The theory that posthospitalization stress might increase the risk of postdischarge complications has never been investigated.OBJECTIVE:
To assess whether serum levels of stress biomarkers at discharge are associated with readmission and death after an acute-care hospitalization.DESIGN:
We prospectively included 346 patients aged ≥50 years admitted to the department of general internal medicine at a large community hospital between April 8, 2013 and September 23, 2013. We measured the serum levels of several biomarkers at discharge: midregional pro-adrenomedullin, copeptin, cortisol, and prolactin. All patients were followed for up to 90 days after discharge (none was lost to follow-up). The main outcome was first unplanned readmission or death within 30 days after hospital discharge. We assessed the additional value of biomarkers to 2 validated readmission prediction scores: the LACE index (Length of stay, Admission Acuity, Charlson Comorbidity Index, and number of Emergency department visits within preceding 6 months) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, and Length of stay).RESULTS:
Forty patients (11.6%) had a 30-day unplanned readmission or death. High serum copeptin and cortisol levels were associated with an increase in the odds of unplanned readmission or death (odds ratios [95% confidence interval] 2.69 [1.29–5.64] and 3.43 [1.36, 8.65], respectively). We found no significant association with midregional pro-adrenomedullin or prolactin. Furthermore, these stress biomarkers increased the performance of two readmission prediction scores (LACE index and HOSPITAL score).CONCLUSION:
High serum levels of copeptin and cortisol at discharge were independently associated with 30-day unplanned readmission or death, supporting a possible negative effect of hospitalization stress during the postdischarge period. Stress biomarkers improved the performance of prediction models and therefore could help better identify high-risk patients.