Validation of a Quality of Life Instrument in Spanish Patients With Rheumatic Diseases

León, Leticia; Rodríguez‐Rodríguez, Luis; Aguilar, M.A.; Jover, Juan A.; Vadillo, Cristina; Redondo, Marta; Abásolo, Lydia

doi:10.1097/rhu.0000000000000774

Cited by 5 publications

(10 citation statements)

References 21 publications

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“…Regarding the former, it includes the clinical notes and comorbidity, or medications prescribed by primary care or other specialists. Regarding the latter, it includes information regarding rheumatology diagnoses (using the ICD9/ICD10), prescribed drugs (using the Spanish Drug and Medical Device Agency (AEMPS) codification system), QoL (using the Rosser Classification System [26]), and the patient’s follow-up plan. This follow-up plan comprises two sections: first, a mandatory “discharge status” (codified as “continued follow-up in our clinic,” “discharge,” “admission,” or “transfer to another center”), and second, an “elaboration of the discharge status”.…”

Section: Methodsmentioning

confidence: 99%

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Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Madrid

Font

Vega-Barbas

et al. 2019

JCM

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Our objective is to develop and validate a predictive model based on the random forest algorithm to estimate the readmission risk to an outpatient rheumatology clinic after discharge. We included patients from the Hospital Clínico San Carlos rheumatology outpatient clinic, from 1 April 2007 to 30 November 2016, and followed-up until 30 November 2017. Only readmissions between 2 and 12 months after the discharge were analyzed. Discharge episodes were chronologically split into training, validation, and test datasets. Clinical and demographic variables (diagnoses, treatments, quality of life (QoL), and comorbidities) were used as predictors. Models were developed in the training dataset, using a grid search approach, and performance was compared using the area under the receiver operating characteristic curve (AUC-ROC). A total of 18,662 discharge episodes were analyzed, out of which 2528 (13.5%) were followed by outpatient readmissions. Overall, 38,059 models were developed. AUC-ROC, sensitivity, and specificity of the reduced final model were 0.653, 0.385, and 0.794, respectively. The most important variables were related to follow-up duration, being prescribed with disease-modifying anti-rheumatic drugs and corticosteroids, being diagnosed with chronic polyarthritis, occupation, and QoL. We have developed a predictive model for outpatient readmission in a rheumatology setting. Identification of patients with higher risk can optimize the allocation of healthcare resources.

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Section: Methodsmentioning

confidence: 99%

“…Regarding QoL, we used the Rosser Classification Index (continuous variable) and its subscale of disability and pain/distress (categorical variables) [26].…”

Section: Methodsmentioning

confidence: 99%

Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Madrid

Font

Vega-Barbas

et al. 2019

JCM

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“…Demographic and clinical information was obtained from our departmental EHR, which is used in all outpatient interactions between health professionals and patients. In each patient visit, information is collected both as free text (including clinical notes, and comorbidity or medications prescribed by other physicians) and codified [including rheumatology diagnoses (using the ICD9/ICD10), prescribed drugs (using the Spanish Drug and Medical Device Agency codification system), HRQoL (using the Rosser Classification Index: RCI [20]), and the patient's follow-up plan.…”

Section: Data Sourcementioning

confidence: 99%

“…The outcome of interest in this study was the patient's HRQoL, measured using the RCI [20][21][22]. This measure of overall health status is a standardized instrument applicable to a wide range of health conditions.…”

Section: Dependent Variablementioning

confidence: 99%

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Factors Associated with Health-Related Quality of Life in Psoriatic Arthritis Patients: A Longitudinal Analysis

et al. 2021

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Introduction: Psoriatic arthritis (PsA) is considered a multifaceted disease, with patients reporting low health-related quality of life (HRQoL). Data on disease burden are substantial and there exists a need for properly designed studies to learn more about the evolution of HRQoL in this condition. This study aims to identify factors associated to HRQoL evolution in PsA patients followed-up in a real-world setting in Spain. Methods: We conducted a retrospective longitudinal observational study including incident patients from the rheumatology outpatient clinic of Hospital Clínico San Carlos (Madrid, Spain), diagnosed for the first time of PsA, Lydia Abasolo and Luis Rodriguez-Rodriguez share senior authorship of this paper.

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Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis

Madrid

León-Mateos

Pato

et al. 2021

Therapeutic Advances in Musculoskeletal

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Introduction: Rheumatic and musculoskeletal diseases (RMDs) have a significant impact on patients’ health-related quality of life (HRQoL) exacerbating disability, reducing independence and work capacity, among others. Predictors’ identification affecting HRQoL could help to place efforts that minimize the deleterious impact of these conditions on patients’ wellbeing. This study evaluates the influence of demographic and clinical predictors on the HRQoL of a cohort of RMD patients, measured using the Rosser classification index (RCI). Methods: We included patients attending the Hospital Clínico San Carlos (HCSC) rheumatology outpatient clinic from 1 April 2007 to 30 November 2017. The primary outcome was the HRQoL assessed in each of the patient’s visits using the RCI. Demographic and clinical variables extracted from a departmental electronic health record (EHR) were used as predictors: RMD diagnoses, treatments, comorbidities, and averaged HRQoL values from previous periods (for this last variable, values were imputed if no information was available). Association between predictors and HRQoL was analyzed using penalized generalized estimating equations (PGEEs). To account for imputation bias, the PGEE model was repeated excluding averaged HRQoL predictors, and common predictors were considered. Discussion: A total of 18,187 outpatients with 95,960 visits were included. From 410 initial predictors, 19 were independently associated with patients’ HRQoL in both PGEE models. Chronic kidney disease (CKD), an episode of prescription of third level analgesics, monoarthritis, and fibromyalgia diagnoses were associated with worse HRQoL. Conversely, the prescription in the previous visit of acid-lowering medication, colchicine, and third level analgesics was associated with better HRQoL. Conclusion: We have identified several diagnoses, treatments, and comorbidities independently associated with HRQoL in a cohort of outpatients attending a rheumatology clinic.

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Validation of a Quality of Life Instrument in Spanish Patients With Rheumatic Diseases

Abstract: The Rosser classification system showed satisfactory reliability and suitable criterion validity for patients with musculoskeletal disorders. The instrument seems to be suitable for clinical decision making and research.

Cited by 5 publications

References 21 publications

Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Factors Associated with Health-Related Quality of Life in Psoriatic Arthritis Patients: A Longitudinal Analysis

Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis

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