PURPOSE: Forecasting hospitalization in @entity1 with @entity1030 has gained significant interest in the field of @entity1030 care. There is a need to find simple tools that can help clinicians to stratify the risk of hospitalization in these @entity1 at the time of care. The perception of quality of life has been reported to be independently associated with hospitalizations, but questionnaires are impractical for daily clinical use. Individual questions from valid questionnaires can have robust predictive abilities, as has been suggested in previous reports, as a way to use @entity1 -reported outcomes to forecast important events like hospitalizations in @entity1030 . Our primary aim was to assess the predictive value of individual questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS) on the risk of hospitalization and to develop a clinically relevant and simple algorithm that clinicians can use in routine practice to identify @entity1 with an increased risk of hospitalization. @entity1 AND METHODS: A total of 493 @entity1 with @entity1030 prospectively recruited from an outpatient pulmonary clinic completed the CRQ-SAS, demographic information, pulmonary function testing, and clinical outcomes. The cohort had a mean age of 70 years, was 54% male, with forced expiratory volume in 1 second percentage predicted 42.8  16.7, and modified Medical Research Council @entity1353 scale score of 2  1.13. RESULTS: Our analysis validated the original CRQ-SAS domains. Importantly, recursive partitioning analysis identified three CRQ-SAS items regarding fear or @entity4697 of breathlessness, @entity1353 with basic activities of daily living, and @entity308 that were highly predictive of hospitalization. We propose a robust (area under the curve =0.70) but short and easy algorithm for daily clinical care to forecast hospitalizations in @entity1 with @entity1030 . CONCLUSION: We identified three themes - fear of breathlessness, @entity1353 with basic activities of daily living, and @entity308 - as important @entity1 -reported outcomes to predict hospitalizations, and propose a short and easy algorithm to forecast hospitalizations in @entity1 with @entity1030 .

Forecasting XXXX hospitalization in the clinic: optimizing the chronic respiratory questionnaire.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient', 'PATIENTS'] @entity308 :: ('MESH:D003866', 'Disease') :: ['depressive symptoms'] @entity4697 :: ('MESH:D016584', 'Disease') :: ['panic'] @entity1353 :: ('MESH:D004417', 'Disease') :: ['dyspnea'] @entity1030 :: ('MESH:D029424', 'Disease') :: ['COPD']
@entity1030 :: (MESH:D029424,Disease) :: ['COPD']
