Study design and participants
This cross-sectional observational study analysed a purposive sample of patients in the Valencian Community. All patients aged ≥60 years who sought primary care services from the health centres of Monóvar, Las Acacias, and Marina Española (province of Alicante, Health Area of Elda), and the Port of Sagunto (province of Valencia, Health Area of Sagunto), from January 2017 to May 2018, were invited to participate. Patients were excluded if they:
did not wish to participate in the study;
were marginalised;
had moderate or severe cognitive decline;
were living in homes for elderly people; or
received home care.
Variables and measurements
The main study variable was defined as the presence of frailty. This was assessed with the FRAIL scale;11,14 a patient was identified as frail when three or more of the five scale criteria — fatigue, resistance, ambulation, illnesses (hypertension, diabetes, cancer, chronic lung disease, heart attack, congestive heart failure, angina pectoris, asthma, arthritis, stroke, kidney disease) and weight loss — were met. The questions were asked during a personal interview with the patient, within appointments made by the patient for another reason, lasting around 5 minutes. The application of the FRAIL scale is recommended in Spain by the Ministry of Health, Social Services, and Equality as a screening tool for frailty,17 which is based on international literature.18,19
In addition, the Charlson Comorbidity Index was used; this assesses the following comorbidities:15
cerebrovascular disease;
diabetes;
chronic obstructive pulmonary disease (COPD);
coronary heart disease (CHD);
congestive heart failure;
dementia;
peripheral vascular disease;
chronic renal disease; and
cancer.
Through the presence or absence of these comorbidities, this index indicates the risk of mortality in patients over a period of 6 months.15 For this study, each of the comorbidities was used independently. Additionally, the following clinical variables were recorded:
Parkinson’s disease;
Arthrosis or advanced musculoskeletal disease;
major auditory or visual deficit;
polypharmacy (at least three drugs prescribed by physicians); or
hospital admission in the last year.
These clinical variables are included in the Valencian Community Regional Department of Health’s definition of the frail older person but were not present in the rest of the questionnaires, as highlighted by Suay Cantos et al,20 which were based on scientific literature.21
Finally, aside from the indicated variables covered by the questionnaires, the following were obtained using the patient’s electronic medical record:
As candidate predictors, the sex and age of the patient were selected, together with those comorbidities or clinical conditions that could have a considerabe effect on the daily life of an older person and, consequently, increase their risk of frailty — namely, AF, stroke, CHD, Parkinson’s disease, COPD, arthrosis or advanced musculoskeletal disease, hearing loss or visual deficit, polypharmacy, hospital admission in the last year, diabetes, dementia, and peripheral vascular disease. Dialysis was not considered, as there were few cases and cancer was not considered because of the great variability of sites and prognoses. To blind the assessment, the patient was asked all the subjective questions of the outcome first, so the answers were not related to the other predictors.
Statistical analysis
Variables were described using absolute and relative frequencies, and means with standard deviations (age). There were no data missing from the study variables. As age is a continuous variable, its functional form was studied through power analysis (likelihood ratio test), finding that the quadratic power did not show differences with linearity. As such, age was included in the multivariate model as a linear predictor. Taking into account the fact that there were 14 potential predictors and the multivariate model could not include more than five (the total number of events was 126, EPV>25), all the models with one, two, three, four, and five predictors were estimated, thereby ensuring evaluation of all the possible combinations. The area under the receiver operating characteristic curve (AUC) was assessed for all combinations, and that with the maximum AUC — that is, the combination with the greatest discrimination — was selected to construct the model.
The calibration of the model was evaluated using soft calibration (splines).Palazón-Bru et al made a review of this topic and they indicated this point for the calibration.24 Calibration and discrimination must be adequate to state that the model is valid, and, when this is done on the same sample on which it was developed, it constitutes an internal validation. This validation was performed through 1000 bootstrap samples.
All analyses were conducted with a significance of 5% and, for each relevant parameter, its associated confidence interval (CI) was calculated. The statistical packages used were SPSS Statistics (version 25) and R (version 3.5.1).
The predictive model has been integrated into a mobile application for Android (Frailty Predictor), which is free to download for all users of Google Play.
