Admission criteria for a cardiovascular short stay unit: a retrospective analysis on a pilot unit

Intern Emerg Med. 2021 Nov;16(8):2087-2095. doi: 10.1007/s11739-021-02700-4. Epub 2021 Mar 26.

Abstract

Rapid intensive observation (RIO) units have been created to guarantee high standards of care in a sustainable health-care system. Within short stay units (SSUs), which are a subgroup of RIOs, only rapidly manageable patients should be admitted. Physicians are unable to predict the length of stay (LOS) as objective criteria to make such a prediction are missing. A retrospective observational study was carried out to identify the objective criteria for admission within a cardiovascular care-oriented SSU. Over a period of 317 days, 340 patients (age 69.4 ± 14.7 years) were admitted to a pilot SSU within our internal medicine department. The most frequent diagnoses were chest pain (45.9%), syncope (12.9%), and supraventricular arrhythmias (11.2%). The median LOS was 4 days (quartile 1:3; quartile 3:7). Predictors of LOS ≤ 96 h were age < 80, hemoglobin > 115 g/L, estimated glomerular filtration rate > 45 mL/min/1.73 m2, Charlson Comorbidity Index < 3, Barthel Index > 40, diagnosis of chest pain, syncope, supraventricular arrhythmias, or acute heart failure. The HEART (history, ECG, age, risk factors, troponin) score was found to be excellent in risk stratification of patients admitted for chest pain. Blood tests and anamnestic variables can be used to predict the LOS and thus SSU admission. The HEART score may help in the classification of patients with chest pain admitted to an SSU.

Keywords: Acute medical unit; Admission criteria; Length of stay; Short stay unit.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Cardiovascular Nursing / organization & administration*
  • Cardiovascular Nursing / standards
  • Cardiovascular Nursing / statistics & numerical data
  • Female
  • Humans
  • Length of Stay / statistics & numerical data
  • Logistic Models
  • Male
  • Middle Aged
  • Patient Admission / statistics & numerical data
  • Patient Admission / trends*
  • Patient Selection*
  • Patients' Rooms / organization & administration
  • Patients' Rooms / statistics & numerical data
  • Retrospective Studies
  • Risk Factors