Algorithm Forecasts COPD Hospitalization Risk

Three important patient-reported outcomes can help predict hospitalizations among patients with chronic obstructive pulmonary disease, potentially helping clinicians to categorize patients' level of risk and design preventive interventions and proper care.

Researchers have identified 3 themes as important patient-reported outcomes that can help predict hospitalizations among patients with chronic obstructive pulmonary disease (COPD), and they propose a simple algorithm to forecast COPD hospitalizations.

Being able to forecast hospitalization in COPD patients is an important way to categorize their level of risk and design preventive interventions and proper care, including palliative care. The algorithm was published in the International Journal of COPD.

“The ultimate goal of this work is to develop a clinically relevant and easy algorithm that clinicians can use in routine practice to identify patients with an increased risk of hospitalization,” the authors wrote.

Beatriz Abascal-Bolado, MD, and colleagues from the Instituto de Investigación Sanitaria Valdecilla in Santander, Spain, and the Mayo Clinic in Rochester, Minnesota, found that responses to 3 questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS) were highly predictive of hospitalization:

  • Fear of breathlessness
  • Dyspnea with basic activities of daily living
  • Depressive symptoms

The study assessed 493 COPD patients at a Mayo Clinic outpatient pulmonary clinic (mean age, 70 years; 54% male; forced expiratory volume in 1 second predicted 42.8 ± 16.7; modified Medical Research Council dyspnea scale score, 2 ± 1.13). Investigators used a rigorous psychometric approach with multiple sensitivity analyses to help forecast risk of hospitalization. They defined specific cut points that allowed for simple interpretation and definition of risk. Factor- and cluster-analysis routines were followed to demonstrate that the internal structure of the original measures was demonstrated in their application setting.

The results support the hypothesis that the feeling of fear or panic due to shortness of breath was the most predictive question in their algorithm (a 57% risk of hospitalization) and further emphasize the importance of symptoms of depression and anxiety (fear) in the health of COPD patients. Anxiety is associated with increased risk of exacerbations, poorer health-related quality of life (QOL), worse physical activity, relapse within 1 month of receiving emergency treatment, and hospital readmission. Depression is associated with increased mortality, impaired health-related QOL, and excessive healthcare utilization rates and costs.

The algorithm presented incorporates patient-reported information that has not been used routinely or systematically in clinical practice, the researchers noted.

“This algorithm to identify patients at higher risk of hospitalization will have false positives and negatives; however, it is not intended as a diagnostic laboratory test, but a supplementary source of information for the clinician,” they concluded.

The investigators said their findings provide a clinically relevant and easy algorithm that clinicians can use in routine practice to identify COPD patients with an increased risk of hospitalization.

“Our work is not implying that we have a better way to predict hospitalization but an alternative way to augment our accuracy in forecasting the risk of hospitalization and the opportunity to provide our patients the most individualized and precise care possible,” the authors wrote.