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Roy JM, Kazim SF, Macciola D, Rangel DN, Rumalla K, Karimov Z, Link R, Iqbal J, Riaz MA, Skandalakis GP, Venero CV, Sidebottom RB, Dicpinigaitis AJ, Kassicieh CS, Tarawneh O, Conlon MS, Thommen R, Alvarez-Crespo DJ, Chhabra K, Sridhar S, Gill A, Vellek J, Nguyen PA, Thompson G, Robinson M, Bowers CA. Frailty as a predictor of postoperative outcomes in neurosurgery: a systematic review. J Neurosurg Sci 2024; 68:208-215. [PMID: 37878249 DOI: 10.23736/s0390-5616.23.06130-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
INTRODUCTION Baseline frailty status has been utilized to predict a wide range of outcomes and guide preoperative decision making in neurosurgery. This systematic review aims to analyze existing literature on the utilization of frailty as a predictor of neurosurgical outcomes. EVIDENCE ACQUISITION We conducted a systematic review following PRISMA guidelines. Studies that utilized baseline frailty status to predict outcomes after a neurosurgical intervention were included in this systematic review. Studies that utilized sarcopenia as the sole measure of frailty were excluded. PubMed, EMBASE, and Cochrane library was searched from inception to March 1st, 2023, to identify relevant articles. EVIDENCE SYNTHESIS Overall, 244 studies met the inclusion criteria. The 11-factor modified frailty index (mFI-11) was the most utilized frailty measure (N.=91, 37.2%) followed by the five-factor modified Frailty Index (mFI-5) (N.=80, 32.7%). Spine surgery was the most common subspecialty (N.=131, 53.7%), followed by intracranial tumor resection (N.=57, 23.3%), and post-operative complications were the most reported outcome (N.=130, 53.2%) in neurosurgical frailty studies. The USA and the Bowers author group published the greatest number of articles within the study period (N.=176, 72.1% and N.=37, 15.2%, respectively). CONCLUSIONS Frailty literature has grown exponentially over the years and has been incorporated into neurosurgical decision making. Although a wide range of frailty indices exist, their utility may vary according to their ability to be incorporated in the outpatient clinical setting.
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Affiliation(s)
- Joanna M Roy
- Topiwala National Medical College, Mumbai, India
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
| | - Syed F Kazim
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
| | - Dylan Macciola
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Dante N Rangel
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Kavelin Rumalla
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
| | - Zafar Karimov
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Remy Link
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Javed Iqbal
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
| | - Muhammad A Riaz
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
| | - Georgios P Skandalakis
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
| | | | | | | | | | - Omar Tarawneh
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Matt S Conlon
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Rachel Thommen
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | | | - Karizma Chhabra
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Sahaana Sridhar
- Burrell College of Osteopathic Medicine, Las Cruces, NM, USA
| | - Amanpreet Gill
- Burrell College of Osteopathic Medicine, Las Cruces, NM, USA
| | - John Vellek
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Phuong A Nguyen
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Grace Thompson
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Myranda Robinson
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Christian A Bowers
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, NM, USA -
- Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, NM, USA
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Roy JM, Bowers CA, Rumalla K, Covell MM, Kazim SF, Schmidt MH. Frailty Indexes in Metastatic Spine Tumor Surgery: A Narrative Review. World Neurosurg 2023; 178:117-122. [PMID: 37499751 DOI: 10.1016/j.wneu.2023.07.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023]
Abstract
Quantification of preoperative frailty is an important prognostic tool in neurosurgical decision making. Metastatic spine tumor patients undergoing surgery are frail and have unfavorable outcomes that include an increased length of stay, unfavorable discharge disposition, and increased readmission rates. These undesirable outcomes result in higher treatment costs. A heterogeneous mixture of various frailty indexes is available with marked variance in their validation, leading to disparate clinical utility. The lack of a universally accepted definition for frailty, let alone in the method of creation or elements required in the formation of a frailty index, has resulted in a body of frailty literature lacking precision for predicting neurosurgical outcomes. In this review, we examine the role of reported frailty indexes in predicting postoperative outcomes after resection of metastatic spine tumors and aim to assist as a frailty guide for helping clinical decision making.
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Affiliation(s)
- Joanna M Roy
- Topiwala National Medical College, Mumbai, India; Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA.
| | - Christian A Bowers
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA; Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, New Mexico, USA
| | - Kavelin Rumalla
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA; Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, New Mexico, USA
| | - Michael M Covell
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA; School of Medicine, Georgetown University, Seattle, Washington DC, USA
| | - Syed Faraz Kazim
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA; Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, New Mexico, USA
| | - Meic H Schmidt
- Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico, USA; Department of Neurosurgery, University of New Mexico Hospital, Albuquerque, New Mexico, USA
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