003e, including formatting, ethical requirements, reference style, figure and table specifications, and submission of the appropriate reporting guideline (STROBE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Author Contributions (ICMJE-Compliant)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAafreen Azmi, BS\u003c/strong\u003e \u0026ndash; Study design, data analysis, statistical modeling, manuscript drafting, figure preparation, revisions, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDaniela A. Perez-Chadid, MD\u003c/strong\u003e \u0026ndash; Study supervision, data interpretation, critical revisions, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUmaima Khan\u003c/strong\u003e \u0026ndash; Data curation, quality control, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMuhammad Sohaib Khan, MBBS\u003c/strong\u003e \u0026ndash; Data acquisition (LRH), patient identification, accuracy verification, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAdnan Khan, MBBS\u003c/strong\u003e \u0026ndash; Data acquisition (LRH), medical record validation, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSyed Shayan Shah, MBBS\u003c/strong\u003e \u0026ndash; Data acquisition (LRH), trauma log review, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAlmas Khattak, MBBS, MPH\u003c/strong\u003e \u0026ndash; Data acquisition (NWGH), data cleaning, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSyeda Shamal, BDS, MHPE\u003c/strong\u003e \u0026ndash; Hospital coordination, clinical variable validation, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWaseem Daad Khan, MBBS\u003c/strong\u003e \u0026ndash; Data curation, data accuracy checks, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRose Calixte, PhD, PStat\u0026reg;\u003c/strong\u003e \u0026ndash; Statistical consultation, model performance review, manuscript revisions, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eErnest J. Barth\u0026eacute;lemy, MD, MPH, MA\u003c/strong\u003e \u0026ndash; Conceptual guidance, interpretation, manuscript revisions, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnil Nanda, MD, MPH\u003c/strong\u003e \u0026ndash; Study oversight, conceptual development, manuscript revisions, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTariq Khan, MBBS\u003c/strong\u003e \u0026ndash; Site coordination, data verification, manuscript review, final approval.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMuhammad Nawaz Khan, MBBS\u003c/strong\u003e \u0026ndash; Senior author, study conception, data oversight, manuscript editing, final approval.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors meet \u003cstrong\u003eall four\u003c/strong\u003e ICMJE criteria for authorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Confirmation of Authorship Requirements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All listed authors meet ICMJE authorship criteria, have approved the final submitted version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Originality and Previous Publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This manuscript is original, has not been published previously, and is not under consideration by any other journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Ethical Approval and Human Subjects Protection\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study was approved by the Institutional Review Board \u0026amp; Ethical Committee of Alliance Healthcare (Pvt) Ltd., Northwest General Hospital (Ref: IRB\u0026amp;EC-2025-GH-0203). This was a retrospective study using de-identified data; informed consent requirements were waived by the ethics board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Conflicts of Interest\u003c/strong\u003e\u003cbr\u003eAll authors declare \u003cstrong\u003eno conflicts of interest\u003c/strong\u003e relevant to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Reporting Guideline Statement\u003c/strong\u003e\u003cbr\u003eThis study adheres to the\u0026nbsp;\u003cstrong\u003eSTROBE\u003c/strong\u003e (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guideline.\u003cbr\u003e\u0026nbsp;The completed STROBE checklist is included at the end of the manuscript and uploaded as a separate file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Funding Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding was received for this study. The authors conducted all work independently without financial support from industry, governmental bodies, or private organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. AI-Use Disclosure\u003c/strong\u003e\u003cbr\u003eAI-assisted language tools (ChatGPT) were used for grammar refinement and organizational editing. AI was \u003cstrong\u003enot\u003c/strong\u003e used for data analysis, statistical modeling, scientific interpretation, figure generation, or literature synthesis. All content was reviewed, verified, and approved by the authors, and no AI-generated text or images were used without human editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDewan MC, Rattani A, Gupta S, et al. Estimating the global incidence of traumatic brain injury. 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Eur J Trauma Emerg Surg. 2023;49(6):2381\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00068-022-02214-4\u003c/span\u003e\u003cspan address=\"10.1007/s00068-022-02214-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Traumatic Brain Injury, Prognosis, Mortality, Glasgow Coma Scale, Neurosurgery","lastPublishedDoi":"10.21203/rs.3.rs-8312895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8312895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Prognostic models can assist neurosurgeons in making early decisions for patients with severe traumatic brain injury (TBI), yet most have been developed in high-resource trauma systems. The MOrtality Score for TBI (MOST) simplifies prediction using readily available clinical variables. Its performance in resource-limited settings remains unknown. Our objective was to externally validate the MOST score in a resource-limited neurosurgical environment and to compare its performance with the CRASH and IMPACT models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We performed a multicentric retrospective cohort study of adults with severe TBI (Glasgow Coma Scale [GCS] \u0026lt; 9) admitted to two neurosurgical referral centers. Demographics, injury characteristics, incident-to-presentation time (minutes), imaging, and corrected MOST/CRASH/IMPACT scores were collected. Primary outcome was in-hospital mortality; secondary outcomes included Glasgow Outcome Scale (GOS) at discharge. Discrimination was assessed with ROC AUCs and pairwise DeLong tests; calibration with Brier scores. Multivariable logistic regression identified independent predictors of mortality and poor functional outcome (GOS ≤ 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 1,105 patients, overall mortality was 12.1% and 94.8% had GOS ≤ 3 at discharge. Median incident-to-presentation time was 360 minutes (IQR 180–540); rural patients arrived later than urban patients (480 vs 180 minutes, p \u0026lt; 0.0001). In adjusted models, higher MOST scores were associated with increased mortality (OR 1.60, 95% CI 1.37–1.86), while higher GCS was protective (OR 0.59, 95% CI 0.42–0.83); IMPACT showed a modest inverse association (OR 0.87, 95% CI 0.78–0.97). MOST showed the best discrimination for mortality (AUC 0.85) versus CRASH (0.79) and IMPACT (0.82) (all pairwise p ≤ 0.026) and the lowest Brier score (0.187).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The MOST maintained robust performance under the system constraints typical of low-resource neurosurgical care, confirming its generalizability beyond high-income trauma networks. These findings highlight the potential role of validated prognostic models in guiding triage and communication in severe TBI.\u003c/p\u003e","manuscriptTitle":"Performance of the MOST Score for Severe Traumatic Brain Injury: First External Validation in Resource-Limited Neurosurgical Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 10:48:54","doi":"10.21203/rs.3.rs-8312895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a707f40-3f67-4580-85a8-7e801a0ad36c","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T18:45:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 10:48:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8312895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8312895","identity":"rs-8312895","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}