Ana Royuela1, Francisco M Kovacs2, Carlos Campillo3, Montserrat Casamitjana4, Alfonso Muriel5, Víctor Abraira6. 1. CIBER Epidemiología y Salud Pública (CIBERESP), C/ Melchor Fernandez Almagro 3-5, 28029 Madrid, Spain; Unidad de Bioestadística Clínica, Hospital Ramón y Cajal, IRICYS, C/ Colmenar Viejo, 9, 28031 Madrid, Spain; Spanish Back Pain Research Network, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain. Electronic address: ana.royuela@hrc.es. 2. Spanish Back Pain Research Network, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain; Departamento Cientıfico, Fundación Kovacs, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain. 3. Servei de Salut de les Illes Balears (Ib-Salut), Calle Reina Esclaramunda 9, 07003 Palma de Majorca, Spain. 4. Regió Sanitària de Barcelona | Consorci Sanitari de Barcelona, Servei Català de la Salut (CatSalut), Parc Sanitari Pere Virgili - Edifici Mestral - Esteve Terradas, 30 4ta planta, 08023 Barcelona, Spain. 5. Unidad de Bioestadística Clínica, Hospital Ramón y Cajal, IRICYS, C/ Colmenar Viejo, 9, 28031 Madrid, Spain; Spanish Back Pain Research Network, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain. 6. CIBER Epidemiología y Salud Pública (CIBERESP), C/ Melchor Fernandez Almagro 3-5, 28029 Madrid, Spain; Unidad de Bioestadística Clínica, Hospital Ramón y Cajal, IRICYS, C/ Colmenar Viejo, 9, 28031 Madrid, Spain; Spanish Back Pain Research Network, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain.
Abstract
BACKGROUND CONTEXT: In the context of shared decision-making, a valid estimation of the probability that a given patient will improve after a specific treatment is valuable. PURPOSE: To develop models that predict the improvement of spinal pain, referred pain, and disability in patients with subacute or chronic neck or low back pain undergoing a conservative treatment. STUDY DESIGN AND SETTING: Analysis of data from a prospective registry in routine practice. PATIENT SAMPLE: All patients who had been discharged after receiving a conservative treatment within the Spanish National Health Service (SNHS) (n=8,778). OUTCOME MEASURES: Spinal pain, referred pain, and disability were assessed before the conservative treatment and at discharge by the use of previously validated methods. METHODS: Improvement in spinal pain, referred pain, and disability was defined as a reduction in score greater than the minimal clinically important change. A predictive model that included demographic, clinical, and work-related variables was developed for each outcome using multivariate logistic regression. Missing data were addressed using multiple imputation. Discrimination and calibration were assessed for each model. The models were validated by bootstrap, and nomograms were developed. RESULTS: The following variables showed a predictive value in the three models: baseline scores for pain and disability, pain duration, having undergone X-ray, having undergone spine surgery, and receiving financial assistance for neck or low back pain. Discrimination of the three models ranged from slight to moderate, and calibration was good. CONCLUSIONS: A registry in routine practice can be used to develop models that estimate the probability of improvement for each individual patient undergoing a specific form of treatment. Generalizing this approach to other treatments can be valuable for shared decision making.
BACKGROUND CONTEXT: In the context of shared decision-making, a valid estimation of the probability that a given patient will improve after a specific treatment is valuable. PURPOSE: To develop models that predict the improvement of spinal pain, referred pain, and disability in patients with subacute or chronic neck or low back pain undergoing a conservative treatment. STUDY DESIGN AND SETTING: Analysis of data from a prospective registry in routine practice. PATIENT SAMPLE: All patients who had been discharged after receiving a conservative treatment within the Spanish National Health Service (SNHS) (n=8,778). OUTCOME MEASURES: Spinal pain, referred pain, and disability were assessed before the conservative treatment and at discharge by the use of previously validated methods. METHODS: Improvement in spinal pain, referred pain, and disability was defined as a reduction in score greater than the minimal clinically important change. A predictive model that included demographic, clinical, and work-related variables was developed for each outcome using multivariate logistic regression. Missing data were addressed using multiple imputation. Discrimination and calibration were assessed for each model. The models were validated by bootstrap, and nomograms were developed. RESULTS: The following variables showed a predictive value in the three models: baseline scores for pain and disability, pain duration, having undergone X-ray, having undergone spine surgery, and receiving financial assistance for neck or low back pain. Discrimination of the three models ranged from slight to moderate, and calibration was good. CONCLUSIONS: A registry in routine practice can be used to develop models that estimate the probability of improvement for each individual patient undergoing a specific form of treatment. Generalizing this approach to other treatments can be valuable for shared decision making.
Authors: Miranda L van Hooff; Wilco C H Jacobs; Paul C Willems; Michel W J M Wouters; Marinus de Kleuver; Wilco C Peul; Raymond W J G Ostelo; Peter Fritzell Journal: Acta Orthop Date: 2015 Impact factor: 3.717
Authors: Francisco M Kovacs; Jesús Seco-Calvo; Borja M Fernández-Félix; Javier Zamora; Ana Royuela; Alfonso Muriel Journal: BMC Musculoskelet Disord Date: 2019-12-26 Impact factor: 2.362
Authors: Ana Royuela; Francisco M Kovacs; Jesús Seco-Calvo; Borja M Fernández-Félix; Víctor Abraira; Javier Zamora Journal: Int J Environ Res Public Health Date: 2021-04-07 Impact factor: 3.390