Published in:
01-10-2019 | Triage | IM - ORIGINAL
Comparison of Reverse Triage with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index to classify medical inpatients of an Italian II level hospital according to their resource’s need
Published in: Internal and Emergency Medicine | Issue 7/2019
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Resource allocation in our overcrowded hospitals would require classification of inpatients according to the severity of illness, the evolving risk and the clinical complexity. Reverse triage (RT) is a method used in disasters to identify inpatients according to their use of hospital resources. The aim of this observational prospective study is to evaluate the use of RT in medical inpatients of an Italian Hospital and to compare the RT score with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index. Cluster sampling was performed on high dependency unit (HDU), geriatrics (Ger) and internal medicine (IM) wards. We calculate RT, NEWS, SOFA and CCI from inpatient charts. Length of stay (LOS), transfer to a higher level of care, death and discharge date were collected after 30 days. We obtained demographics, comorbidities, severity and clinical complexity of 260 inpatients. We highlighted differences in NEWS, SOFA and CCI in the three divisions. On the contrary RT score was uniformly high (median 7), with 85% of patients with RT = 8. NEWS, SOFA and CCI were higher in patients with higher RT score. We used the sum of the interventions listed by RT (RT sum) as a proxy of the level of care needed. RT-sum showed moderate correlation with NEWS (r = 0.52 Spearman, p < 0.001). RT-sum was the highest in HDU, related to the evolving severity of HDU patients. Ger patients that showed the highest CCI score (with all patients in the CCI ≥ 3 category) had the second highest RT-sum. RT score showed similar values in the majority of the inpatients regardless of differences in NEWS, SOFA and CCI in different ward subgroups. RT-sum is related both to evolving severity (NEWS) and to clinical complexity (CCI). RT and NEWS could predict inpatient level of care and resource need associated with CCI.