Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Renan C Castillo; Yanjie Huang; Daniel Scharfstein; Katherine Frey; Michael J Bosse; Andrew N Pollak; Heather A Vallier; Kristin R Archer; Robert A Hymes; Anna B Newcomb; Ellen J MacKenzie; Stephen Wegener; and the Major Extremity Trauma Research Consortium (METRC); Joseph R Hsu; Madhav A Karunakar; Rachel B Seymour; Stephen H Sims; Eileen Flores; Christine Churchill; David J Hak; Corey E Henderson; Hassan R Mir; Daniel S Chan; Anjan R Shah; Barbara Steverson; Jerald Westberg; Joshua L Gary; Timothy S Achor; Andrew Choo; John W Munz; Melissa Porrey; Sarah Hendrickson; Mary A Breslin; Todd O McKinley; Greg E Gaski; Laurence B Kempton; Anthony T Sorkin; Walter W Virkus; Lauren C Hill; Clifford B Jones; Debra L Sietsema; Robert V O’Toole; Katherine Ordonio; Andrea L Howe; Timothy J Zerhusen, Jr; William Obremskey; Robert H Boyce; A Alex Jahangir; Cesar S Molina; Manish K Sethi; Susan W Vanston; Eben A Carroll; Danielle Yemiola Drye; Martha B Holden; Susan C Collins; Elizabeth Wysocki

doi:10.1001/jamasurg.2018.4824

. 2018 Dec 19;154(2):e184824. doi: 10.1001/jamasurg.2018.4824

Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Renan C Castillo ^1,^✉, Yanjie Huang ¹, Daniel Scharfstein ¹, Katherine Frey ¹, Michael J Bosse ², Andrew N Pollak ³, Heather A Vallier ⁴, Kristin R Archer ⁵, Robert A Hymes ⁶, Anna B Newcomb ⁶, Ellen J MacKenzie ¹, Stephen Wegener ⁷; and the Major Extremity Trauma Research Consortium (METRC), Joseph R Hsu ², Madhav A Karunakar ², Rachel B Seymour ², Stephen H Sims ², Eileen Flores ², Christine Churchill ², David J Hak ⁸, Corey E Henderson ⁸, Hassan R Mir ⁹, Daniel S Chan ⁹, Anjan R Shah ⁹, Barbara Steverson ⁹, Jerald Westberg ¹⁰, Joshua L Gary ¹¹, Timothy S Achor ¹¹, Andrew Choo ¹¹, John W Munz ¹¹, Melissa Porrey ⁶, Sarah Hendrickson ⁴, Mary A Breslin ⁴, Todd O McKinley ¹², Greg E Gaski ¹², Laurence B Kempton ¹², Anthony T Sorkin ¹², Walter W Virkus ¹², Lauren C Hill ¹², Clifford B Jones ¹³, Debra L Sietsema ¹³, Robert V O’Toole ³, Katherine Ordonio ³, Andrea L Howe ³, Timothy J Zerhusen Jr ³, William Obremskey ⁵, Robert H Boyce ⁵, A Alex Jahangir ⁵, Cesar S Molina ⁵, Manish K Sethi ⁵, Susan W Vanston ⁵, Eben A Carroll ¹⁴, Danielle Yemiola Drye ¹⁴, Martha B Holden ¹⁴, Susan C Collins ¹, Elizabeth Wysocki ¹

¹METRC Coordinating Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

²Carolinas Medical Center, Charlotte, North Carolina

³University of Maryland R Adams Cowley Shock Trauma Center, Baltimore

⁴MetroHealth Medical Center, Cleveland, Ohio

⁵Vanderbilt University Medical Center, Nashville, Tennessee

⁶Inova Fairfax Hospital, Falls Church, Virginia

⁷Johns Hopkins University School of Medicine, Baltimore, Maryland

⁸Denver Health and Hospital Authority, Denver, Colorado

⁹Florida Orthopedic Institute/Tampa General Hospital, Tampa

¹⁰Hennepin County Medical Center/Regions Hospital, Minneapolis, Minnesota

¹¹University of Texas Health Science Center at Houston

¹²Methodist Hospital, Indianapolis, Indiana

¹³Orthopaedic Associates of Michigan, Grand Rapids

¹⁴Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina

Group Information: The Major Extremity Trauma Research Consortium (METRC) authors appear at the end of this article.

Accepted for Publication: September 14, 2018.

^✉

Corresponding Author: Renan C. Castillo, PhD, MS, Johns Hopkins Bloomberg School of Public Health, 415 N Washington St, Baltimore, MD 21231 (rcastill@jhsph.edu).

Published Online: December 19, 2018. doi:10.1001/jamasurg.2018.4824

The Major Extremity Trauma Research Consortium (METRC) Authors: Joseph R. Hsu, MD; Madhav A. Karunakar, MD; Rachel B. Seymour, PhD; Stephen H. Sims, MD; Eileen Flores, MSW, LCSW; Christine Churchill, MA; David J. Hak, MD; Corey E. Henderson, MS; Hassan R. Mir, MD, MBA; Daniel S. Chan, MD; Anjan R. Shah, MD; Barbara Steverson, MHA; Jerald Westberg, BA; Joshua L. Gary, MD; Timothy S. Achor, MD; Andrew Choo, MD; John W. Munz, MD; Melissa Porrey, MA, NCC, LPC; Sarah Hendrickson, MEd; Mary A. Breslin, BA; Todd O. McKinley, MD; Greg E. Gaski, MD; Laurence B. Kempton, MD; Anthony T. Sorkin, MD; Walter W. Virkus, MD; Lauren C. Hill, BS, CCRC; Clifford B. Jones, MD; Debra L. Sietsema, PhD, MSN; Robert V. O’Toole, MD; Katherine Ordonio, MPH; Andrea L. Howe, BS; Timothy J. Zerhusen Jr, BS; William Obremskey, MD, MPH, MMHC; Robert H. Boyce, MD; A. Alex Jahangir, MD, MMHC; Cesar S. Molina, MD; Manish K. Sethi, MD; Susan W. Vanston, MS, PT; Eben A. Carroll, MD; Danielle Yemiola Drye, EdS, MA; Martha B. Holden, AAS, AA; Susan C. Collins, MSc; Elizabeth Wysocki, MS.

Affiliations of The Major Extremity Trauma Research Consortium (METRC) Authors: METRC Coordinating Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Collins, Wysocki); Carolinas Medical Center, Charlotte, North Carolina (Hsu, Karunakar, Seymour, Sims, Flores, Churchill); University of Maryland R Adams Cowley Shock Trauma Center, Baltimore (O’Toole, Ordonio, Howe, Zerhusen); MetroHealth Medical Center, Cleveland, Ohio (Hendrickson, Breslin); Vanderbilt University Medical Center, Nashville, Tennessee (Obremskey, Boyce, Jahangir, Molina, Sethi, Vanston); Inova Fairfax Hospital, Falls Church, Virginia (Porrey); Denver Health and Hospital Authority, Denver, Colorado (Hak, Henderson); Florida Orthopedic Institute/Tampa General Hospital, Tampa (Mir, Chan, Shah, Steverson); Hennepin County Medical Center/Regions Hospital, Minneapolis, Minnesota (Westberg); University of Texas Health Science Center at Houston (Gary, Achor, Choo, Munz); Methodist Hospital, Indianapolis, Indiana (McKinley, Gaski, Kempton, Sorkin, Virkus, Hill); Orthopaedic Associates of Michigan, Grand Rapids (Jones, Sietsema); Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina (Carroll, Drye, Holden).

Author Contributions: Dr Castillo had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Castillo, Scharfstein, Frey, Bosse, Pollak, Vallier, Hymes, Newcomb, Mackenzie, Wegener, Sims, Hak, Hendrickson, Hill, Jones, Sietsema, Howe, Zerhusen, Obremskey, Sethi, Wysocki.

Acquisition, analysis, or interpretation of data: Castillo, Huang, Scharfstein, Frey, Bosse, Vallier, Archer, Hymes, Newcomb, Mackenzie, Wegener, Hsu, Karunakar, Seymour, Sims, Flores, Churchill, Henderson, Mir, Chan, Shah, Steverson, Westberg, Gary, Achor, Choo, Munz, Porrey, Hendrickson, Breslin, McKinley, Gaski, Kempton, Sorkin, Virkus, Sietsema, O’Toole, Ordonio, Howe, Zerhusen, Boyce, Jahangir, Molina, Sethi, Vanston, Carroll, Drye, Holden, Collins.

Drafting of the manuscript: Castillo, Huang, Scharfstein, Frey, Mackenzie, Wegener, Hill, Jones, Zerhusen, Sethi, Wysocki.

Critical revision of the manuscript for important intellectual content: Castillo, Scharfstein, Frey, Bosse, Pollak, Vallier, Archer, Hymes, Newcomb, Mackenzie, Wegener, Hsu, Karunakar, Seymour, Sims, Flores, Churchill, Hak, Henderson, Mir, Chan, Shah, Steverson, Westberg, Gary, Achor, Choo, Munz, Porrey, Hendrickson, Breslin, McKinley, Gaski, Kempton, Sorkin, Virkus, Jones, Sietsema, O’Toole, Ordonio, Howe, Zerhusen, Obremskey, Boyce, Jahangir, Molina, Sethi, Vanston, Carroll, Drye, Holden, Collins.

Statistical analysis: Castillo, Huang, Scharfstein, Sethi.

Obtained funding: Castillo, Scharfstein, Bosse, Mackenzie, Wegener, Obremskey, Sethi.

Administrative, technical, or material support: Frey, Pollak, Vallier, Archer, Hymes, Newcomb, Mackenzie, Sims, Flores, Churchill, Henderson, Mir, Steverson, Westberg, Gary, Achor, Choo, Munz, Breslin, McKinley, Gaski, Kempton, Hill, Jones, O’Toole, Ordonio, Howe, Boyce, Molina, Sethi, Carroll, Drye, Holden, Collins, Wysocki.

Supervision: Castillo, Scharfstein, Frey, Vallier, Hymes, Newcomb, Mackenzie, Hsu, Karunakar, Sims, Hak, Jones, O'Toole, Obremskey, Molina, Sethi.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study is supported in part by grant CDMRP W81XWH-10-2-0090 from the United States Department of Defense.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

^✉

Corresponding author.

PMCID: PMC6439663 PMID: 30566192

Abstract

Importance

Numerous studies have demonstrated that long-term outcomes after orthopedic trauma are associated with psychosocial and behavioral health factors evident early in the patient’s recovery. Little is known about how to identify clinically actionable subgroups within this population.

Objectives

To examine whether risk and protective factors measured at 6 weeks after injury could classify individuals into risk clusters and evaluate whether these clusters explain variations in 12-month outcomes.

Design, Setting, and Participants

A prospective observational study was conducted between July 16, 2013, and January 15, 2016, among 352 patients with severe orthopedic injuries at 6 US level I trauma centers. Statistical analysis was conducted from October 9, 2017, to July 13, 2018.

Main Outcomes and Measures

At 6 weeks after discharge, patients completed standardized measures for 5 risk factors (pain intensity, depression, posttraumatic stress disorder, alcohol abuse, and tobacco use) and 4 protective factors (resilience, social support, self-efficacy for return to usual activity, and self-efficacy for managing the financial demands of recovery). Latent class analysis was used to classify participants into clusters, which were evaluated against measures of function, depression, posttraumatic stress disorder, and self-rated health collected at 12 months.

Results

Among the 352 patients (121 women and 231 men; mean [SD] age, 37.6 [12.5] years), latent class analysis identified 6 distinct patient clusters as the optimal solution. For clinical use, these clusters can be collapsed into 4 groups, sorted from low risk and high protection (best) to high risk and low protection (worst). All outcomes worsened across the 4 clinical groupings. Bayesian analysis shows that the mean Short Musculoskeletal Function Assessment dysfunction scores at 12 months differed by 7.8 points (95% CI, 3.0-12.6) between the best and second groups, by 10.3 points (95% CI, 1.6-20.2) between the second and third groups, and by 18.4 points (95% CI, 7.7-28.0) between the third and worst groups.

Conclusions and Relevance

This study demonstrates that during early recovery, patients with orthopedic trauma can be classified into risk and protective clusters that account for a substantial amount of the variance in 12-month functional and health outcomes. Early screening and classification may allow a personalized approach to postsurgical care that conserves resources and targets appropriate levels of care to more patients.

This cohort study of patients with severe orthopedic injuries examines whether risk and protective factors measured at 6 weeks after injury could classify individuals into risk clusters and evaluates whether these clusters explain variations in 12-month outcomes.

Key Points

Question

Can risk and protective factors effectively classify patients with orthopedic trauma into risk clusters, and can these clusters explain variation in outcomes 1 year after injury?

Findings

This cohort study demonstrates that patients with orthopedic trauma can be classified early in the recovery process into clinically useful risk and protective clusters. These clusters account for a substantial amount of variance for a wide range of 12-month functional and health outcomes.

Meaning

The present work lays the foundation for a stratified approach to postsurgical care of orthopedic trauma.

Introduction

A growing body of literature has found psychological and social sequelae of trauma to be important factors associated with long-term outcomes.^{1,2,3,4,5,6,7,8,9,10} The factors with the strongest association have consistently been shown to be pain, depression, posttraumatic stress disorder (PTSD), tobacco use, and alcohol abuse. Protective factors include resilience, social support, and self-efficacy. All these factors can be measured early in the recovery process and may be modifiable with targeted interventions.^11,12 However, little is known about how these factors are associated or whether clinically actionable subgroups exist.

Several studies have shown a strong, and potentially etiological, link between pain during early recovery and subsequent psychological distress, specifically depression and PTSD. The Lower Extremity Assessment Project (LEAP), which compared amputation with reconstruction after limb-threatening injuries, underscored the effect of early pain intensity on the development of chronic pain^9,13: patients with higher levels of pain at 3 months after injury had greater disability and lower rates of returning to work.¹¹ Numerous other studies have demonstrated the importance of early pain intensity to be associated with pain at 6 to 12 months after injury.^14,15,16

Two psychological conditions significantly affect recovery after trauma: depression and anxiety or PTSD. The LEAP participants reported elevated levels of psychological distress, including depression, anxiety, and phobia, compared with age- and sex-matched norms.⁷ A strong, persistent association was observed between early anxiety and depression and both functional outcomes and return to work at 2 and 7 years after trauma.¹¹ In longitudinal analyses with the same data, early pain, depression, and anxiety symptoms affected function at later time points,¹⁰ and both anxiety and depression were found to largely mediate the effect of pain on function.⁹

A growing body of evidence demonstrates a strong association between tobacco use and complications in fracture repair. In a meta-analysis of 10 studies with 1221 fractures across multiple fracture sites, smoking was associated with increased rates of nonunion, wound dehiscence, and surgical infection.¹⁷ The LEAP identified more complications among smokers vs nonsmokers.¹⁸ Although alcohol use has not been consistently found to be a major factor associated with poor outcomes in prior studies,¹⁹ at least 1 study indicates that the widespread use of screening and brief interventions results in significant improvements for illicit drug use and heavy alcohol use, which are more likely to be associated with poor outcomes.²⁰

Positive psychological factors such as resilience, social support, and self-efficacy have demonstrated protective effects on long-term outcomes, including pain and employment.^11,21,22 Resilience has been described as the capacity to maintain healthy psychological function after exposure to a highly disruptive event.^23,24 Resilience has been conceptualized as both an inherited, stable trait and as a statelike variable comprising behaviors, thoughts, and actions that can be taught and enhanced.²⁵ Although no studies exist specific to orthopedic trauma, resilience has been associated with better outcomes in individuals with other types of traumatic injury.²⁶ In the LEAP, individuals with the highest self-efficacy and social support scores at 3 months were more likely to return to work at 2 and 7 years than were those with lower scores.¹¹ Several additional studies have confirmed the association between self-efficacy and pain and disability within 3 months of traumatic injury.^27,28

In this study, we leverage data from an existing prospective cohort of patients with orthopedic trauma that measured all these factors early in the recovery process and followed patients 12 months after injury. The aims of this analysis were to examine the distribution of known risk and protective factors in a population with orthopedic trauma, examine whether these factors effectively classify individuals into risk clusters, and evaluate whether these clusters explain variation in outcomes at 12 months after trauma.

Methods

This analysis was conducted as part of a larger study to evaluate the effectiveness of trauma collaborative care (TCC), which has been described elsewhere.²⁹ Briefly, TCC is an intervention to address the psychosocial needs of patients and improve transition to home and reintegration into society. Twelve level I trauma centers participated in a cluster trial of the TCC Program, conducted between July 16, 2013, and January 15, 2016, and enrolled 896 patients. The TCC Program was fully implemented in 6 centers; patients at the other 6 centers continued to receive services as usual. Included were patients 18 to 60 years of age with at least 1 orthopedic injury (upper extremity, lower extremity, spine, or pelvis) with an Abbreviated Injury Scale score of 3 or greater and a length of stay of either 5 or more days or 3 or more days with a planned readmission for additional procedures. To avoid bias owing to intervention effects (patients in the TCC Program received services prior to 6 weeks and the TCC Program targeted patients with interventions in response to the risk factors measured at that time), data from only the participants in the 6 control centers were used in the analyses reported here. Among 419 participants in this control group, 352 had a 6-week assessment (conducted within 17 weeks after injury), 341 had complete data on risk and protective factors, 322 had 12-month visit data, and 312 had both. Institutional Review Board approval was obtained from Carolinas Medical Center, Inova Fairfax Hospital, MetroHealth Medical Center, University of Maryland, R Adams Cowley Shock Trauma Center, Vanderbilt University Medical Center, Wake Forest University Baptist Medical Center, Denver Health and Hospital Authority (Denver Health Medical Center), Florida Orthopaedic Institute (Tampa General Hospital), Hennepin County Medical Center, OrthoIndy (Methodist Hospital), Orthopaedic Associates of Michigan (Spectrum Health), University of Texas Houston, and St Joseph’s Hospital. Written consent was obtained from all participants.

Data Collection

At 6 weeks after injury, patients enrolled in the control arm were scheduled to receive a telephonic recovery assessment consisting of several standard screening instruments, including 5 risk factors and 4 protective factors. Risk factors measured were: (1) pain, using an 11-point pain intensity numerical rating scale (0-4, mild; 5-6, moderate; and 7-10, severe³⁰); (2) depression, measured by Patient Health Questionnaire 9³¹ (0-4, minimal; 5-9, mild; 10-14, moderate; 15-19, moderately severe; and 20-27, severe); (3) PTSD, measured by the PTSD Checklist (17-24, minimal; 25-35, mild; 36-50, moderate; and 51-85, severe³²); (4) alcohol abuse, using items from the National Institute on Alcohol Abuse and Alcoholism’s Alcohol Screening and Brief Intervention (answers categorized as yes vs no³³); and (5) tobacco use (categorized as yes vs no). The protective factors were (1) resilience, measured by the 2-item Connor-Davidson Resilience scale (0-5, weak; 6-7, moderate; and 8, strong³⁴); (2) self-efficacy for return to work, measured by a 10-point self-efficacy scale for return to usual activity (1-4, low; 5-7, moderate; and 8-10, strong); (3) self-efficacy for managing the financial consequences of the injury, measured by a 10-point self-efficacy scale for managing the financial demands of recovery (1-4, low; 5-7, moderate; and 8-10, strong⁶); and (4) social support, measured by Behavioral Risk Factor Surveillance System single-item scale³⁵ (0-2, weak; 3, moderate; and 4, strong). Both self-efficacy scales are based on an existing scale³⁶ but were modified to capture 2 items most relevant to the population with trauma (https://www.traumasurvivorsnetwork.org/recovery_assessments/). Twelve months after baseline, participants’ functional outcomes and health conditions were assessed using (1) the Short Musculoskeletal Function Assessment (SMFA),³⁷ (2) self-rated health (measured using the first item of the Veterans RAND 12 Item Health Survey, a question that has been found to be both associated with future health and to capture a significant proportion of the variance of longer health status measures),^38,39,40 (3) depression (measured using the Patient Health Questionnaire 9),³¹ and (4) PTSD (measured using the PTSD Checklist).³² The outcomes for this study have been described in detail elsewhere.²⁹

Statistical Analysis

Statistical analysis was conducted from October 9, 2017, to July 13, 2018. Latent class analysis was used to identify an optimal number of distinct clusters based on patients’ profile of risk and protective factors measured at 6 weeks. A sequence of models, indexed by cluster size, were fit starting with 1 cluster. The goal was to identify the minimum number of clusters best describing the complexity of the data. The optimal number of clusters was determined, in part, using Akaike information criterion, Bayesian information criterion, sample-size adjusted Bayesian information criterion, bootstrap likelihood ratio test, and residual plots.⁴¹

Using the optimal number of clusters, Bayesian analysis was then used to further understand differences across clusters and whether clusters could be collapsed and ordered. For each outcome, posterior distributions for the cluster-specific means were computed (using normal models for continuous outcomes and binomial models for binary outcomes, and flat priors). From these distributions, the posterior probability of all possible orderings of the cluster-specific means was computed. These results were used to collapse and order clusters. Posterior difference in means and 95% CIs for successive risk clusters (after collapsing and ordering) are presented.

R² statistics were used to compare the various cluster solutions as well as the individual risk and protective factors with respect to their ability to explain variability in outcomes at 12 months. For each outcome, the sample size was based on both the set of patients with complete information on risk and protective factors at 6 weeks and the available data on the outcome (sample sizes ranged from 303 to 312). Statistical analyses were performed using R, version 3.4.2 (R Project for Statistical Computing).

Results

Among the 352 patients in this analysis, ages ranged from 18 to 60 years (mean [SD], 37.6 [12.5] years). A total of 252 patients (71.6%) were non-Hispanic white, 231 (65.6%) were men, and 216 (61.4%) received a college-level or higher education. Table 1 shows the distribution of risk and protective factors at 6 weeks after discharge. About half of the patients (175 [49.7%]) reported moderate to severe pain (pain intensity scores, >4), 124 (35.2%) reported moderate or greater symptoms of depression (scores on Patient Health Questionnaire 9, ≥10), and 126 (35.8%) reported moderate or greater symptoms of PTSD (scores on the PTSD Checklist, >35). Regarding protective factors, 269 patients (76.4%) reported strong resilience (score, ≥6), 273 (77.6%) reported strong social support (score, ≥3), 202 (57.4%) reported high self-efficacy (score, >7) for return to work, and 142 (40.3%) reported high self-efficacy (score, >7) for managing finances. Finally, 64 patients (18.2%) reported alcohol abuse and 92 (26.1%) reported tobacco use. Table 1 also shows the differences between participants with and without 12-month follow up data. Participants lost to follow-up (n = 17) or unavailable for follow-up (n = 13) at 12 months had higher risk and lower protective levels, on average, for all measured characteristics.

Table 1. Overall Distribution of Risk and Protective Factors at 6 Weeks.

Factor	Patients, No. (%)
Factor	Overall (N = 352)	With 12-mo Visit (n = 322)	Without 12-mo Visit (n = 30)
Risk Factors
Pain score
Mean (SD) score	4.7 (2.5)	4.6 (2.5)	5.7 (2.5)
0-4	176 (50.0)	165 (51.2)	11 (36.7)
5-6	86 (24.4)	79 (24.5)	7 (23.3)
7-10	89 (25.3)	77 (23.9)	12 (40.0)
Unknown	1 (0.3)	1 (0.3)	0
Depression score
Mean (SD) score	8.2 (6.1)	8.1 (6.1)	9.6 (6.7)
0-4,	127 (36.1)	120 (37.3)	7 (23.3)
5-9	101 (28.7)	92 (28.6)	9 (30.0)
10-14	61 (17.3)	55 (17.1)	6 (20.0)
15-19	36 (10.2)	32 (9.9)	4 (13.3)
20-27	27 (7.7)	23 (7.1)	4 (13.3)
PTSD score
Mean (SD) score	34.2 (14.8)	33.9 (14.7)	37.4 (14.7)
17-24	110 (31.3)	102 (31.7)	8 (26.7)
25-35	116 (33.0)	109 (33.9)	7 (23.3)
36-50	69 (19.6)	61 (18.9)	8 (26.7)
51-85	57 (16.2)	50 (15.5)	7 (23.3)
Alcohol abuse
Yes	64 (18.2)	58 (18.0)	6 (20.0)
No	279 (79.3)	256 (79.5)	23 (76.7)
Unknown	9 (2.6)	8 (2.5)	1 (3.3)
Tobacco use
Yes	92 (26.1)	82 (25.5)	10 (33.3)
No	260 (73.9)	240 (74.5)	20 (66.7)
Protective Factors
Resilience score
Mean (SD) score	6.6 (1.6)	6.6 (1.6)	6.4 (2.0)
0-5	82 (23.3)	72 (22.4)	10 (33.3)
6-7	127 (36.1)	121 (37.6)	6 (20.0)
8	142 (40.2)	128 (39.8)	14 (46.7)
Unknown	1 (0.3)	1 (0.3)	0
Social support score
Mean (SD) score	3.1 (1.1)	3.2 (1.1)	3.0 (1.3)
0-2	79 (22.4)	69 (21.4)	10 (33.3)
3	96 (27.3)	93 (28.9)	3 (10.0)
4	177 (50.3)	160 (49.7)	17 (56.7)
Self-efficacy: return to work score
Mean (SD) score	7.2 (2.9)	7.2 (2.8)	6.7 (3.1)
1-4	65 (18.5)	58 (18.0)	7 (23.3)
5-7	85 (24.1)	76 (23.6)	9 (30.0)
8-10	202 (57.4)	188 (58.4)	14 (46.7)
Self-efficacy: manage finances score
Mean (SD) score	5.8 (3.3)	5.9 (3.2)	5.2 (3.5)
1-4	118 (33.5)	105 (32.6)	13 (43.3)
5-7	91 (25.9)	84 (26.1)	7 (23.3)
8-10	142 (40.3)	132 (41.0)	10 (33.3)
Unknown	1 (0.3)	1 (0.3)	0

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Abbreviation: PTSD, posttraumatic stress disorder.

eTable 1 in the Supplement shows the Akaike information criterion, Bayesian information criterion, and sample-size adjusted Bayesian information criterion for cluster sizes ranging from 1 to 10. The Bayesian information criterion indicates the 2-cluster solution as optimal, although the 3-cluster solution yields a similar result. The sample-size adjusted Bayesian information criterion indicates the 6-cluster solution as optimal, although there is little difference between the 4-cluster, 5-cluster, and 6-cluster solutions. The Akaike information criterion indicates the 7-cluster solution as optimal. Finally, the likelihood ratio test clearly identifies a 6-cluster solution as optimal. The residual plots (eFigures 1A-C in the Supplement) suggest that solutions with 5 or more clusters provide comparable fits to the data. Taken together, these analyses suggest a 6-cluster solution.

Table 2 presents the 6-week profile of risk and protective factors for each of the 6 clusters. Differences were noted in the distribution of factors between clusters, and names were assigned to factors based on these differences. Cluster 1 (82 participants [23.3%]) and cluster 2 (108 participants [30.7%]) have consistently low levels of all risk factors and very high protective factors. However, in every category, cluster 2 is slightly worse than cluster 1. We labeled cluster 1 as “best” and cluster 2 as “second best.” Cluster 6 patients (54 participants [15.3%]) are far above clinical thresholds for pain, depression, and PTSD and have very low psychological protection during recovery; we labeled cluster 6 as “worst.” The 3 remaining clusters—3, 4 and 5—can all be classified as having medium protection and medium risk. In addition to medium risk and medium protection, cluster 4 exhibits very high pain and the highest levels of alcohol abuse of any cluster. Cluster 5 exhibits the lowest self-efficacy. We labeled cluster 3 as “medium,” cluster 4 as “medium-high pain,” and cluster 5 as “medium-low self-efficacy.” The distribution of clusters by selected participant characteristics, injury severity, and hospital region are in eTable 2 in the Supplement.

Table 2. Distribution of 6-Week Risk and Protective Factors Based on 6-Cluster Solution Among 352 Patients.

Factor	Cluster 1: Best (n = 82 [23.3%])	Cluster 2: Second Best (n = 108 [30.7%])	Cluster 3: Medium (n = 50 [14.2%])	Cluster 4: Medium-High Pain (n = 29 [8.2%])	Cluster 5: Medium-Low Self-efficacy (n = 29 [8.2%])	Cluster 6: Worst (n = 54 [15.3%])
Risk Factors
Pain score, mean (SD)	3.0 (2.0)	3.5 (1.7)	5.0 (1.9)	7.8 (1.1)	4.3 (1.3)	7.7 (1.3)
Depression score, mean (SD)	3.2 (3.0)	4.7 (2.5)	13.3 (3.1)	8.8 (5.4)	9.1 (3.3)	17.3 (4.4)
PTSD score, mean (SD)	20.4 (2.6)	28.3 (6.4)	40.9 (9.9)	29.1 (4.5)	41.8 (8.9)	59.1 (10.7)
Alcohol abuse, No. (%)	6 (7.3)	25 (23.1)	8 (16.0)	9 (31.0)	8 (27.6)	8 (14.8)
Tobacco use, No. (%)	2 (2.4)	30 (27.8)	4 (8.0)	13 (44.8)	12 (41.4)	31 (57.4)
Protective Factors
Resilience score, mean (SD)	7.2 (1.4)	6.8 (1.3)	5.6 (2.0)	7.4 (1.2)	6.1 (1.8)	5.7 (1.7)
Social support score, mean (SD)	3.9 (0.4)	3.2 (0.8)	2.8 (1.2)	3.7 (0.7)	2.6 (1.5)	2.4 (1.3)
Self-efficacy: return to work score, mean (SD)	9.3 (1.3)	8.3 (1.8)	7.2 (1.7)	5.0 (3.3)	3.7 (2.4)	4.8 (3.0)
Self-efficacy: manage finances score, mean (SD)	8.6 (1.8)	6.6 (2.5)	4.5 (2.6)	4.4 (3.4)	2.1 (2.2)	4.2 (3.3)

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Abbreviation: PTSD, posttraumatic stress disorder.

Table 3 displays summaries (means and proportions) of the distribution of 12-month outcomes based on the 6-cluster solution. All outcomes at 12 months, except self-rated health, display a consistent pattern: outcomes worsen between cluster 1 and cluster 2, between cluster 2 and clusters 3 to 5, and between clusters 3 to 5 and cluster 6. Table 3 displays the posterior probability of the ordering of means across the 6 clusters for each outcome. This analysis confirms strong evidence for all outcomes, except self-rated health and positive screening results for depression, of the following ordering: cluster 1 better than cluster 2 better than clusters 3, 4, and 5 better than cluster 6. After combining the “medium” clusters (ie, clusters 3, 4, and 5) to produce 4 clinical groups, the posterior differences between clusters show that the 12-month outcomes worsen by risk clusters (Table 3). As an example, the posterior mean SMFA dysfunction scores increased by 7.8 points (95% CI, 3.0-12.6) between cluster 1 and cluster 2; by 10.3 points (95% CI, 1.6-20.2) between cluster 2 and combined clusters 3, 4, and 5; and by 18.4 points (95% CI, 7.7-28.0) between the combined clusters 3, 4, and 5 and cluster 6. The trend between the collapsed risk clusters and 12-month health status holds across all the outcomes. The Figure shows the posterior distributions for SFMA and self-rated health outcomes; 12-month SMFA, depression, and PTSD outcomes are displayed in eFigure 2 in the Supplement. Although overlap exists between clusters 1 and 2 for positive screening results for depression, and between cluster 2 and combined clusters 3, 4, and 5 for self-rated health, the posterior distributions indicate that the 4 clinical groups have a differentiated health status at 12 months.

Table 3. Distribution of 12-Month Outcomes Based on 6-Cluster Solution Among 322 Patients.

12-mo Outcomes	Cluster 1: Best (n = 77)	Cluster 2: Second Best (n = 102)	Cluster 3: Medium (n = 47)	Cluster 4: Medium-High Pain (n = 25)	Cluster 5: Medium-Low SE (n = 24)	Cluster 6: Worst (n = 47)	Bayesian Analysis
							Posterior Probability of Risk Order^a	Posterior Difference Between Clusters (95% CI)
							Posterior Probability of Risk Order^a	Cluster 2 vs Cluster 1	Clusters 3, 4, and 5 vs Cluster 2	Cluster 6 vs Clusters 3, 4, and 5
SMFA: dysfunction index
Score, mean (SD)	14.9 (15.3)	22.6 (17.3)	29.3 (17.3)	37.3 (15.1)	35.6 (19.3)	51.3 (17.7)	12***6: 98.3%; 123546: 53.5%; 123456: 34.6%	7.8 (3.0 to 12.6)	10.3 (1.6 to 20.2)	18.4 (7.7 to 28.0)
Daily activities category, score, mean (SD)^b	16.3 (20.3)	25.9 (24.7)	33.9 (23.6)	41.2 (21.6)	42.3 (25.8)	58.5 (21.6)	12***6: 96.1%; 123456: 46.1%; 123546: 33.8%	9.6 (3.0 to 16.3)	12.0 (1.0 to 24.8)	20.6 (7.1 to 32.3)
Emotional status category, score, mean (SD)	20.2 (20.0)	28.6 (20.5)	40.3 (21.8)	48.1 (19.8)	43.9 (25.8)	66.2 (20.1)	12***6: 99.3%; 123546: 46.4%; 124536: 24.6%	8.4 (2.4 to 14.4)	14.6 (4.8 to 26.1)	22.9 (10.7 to 33.6)
Mobility category, score, mean (SD)	18.6 (19.4)	27.2 (21.5)	34.2 (20.0)	46.3 (20.0)	41.1 (25.4)	59.4 (18.4)	12***6: 96.0%; 123546: 64.1%; 123456: 19.9%	8.6 (2.6 to 14.6)	11.8 (0.8 to 25.5)	20.4 (6.2 to 32.0)
SMFA: bother index^c
Score, mean (SD)	13.1 (15.4)	21.2 (18.8)	31.8 (18.1)	42.9 (20.3)	39.2 (27.1)	59.7 (24.7)	12***6: 99.5%; 123546: 58.4%; 123456: 28.7%	8.0 (3.0 to 13.1)	15.4 (5.0 to 28.7)	23.1 (8.2 to 35.8)
Depression (PHQ-9)
Score, mean (SD)	3.0 (5.3)	4.9 (5.2)	7.5 (6.2)	8.1 (5.9)	8.2 (7.5)	15.7 (6.6)	12***6: 96.3%; 123456: 26.9%; 123546: 20.1%	1.9 (0.3 to 3.4)	3.0 (0.6 to 5.8)	7.9 (4.7 to 10.8)
Patients with positive screening results (score, >9), %	12	15	32	28	38	77	12***6: 64.7%; 124356: 23.4%; 123456: 14.7%	3 (−8 to 13)	18 (1 to 37)	42 (20 to 62)
PTSD (PCL)
Score, mean (SD)	22.9 (10.4)	29.0 (11.8)	36.1 (14.9)	37.0 (13.9)	40.0 (18.5)	55.9 (16.7)	12***6: 99.1%; 123456: 38.8%; 124356: 29.1%	6.1 (2.8 to 9.3)	8.3 (2.3 to 16.2)	18.6 (9.9 to 26.0)
Patients with positive screening results (score, >35), %	9	25	45	52	54	85	12***6: 97.4%; 123456: 32.1%; 123546: 26.1%	16 (5 to 26)	23 (4 to 44)	35 (13 to 54)
Self-rated health^d
Score, mean (SD)	2.1 (0.9)	2.6 (1.1)	2.9 (1.0)	2.5 (1.0)	2.8 (1.2)	3.5 (1.0)	12***6: 21.4%^e; 124356: 7.2%; 125346: 7.0%	0.6 (0.3 to 0.8)	0.1 (−0.4 to 0.6)	0.7 (0.2 to 1.3)

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Abbreviations: PCL, PTSD Checklist; PHQ-9, Patient Health Questionnaire 9; PTSD, posttraumatic stress disorder; SE, self-efficacy; SMFA, Short Musculoskeletal Function Assessment.

^{^a}

For Bayesian analysis results, the posterior probabilities of 12***6 (ie, cluster 1 better than cluster 2 better than clusters 3, 4, and 5 [in any possible order] better than cluster 6) and the 2 most common orders in addition to 12***6 were reported.

^{^b}

One missing, unknown, or refused in cluster 2.

^{^c}

Two missing, unknown, or refused in cluster 1; 1 missing, unknown, or refused in cluster 2; 2 missing, unknown, or refused in cluster 3; 1 missing, unknown, or refused in cluster 5; and 4 missing, unknown, or refused in cluster 6.

^{^d}

Two missing, unknown, or refused in cluster 6.

^{^e}

For self-rated health, the 5 most common orders are 135246 (20.9%), 134256 (20.3%), 145236 (11.8%), 124356 (7.2%), and 125346 (7.0%). For self-rated health, cluster 2 and cluster 3, 4, and 5 are not well-differentiated.

Figure. — A, SMFA dysfunction Index. B, SMFA dysfunction index–mobility. C, SMFA bother index. D, Self-rated health. Number of simulations: 1 million. SE indicates self-efficacy.

Table 4 shows the amount of variability explained by each risk factor, each protective factor, and the various cluster solutions for each outcome at 12 months. Overall, the cluster solutions perform better than each individual risk and protective factor. The proportions of variation in 12-month outcomes explained by the cluster solutions range from 12.2% to 39.7%. With the exception of self-rated health, there is little difference between the cluster solutions. For self-rated health, the 5 and 6 latent class analysis solutions and 4 clinical clusters yield the best results.

Table 4. Proportion of Variation in 12-Month Outcomes Explained by Each Risk Factor, Each Protective Factor, and Multiple Cluster Solutions.

12-mo Outcome	No.	Risk Factor					Protective Factor				Cluster
12-mo Outcome	No.	Pain	Depression	PTSD	Alcohol Abuse	Tobacco Use	Resilience	Social Support	Return to Work	Manage Finances	Analysis Based: 4 Clusters	Analysis Based: 5 Clusters	Analysis Based: 6 Clusters	Clinical Use: 4 Clusters
SMFA: dysfunction index	312	0.3028	0.2441	0.2238	0.0006	0.0515	0.0436	0.0546	0.2050	0.1479	0.3298	0.3397	0.3399^a	0.3283
Daily activities category	311	0.2360	0.1912	0.1919	0.0009	0.0321	0.0323	0.0439	0.1680	0.1096	0.2702	0.2732^a	0.2671	0.2592
Emotional status category	312	0.3279	0.2811	0.2361	0.0005	0.0503	0.0504	0.0575	0.1860	0.1474	0.3388	0.3479	0.3480^a	0.3417
Mobility category	312	0.2852	0.2158	0.1942	0.0031	0.0380	0.0467	0.0441	0.1624	0.1356	0.3056	0.3190^a	0.3144	0.2985
SMFA: bother index	303	0.3123	0.2903	0.2568	0.0000	0.0620	0.0429	0.0582	0.2041	0.1353	0.3778	0.3781	0.3847^a	0.3705
Depression	312	0.2077	0.3118	0.3968	0.0002	0.0534	0.0702	0.0978	0.1554	0.1517	0.3974^a	0.3825	0.3882	0.3855
PTSD	312	0.2279	0.2875	0.2902	0.0002	0.0639	0.0522	0.0870	0.1118	0.1144	0.3459^a	0.3303	0.3325	0.3316
Self-rated health	310	0.0576	0.0802	0.1221	0.0016	0.0334	0.0462	0.0297	0.0529	0.0612	0.1234	0.1436	0.1496^a	0.1435

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Abbreviations: PTSD, posttraumatic stress disorder; SMFA, Short Musculoskeletal Function Assessment.

^{^a}

Highest value.

Discussion

Overall, these data indicate that it is possible to identify clusters of patients based on known risk and protective factors, measured early during recovery. Furthermore, while some individual factors, such as pain, are highly associated with selected outcomes (eg, physical function), the clustering of patients using multiple factors appears to provide better stratification of persons at risk for a wide range of outcomes.

The 6 clusters and 4 clinical groups we identified suggest distinct monitoring and treatment pathways. The first 2 groups, capturing the largest percentage of patients (190 [54.0%]), were characterized by low risk and high protection. Both of these groups had mean SMFA dysfunction scores at 1 year within population norms and mean depression and PTSD scores below disability thresholds, although cluster 2 consistently scored worse by at least 1 minimal clinically important difference.⁴² Although both clusters can be expected to do well, patients in cluster 1 should achieve full recovery, barring clinical complications. Standard clinical care plus monitoring for the development of secondary conditions is likely sufficient for these patients. Patients in cluster 2, however, may benefit from additional monitoring.

The next 3 clusters, comprising 108 patients (30.7%), shared mostly subclinical levels of risk and medium levels of protection. There is significant evidence that subthreshold levels in risk factors such as pain, anxiety, and depression play an important role in poor outcomes.¹¹ These patients may benefit from involvement in peer support and self-management programs. Peer visitors and support groups may provide social support and successful role models to improve social support and resilience. Self-management programs characterized by self-monitoring, education, skill training, and goal setting can improve patients’ self-efficacy and coping skills, develop pain management strategies, and aid in symptom management.

Two clusters within this group may require additional attention. Patients in cluster 4, the medium-high pain cluster, appear predisposed for poor pain management. This possible outcome is due to high reported levels of pain, which has been shown to be associated with chronic pain,¹³ but also due to high levels of alcohol abuse, which has been associated with poor pain management outcomes.⁴³ Individuals in this cluster may benefit from more comprehensive pain management programs incorporating both pharmacologic and nonpharmacologic treatment. Ongoing substance abuse screening, assessment, and referral is also indicated for patients in this cluster. Patients in cluster 5, the medium-low self-efficacy cluster, may benefit from more aggressive enrollment in self-management interventions, which have been shown to improve self-efficacy in persons with chronic health conditions.⁴⁴

Cluster 6, with high risk and low protection, comprised 54 patients (15.3%). These patients are at very high risk for numerous conditions and generally have assessment scores that fall above screening thresholds, even at 6 weeks after injury. At 1 year, more than 90% exceed SMFA disability thresholds, and about 75% have positive screening results for depression and PTSD. These patients would likely benefit from aggressive referral to mental health and pain management services along with ongoing care coordination early in the recovery period to prevent poor long-term outcomes. Trauma centers may need to significantly increase the availability of comprehensive services if they are to meet the needs of the patients in this cluster.

The current emphasis on personalized medicine, broadly defined as identification of patients who can benefit from targeted therapies,⁴⁵ has largely focused on using biomarkers and genetic information to guide care. A broader definition expands the range of personal factors to include biological, psychological, and environmental factors to estimate susceptibility, recovery, or response to treatment in order to guide treatment to improve outcomes.⁴⁶ These data suggest that it is possible to stratify individuals after trauma according to self-reported information, laying the foundation for matching patients with appropriate levels of intervention. This approach has led to cost-effective approaches for treating low back pain.⁴⁷ By taking a stratified approach to care after trauma, it may be possible to match patients with the appropriate level of support and care and improve outcomes while conserving resources.

Limitations

Although we studied a moderate-sized sample of 352 patients from 6 trauma centers, weaknesses of this study include the heterogeneity of the sample based on type and severity of orthopedic injury and injuries to other body systems. This sample from 6 trauma centers is unlikely to be fully representative of the overall population of patients with orthopedic trauma. In addition, there were a number of measurement challenges. Although most instruments used are established, validated, reliable, and responsive measures, possible limitations include the use of an adapted measure of self-efficacy as well as patient fatigue and insufficient literacy.^{31,34,36,37,38,39,40} Furthermore, the SMFA has been shown to have a ceiling effect for certain populations.^30,37,42 Similarly, our measurements of resilience also have the potential for a ceiling effect in groups with moderate or high function. Furthermore, inclusion of additional psychosocial factors may have improved the effectiveness of our methods.²⁶

Conclusions

Overall, our study demonstrates that patients with orthopedic trauma can be classified early in the recovery process into clinically useful risk and protective clusters that account for much of the variance for a wide range of 12-month functional and health outcomes. These clusters may help develop a theoretical framework on which to build future clinical tools. These clinical tools will need to be developed using predictive modeling approaches, a validation data set, and adequate recognition of the dangers in creating arbitrary categories to describe a risk continuum.⁴⁸ The present work lays the foundation for a clinical model that can be used to assess patients to deliver a personalized approach to postsurgical care.

Supplement.

eTable 1. Selection of the Number of Clusters

eTable 2. Baseline Participant Characteristics by Cluster

eFigure 1. Pearson Residual Plots

eFigure 2. Posterior Distribution of 12-month SMFA, Depression and PTSD Outcomes Based on Six-Cluster Solution

Click here for additional data file.^{(1.1MB, pdf)}

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48.Vickers AJ, Basch E, Kattan MW. Against diagnosis. Ann Intern Med. 2008;149(3):200-203. doi: 10.7326/0003-4819-149-3-200808050-00010 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eTable 1. Selection of the Number of Clusters

eTable 2. Baseline Participant Characteristics by Cluster

eFigure 1. Pearson Residual Plots

eFigure 2. Posterior Distribution of 12-month SMFA, Depression and PTSD Outcomes Based on Six-Cluster Solution

Click here for additional data file.^{(1.1MB, pdf)}

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PERMALINK

Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Renan C Castillo, PhD, MS

Yanjie Huang, ScM

Daniel Scharfstein, ScD

Katherine Frey, PhD, MS, MPH

Michael J Bosse, MD

Andrew N Pollak, MD

Heather A Vallier, MD

Kristin R Archer, PhD, DPT

Robert A Hymes, MD

Anna B Newcomb, PhD, MSW

Ellen J MacKenzie, PhD

Stephen Wegener, PhD

Joseph R Hsu, MD

Madhav A Karunakar, MD

Rachel B Seymour, PhD

Stephen H Sims, MD

Eileen Flores, MSW, LCSW

Christine Churchill, MA

David J Hak, MD

Corey E Henderson, MS

Hassan R Mir, MD, MBA

Daniel S Chan, MD

Anjan R Shah, MD

Barbara Steverson, MHA

Jerald Westberg, BA

Joshua L Gary, MD

Timothy S Achor, MD

Andrew Choo, MD

John W Munz, MD

Melissa Porrey, MA, NCC, LPC

Sarah Hendrickson, MEd

Mary A Breslin, BA

Todd O McKinley, MD

Greg E Gaski, MD

Laurence B Kempton, MD

Anthony T Sorkin, MD

Walter W Virkus, MD

Lauren C Hill, BS, CCRC

Clifford B Jones, MD

Debra L Sietsema, PhD, MSN

Robert V O’Toole, MD

Katherine Ordonio, MPH

Andrea L Howe, BS

Timothy J Zerhusen Jr, BS

William Obremskey, MD, MPH, MMHC

Robert H Boyce, MD

A Alex Jahangir, MD, MMHC

Cesar S Molina, MD

Manish K Sethi, MD

Susan W Vanston, MS, PT

Eben A Carroll, MD

Danielle Yemiola Drye, EdS, MA

Martha B Holden, AAS, AA

Susan C Collins, MSc

Elizabeth Wysocki, MS

Abstract

Importance

Objectives

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Key Points

Question

Findings

Meaning

Introduction

Methods

Data Collection

Statistical Analysis

Results

Table 1. Overall Distribution of Risk and Protective Factors at 6 Weeks.

Table 2. Distribution of 6-Week Risk and Protective Factors Based on 6-Cluster Solution Among 352 Patients.

Table 3. Distribution of 12-Month Outcomes Based on 6-Cluster Solution Among 322 Patients.

Figure. Posterior Distribution of 12-Month Short Musculoskeletal Function Assessment (SMFA) and Self-rated Health Outcomes Based on 6-Cluster Solution.

Table 4. Proportion of Variation in 12-Month Outcomes Explained by Each Risk Factor, Each Protective Factor, and Multiple Cluster Solutions.

Discussion

Limitations