Prognostication survey

Survey Purpose

Medical providers are commonly called upon to synthesize available clinical information to estimate a patient's prognosis and make treatment recommendations. In patients with severe brain injury, accurate prognostication can be challenging. At the same time, treatment recommendations (e.g. long-term support or withdrawal of life-sustaining therapy) are important.

This survey focuses on the specific example of prognostication of outcome in comatose survivors of cardiac arrest with anoxic brain injury. Our goal is to understand provider values and preferences so that new prognostication strategies we are developing meet providers' needs. The survey is intended for any healthcare provider who cares for patients with acute brain injury.

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* 1. What is your current primary job title? (Primary job is based on the job you perform for more than 50 percent of your overall time working)

Attending Physician

Fellow

Resident

Pharmacist

Advanced Practice Provider

Nurse

Researcher (PhD, MS, etc)

Student

Other (please specify)

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* 2. How many years have you been involved in clinical patient care?

0 - I do not take care of patients clinically

<5 years

5-10 years

>10 years

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* 3. What is the main clinical setting in which you work?

Teaching hospital

Non-teaching hospital

Non-hospital clinical setting (e.g. clinic)

None - I do not work clinically

Other (please specify)

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* 4. What medical specialty do you practice for most of your clinical work?

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* 5. What is your gender?

Female

Male

Other/Prefer not to answer

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* 6. Where in the world do you practice?

North America

South America

Asia

Europe

Africa

Australia

Other

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* 7. Which belief system do you most associate with?

Catholic

Jewish

Muslim

Hindu

Protestant

Prefer not to answer

Not religious

Other (please specify)

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* 8. Which prognostic tests do you currently use in your practice? (Choose all that apply)

S100b serum biomarker

NSE serum biomarker

CT scan

Physical exam

Somatosensory evoked potentials

EEG

MRI

Prognostic uncertainty

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* 9. Decisions based on diagnostic bad test results:
You perform a prognostic test that predicts a 50 year old previously healthy patient will not awaken from coma. Family has told you that if she could not awaken with good functional outcome, she would not want long-term life-sustaining therapy. Thus, you advise withdrawal of life-sustaining therapy with a transition to comfort-oriented care.

If the objective is to never make a mistake, life-sustaining therapy can never be withdrawn because no test is perfect.

What is an acceptable error rate in this scenario (i.e. the patient could actually have awakened with good functional outcome had life-sustaining therapy been continued)?

1 error in 10,000 cases (0.01% false positive rate)

1 error in 1,000 cases (0.1% false positive rate)

1 error in 100 cases (1% false positive rate)

1 error in 10 cases (10% false positive rate)

1 error in 5 cases (20% false positive rate)

1 error in 2 cases (50% false positive rate)

This error rate doesn't matter to me

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* 10. Same scenario, good diagnostic test result:
You perform a prognostic test that predicts a 50 year old previously healthy patient will awaken and experience a good recovery. Thus, based on your patient's values and preferences, you advise life-sustaining therapy be continued and proceed with tracheostomy, PEG tube placement, and transition to long-term care.

If the objective is to never make a mistake, life-sustaining therapy should never be continued based on a favorable test result, because no test is perfect.

What is an acceptable error rate in this scenario (i.e. long-term care is continued in a patient with no potential to regain consciousness)?

1 error in 10,000 cases (0.01% false positive rate)

1 error in 1,000 cases (0.1% false positive rate)

1 error in 100 cases (1% false positive rate)

1 error in 10 cases (10% false positive rate)

1 error in 5 cases (20% false positive rate)

1 error in 2 cases (50% false positive rate)

This error rate doesn't matter to me

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* 11. One way to improve accuracy in a prognostic decision is to delay the decision until more information is available. However, delaying decisions has financial, ethical and emotional costs to patients, families, providers and society.

For the most useful prognostic tool, what is the best balance between accuracy and length of time before data are available?

Highly accurate

Very timely

Clear

i We adjusted the number you entered based on the slider’s scale.

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* 12. Please rank the prognostic tool that would be most useful to you:

Perfectly accurate in predicting good outcome, but based on data not available for at least 14 days after admission

Perfectly accurate in predicting bad outcome, but based on data not available for at least 14 days after admission

Very accurate in predicting good outcome, based on data available in the first 14 days after admission

Very accurate in predicting bad outcome, based on data available in the first 14 days after admission

Moderately accurate in predicting good outcome, based on data available in the first few days after admission

Moderately accurate in predicting bad outcome, based on data available in the first few days after admission

Somewhat accurate in predicting good outcome, based on data available in the first few hours after admission

Somewhat accurate in predicting bad outcome, based on data available in the first few hours after admission

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* 13. I prefer my prognostic test be optimized to:

Predict bad outcomes accurately

Predict good outcomes accurately

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* 14. You are asked to advise the family of a 65y/o previously functional patient with several pre-existing medical comorbidities whether there is a chance of good outcome. They would withdraw life-sustaining therapy if the patient "has no chance" of experiencing this outcome.

At day 3, I would be willing to wait 24h longer for additional data that would let me refine my prediction of good outcome from:

	No	Yes
0-60% good outcome to 0-40% good outcome	0-60% good outcome to 0-40% good outcome No	0-60% good outcome to 0-40% good outcome Yes
0-40% good outcome to 0-20% good outcome	0-40% good outcome to 0-20% good outcome No	0-40% good outcome to 0-20% good outcome Yes
0-20% good outcome to 0-10% good outcome	0-20% good outcome to 0-10% good outcome No	0-20% good outcome to 0-10% good outcome Yes
0-10% good outcome to 0-5% good outcome	0-10% good outcome to 0-5% good outcome No	0-10% good outcome to 0-5% good outcome Yes
0-5% good outcome to 0-2% good outcome	0-5% good outcome to 0-2% good outcome No	0-5% good outcome to 0-2% good outcome Yes
0-2% good outcome to 0-1% good outcome	0-2% good outcome to 0-1% good outcome No	0-2% good outcome to 0-1% good outcome Yes
0-1% good outcome to 0-0.5% good outcome	0-1% good outcome to 0-0.5% good outcome No	0-1% good outcome to 0-0.5% good outcome Yes
0-0.5% good outcome to 0-0.1% good outcome	0-0.5% good outcome to 0-0.1% good outcome No	0-0.5% good outcome to 0-0.1% good outcome Yes
0-0.1% good outcome to 0-0.01% good outcome	0-0.1% good outcome to 0-0.01% good outcome No	0-0.1% good outcome to 0-0.01% good outcome Yes

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* 15. You are asked to advise the family of a 65y/o previously functional patient with several pre-existing medical comorbidities whether there is a chance of good outcome. They would withdraw life-sustaining therapy if the patient "has no chance" of experiencing this outcome.

At day 3, I would be willing to wait 5 to 7 days longer for additional data that would let me refine my prediction of good outcome from:

	No	Yes
0-60% good outcome to 0-40% good outcome	0-60% good outcome to 0-40% good outcome No	0-60% good outcome to 0-40% good outcome Yes
0-40% good outcome to 0-20% good outcome	0-40% good outcome to 0-20% good outcome No	0-40% good outcome to 0-20% good outcome Yes
0-20% good outcome to 0-10% good outcome	0-20% good outcome to 0-10% good outcome No	0-20% good outcome to 0-10% good outcome Yes
0-10% good outcome to 0-5% good outcome	0-10% good outcome to 0-5% good outcome No	0-10% good outcome to 0-5% good outcome Yes
0-5% good outcome to 0-2% good outcome	0-5% good outcome to 0-2% good outcome No	0-5% good outcome to 0-2% good outcome Yes
0-2% good outcome to 0-1% good outcome	0-2% good outcome to 0-1% good outcome No	0-2% good outcome to 0-1% good outcome Yes
0-1% good outcome to 0-0.5% good outcome	0-1% good outcome to 0-0.5% good outcome No	0-1% good outcome to 0-0.5% good outcome Yes
0-0.5% good outcome to 0-0.1% good outcome	0-0.5% good outcome to 0-0.1% good outcome No	0-0.5% good outcome to 0-0.1% good outcome Yes
0-0.1% good outcome to 0-0.01% good outcome	0-0.1% good outcome to 0-0.01% good outcome No	0-0.1% good outcome to 0-0.01% good outcome Yes

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* 16. You are asked to advise the family of a 65y/o previously functional patient with several pre-existing medical comorbidities whether there is a chance of good outcome. They would withdraw life-sustaining therapy if the patient "has no chance" of experiencing this outcome.

At day 14, I would be willing to wait 5 to 7 days longer for additional data that would let me refine my prediction of good outcome from:

	No	Yes
0-60% good outcome to 0-40% good outcome	0-60% good outcome to 0-40% good outcome No	0-60% good outcome to 0-40% good outcome Yes
0-40% good outcome to 0-20% good outcome	0-40% good outcome to 0-20% good outcome No	0-40% good outcome to 0-20% good outcome Yes
0-20% good outcome to 0-10% good outcome	0-20% good outcome to 0-10% good outcome No	0-20% good outcome to 0-10% good outcome Yes
0-10% good outcome to 0-5% good outcome	0-10% good outcome to 0-5% good outcome No	0-10% good outcome to 0-5% good outcome Yes
0-5% good outcome to 0-2% good outcome	0-5% good outcome to 0-2% good outcome No	0-5% good outcome to 0-2% good outcome Yes
0-2% good outcome to 0-1% good outcome	0-2% good outcome to 0-1% good outcome No	0-2% good outcome to 0-1% good outcome Yes
0-1% good outcome to 0-0.5% good outcome	0-1% good outcome to 0-0.5% good outcome No	0-1% good outcome to 0-0.5% good outcome Yes
0-0.5% good outcome to 0-0.1% good outcome	0-0.5% good outcome to 0-0.1% good outcome No	0-0.5% good outcome to 0-0.1% good outcome Yes
0-0.1% good outcome to 0-0.01% good outcome	0-0.1% good outcome to 0-0.01% good outcome No	0-0.1% good outcome to 0-0.01% good outcome Yes