DOI: 10.5061/dryad.z8w9ghx78
title: Concerns of surrogate decision makers for acute brain injury patients: a US population survey

description: Objective: To determine whether groups of surrogates for severe acute
 brain injury (SABI) patients with poor prognosis can be identified based
 on their prioritization of goals-of-care (GOC) decisional concerns, an
 online survey of 1588 adults recruited via a probability-based panel
 representative of the US population was conducted.   Methods:
 Participants acted as a surrogate for a GOC decision for a hypothetical
 SABI patient and were randomized to one of two prognostic scenarios: the
 patient likely being left with a range of severe functional disability
 (SD) or remaining in a vegetative state (VS). Participants prioritized a
 list of 12 decisional concerns via Best-Worst Scaling. Latent class
 analysis (LCA) was used to discover decisional groups. Results: The
 completion rate was 44.6%; data weighting was conducted to mitigate
 nonresponse bias. For 792 SD respondents, LCA revealed four groups. All
 groups shared concerns regarding respecting patient wishes and minimizing
 suffering. The four groups were otherwise distinguished by unique concerns
 that their members highlighted: an older adult remaining severely disabled
 (34.4%), family consensus (26.4%), doubt regarding prognostic accuracy
 (20.7%), and cost of long-term care (18.6%).  For the 796 VS
 respondents, LCA revealed five groups. Four of the five groups had similar
 concern profiles to the four SD groups. The largest (29.0%) expressed the
 most prognostic doubt. An additional group (15.8%) prioritized religious
 concerns. Conclusions: While surrogate decision makers for patients with
 SABI are concerned with respecting patient wishes and minimizing
 suffering, certain groups highly prioritize other specific decisional
 factors.  These data can help inform future interventions for
 supporting decision makers. 
description: The survey versions for this study were developed in 2015, with
 the main data for this study then collected between February 18 and 29,
 2016, from a nationally representative online survey of US adults 30 years
 and older. The following sections outline the survey development process
 first; followed by descriptions of the sample, data collection, and
 statistical design for the main study.  
 Standard protocol approval The study was
 approved by the Yale Human Investigation Committee (protocols #1406014207
 and #1505015893).   Survey
 instrument development Two versions of the survey
 instrument were drafted by the authors that both asked a participant to
 consider an 85-year-old, previously healthy, close family member admitted
 to an ICU following a severe intracerebral hemorrhage (ICH) and remaining
 intubated and poorly responsive despite two weeks of care without
 limitations (full surveys available in Appendix e-1). Both versions of the
 survey then asked the participant to imagine that he or she was the
 patient’s surrogate decision maker for possible tracheostomy and
 gastrostomy placement (i.e., LST). The two survey
 versions that were developed differed with regards to the long-term poor
 prognosis presented. One version—the more common real-life scenario—stated
 that the patient would most likely remain unresponsive, with a small but
 unlikely chance that the patient could gradually recover the ability to
 speak, eat, recognize others, and/or travel in a wheelchair with time (the
 “severe disability” survey). The other version stated a less common but
 more severe scenario that the patient would remain bedbound and
 unresponsive long-term with certainty (the “vegetative state” survey).
 Both survey versions specified that the patient would be ventilator-free
 following tracheostomy and that the patient did not have an advance
 directive. For both
 versions of the survey, a list of concerns that a surrogate could
 prioritize when deciding on LST versus CMO for an intubated patient with
 SABI was also developed (Table e-1). To review the drafted hypothetical
 scenarios and to create the list of decision making concerns,
 semi-structured interviews were conducted with 11 family members of former
 patients in the Yale New Haven Hospital (YNHH) Neuroscience ICU (six of
 whom had requested the ICU make their relatives CMO). Family members who
 provided input were recruited by telephone within 8 months of their
 neuroscience ICU experience. Detailed notes were taken as the family
 members (1) provided feedback on the wording of the drafted hypothetical
 scenarios and (2) listed what their own top decisional concerns would be
 if acting as a surrogate decision maker. They were also asked if they
 agreed or disagreed with concerns that other families had expressed in
 prior published qualitative research regarding similar ICU goals-of-care
 decisions. Interviews with
 family members were conducted until thematic saturation regarding the
 decision making concerns was achieved, with a total of 10 concerns
 attained. Fifteen national experts—in the fields of neurology, critical
 care, palliative care, geriatrics, ethics, and medical decision making
 with publication track records on the topic of ICU GOC decision
 making—were then contacted by e-mail, given revised copies of the
 hypothetical scenarios and the list of 10 concerns elicited from the
 family interviews, and asked to provide additional feedback
 (Table e-1). The final list of 12 decision making
 concerns in Table 1 were used to create a 12-question Best-Worst Scaling
 (BWS) exercise utilized in both survey versions to allow participants to
 prioritize their concerns based on the hypothetical scenario presented. A
 BWS question asks respondents to choose the “best” (or most concerning)
 and the “worst” (or least concerning) item from a series of sets
 containing different combinations of items from a master list. An example
 is provided in Appendix e-1. BWS questions are easy to understand, and
 respondents from diverse socioeconomic backgrounds have been shown to
 provide reliable data. The format avoids scale-related response bias and
 is thus more efficient than rating scales. BWS has been employed to assess
 patients’ preferences in a wide variety of health care settings.
 The question design used in the BWS
 application was based on a Youden balanced incomplete block design. Each
 of the 12 questions presented four potential decision making concerns,
 with each concern presented an equal number of times in each version of
 the survey, and each concern presented with each of the other 11 concerns
 an equal number of times. Following the BWS exercise, both survey
 versions asked participants about their decision regarding requesting LST
 versus CMO for the hypothetical patient, as well as their certainty about
 the decision on a 4-point scale. Surveys were designed using Sawtooth
 Software, Inc. (SSI) Web v8.2. Both versions of the survey underwent
 cognitive testing among a total of 11 ethnically diverse subjects
 recruited from the local community around YNHH. The final survey versions
 incorporated feedback and were both expected to take at least eight
 minutes to complete based on testing.  
 Sample Following survey instrument
 development, participants for the main study were recruited from the GfK
 (now Ipsos) KnowledgePanel, a probability-based online panel designed to
 be representative of all non-institutionalized civilian adults living in
 US households. The 55,000 active panel members cover 97% of US households,
 and the panel sample is updated every three months through address-based
 sampling, to include those that are served only by cellphones or have no
 telephone service. Selected panel members who do not have computers or
 internet service are provided with both. Because the
 minimum age of real-life surrogate decision makers in prior studies in
 Neuro ICUs was approximately 30 years old, this age was used as the
 minimum age for recruitment of participants from the survey
 panel.   Data collection
 Following a two-day operational test period with 36 participants,
 the KnowledgePanel members accessed the surveys using a link sent by the
 company and were randomized to one of the two survey versions, with their
 differing prognostic scenarios. Any respondent completing a survey in less
 than eight minutes was excluded. The KnowledgePanel provided the following
 basic demographic information regarding participants: age, gender, race,
 education, household internet access, region of residence, marital status,
 employment status, and income. All other demographic variables were
 self-reported by respondents directly in response to additional survey
 questions.   Statistical
 analysis Data from the severe disability and vegetative
 state survey versions were analyzed separately. To minimize bias due to
 differential nonresponse in the KnowledgePanel recruitment process as well
 as nonresponse during the field period, iterative proportional fitting was
 used to weight the survey data and geodemographically match the U.S.
 population age 30 and older, based on the 2015 Current Population
 Survey. Latent class analysis was performed on the data
 from the surveys’ BWS exercise using Sawtooth Software’s Latent Class
 Segmentation Module v4 to estimate part-worth utilities (i.e., importance
 scores for each of the concerns listed in the surveys), scaled from zero
 to 100, and to determine previously unmeasured class membership among
 participants. Class solutions were replicated 20 times from random
 starting seeds, with a maximum of five groups to allow for possible
 heterogeneity of survey responses. For both versions of
 the survey, the Bayesian Information Criterion (BIC) and Akaike
 Information Criterion (AIC) decreased as the number of groups increased,
 suggesting improving fit. However, for the severe disability survey, two
 of the groups in the five-group solution had utility distributions that
 were nearly identical (Figure e-1), which made four groups the best
 practical solution. For both survey versions,
 associations between participant characteristics and group membership were
 explored by comparing univariate data and subsequently performing
 multinomial logistic regression including only those surveys with all
 questions answered. Analyses were conducted using SAS software v14 (SAS
 Institute Inc., Cary, NC).   Sample
 size Factors affecting the performance of latent class
 analyses include the number of items in the BWS exercise, the manner in
 which different concerns cluster among participants, and the eventual size
 of each group in the best solutions. Meaningful segmentation analyses have
 been performed in the medical literature with as few as 183 respondents
 for a survey that contained as many as 18 items to prioritize. In
 comparison, recruitment for this study obtained nearly 800 respondents for
 each of two 12-item BWS exercises.  
 Data availability Anonymized data from this
 study can be shared by request by any qualified investigator for the
 purposes of replicated procedures and results.