TY - JOUR
T1 - Preoperative Mobile Health Data Improve Predictions of Recovery from Lumbar Spine Surgery
AU - Greenberg, Jacob K.
AU - Frumkin, Madelyn
AU - Xu, Ziqi
AU - Zhang, Jingwen
AU - Javeed, Saad
AU - Zhang, Justin K.
AU - Benedict, Braeden
AU - Botterbush, Kathleen
AU - Yakdan, Salim
AU - Molina, Camilo A.
AU - Pennicooke, Brenton H.
AU - Hafez, Daniel
AU - Ogunlade, John I.
AU - Pallotta, Nicholas
AU - Gupta, Munish C.
AU - Buchowski, Jacob M.
AU - Neuman, Brian
AU - Steinmetz, Michael
AU - Ghogawala, Zoher
AU - Kelly, Michael P.
AU - Goodin, Burel R.
AU - Piccirillo, Jay F.
AU - Rodebaugh, Thomas L.
AU - Lu, Chenyang
AU - Ray, Wilson Z.
N1 - Publisher Copyright:
© 2024 Congress of Neurological Surgeons 2024. All rights reserved.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - BACKGROUND AND OBJECTIVES: Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. METHODS: Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic. RESULTS: A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54). CONCLUSION: Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.
AB - BACKGROUND AND OBJECTIVES: Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. METHODS: Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic. RESULTS: A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54). CONCLUSION: Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.
KW - Ecological momentary assessment
KW - Lumbar spine surgery
KW - Mobile health data
KW - Outcome prediction
UR - http://www.scopus.com/inward/record.url?scp=85193546702&partnerID=8YFLogxK
U2 - 10.1227/neu.0000000000002911
DO - 10.1227/neu.0000000000002911
M3 - Article
C2 - 38551340
AN - SCOPUS:85193546702
SN - 0148-396X
VL - 95
SP - 617
EP - 626
JO - Neurosurgery
JF - Neurosurgery
IS - 3
ER -