Sara J E Beishuizen1, Barbara C van Munster2,3, Annemarieke de Jonghe4, Ameen Abu-Hanna5, Bianca M Buurman1, Sophia E de Rooij1,3. 1. Department of Internal Medicine, Geriatrics Section, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. 2. Department of Geriatrics, Gelre Hospitals, Apeldoorn, The Netherlands. 3. Department of Internal Medicine, University Center of Geriatric Medicine, University Medical Center Groningen, Groningen, The Netherlands. 4. Department of Geriatrics, Tergooi Hospitals, Hilversum, The Netherlands. 5. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Abstract
OBJECTIVES: Change in cognitive functioning is often observed after hip fracture. Different patterns, with both improvement and decline, are expected, depending on premorbid cognitive functioning and events that occur during hospitalization. These patterns are unknown and important for older hip fracture patients with different levels of premorbid cognitive functioning. DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS: We conducted a secondary analysis of a multi-center randomized controlled trial. 302 consecutive patients aged 65-102 years old, admitted for hip fracture surgery, were enrolled. The Mini Mental State Examination (MMSE) was obtained at hospital admission, at discharge, and at 3 and 12 months after discharge. Cognitive trajectories were identified with Group Based Trajectory Modelling, using the repeated MMSE measurements as outcome variable. To illustrate the specific characteristics of this relative novel methodological approach, it was contrasted with results obtained from linear mixed effects modeling. RESULTS: 146 (48.3%) patients had premorbid cognitive impairment and 85 patients (28.1%) experienced delirium during admission. Three distinct cognitive trajectories were identified and labeled based on different MMSE course over time: improvement (57.9%), stable (28.1%), and rapid decline (13.9%), with an annual MMSE change of 1.7, 0.8, and -3.5 points respectively. With mixed effects modeling an overall annual increase of 0.7 MMSE points was estimated for the group as a whole. CONCLUSION: Three distinct cognitive trajectories were identified in a population of older hip fracture patients. These trajectory groups can be used as a starting point to inform patients and caregivers on the possible prognosis after hip fracture. Group based trajectory modelling is a useful technique when the purpose is to describe patterns of change within a population and a variety of trajectories are expected to exist.
OBJECTIVES: Change in cognitive functioning is often observed after hip fracture. Different patterns, with both improvement and decline, are expected, depending on premorbid cognitive functioning and events that occur during hospitalization. These patterns are unknown and important for older hip fracture patients with different levels of premorbid cognitive functioning. DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS: We conducted a secondary analysis of a multi-center randomized controlled trial. 302 consecutive patients aged 65-102 years old, admitted for hip fracture surgery, were enrolled. The Mini Mental State Examination (MMSE) was obtained at hospital admission, at discharge, and at 3 and 12 months after discharge. Cognitive trajectories were identified with Group Based Trajectory Modelling, using the repeated MMSE measurements as outcome variable. To illustrate the specific characteristics of this relative novel methodological approach, it was contrasted with results obtained from linear mixed effects modeling. RESULTS: 146 (48.3%) patients had premorbid cognitive impairment and 85 patients (28.1%) experienced delirium during admission. Three distinct cognitive trajectories were identified and labeled based on different MMSE course over time: improvement (57.9%), stable (28.1%), and rapid decline (13.9%), with an annual MMSE change of 1.7, 0.8, and -3.5 points respectively. With mixed effects modeling an overall annual increase of 0.7 MMSE points was estimated for the group as a whole. CONCLUSION: Three distinct cognitive trajectories were identified in a population of older hip fracture patients. These trajectory groups can be used as a starting point to inform patients and caregivers on the possible prognosis after hip fracture. Group based trajectory modelling is a useful technique when the purpose is to describe patterns of change within a population and a variety of trajectories are expected to exist.
Authors: Lori A Daiello; Annie M Racine; Ray Yun Gou; Edward R Marcantonio; Zhongcong Xie; Lisa J Kunze; Kamen V Vlassakov; Sharon K Inouye; Richard N Jones; David Alsop; Thomas Travison; Steven Arnold; Zara Cooper; Bradford Dickerson; Tamara Fong; Eran Metzger; Alvaro Pascual-Leone; Eva M Schmitt; Mouhsin Shafi; Michele Cavallari; Weiying Dai; Simon T Dillon; Janet McElhaney; Charles Guttmann; Tammy Hshieh; George Kuchel; Towia Libermann; Long Ngo; Daniel Press; Jane Saczynski; Sarinnapha Vasunilashorn; Margaret O'Connor; Eyal Kimchi; Jason Strauss; Bonnie Wong; Michael Belkin; Douglas Ayres; Mark Callery; Frank Pomposelli; John Wright; Marc Schermerhorn; Tatiana Abrantes; Asha Albuquerque; Sylvie Bertrand; Amanda Brown; Amy Callahan; Madeline D'Aquila; Sarah Dowal; Meaghan Fox; Jacqueline Gallagher; Rebecca Anna Gersten; Ariel Hodara; Ben Helfand; Jennifer Inloes; Jennifer Kettell; Aleksandra Kuczmarska; Jacqueline Nee; Emese Nemeth; Lisa Ochsner; Kerry Palihnich; Katelyn Parisi; Margaret Puelle; Sarah Rastegar; Margaret Vella; Guoquan Xu; Margaret Bryan; Jamey Guess; Dee Enghorn; Alden Gross; Yun Gou; Daniel Habtemariam; Ilean Isaza; Cyrus Kosar; Christopher Rockett; Douglas Tommet; Ted Gruen; Meg Ross; Katherine Tasker; James Gee; Ann Kolanowski; Margaret Pisani; Sophia de Rooij; Selwyn Rogers; Stephanie Studenski; Yaakov Stern; Anthony Whittemore; Gary Gottlieb; John Orav; Reisa Sperling Journal: Anesthesiology Date: 2019-09 Impact factor: 7.892
Authors: Paolo Astrone; Monica Rodrigues Perracini; Finbarr C Martin; David R Marsh; Matteo Cesari Journal: Aging Clin Exp Res Date: 2022-07-13 Impact factor: 4.481
Authors: Emerson M Wickwire; M Doyinsola Bailey; Virend K Somers; Mukta C Srivastava; Steven M Scharf; Abree M Johnson; Jennifer S Albrecht Journal: J Clin Sleep Med Date: 2021-06-01 Impact factor: 4.324
Authors: Michelle Takemoto; Todd M Manini; Dori E Rosenberg; Amanda Lazar; Zvinka Z Zlatar; Sai Krupa Das; Jacqueline Kerr Journal: Am J Prev Med Date: 2018-10 Impact factor: 5.043