RESUMO
OBJECTIVES: To develop a mortality-predictive model for correct identification of patients with non-cancer multiple chronic conditions who would benefit from palliative care, recognise predictive indicators of death and provide with tools for individual risk score calculation. DESIGN: Retrospective observational study with multivariate logistic regression models. PARTICIPANTS: All patients with high-risk multiple chronic conditions incorporated into an integrated care strategy that fulfil two conditions: (1) they belong to the top 5% of the programme's risk pyramid according to the adjusted morbidity groups stratification tool and (2) they suffer simultaneously at least three selected chronic non-cancer pathologies (n=591). MAIN OUTCOME MEASURE: 1 year mortality since patient inclusion in the programme. RESULTS: Among study participants, 201 (34%) died within the 1 year follow-up. Variables found to be independently associated to 1 year mortality were the Barthel Scale (p<0.001), creatinine value (p=0.032), existence of pressure ulcers (p=0.029) and patient global status (p<0.001). The area under the curve (AUC) for our model was 0.751, which was validated using bootstrapping (AUC=0.751) and k-fold cross-validation (10 folds; AUC=0.744). The Hosmer-Lemeshow test (p=0.761) showed good calibration. CONCLUSIONS: This study develops and validates a mortality prediction model that will guide transitions of care to non-cancer palliative care services. The model determines prognostic indicators of death and provides tools for the estimation of individual death risk scores for each patient. We present a nomogram, a graphical risk calculation instrument, that favours a practical and easy use of the model within clinical practices.
