<p>A clinical prediction model for hospitalized COPD exacerbations based on &ldquo;treatable traits&rdquo;</p>

Abstract

Background Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or “treatable traits” also contribute to risk of exacerbation. Objective The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits. Patients and methods A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV 1 60%±20% predicted, formed the derivation cohort. Hospitalized exacerbation rate was modeled using zero-inflated negative binomial regression. Calibration was assessed by graphically comparing the agreement between predicted and observed annual hospitalized exacerbation rates. Predictive (discriminative) accuracy of the model for identifying high-risk patients (defined as experiencing ≥1 hospitalized exacerbations) was assessed with area under the curve (AUC) and receiver operating characteristics analyses, and compared to other existing risk indices. We externally validated the prediction model using a multicenter dataset comprising 419 COPD patients. Results The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good. Conclusion We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

“…All these variables have also shown to be predictive for hospital readmissions (e.g. [ 3 , 40 , 66 , 68 , 69 ] that consequently, the development of a prediction model including these variables is the evident following task. As this model would additionally help to identify risk factors causing readmissions, even more tailored strategies for prevention of these events could be designed by clinicians or policy makers.…”

Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge

“…Previously, the history of exacerbation was reported to an independent predictor for future exacerbations in patients [ 26 ]. A study of Bernabeu-Mora et al indicated that the number of hospitalizations due to exacerbations in the previous year increased the risk of readmission by 4.44 times [ 27 ].…”

Comparison of Immediate and Sequential Withdrawal of a Systemic Glucocorticoid in the Treatment of Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Multicenter, Randomized, Double-Blind, Parallel-Controlled, Open-Label Study

“…Different studies recommend different durations of glucocorticoid application, Yii et al (2019) found that a history of frequent acute exacerbations is a strong and independent predictor of prognosis. In addition, the history of exacerbations is independent of severe exacerbations ( Han et al, 2017 ).…”