Prediction Model of In-Hospital Death for Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients Admitted to Intensive Care Unit: The PD-ICU Score.

INTRODUCTION

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to intensive care unit (ICU) are exposed to poor clinical outcomes, and no specific prognostic models are available among this population. We aimed to develop and validate a risk score for prognosis prediction for these patients.

METHODS

This was a multicenter observation study. AECOPD patients admitted to ICU were included for model derivation from a prospective, multicenter cohort study. Logistic regression analysis was applied to identify independent predictors for in-hospital death and establish the prognostic risk score. The risk score was further validated and compared with DECAF, BAP-65, CURB-65, and APACHE II score in another multicenter cohort.

RESULTS

Five variables were identified as independent predictors for in-hospital death in APCOPD patients admitted to ICU, and a corresponding risk score (PD-ICU score) was established, which was composed of procalcitonin >0.5 μg/L, diastolic blood pressure <60 mm Hg, need for invasive mechanical ventilation, disturbance of consciousness, and blood urea nitrogen >7.2 mmol/L. Patients were classified into three risk categories according to the PD-ICU score. The in-hospital mortality of low-risk, intermediate-risk, and high-risk patients was 0.3%, 7.3%, and 27.9%, respectively. PD-ICU score displayed excellent discrimination ability with an area under the receiver-operating characteristic curve (AUC) of 0.815 in the derivation cohort and 0.754 in the validation cohort which outperformed other prognostic models.

CONCLUSION

We derived and validated a simple and clinician-friendly prediction model (PD-ICU score) for in-hospital mortality among AECOPD patients admitted to ICU. With good performance and clinical practicability, this model may facilitate early risk stratification and optimal decision-making among these patients.