Predicting in-hospital mortality among non-trauma patients based on vital sign changes between prehospital and in-hospital: An observational cohort study

PLoS One. 2019 Jan 31;14(1):e0211580. doi: 10.1371/journal.pone.0211580. eCollection 2019.

Abstract

Objective: To prevent misjudgment of the severity of patients in the emergency department who initially seem non-severe but are in a critical state, methods that differ from the conventional viewpoint are needed. We aimed to determine whether vital sign changes between prehospital and in-hospital could predict in-hospital mortality among non-trauma patients.

Methods: This observational cohort study was conducted in two tertiary care hospitals. Patients were included if they were transported by ambulance for non-trauma-related conditions but were excluded if they experienced prehospital cardiopulmonary arrest, were pregnant, were aged <15 years, had undergone inter-hospital transfer, or had complete missing data regarding prehospital or in-hospital vital signs. The main outcome was in-hospital mortality, and the study variables were changes in vital signs, pulse pressure, and/or shock index between the prehospital and in-hospital assessments. Logistic regression analyses were performed to obtain adjusted odds ratios for each variable. Receiver operating characteristic curve analyses were performed to identify cut-off values that produced a positive likelihood ratio of ≥2.

Results: Among the 2,586 eligible patients, 170 died in the two hospitals. Significantly elevated risks of in-hospital mortality were associated with changes in the Glasgow Coma Scale (cut-off ≤-3), respiratory rate (no clinically significant cut-off), systolic blood pressure (cut-off ≥47 mmHg), pulse pressure (cut-off ≥55 mmHg), and shock index (cut-off ≥0.3).

Conclusions: Non-trauma patients who exhibit changes in some vital signs between prehospital and in-hospital have an increased risk of in-hospital mortality. Therefore, it is useful to incorporate these changes in vital signs to improve triaging and predict the occurrence of in-hospital mortality.

Publication types

  • Observational Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Brain Diseases / mortality*
  • Emergency Medical Services / statistics & numerical data*
  • Female
  • Hospital Mortality / trends*
  • Humans
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • ROC Curve
  • Respiration Disorders / mortality*
  • Retrospective Studies
  • Severity of Illness Index*
  • Survival Rate
  • Vital Signs / physiology*

Grants and funding

The authors received no specific funding for this work.