To Do or Not to Do: Decompressive Craniectomy for Severe Traumatic Brain Injury

Abstract

Globally, severe traumatic brain injury (TBI) is a significant cause of death and disability, particularly among young adults in their productive years. The management of elevated intracranial pressure (ICP) following TBI remains one of the greatest challenges in neurotrauma care, with decompressive craniectomy (DC) being a prominent, albeit contentious, treatment option. DC, a surgical procedure that involves removing a portion of the skull to accommodate brain swelling, has emerged as a potential life-saving intervention in such scenarios. The rationale is that by reducing ICP and enhancing cerebral perfusion, DC may mitigate further neurological damage. However, while DC effectively reduces mortality, its association with a high prevalence of severe disability and poor long-term functional outcomes has led to ongoing debate regarding its clinical utility, ethical justification, and cost-effectiveness. From a health care economics standpoint, DC has been shown to be more cost-effective than alternatives like barbiturate coma, particularly in younger patients with less severe injuries. Yet, this advantage diminishes in older populations or those with profound neurological impairment, where survival often comes at the cost of substantial long-term care needs and significantly impaired quality of life. Additionally, the decision to perform DC often occurs under critical circumstances where inherent prognostic uncertainty of early outcome prediction and emotional stress further complicate the shared decision-making process. To aid in navigating these complex choices and to guide ethical resource allocation, prognostic models such as Corticosteroid Randomization After Significant Head injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) have been developed, offering evidence-based predictions of functional outcomes based on preoperative clinical and radiographic variables. Nevertheless, these models have limitations. This review synthesizes current evidence on the clinical effectiveness, cost utility, and ethical dimensions of DC in severe TBI. It also explores the role of predictive tools in facilitating evidence-informed and ethically responsible decisions. A literature review was conducted using major biomedical databases to identify and synthesize clinical, ethical, and economic evidence related to DC in severe TBI. We also sought the opinion of various experts and tried to provide a comprehensive, multidimensional understanding of DC in neurotrauma care to support clinicians in navigating the complexities of managing severe TBI patients.

Keywords

decompressive craniectomy - resource allocation - role of rescue - traumatic brain injury - prognostic models - CRASH - IMPACT

Introduction

This narrative review was conducted to evaluate the clinical effectiveness, cost utility, ethical considerations, and outcome prediction tools related to decompressive craniectomy (DC) in severe traumatic brain injury (TBI). A comprehensive literature search was performed using electronic databases including PubMed, Scopus, and Embase covering articles published up to May 2025. Search was performed using keywords: decompressive craniectomy, traumatic brain injury, resource allocation, prognostic models, CRASH (Corticosteroid Randomization After Significant Head injury), IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury), cost-effectiveness, and ethical decision-making.

Articles were screened by title and abstract, followed by full-text review of the relevant articles. Inclusion criteria focused on studies involving adult patients with severe TBI undergoing DC, reporting clinical outcomes, prognostic model performance, cost-effectiveness, or ethical considerations, as well as relevant review articles and expert opinions. Data for each study was extracted separately according to study design and outcomes. Extracted data included but was not limited to outcomes, intervention details, key points, and conclusions, which formed the final review. An imaginary case example was provided with the consultation of experts, reflecting a typical severe TBI patient scenario.

Case Example

A young patient is brought to the emergency department after falling from a three-story building. Upon arrival, the patient is unresponsive, with a Glasgow Coma Scale (GCS) score of 3. The pupils are bilaterally dilated and fixed, and vital signs reveal severe hypertension and bradycardia, consistent with Cushing's reflex. A computed tomography (CT) scan shows diffuse cerebral edema indicative of intracranial hypertension.

Despite immediate resuscitative efforts including intubation, hyperventilation, hyperosmolar therapy, and sedation, his intracranial pressure (ICP) remains critically elevated at over 40 mm Hg, reflecting severe refractory intracranial hypertension. The neurosurgical team is considering performing an emergent DC as a live-saving intervention. However, while DC may stabilize the patient and prevent immediate death, it carries a high risk of severe disability or a vegetative state.

The patient's family insists that everything possible should be done to save his life; however, the clinicians are concerned that they may be converting death into what the family and the patient may find to be an unacceptable degree of severe functional and neurocognitive disability: The ethical dilemma of quality of life and resource management versus survival.

Decompressive Craniectomy

DC is widely recognized as a life-saving intervention in the context of severe TBI. The procedure is technically straightforward and consists of removing a section of the skull to provide extra space into which the injured swelling brain can expand.[1] [2] DC itself, does not differ much from other routine neurosurgical procedures in terms of surgery and in-hospital care costs. It has been reported by cost-effectiveness studies that DC is still the most cost-effective option in contrast to other alternative treatments for ICP reduction such as barbiturate coma, even at ages up to 80.[3] Yet, when it comes to cost-effectiveness over time, it is known to be costly due to poor outcomes following severe injuries.[4] The surgical decompression will not reverse the neurological damage from the initial injury. A number of trials have demonstrated that when the initial injury is severe, increased survival comes as an almost direct increase in the number of survivors with very high disability. The long-term costs of caring for these survivors will also be very significant.[4] [5] [6]

Outcome for Decompressive Craniectomy: Effectiveness and Costs

Most studies use the Extended Glasgow Outcome Scale (GOS-E) to assess outcome which is usually dichotomized into favorable and unfavorable. Favorable outcome is defined as moderate disability to good recovery (GOS-E score of 1–4), and unfavorable outcome is usually defined as death, a vegetative state, or severe disability with survivors being fully dependent (GOS-E score of 5–8). More recently in the RESCUEicp study, which is described further in the article, upper severe disability was included in the category of favorable outcome which is somewhat controversial.[7] [8]

Overall, outcome following DC is reported to be favorable in 30 to 50% of patients, with 20 to 25% rate of in-hospital mortality. In general, outcome is highly dependent on prognostically important factors such as age, pupil reactivity, radiological findings, and injury severity. In some cases, the injuries are so severe that even if DC manages to reduce ICP successfully, survivors are most likely to live with severe neurological disability or may even die weeks or months later.[2] [4] [5] [6]

A large trial study randomly assigned 408 patients with TBI and refractory elevated ICP of more than 25 mm Hg to undergo DC or receive ongoing medical care (MC).[5] Outcomes at 6 months showed the DC group to have lower rates of death but higher vegetative state and higher severe disability. This is because DC, as a life-saving intervention, is usually performed on severely injured patients who if not receive the treatment are very likely to expire. This makes DC statistically associated with more long-term unfavorable outcomes of vegetative state and severe disability ([Plot 1]).[2] [5]

Plot 1 Outcome of trial of decompressive craniectomy (DC) for traumatic intracranial hypertension: Extended Glasgow Outcome Scale (GOS-E) distributions at 6-month time point for the surgical care group (DC) and ongoing medical care group. (Data from Hutchinson et al. Trial of Decompressive Craniectomy for Traumatic Intracranial Hypertension. N Engl J Med. 2016.)

As the initial injury gets more severe, the costs of patients' nursing and care over time increase, and their satisfaction and life quality decrease. This results in a decline in cost-effectiveness for more severe injuries. For patients with a GCS of 3 and bilaterally fixed, dilated pupils, the mortality rate is reported to exceed 90%. Factors other than injury severity can also affect the outcome. A systematic review reported that in areas with limited access to advanced MC, DC can be beneficial if performed within 5 hours of trauma, particularly in younger patients with a GCS higher than 5. Yet, this was not based on high-quality data and did not take important baseline differences such as structural injury variabilities into account.[9] [10] [11] [12] Cost utility analyses have reported highly variable costs per quality-adjusted life years (QALYs). This is partially due to variabilities in health care systems of different settings and countries. Overall, they reveal that DC might not be so cost-effective in very severe cases and older ages for health care systems.[3] [13] [14] [15] [16] [17]

Patient and Family Preferences

Besides cost-effectiveness, clinician should also consider that outcome following surgery might not be acceptable to the individual and their family. When making the clinical decision or discussing the situation with families, it is important not to dichotomize the outcome into life or death, because adopting this position fails to recognize that many survivors may be left with lifelong disability and dependency, a condition that some people and their families might not choose over death. Given the nature of severe TBI, it is nearly impossible to know a patient's preferences at the time of the surgery due to decreased awareness. An advanced directive may be helpful in these circumstances; however, very few people, especially among younger patients, have even considered these issues. Promoting and universalizing the concept of advanced health care directives would be beneficial for many health care issues such as this one.[18] [19] [20] [21]

To address the issue of patients' preference, one study interviewed 500 health care workers of different specialties and backgrounds. The results revealed that participants were more likely to suggest DC for their patients than they would do for their own. After they were given the predicted risks of unfavorable outcomes and the observed outcomes at 18 months, their preferences for having the surgery for themselves was considerably reduced regardless of their religious backgrounds or their specialties. This suggests that the participants found the concept of survival with severe disability unacceptable and that access to reliable information regarding outcome is important and can be influential.[16] [22] [23]

Ethical Dilemma of Resource Allocation for Poor Outcome Procedures

Clinical and moral reasoning are fully integrated in morally complex situations. In other words, a good clinical decision cannot be made unless it is based on good moral reasoning and this requires the clinician to be familiar with ethical considerations and concepts.[24] [25]

The rule of rescue is one such concept that describes the strong ethical proclivity to save an identified individual in immediate danger of death or serious harm regardless of risks and costs.[26] An example would be a group of firefighters entering a burning building to rescue a single individual placing them all in great danger. While on an emotional level this seems reasonable it is somewhat at odds with the principles of fairness, justice, and even beneficence. From a utilitarian point of view, health care resources must be allocated precisely to maximize the outcome of the system, and this is often objectively quantified by QALYs. In certain circumstances resources for some patients can be reallocated to where they may benefit more. At these times, health care resources need to be rationed such as during the recent coronavirus disease pandemic.[27] [28]

Some believe that rationing health care goes against basic human instincts and take the position that is more in keeping with the rule of rescue that emphasizes on worth and sanctity of life, no matter what the cost. However, it must be recognized that while making great efforts to save a life, this moral worth can be challenged if the eventual outcome is unacceptable to the patient and their families, especially if this is at considerable costs and burdens on the health care system. Indeed, it could be argued that this would be against the principle of nonmaleficence.[21] [26] [28] [29]

For a patient to survive a DC and remain in a vegetative state, the achieved QALYs can result in a greater loss of QALYs for which the families must care for their beloved one. In these circumstances, the surgery could be considered futile even without considering the monetary and material burden on the family and society in general.

Unfortunately, the clinical decision to surgically intervene must be made during moments of genuine crisis. While families are almost always under so much emotional burden and outcome to them is often dichotomized into life or death. Understandably, they want to try every last option, hopefully wishing to save their loved one's life without considering long-term efficacy and beneficence.[23] [26] [29] [30] [31]

It must also be acknowledged that surgeons are also not immune to cognitive fallacies. They need to keep a well-balanced stance based on the available data in order to avoid falling into the well-known pitfall of omission bias. This is the tendency to preferentially observe the harm following omission and inaction in contrast to equal or less harm caused by the actions. This phenomenon is mostly prevalent when the health care provider is faced with the probability of negative results in case of a clinical action. These results may put them at risk of a false judgment due to a false causal assignment made by the others, including the family and sometimes even other colleagues.[19] [32] [33]

Decreasing a clinician's decision-making burden can possibly be achieved by sharing it with the ethical committees and the family. There are plenty of benefits to this, including less decision fatigue for the physician, more acceptance by the family, and sometimes achieving a more acceptable and tailored outcome.[34] [35]

In the context of severe TBI, when the surgeon is faced with the ethical dilemma of whether to perform DC or not, there are a variety of factors that the clinician must take into account. Of the most important ones is the predicted outcome of the surgery.

Outcome Prediction

To address the issue of accurate prognosis, many studies have utilized previously obtained patients' data to develop models of outcome prediction. These studies have used a large amount of data gathered from TBI patients who underwent DC surgery before the surgery and their consequent outcomes during the follow-up.[36] [37] [38] [39] [40] [41]

Prognostic Models: CRASH and IMPACT

Two of the most relevant and widely tested models include the CRASH model and the model from the IMPACT group. The CRASH trial was originally a placebo-controlled trial evaluating the effect of corticosteroids on neurological disability and death following consciousness-impairing head injuries among adults. Although the corticosteroids were proven to be unhelpful, this study provided researchers with data from which to build a Web-based prognostic model for evaluation of TBI patients. The model provides predicted risk of an unfavorable outcome at 6 months from the injury, defined as severely disabled, vegetative state, or death. The IMPACT model evaluated the risk of mortality at 6 months following the injury using the data from nearly 8,500 patients and externally validated its results on 6,681 patients from the CRASH trial. They proposed that age, motor score, pupillary reactions, and specific CT scan characteristics were the most important prognostic outcome predictors.[42] [43] [44] [45] [46] [47]

Prognostic Models: Limitations

Both models have been externally validated by various authors and have shown to be reliable with nearly the same prognostic value. Despite this, some validation studies reported outcomes significantly better than the models' predictions. These studies form a smaller proportion of the literature in contrast to those that have reported good accuracies for the models. Nevertheless, this is an important finding and must not be neglected, as clinical decisions regarding life or death may depend on it. One major criticism of these models is that they are primarily based on data derived from high-income, developed countries. Some studies have attempted to validate these models in different settings, such as low-income regions. Yet, the socioeconomic status of the regions, in general, is only one of the factors that must be taken into account and should not become a slippery slope to justify inaction. We suggest further validation studies since these models need to be evaluated, calibrated, and validated for utilization in each setting in order to maximize accuracy prior to usage.[36] [37] [38] [39] [40] [41] [48] [49] [50] [51] [52] [53] [54]

Prognostic Models: Recent Achievements

Recently, other prognostic models have been developed using new attractive approaches, especially machine learning (ML) and network analysis-based models. Some of these methods benefit from the processing of patients' imaging data (CT scan/magnetic resonance imaging). Although less widely used and validated, they are likely to produce reliable results even with smaller-size data. One of the benefits of such models is the ease of calibration for usage in each setting based on previously collected and registered patients' data. It is important not to forget that these models, especially ML-based models need to be cross-validated and externally validated with different samples and within different settings to produce the most efficient, reliable, and true outcome. Recently, a meta-analysis study reported that ML models are accurate in predicting disorders of consciousness in TBI patients. Since the learning phase and testing phase are often conducted on the same sort of data without further cross-validation, we believe that external validation studies on each model are needed to be able to fully trust and rely on it.[49] [55] [56] [57]

Prognostic Models: Setting Thresholds of Risk, Benefits, and Limitations

There are a handful of studies on the validation of TBI prognostic models. Some authors have suggested methods involving thresholds of risk for unfavorable outcomes when deciding about performing DC surgery. This is based on the idea that there are significant cutoff points in which the outcome of the surgery dramatically changes. One study applied the CRASH Web-based prognostic model to a cohort of patients who had received a DC.[20] In this study, 19 of 27 patients with a CRASH score of < 75% were able to return to work after 18 months, but none of the 14 patients with more than 75% risk of unfavorable outcomes could do so. Another study by the same author set the threshold to 80%. Based on their collected data of 164 adult patients with neurotrauma who underwent DC surgery, they reported that from 43 patients with a predicted risk of unfavorable outcome higher than 80%, 24 survived with severe disability and 3 with moderate disability. Whereas among 121 patients with a predicted risk of less than 80%, 102 had returned home, 7 required home nursing care, and one patient remained in rehabilitation.[20] [21]

This threshold-setting method can be useful for the sake of ease during stress-driven moments of clinical decision-making. However, there are important considerations when it comes to patient stratification. First, the threshold itself is driven from the data after initial evaluation, and the data is not the same for each setting. Calibration of the threshold and updating it is crucial for each environment. Another problem is the individual varieties among the patients and situations. Moderate to severe disability might be an acceptable and affordable outcome for one individual and their family, while this can be an unacceptable outcome for another. Providing them with accurate information regarding the outcome of the surgery can be useful, beneficial, and from our point of view necessary. It is important to have in mind the individual diversity between the families and the patients and guide them concerning their ideas and differences.[17] [21] [23]

Prognostic Models: Conclusion

Finally, we must know that prognostic models should not be replaced with clinical decisions, at least for now. While adopting these models can help the clinician to have a better assessment and awareness of the situation, they can only be used to guide clinical management and not to dictate decision-making. A case-by-case approach can be taken in order to optimize the results for each individual. Although to some degree this may place the clinician at the risk of cognitive biases, it still is the most widely used and accepted framework.[58] [59]

What To Do If Surgery is Not Beneficial?

In a boundary situation, when the clinical decision is not to perform a DC it is best to sit down with the families if available, go over the possibilities, pros and cons of surgery, and talk to them about the situation while providing them with accurate and valid information. If the most likely outcome is severe disability and dependency, this must be clearly explained. Choosing the right words and language is important and should be considered. Research supports that a structured approach, like shared decision-making (SDM), is valuable in these situations. In SDM, clinicians provide patients or families with accurate and clear information, and they are encouraged to take an active role in deciding their care, sometimes opting for a second consultation to clarify doubts. This process is especially helpful in cases where surgery may not be the best option, allowing time for a more reflective decision-making process that considers both the clinical evidence and the ethical implications of the decision. Moreover, it is important not to forget that the process of clinical decision-making in medicine is inherently probabilistic and often guided by Bayesian reasoning, where clinicians must evaluate the risks and benefits of an intervention based on prior knowledge and new data. Summarizing all in a sentence, the clinician's goal should always be maximizing benefits while trying to avoid harm.[60] [61]

Conflict of Interest

None declared.

Acknowledgments

The authors would like to express their sincere gratitude to the following individuals for sharing their clinical insights and perspectives on decompressive craniectomy in severe traumatic brain injury with us: The young professors of neurosurgery, anesthesiology, and neurology at Sina Hospital in Iran; Dr. Michael Flynn in Canada; and Dr. Bijan Arabi in the United States. Their thoughtful input contributed meaningfully to the development of this work.

Authors' Contributions

E.R. was a major contributor in investigation, writing - original draft, and conceptualization. M.H. was a major contributor in writing – review and editing of the manuscript. H.N. was a medical ethics professional and advisor and was a major contributor in writing – review and editing of the manuscript. S.H. was a neurotrauma and medical ethics expert advisor and a major contributor in writing – review and editing of the manuscript, and validation of the project. V.R. was a neurosurgery and neurotrauma expert advisor and was a major contributor in writing – review and editing of the manuscript and has made substantial contribution in conception, validation, and administration of the project. All authors have read and approved the final manuscript.

• References

• 1 Aarabi B, Hesdorffer DC, Ahn ES, Aresco C, Scalea TM, Eisenberg HM. Outcome following decompressive craniectomy for malignant swelling due to severe head injury. J Neurosurg 2006; 104 (04) 469-479
• 2 Cooper DJ, Rosenfeld JV, Murray L. et al; DECRA Trial Investigators, Australian and New Zealand Intensive Care Society Clinical Trials Group. Decompressive craniectomy in diffuse traumatic brain injury. N Engl J Med 2011; 364 (16) 1493-1502
• 3 Alali AS, Naimark DM, Wilson JR. et al. Economic evaluation of decompressive craniectomy versus barbiturate coma for refractory intracranial hypertension following traumatic brain injury. Crit Care Med 2014; 42 (10) 2235-2243
• 4 Kolias AG, Adams H, Timofeev IS. et al; RESCUEicp Trial Collaborators. Evaluation of outcomes among patients with traumatic intracranial hypertension treated with decompressive craniectomy vs standard medical care at 24 months: a secondary analysis of the RESCUEicp randomized clinical trial. JAMA Neurol 2022; 79 (07) 664-671
• 5 Hutchinson PJ, Kolias AG, Timofeev IS. et al; RESCUEicp Trial Collaborators. Trial of decompressive craniectomy for traumatic intracranial hypertension. N Engl J Med 2016; 375 (12) 1119-1130
• 6 Guerra WK, Gaab MR, Dietz H, Mueller JU, Piek J, Fritsch MJ. Surgical decompression for traumatic brain swelling: indications and results. J Neurosurg 1999; 90 (02) 187-196
• 7 Wilson JT, Pettigrew LE, Teasdale GM. Structured interviews for the Glasgow Outcome Scale and the extended Glasgow Outcome Scale: guidelines for their use. J Neurotrauma 1998; 15 (08) 573-585
• 8 Honeybul S, Ho KM, Gillett GR. Long-term outcome following decompressive craniectomy: an inconvenient truth?. Curr Opin Crit Care 2018; 24 (02) 97-104
• 9 Barthélemy EJ, Melis M, Gordon E, Ullman JS, Germano IM. Decompressive craniectomy for severe traumatic brain injury: a systematic review. World Neurosurg 2016; 88: 411-420
• 10 Howard JL, Cipolle MD, Anderson M. et al. Outcome after decompressive craniectomy for the treatment of severe traumatic brain injury. J Trauma 2008; 65 (02) 380-385 , discussion 385–386
• 11 Tang Z, Yang R, Zhang J. et al. Outcomes of traumatic brain-injured patients with Glasgow Coma Scale < 5 and bilateral dilated pupils undergoing decompressive craniectomy. Front Neurol 2021; 12: 656369
• 12 Tien HC, Cunha JR, Wu SN. et al. Do trauma patients with a Glasgow Coma Scale score of 3 and bilateral fixed and dilated pupils have any chance of survival?. J Trauma 2006; 60 (02) 274-278
• 13 Malmivaara K, Kivisaari R, Hernesniemi J, Siironen J. Cost-effectiveness of decompressive craniectomy in traumatic brain injuries. Eur J Neurol 2011; 18 (04) 656-662
• 14 Behranwala R, Aojula N, Hagana A, Houbby N, de Preux DL. An economic evaluation for the use of decompressive craniectomy in the treatment of refractory traumatic intracranial hypertension. Brain Inj 2021; 35 (04) 444-452
• 15 van Dijck JT, Reith FC, van Erp IA. et al. Decision making in very severe traumatic brain injury (Glasgow Coma Scale 3-5): a literature review of acute neurosurgical management. J Neurosurg Sci 2018; 62 (02) 153-177
• 16 Jamous M, Barbarawi M, Samrah S, Khabaz MN, Al-Jarrah M, Dauod S. Emergency decompressive craniectomy for trauma patients with Glasgow Coma Scale of 3 and bilateral fixed dilated pupils. Eur J Trauma Emerg Surg 2010; 36 (05) 465-469
• 17 Ho KM, Honeybul S, Lind CR, Gillett GR, Litton E. Cost-effectiveness of decompressive craniectomy as a lifesaving rescue procedure for patients with severe traumatic brain injury. J Trauma 2011; 71 (06) 1637-1644 , discussion 1644
• 18 Gordy S, Klein E. Advance directives in the trauma intensive care unit: do they really matter?. Int J Crit Illn Inj Sci 2011; 1 (02) 132-137
• 19 Iserson KV. Getting advance directives to the public: a role for emergency medicine. Ann Emerg Med 1991; 20 (06) 692-696
• 20 Honeybul S, Ho KM, Lind CR, Corcoran T, Gillett GR. The retrospective application of a prediction model to patients who have had a decompressive craniectomy for trauma. J Neurotrauma 2009; 26 (12) 2179-2183
• 21 Honeybul S, Gillett GR, Ho KM, Lind CR. Neurotrauma and the rule of rescue. J Med Ethics 2011; 37 (12) 707-710
• 22 Honeybul S, Ho K, O'Hanlon S. Access to reliable information about long-term prognosis influences clinical opinion on use of lifesaving intervention. PLoS One 2012; 7 (02) e32375
• 23 Honeybul S, Gillett G, Ho K, Lind C. Ethical considerations for performing decompressive craniectomy as a life-saving intervention for severe traumatic brain injury. J Med Ethics 2012; 38 (11) 657-661
• 24 Inguaggiato G, Metselaar S, Molewijk B, Widdershoven G. How moral case deliberation supports good clinical decision making. AMA J Ethics 2019; 21 (10) E913-E919
• 25 Singer PA, Pellegrino ED, Siegler M. Clinical ethics revisited. BMC Med Ethics 2001; 2: E1
• 26 McKie J, Richardson J. The rule of rescue. Soc Sci Med 2003; 56 (12) 2407-2419
• 27 Olson S, Rosenfeld JV, Honeybul S. Neurotrauma, COVID and the rationing of intensive care: an ethical approach. Br J Neurosurg 2022; 36 (05) 594-599
• 28 Sheehan M. Resources and the rule of rescue. J Appl Philos 2007; 24 (04) 352-366
• 29 Cookson R, McCabe C, Tsuchiya A. Public healthcare resource allocation and the rule of rescue. J Med Ethics 2008; 34 (07) 540-544
• 30 Scheunemann LP, White DB. The ethics and reality of rationing in medicine. Chest 2011; 140 (06) 1625-1632
• 31 Knapp M. The cost-effectiveness challenge: is it worth it?. Alzheimers Res Ther 2015; 7 (01) 10
• 32 Little AS, Wu SJ. Cognitive bias and neurosurgical decision making. J Neurosurg 2021; 137 (01) 307-312
• 33 Baron J, Ritov I. Omission bias, individual differences, and normality. Organ Behav Hum Decis Process 2004; 94 (02) 74-85
• 34 Baker EF, Geiderman JM, Kraus CK, Goett R. The role of hospital ethics committees in emergency medicine practice. J Am Coll Emerg Physicians Open 2020; 1 (04) 403-407
• 35 Hajibabaee F, Joolaee S, Cheraghi MA, Salari P, Rodney P. Hospital/clinical ethics committees' notion: an overview. J Med Ethics Hist Med 2016; 9: 17
• 36 Roozenbeek B, Lingsma HF, Lecky FE. et al; International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Study Group, Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, Trauma Audit and Research Network (TARN). Prediction of outcome after moderate and severe traumatic brain injury: external validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models. Crit Care Med 2012; 40 (05) 1609-1617
• 37 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 (01) e27
• 38 Wongchareon K, Thompson HJ, Mitchell PH, Barber J, Temkin N. IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort. Inj Prev 2020; 26 (06) 546-554
• 39 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) 1146-1152
• 40 de Cássia Almeida Vieira R, Silveira JCP, Paiva WS. et al. Prognostic models in severe traumatic brain injury: a systematic review and meta-analysis. Neurocrit Care 2022; 37 (03) 790-805
• 41 Dijkland SA, Foks KA, Polinder S. et al. Prognosis in moderate and severe traumatic brain injury: a systematic review of contemporary models and validation studies. J Neurotrauma 2020; 37 (01) 1-13
• 42 Perel P, Arango M, Clayton T. et al; MRC CRASH Trial Collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 2008; 336 (7641) 425-429
• 43 Steyerberg EW, Mushkudiani N, Perel P. et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (08) e165 , discussion e165 discussion e
• 44 Marmarou A, Lu J, Butcher I. et al. IMPACT database of traumatic brain injury: design and description. J Neurotrauma 2007; 24 (02) 239-250
• 45 Eagle SR, Nwachuku E, Elmer J, Deng H, Okonkwo DO, Pease M. Performance of CRASH and IMPACT prognostic models for traumatic brain injury at 12 and 24 months post-injury. Neurotrauma Rep 2023; 4 (01) 118-123
• 46 Agrawal D, Ahmed S, Khan S, Gupta D, Sinha S, Satyarthee GD. Outcome in 2068 patients of head injury: experience at a level 1 trauma centre in India. Asian J Neurosurg 2016; 11 (02) 143-145
• 47 The CRASH trial management group, The CRASH trial collaborators. The CRASH trial protocol (Corticosteroid randomisation after significant head injury) [ISRCTN74459797]. BMC Emerg Med 2001; 1 (01) 1
• 48 Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak 2006; 6 (01) 38
• 49 Elahi C, Adil SM, Sakita F. et al. Corticosteroid Randomization after Significant Head Injury and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury models compared with a machine learning-based predictive model from Tanzania. J Neurotrauma 2022; 39 (1-2): 151-158
• 50 Hashemi B, Amanat M, Baratloo A. et al. Validation of CRASH model in prediction of 14-day mortality and 6-month unfavorable outcome of head trauma patients. Emergency (Tehran) 2016; 4 (04) 196-201
• 51 Fazel M, Ahmadi S, Hajighanbari MJ. et al. Validation of CRASH model in prediction of 14-day mortality and 6-month unfavourable outcome of pediatric traumatic brain injury. J Pediat Perspect 2019; 7 (12) 10413-10422
• 52 Honeybul S, Ho KM, Lind CR, Gillett GR. Validation of the CRASH model in the prediction of 18-month mortality and unfavorable outcome in severe traumatic brain injury requiring decompressive craniectomy. J Neurosurg 2014; 120 (05) 1131-1137
• 53 Honeybul S, Ho KM, Lind CR, Gillett GR. Observed versus predicted outcome for decompressive craniectomy: a population-based study. J Neurotrauma 2010; 27 (07) 1225-1232
• 54 Majdan M, Lingsma HF, Nieboer D, Mauritz W, Rusnak M, Steyerberg EW. Performance of IMPACT, CRASH and Nijmegen models in predicting six month outcome of patients with severe or moderate TBI: an external validation study. Scand J Trauma Resusc Emerg Med 2014; 22 (01) 68
• 55 Lee SH, Lee CH, Hwang SH, Kang DH. A machine learning-based prognostic model for the prediction of early death after traumatic brain injury: comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) model. World Neurosurg 2022; 166: e125-e134
• 56 Zhu X, Gao L, Luo J. A meta-analysis of predicting disorders of consciousness after traumatic brain injury by machine learning models. Alpha Psychiatry 2024; 25 (03) 290-303
• 57 Adil SM, Elahi C, Patel DN. et al. Deep learning to predict traumatic brain injury outcomes in the low-resource setting. World Neurosurg 2022; 164: e8-e16
• 58 Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine. Science 2019; 363 (6429) 810-812
• 59 Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive biases associated with medical decisions: a systematic review. BMC Med Inform Decis Mak 2016; 16 (01) 138
• 60 Ubbink DT. Shared decision-making should be a standard part of surgical care. Br J Surg 2022; 109 (11) 1049-1050
• 61 Hawkins AT, Fayanju OM, Maduekwe UN. Shared decision-making in the surgical sciences. JAMA Surg 2023; 158 (11) 1121-1122

Publication History

Article published online:
28 November 2025