Predictive Model of Gait Recovery at One Month after Hip Fracture from a National Cohort of 25,607 Patients: The Hip Fracture Prognosis (HF-Prognosis) Tool

Cristina González de Villaumbrosia, Pilar Sáez López, Isaac Martín de Diego, Carmen Lancho Martín, Marina Cuesta Santa Teresa, Teresa Alarcón, Cristina Ojeda Thies, Rocío Queipo Matas, Juan Ignacio González-Montalvo and on behalf of the Participants in the Spanish National Hip Fracture Registry
Additional contact information
Cristina González de Villaumbrosia: Hospital Universitario Rey Juan Carlos, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Pilar Sáez López: Hospital Universitario Fundación Alcorcón, Instituto de Investigación Hospital Universitario La Paz, 28046 Madrid, Spain
Isaac Martín de Diego: Data Science Lab, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Carmen Lancho Martín: Data Science Lab, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Marina Cuesta Santa Teresa: Data Science Lab, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Teresa Alarcón: Hospital Universitario La Paz, Instituto de Investigación Hospital Universitario La Paz, 28046 Madrid, Spain
Cristina Ojeda Thies: Hospital Universitario 12 De Octubre, 28041 Madrid, Spain
Rocío Queipo Matas: Data Science Lab, Universidad Europea de Madrid, 28005 Madrid, Spain
Juan Ignacio González-Montalvo: Hospital Universitario La Paz, Instituto de Investigación Hospital Universitario La Paz, 28046 Madrid, Spain
on behalf of the Participants in the Spanish National Hip Fracture Registry: SNHFR are listed in acknowledgments.

IJERPH, 2021, vol. 18, issue 7, 1-17

Abstract: The aim of this study was to develop a predictive model of gait recovery after hip fracture. Data was obtained from a sample of 25,607 patients included in the Spanish National Hip Fracture Registry from 2017 to 2019. The primary outcome was recovery of the baseline level of ambulatory capacity. A logistic regression model was developed using 40% of the sample and the model was validated in the remaining 60% of the sample. The predictors introduced in the model were: age, prefracture gait independence, cognitive impairment, anesthetic risk, fracture type, operative delay, early postoperative mobilization, weight bearing, presence of pressure ulcers and destination at discharge. Five groups of patients or clusters were identified by their predicted probability of recovery, including the most common features of each. A probability threshold of 0.706 in the training set led to an accuracy of the model of 0.64 in the validation set. We present an acceptably accurate predictive model of gait recovery after hip fracture based on the patients’ individual characteristics. This model could aid clinicians to better target programs and interventions in this population.

Keywords: predictive model; hip fracture; gait recovery (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:7:p:3809-:d:530702

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