Surgical decision making in the setting of severe traumatic brain injury: A survey of neurosurgeons.

BACKGROUND: Surgical decision-making in severe traumatic brain injury (TBI) is complex. Neurosurgeons weigh risks and benefits of interventions that have the potential to both maximize the chance of recovery and prolong suffering. Inaccurate prognostication can lead to over- or under-estimation of outcomes and influence treatment recommendations.
OBJECTIVE: To evaluate the impact of evidence-based risk estimates on neurosurgeon treatment recommendations and prognostic beliefs in severe TBI.
METHODS: In a survey-based randomized experiment, a total of 139 neurosurgeons were presented with two hypothetical patient with severe TBI and subdural hematoma; the intervention group received additional evidence-based risk estimates for each patient. The main outcome was neurosurgeon treatment recommendation of non-surgical management. Secondary outcomes included prediction of functional recovery at six months.
RESULTS: In the first patient scenario, 22% of neurosurgeons recommended non-surgical management and provision of evidence-based risk estimates increased the propensity to recommend non-surgical treatment (odds ratio [OR]: 2.81, 95% CI: 1.21-6.98; p = 0.02). Neurosurgeon prognostic beliefs of 6-month functional recovery were variable in both control (median 20%, IQR: 10%-40%) and intervention (30% IQR: 10%-50%) groups and neurosurgeons were less likely to recommend non-surgical management when they believed prognosis was favorable (odds ratio [OR] per percentage point increase in 6-month functional recovery: 0.97, 95% confidence interval [CI]: 0.95-0.99). The results for the second patient scenario were qualitatively similar.
CONCLUSIONS: Our findings show that the provision of evidence-based risk predictions can influence neurosurgeon treatment recommendations and prognostication, but the effect is modest and there remains large variability in neurosurgeon prognostication.

RCT Entities:   Population Interventions Outcomes

Introduction

Severe traumatic brain injury (TBI) accounts for 52,000 patient deaths and 5.3 million newly disabled Americans yearly[1-4]. For patients diagnosed with severe TBI with subdural hematoma, there are three dominant treatment modalities: craniotomy, non-surgical medical treatment and withdrawal or withholding of treatment. TBI patients are usually in critical condition and treatment decisions are time-sensitive. Physician recommendations play a critical role in decision-making because surrogate decision makers are faced with an overwhelming and unfamiliar scenario. When making the treatment recommendation, neurosurgeons, emergency, and neurocritical care providers face a critical trade-off: on the one hand, performing a craniotomy can relieve pressure from subdural hematoma and thus increase the chance of recovery and minimize the risk of neurologic decline; on the other hand, there is a non-negligible risk, especially in elderly patients, that the invasive procedure may prolong suffering without benefit[5, 6]. Making this decision even more difficult, there is large uncertainty about which patients will benefit from craniotomy in the setting of severe TBI and uncertainty about TBI outcomes[7, 8]. According to a position statement by the Neurocritical Care Society, there is concern that uncertain neuroprognostication limits treatment decisions[9]. Indeed, a previous study found that neurosurgeons tend to overestimate poor outcomes and underestimate positive outcomes in the setting of severe TBI[10].

Over the past years, there has been an increased effort to derive evidence-based treatment recommendations for patients with TBI. Several studies have used cohort and registry data to develop and validate quantitative risk prediction models based on clinical features[11, 12]. Furthermore, through the development of web-based risk calculators these evidence-based outcome predicitions are now readily accessible to clinicians. Two such risk calculators are CRASH[11] and IMPACT[12], which both incorporate patient presentation data to predict multiple short and long-term outcomes[13, 14]. Although validated, these calculators are not widely used in clinical practice[15]. Therefore, little is known about the effect of prognostic calculators on neurosurgeon prognostic beliefs and treatment recommendations.

The objective of this study was two-fold: 1) to characterize neurosurgeon decision making in severe TBI and 2) to evaluate the impact of evidence-based prognostic risk estimates on neurosurgeon prognostic beliefs and treatment decisions. To this end, we performed a survey of neurosurgeons at an international meeting and assessed their beliefs and recommendations in hypothetical scenarios of patients with severe TBI. The survey included a randomized experiment to evaluate the effect of providing evidence-based prognostic risk estimates.

Methods

Survey design

We surveyed a convenience sample of neurosurgeons attending the Congress of Neurological Surgeons Annual Meeting in 2017 to determine their prognostic estimates and treatment recommendations for hypothetical patients with severe traumatic brain injury. Given their integral involvement in neurosurgical decision-making, attending, fellow, and resident level neurosurgeons were eligible for the study. The study was open to neurosurgeons from any country. Physicians from other specialties and medical students were excluded. Ethics approval for this study was obtained from the DukeHealth Institutional Review Board. The survey contained a statement informing participants of the goals and details of the study and giving an opportunity for consenting to or declining participation.

The control survey contained two hypothetical patient scenarios and no prognostic estimate information (Table 1 and Supplemental Digital Content): one patient was a 77-year-old patient, Glasgow Coma Scale Total Score five, with reactive pupils (Scenario one); and a 60-year-old patient, Glasgow Coma Total Score four with fixed and dilated pupils (Scenario two). In both scenarios, patients were described as having one centimeter subdural hematomas on computed tomography (CT) scan (laterality unspecified) with one centimeter of midline shift. The order of presentation of scenarios was the same on all surveys. The intervention survey contained the same hypothetical patient scenarios as the control survey, and additionally provided prognostic estimates based on the CRASH calculator[14]. Prognostic estimates in the CRASH survey were comprised of the risk of 14-day mortality and the risk of 6-month poor functional outcome, defined as death, vegetative state, or severe disability[14]. For scenario one, the CRASH calculator estimated a 14-day mortality of 65.6% and a 6-month risk of poor functional outcome of 93%; for scenario two, the CRASH calculator predicted a 14-day mortality of 73.1% and a 6-month risk of poor functional outcome of 93%. Upon reading the patient scenarios, the survey respondents were first asked to estimate the percent chance of 30-day survival and the percent chance that at six months the patient would be able to communicate and perform activities of daily living (6-month ADL). Next, respondents were asked to answer a hypothetical family member’s question about treatment recommendation with the following choices: craniotomy, medical management, or comfort care, as defined as the decision to withdraw or withhold life-sustaining intervention.

Table 1

Scenario descriptions.

Hypothetical patient characteristics presented to neurosurgeon participants.

	Patient 1	Patient 2
Age (years)	77	60
Mechanism of Injury	Fall
Time Since Injury	Unknown
Comorbidities	Unknown
GCS	5	4
CT brain	1cm acute subdural hematoma, 1cm midline shift, scattered traumatic subarachnoid hemorrhage
Labs	Glucose 90mg/dL, coagulation within normal limits, hemoglobin 9g/dL
CRASH estimate of 14-day mortality risk	65.60%	73.10%
CRASH estimate of 6-month unfavorable outcome risk	93.00%

In addition to the scenario responses, participants were asked to provide the following demographic information: age, experience (defined as the time since graduation from medical school), gender, race/ethnicity, training level (intern, resident, fellow or attending), and hospital type (level 1 trauma center: yes/no).

Sample size

We hypothesized that respondents receiving the intervention survey including evidence-based risk estimates would both predict poorer prognoses and be less likely to recommend craniotomy than respondents receiving the control survey. The experiment was powered on the effect of evidence-based risk estimates on craniotomy recommendation in the first scenario. Based on a single-institution pilot study of 50 neurosurgeons, we estimated that we needed 200 respondents (100 per group) to have 80% power to detect a medium effect size (Cohen’s D = 0.4) for the impact of CRASH estimates on craniotomy recommendations, assuming a type I error rate of 0.05.

Statistical analyses

Survey responses were double-entered and discrepancies were resolved by referring back to the written survey. Surveys were considered complete if at least 50% of the questions were answered; incomplete surveys and surveys with missing treatment recommendation were excluded from the analysis. Continuous prognostic predictions between the two study groups were compared using the two-sided t-test. The association between survey version and recommendation of non-surgical management was estimated using univariable logistic regression models and odds ratios (OR) were calculated. The association between survey version and the three management options of craniotomy, medical management and comfort care was estimated using multinomial logistic regression models, and corresponding ORs were calculated. For exploratory mediation analyses[16], the effect of neurosurgeon prognostic beliefs on treatment recommendation was evaluated using univariable and multivariable logistic regression models, the latter adjusted for neurosurgeon age, race, gender, experience, training level, hospital type and survey version. All statistical tests performed were two-sided. Analyses were performed in STATA (version 15.0.587, StataCorp. 2017, College Station, TX) and R (version 3.5.1, R Foundation for Statistical Computing, Vienna, Austria).

Results

Demographics

A total of 139 neurosurgeons completed the survey. After removal of surveys with incomplete treatment recommendations, 138 and 136 surveys were included for the analyses of scenarios one and two, respectively. Among the 138 respondents of scenario one (Table 2), the majority were male (80%), and median age was 40 (interquartile range [IQR]: 30–55) years. The majority of respondents were either fellows or attending surgeons (65%), and median professional experience was 13 (IQR: 5–27) years. A total of 68 (49%) participants received the control version of the survey without CRASH estimates and 70 (51%) received the intervention version with the CRASH estimates.

Table 2

Survey participant demographics for scenario 1.

	All participants (n = 138*)	Control (n = 68*)	Intervention (n = 70*)
Age [y], median (IQR)	40 (30–55)	40 (32–53)	40 (33–57)
Experience [y], median (IQR)	13 (5–27)	13.5 (2–26)	13 (6–31)
Gender, n (%)
Male	110 (80)	52 (77)	58 (83)
Female	26 (19)	15 (22)	11 (16)
Unknown	2 (1)	1 (1)	1 (1)
Race/Ethnicity, n (%)
White	78 (57)	38 (56)	40 (57)
Black	11 (8)	3 (4)	8 (11)
Asian	24 (17)	14 (21)	10 (14)
Hispanic	13 (9)	7 (10)	6 (9)
Unknown	12 (9)	6 (9)	6 (9)
Training level, n (%)
Resident	45 (33)	23 (34)	22 (32)
Attending/Fellow	90 (65)	43 (63)	47 (67)
Unknown	3 (2)	2 (3)	1 (1)
Hospital type, n (%)
Level 1 trauma center	91 (66)	43 (32)	22 (31)
Other	44 (32)	22 (63)	48 (69)
Unknown	3 (2)	3 (4)	0 (0)

*For the analysis of scenario 2, n = 2 participants were excluded due to missing treatment recommendation.

Prognostic beliefs

Overall, neurosurgeon predictions of 30-day survival and 6-month ADL were widely variable regardless of survey version received (Fig 1). In scenario one (Fig 1A), the median 30-day survival prediction among neurosurgeons who received evidence-based risk estimates was 40% (IQR: 30%-68%), compared to 58% (IQR: 30%-80%) among those who did not receive the risk estimates. For 6-month ADL, the median prediction among neurosurgeons who received risk estimates was 20% (IQR: 10%-40%), compared to 30% (IQR: 10%-50%) among those who did not receive estimates.

Fig 1

Neurosurgeon prognostic beliefs.

Neurosurgeon prognostic beliefs about 30-day survival and 6-month ADL are shown for the hypothetical scenarios 1 (A) and 2 (B). Each dot represents an individual neurosurgeon prognostic estimate. Red dots represent neurosurgeons who recommended a craniotomy for the hypothetical patient and black dots represent neurosurgeons who did not recommend a craniotomy. Horizontal lines represent the median (middle line) and interquartile range (top and bottom lines) of the predictions, respectively.

In scenario two (Fig 1B), the median 30-day survival prediction was lower among neurosurgeons who received risk estimates (28%, IQR: 15%-50%) compared to those who did not (40%, IQR: 10%-60%). Similarly, the median 6-month ADL prediction among those who received risk estimates (6%, IQR: 1%-24%) was lower compared to those who did not (10%, IQR: 1%-50%).

Treatment recommendations

In both survey groups, neurosurgeons recommended non-surgical treatment (including medical management or withdrawal of life sustaining treatment) over a wide range of prognostic beliefs (Fig 1, black dots). Overall, 22% and 42% of neurosurgeons recommended non-surgical treatment in scenarios one and two, respectively (Table 3). In scenario one, neurosurgeons who received risk estimates were more likely to recommend non-surgical treatment (OR: 2.81, 95% CI: 1.21–6.98; p = 0.02). In scenario two, there was no difference in non-surgical management choices between the control and intervention groups (OR: 1.37, 95% CI: 0.69–2.74). When further stratifying the non-surgical management recommendations into medical treatment and withdrawal of life-sustaining intervention in scenario one, we found that neurosurgeons receiving risk estimates were more likely to choose withdrawal (OR: 5.42, 95% CI: 1.12–26.25), but not medical treatment (OR: 2.06, 95% CI: 0.75–5.65). No effect was found in scenario two.

Table 3

Effect of providing risk estimates on treatment choice.

	Scenario One			Scenario Two
	Control (N = 68)	Intervention (N = 70)	Odds ratio (95% CI)	Control (N = 67)	Intervention (N = 69)	Odds ratio (95% CI)
n (%)
Non-surgical management	9 (13)	21 (30)	2.8 (1.2–7.0)*	25 (37)	31 (45)	1.4 (0.7–2.7)
Non-surgical management, by type
Medical management	7 (10)	12 (17)	2.1 (0.8–5.6)	11 (16)	9 (13)	0.9 (0.3–2.4)
Comfort care	2 (3)	9 (13)	5.4 (1.1–26.2)	14 (21)	22 (32)	1.7 (0.8–3.9)

*p = 0.02

Exploratory mediation analysis for scenario one

In scenario one, the provision of evidence-based risk estimates had an effect on both 6-month functional recovery beliefs (p = 0.06, t-test) and treatment recommendations (p = 0.02, Table 3). We thus performed an exploratory mediation analysis to evaluate the potential role of 6-month ADL as a mediator for the effect of risk estimate provision on treatment recommendation (Fig 2). In a univariable analysis of 6-month ADL as a predictor for treatment recommendation, there was a negative association between 6-month ADL estimates and non-surgical treatment recommendation (OR per percentage point increase in 6-month ADL: 0.97, 95% CI: 0.95–0.99, p = 0.01; Table 4). When adjusting the analysis for neurosurgeon characteristics and survey version, the strength of the association between 6-month ADL and non-surgical recommendation remained unchanged. Importantly, in the multivariable analysis, the survey version was no longer a predictor for the treatment recommendation (Table 4). Together, these results suggest that prognostic beliefs (6-month ADL) may be a potential mediator in the association of survey version and treatment recommendation. In scenario two, a mediation analysis was not warranted because provision of evidence-based risk estimates had no effect on treatment recommendation.

Fig 2

Exploratory mediation analysis.

Relationship between receipt of evidence-based risk estimates, prognostic beliefs and treatment recommendation. When including prognostic estimates in the model, the relationship between receipt of evidence-based risk estimates and treatment recommendation loses statistical significance.

Table 4

Effect of prognostic beliefs (6-month ADL) on recommendation of non-surgical management.

6-Month ADL
Analysis	Variable	Odds ratio (95% CI)	p-value
Univariable	6-month ADL	0.97 (0.95–0.99)	0.01
Multivariable adjusted	6-month ADL (percentage points)	0.97 (0.94–0.99)	0.01
	Gender: male	1.49 (0.44–5.86)	0.54
	Race: white	0.34 (0.12–0.90)	0.03
	Age (years)	1.04 (0.88–1.20)	0.62
	Hospital type: Level 1 trauma center	0.95 (0.27–3.44)	0.94
	Position: intern/resident	0.68 (0.18–2.55)	0.57
	Experience (years)	0.93 (0.81–1.09)	0.34
	Version: CRASH	2.03 (0.76–5.84)	0.17

Discussion

We conducted a survey to evaluate the role of evidence-based risk predictions in neurosurgeon prognostication and decision-making in severe TBI. Our study yielded three main insights. First, neurosurgeon prognostic predictions were highly variable and only modestly influenced by the provision of evidence-based risk predictions. Second, the majority of neurosurgeons recommended craniotomy for hematoma evacuation, although they were less likely to do so when they believed the prognosis was very poor. Third, the provision of evidence-based risk predictions decreased the propensity to recommend craniotomy, and this effect may have been partially mediated by a change in neurosurgeon belief about prognosis.

In both scenarios, there was very little prognostic agreement among neurosurgeons, an observation that applied to both the control and intervention groups of the study. This variability in prognostication and decision making in severe TBI may reflect tensions in the existing prognostic literature. On one hand, there is rising evidence of delayed neurologic recovery in patients previously believed to have devastating prognoses[17, 18]. On the other hand, there is growing evidence of over-utilization of surgery at the end of life[6], limited cost-effectiveness of craniotomy for TBI in patients with very poor prognoses[19], and the fear of leaving patients in a state they would find unacceptable[20]. It should be noted that both hypothetical patients had poor prognoses according to the CRASH calculator. Although these scenarios had similar CRASH estimates for prognosis, the patient presentations varied in ways that led neurosurgeons to different conclusions which is an interesting area of future study. Finally, the available data about the patient was limited purposefully as many of these decisions are made quickly with little clinical information in an emergent setting.

Overall, provision of evidence-based risk estimates had a limited impact on neurosurgeon prognostic beliefs. In the first scenario, there was a modest negative influence of CRASH estimates on 6-month ADL prognosis. There was no such effect of risk estimate provision on prognostic beliefs in the second scenario, which may partially be due to the overall poorer perceived prognosis for this hypothetical patient. The limited effect of CRASH estimates may be due to the fact that neurosurgeons are not confident in the validity of the tool, that they have fixed beliefs about prognosis, or that they primarily rely on personal experience and training to make decisions. Indeed, a recent survey of neurosurgeons found that the use of risk calculators is rare in practice, and that neurosurgeons are unlikely to rely on them for decision making[15]. Finally, it is possible that neurosurgeons depending on practice type, experience, or health system in which they practice could have different responses to evidence-based risk estimates. This study included a diverse population of neurosurgeons but was not designed to delineate these differences. This represents an interesting area of future study.

Neurosurgeons who received evidence-based risk estimates were less likely to recommend craniotomy and more likely to recommend withdrawal of life-sustaining intervention. However, the majority of neurosurgeons in both scenarios, regardless of receiving the evidence-based risk estimates, recommended craniotomy. Although the effect of risk predictions was limited, exploratory analyses suggested that the impact of CRASH estimates on treatment recommendations may in part be mediated by beliefs about long-term outcomes such as functional recovery.

In the first patient scenario, belief about the patient’s prognosis was associated with the decision to offer or not offer a craniotomy. In a previous study, neurosurgeons stated they would consider not offering aggressive treatment when the chance of specific poor prognoses was 80% or greater[15]. Interestingly, in our study, although poor prognosis correlated with fewer craniotomies, many neurosurgeons were still willing to recommend craniotomy at a very low chance of functional recovery. This raises important questions about the potential of prognostic models like CRASH and IMPACT to influence neurosurgeon decision-making in practice and provides an interesting avenue for future work that seeks to understand the mechanisms of neurosurgeon decision making. Additionally, future work is required to assess the relationship between emergent decision making and recent data and recommendations for delaying prognostication in severe TBI21.

This study suggests that neurosurgeon decision making in severe TBI is highly variable, even in the presence of evidence-based prognostic estimates. Because prognostic estimates do modestly change prognostic belief, future work should address improving these estimates as well as exploring barriers to the use of prognostic prediction models by clinicians. In doing so, they must acknowledge that prognostication in severe TBI may be limited by self-fulfilling prophecy and inappropriately early decisions[21]. Additionally, while risk-based estimates provide insight into decision-making in severe TBI, there are other important factors to address such as physician communication of risk, bias, and consideration of uncertainty and timing of decisions. Because prognostication data and decision making in severe TBI are complex, there is also need for future work to characterize surgeon attitudes about the relationship between prognosis and treatment recommendation.

Limitations

Our study has several important limitations. First, we studied a non-representative convenience sample, and the sample size was less than projected (139 instead of 200). A limited sample size could decrease the significance of our findings. However this study represents a larger than typical sample of neurosurgeons and identifies trends that are interesting for further study. Second, the survey results reflect clinician perception in hypothetical scenarios and it is possible that physicians may respond differently in comparable real-world situations[22]. Not all neurosurgeons are trained alike and although we looked at hospital type and training level, a future study could look at the effects of training and demographics on surgical decision making in TBI. Third, an order effect could have influenced responses to the second scenario based on responses to the first scenario[23]. Finally, in neurosurgical practice, family preferences and communication play an important role in the shared decision making process. Our study was not designed to evaluate the role of physician-patient interaction in the decision making process and this should be addressed in future work. Finally, this work focused on neurosurgeons; decision making practices of other relevant specialists, including neurologists and neurointensivists, is an important area of future study.

Conclusion

Neurosurgeons who received evidence-based risk estimates prior to making a treatment recommendation were less likely to recommend craniotomy and more likely to suggest withholding aggressive treatment. Prognostic beliefs for the same hypothetical patients were highly variable, and many neurosurgeons recommended craniotomy even if their own prognostic estimates were poor. The provision of risk estimates had limited effect on prognostic beliefs, but nevertheless, risk estimates may partially mediate the neurosurgeons’ decision to recommend craniotomy, meaning that evidence-based estimates have the potential to influence neurosurgeon decision making. These findings raise important questions about if and how prognostic tools can be best incorporated into decision making in severe TBI.

Survey provided to neurosurgeons.

(DOCX)

Click here for additional data file.

18 Nov 2019

PONE-D-19-24519

Surgical Decision Making in the Setting of Severe Traumatic Brain Injury: A Survey of Neurosurgeons

PLOS ONE

Dear Dr. williamson,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

We would appreciate receiving your revised manuscript by Jan 02 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Panagiotis Kerezoudis, M.D., M.S.

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2.  Thank you for including your ethics statement:

"Ethics approval for this study was obtained from the university institutional review board. The survey contained a statement informing participants of the goals and details of the study and giving an opportunity for consenting to or declining participation."

i) Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

ii) Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

3. Thank you for stating the following financial disclosure:

"No".

Please provide an amended Funding Statement that declares *all* the funding or sources of support received during this specific study (whether external or internal to your organization) as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now.

Please state what role the funders took in the study.  If any authors received a salary from any of your funders, please state which authors and which funder. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

* Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

4.  Thank you for stating the following in your Competing Interests section:

"No".

i) Please complete your Competing Interests on the online submission form to state any Competing Interests. If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now

ii)  This information should be included in your cover letter; we will change the online submission form on your behalf.

5. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

6. Please amend your list of authors on the manuscript to ensure that each author is linked to an affiliation. Authors’ affiliations should reflect the institution where the work was done (if authors moved subsequently, you can also list the new affiliation stating “current affiliation:….” as necessary).

7. ** Please include your tables as part of your main manuscript and remove the individual files **. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall

The two scenarios are essentially treated as two separate questions for much of the paper, which brings up the point of multiple comparisons. Either the second scenario is a “control” which the authors did not anticipate would vary with providing prognostic data, or the issue of multiple comparisons needs to be addressed/taken into account appropriately during analyses for significance. Alternatively, the authors could have performed analyses while somehow removing/regressing out the effect of the patient scenario to see the global effect across the entire study. Either way, the possibility of performing multiple comparisons needs to be addressed.

Abstract

Methods – Please more clearly explain the groups in methods, else the results section is less clear to the reader.

Results – As written it is not clear that the surgery recommendation changing is only for one of the two groups.

Paper

194 – The effect was borderline and not a significant effect, therefore one can claim it at most as a trend only.

195 – Is the mediation analysis for both scenarios or just the 1st ?

203 – The results of a multivariable analysis are less convincing when independent variable A (6-month ADL) is relatively correlated with variable D (whether the participant received CRASH data). There seems to be a decent degree of multicollinearity between 6-month ADL and test version, which depending on the strength may be an argument against performing this particular analysis at all. Did the authors validate the use of multiple linear regression by testing Variance Inflation Factor values?

Figures and Tables:

Table 2 – In the trauma center hospital section the number and percent data don’t make sense. I think the percent data in row “other” for control and intervention columns should be in the “Level 1” row AND the number data for intervention “other” should be in the level 1 row.

Reviewer #2: The authors present a survey analysis from a random convenience sample of 139 neurosurgeons from the Congress of Neurological Surgeons (CNS) meeting 2017. The goal of the study was to evaluate surgical decision making in the setting of severe TBI and to determine the impact of availability of evidence-based risk estimates on prognostication and treatment recommendation. The authors found that prognostic beliefs and surgical recommendation were highly variable and could be modestly influenced by evidence-based risk estimates. The manuscript is well-written and relevant findings appropriately discussed. I have the following comments:

1. I would advise the authors to avoid beginning sentences in the results section of the abstract with a number.

2. Although neurotrauma training is pretty standard across most residency programs, did the authors collect information about the type of practice for attending neurosurgeons? Were there differences based on type of fellowship training? Were spine surgeons more or less likely to recommend craniotomy? Did having additional neuro-critical care fellowship training influence treatment recommendation? If this information was not collected, the authors should mention the possibility of residual confounding due to such factors as one of the limitations of the results.

3. In the multivariable analysis presented in Table 4, I notice the authors did not adjust for other collected demographic characteristics like surgeon race/ethnicity, age and years of experience. Can the authors comment on that or provide a revised analysis adjusted for these covariates?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

21 Jan 2020

We thank the editors and reviewers for their thorough review and for providing constructive critique on the manuscript. In response to your thoughtful review, we carefully re-analyzed our paper including our methods and discussion sections to make our thought process and data clearer. We feel that the reviews have significantly improved our manuscript and look forward to your comments going forward.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

28 Jan 2020

Surgical Decision Making in the Setting of Severe Traumatic Brain Injury: A Survey of Neurosurgeons

PONE-D-19-24519R1

Dear Dr. williamson,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Panagiotis Kerezoudis, M.D., M.S.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

The authors have addressed all of the reviewers' comments and the manuscript is now ready for publication.

18 Feb 2020

PONE-D-19-24519R1

Surgical Decision Making in the Setting of Severe Traumatic Brain Injury: A Survey of Neurosurgeons

Dear Dr. Williamson:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Panagiotis Kerezoudis

Academic Editor

PLOS ONE