The development of the ADO-SQ model to predict 1-year mortality in patients with COPD

• Published in: Palliative medicine 2022-05, Vol.36 (5), p.821-829
• Main Authors: Owusuaa, Catherine, van der Leest, Cor, Helfrich, Gea, Heller-Baan, Roxane, Loenhout, CJ van, Herbrink, Jacobine W, Nieboer, Daan, van der Rijt, Carin CD, van der Heide, Agnes
• Format: Article
• Language: English
• Subjects: Aged; Air flow; Body mass index; Chronic obstructive pulmonary disease; Comorbidity; Death & dying; Dyspnea; End of life decisions; Female; Hospice care; Hospitals; Humans; Life expectancy; Life goals; Male; Medical prognosis; Models, Statistical; Mortality; Nomograms; Obstruction; Original; Palliative care; Prediction models; Prognosis; Prospective Studies; Pulmonary Disease, Chronic Obstructive
• ISSN: 0269-2163; 1477-030X
• DOI: 10.1177/02692163221080662

Background: Goals of end-of-life care must be adapted to the needs of patients with chronic obstructive pulmonary disease (COPD) who are in the last phase of life. However, identification of those patients is limited by moderate performances of existing prognostic models and by limited validation of the often-recommended surprise question. Aim: To develop a clinical prediction model to predict 1-year mortality in patients with COPD. Design: Prospective study using logistic regression to develop a model in two steps: (1) external validation of the ADO, BODEX, or CODEX models (A = age; B = body mass index; C = comorbidity; D = dyspnea; EX = exacerbations; O = airflow obstruction); (2) updating of best performing model and extending it with the surprise question. Discriminative performance of the new model was assessed using internal-external validation and measured with area under the curve (AUC). A nomogram and web application were developed. Settings/participants: Patients with COPD from five hospitals (September–November 2017). Results: Of the 358 included patients (median age 69.5 years, 50% male), 63 (17%) died within a year. The ADO index (AUC 0.73) had the best discriminative ability compared to the BODEX (AUC 0.71) or CODEX (AUC 0.68), and was extended with the surprise question. The resulting ADO-surprise question (SQ) model had an AUC of 0.79. Conclusion: The ADO-SQ model offers improved discriminative performance for predicting 1-year mortality compared to the surprise question, ADO, BODEX, or CODEX. A user-friendly nomogram and web application (https://dnieboer.shinyapps.io/copd) were developed. Further external validation of the ADO-SQ in patient groups is needed.