Association between 6-Week Postdischarge Risk Classification and 12-Month Outcomes after Orthopedic Trauma

Renan Carlos Castillo, Yanjie Huang, Daniel O 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 T Wegener, Joseph R. Hsu, Madhav A. Karunakar, Rachel B. Seymour, Stephen H. Sims, Eileen Flores, Christine Churchill, David J. Hak, Corey E. HendersonHassan 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, 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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJAMA Surgery
DOIs
StateAccepted/In press - Jan 1 2018

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Orthopedics
Wounds and Injuries
Self Efficacy
Post-Traumatic Stress Disorders
Health
Depression
Bayes Theorem
Patient Discharge
Trauma Centers
Tobacco Use
Social Support
Alcoholism
Observational Studies
Outcome Assessment (Health Care)
Prospective Studies
Pain
Population
Protective Factors

ASJC Scopus subject areas

  • Surgery

Cite this

Association between 6-Week Postdischarge Risk Classification and 12-Month Outcomes after Orthopedic Trauma. / Castillo, Renan Carlos; Huang, Yanjie; Scharfstein, Daniel O; Frey, Katherine; Bosse, Michael J.; Pollak, Andrew N.; Vallier, Heather A.; Archer, Kristin R.; Hymes, Robert A.; Newcomb, Anna B.; Mackenzie, Ellen J; Wegener, Stephen T; Hsu, Joseph R.; Karunakar, Madhav A.; Seymour, Rachel B.; Sims, Stephen H.; Flores, Eileen; Churchill, Christine; Hak, David J.; Henderson, Corey E.; Mir, Hassan R.; Chan, Daniel S.; Shah, Anjan R.; Steverson, Barbara; Westberg, Jerald; Gary, Joshua L.; Achor, Timothy S.; Choo, Andrew; Munz, John W.; Porrey, Melissa; Hendrickson, Sarah; Breslin, Mary A.; McKinley, Todd O.; Gaski, Greg E.; Kempton, Laurence B.; Sorkin, Anthony T.; Virkus, Walter W.; Hill, Lauren C.; Jones, Clifford B.; Sietsema, Debra L.; O'Toole, Robert V.; Ordonio, Katherine; Howe, Andrea L.; Zerhusen, Timothy J.; Obremskey, William; Boyce, Robert H.; Jahangir, A. Alex; Molina, Cesar S.; Sethi, Manish K.; Vanston, Susan W.; Carroll, Eben A.; Drye, Danielle Yemiola; Holden, Martha B.; Collins, Susan C.; Wysocki, Elizabeth.

In: JAMA Surgery, 01.01.2018.

Research output: Contribution to journalArticle

Castillo, RC, Huang, Y, Scharfstein, DO, Frey, K, Bosse, MJ, Pollak, AN, Vallier, HA, Archer, KR, Hymes, RA, Newcomb, AB, Mackenzie, EJ, Wegener, ST, Hsu, JR, Karunakar, MA, Seymour, RB, Sims, SH, Flores, E, Churchill, C, Hak, DJ, Henderson, CE, Mir, HR, Chan, DS, Shah, AR, Steverson, B, Westberg, J, Gary, JL, Achor, TS, Choo, A, Munz, JW, Porrey, M, Hendrickson, S, Breslin, MA, McKinley, TO, Gaski, GE, Kempton, LB, Sorkin, AT, Virkus, WW, Hill, LC, Jones, CB, Sietsema, DL, O'Toole, RV, Ordonio, K, Howe, AL, Zerhusen, TJ, Obremskey, W, Boyce, RH, Jahangir, AA, Molina, CS, Sethi, MK, Vanston, SW, Carroll, EA, Drye, DY, Holden, MB, Collins, SC & Wysocki, E 2018, 'Association between 6-Week Postdischarge Risk Classification and 12-Month Outcomes after Orthopedic Trauma', JAMA Surgery. https://doi.org/10.1001/jamasurg.2018.4824
Castillo, Renan Carlos ; Huang, Yanjie ; Scharfstein, Daniel O ; Frey, Katherine ; Bosse, Michael J. ; Pollak, Andrew N. ; Vallier, Heather A. ; Archer, Kristin R. ; Hymes, Robert A. ; Newcomb, Anna B. ; Mackenzie, Ellen J ; Wegener, Stephen T ; Hsu, Joseph R. ; Karunakar, Madhav A. ; Seymour, Rachel B. ; Sims, Stephen H. ; Flores, Eileen ; Churchill, Christine ; Hak, David J. ; Henderson, Corey E. ; Mir, Hassan R. ; Chan, Daniel S. ; Shah, Anjan R. ; Steverson, Barbara ; Westberg, Jerald ; Gary, Joshua L. ; Achor, Timothy S. ; Choo, Andrew ; Munz, John W. ; Porrey, Melissa ; Hendrickson, Sarah ; Breslin, Mary A. ; McKinley, Todd O. ; Gaski, Greg E. ; Kempton, Laurence B. ; Sorkin, Anthony T. ; Virkus, Walter W. ; Hill, Lauren C. ; Jones, Clifford B. ; Sietsema, Debra L. ; O'Toole, Robert V. ; Ordonio, Katherine ; Howe, Andrea L. ; Zerhusen, Timothy J. ; Obremskey, William ; Boyce, Robert H. ; Jahangir, A. Alex ; Molina, Cesar S. ; Sethi, Manish K. ; Vanston, Susan W. ; Carroll, Eben A. ; Drye, Danielle Yemiola ; Holden, Martha B. ; Collins, Susan C. ; Wysocki, Elizabeth. / Association between 6-Week Postdischarge Risk Classification and 12-Month Outcomes after Orthopedic Trauma. In: JAMA Surgery. 2018.
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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.",
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TY - JOUR

T1 - Association between 6-Week Postdischarge Risk Classification and 12-Month Outcomes after Orthopedic Trauma

AU - Castillo, Renan Carlos

AU - Huang, Yanjie

AU - Scharfstein, Daniel O

AU - Frey, Katherine

AU - Bosse, Michael J.

AU - Pollak, Andrew N.

AU - Vallier, Heather A.

AU - Archer, Kristin R.

AU - Hymes, Robert A.

AU - Newcomb, Anna B.

AU - Mackenzie, Ellen J

AU - Wegener, Stephen T

AU - Hsu, Joseph R.

AU - Karunakar, Madhav A.

AU - Seymour, Rachel B.

AU - Sims, Stephen H.

AU - Flores, Eileen

AU - Churchill, Christine

AU - Hak, David J.

AU - Henderson, Corey E.

AU - Mir, Hassan R.

AU - Chan, Daniel S.

AU - Shah, Anjan R.

AU - Steverson, Barbara

AU - Westberg, Jerald

AU - Gary, Joshua L.

AU - Achor, Timothy S.

AU - Choo, Andrew

AU - Munz, John W.

AU - Porrey, Melissa

AU - Hendrickson, Sarah

AU - Breslin, Mary A.

AU - McKinley, Todd O.

AU - Gaski, Greg E.

AU - Kempton, Laurence B.

AU - Sorkin, Anthony T.

AU - Virkus, Walter W.

AU - Hill, Lauren C.

AU - Jones, Clifford B.

AU - Sietsema, Debra L.

AU - O'Toole, Robert V.

AU - Ordonio, Katherine

AU - Howe, Andrea L.

AU - Zerhusen, Timothy J.

AU - Obremskey, William

AU - Boyce, Robert H.

AU - Jahangir, A. Alex

AU - Molina, Cesar S.

AU - Sethi, Manish K.

AU - Vanston, Susan W.

AU - Carroll, Eben A.

AU - Drye, Danielle Yemiola

AU - Holden, Martha B.

AU - Collins, Susan C.

AU - Wysocki, Elizabeth

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

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