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

Date Published: January 31, 2019

Publisher: Public Library of Science

Author(s): Yohei Kamikawa, Hiroyuki Hayashi, Biswadev Mitra.

http://doi.org/10.1371/journal.pone.0211580

Abstract

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.

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. 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). 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.

Partial Text

There is limited to time to judge the severity or emergency of patients, especially in the crowded emergency department (ED). To prevent missing patients whose condition seems non-severe initially but is actually critical in a busy ED, measurement of vital signs in the ED is known to be useful for triaging patients who arrive via ambulance transportation. Prehospital vital signs are also informative for trauma patients in preparing immediate interventions.[1] This approach is advantageous in rural settings or in developing countries, as vital sign measurement is simpler, easier, and more rapid than blood testing.[2] Furthermore, a combination of vital signs can predict the risk of mortality, which has led to the development of various diagnostic tools.[2–5] Nevertheless, it is still difficult to accurately predict the risk of mortality based on a single vital sign, which typically cannot provide good specificity and sensitivity.[6] In addition, vital signs can vary considerably in emergent or special states, such as pregnancy, advanced age, intracranial disease, and chronic hypoxemia.[7–9] Thus, to not miss critical patients, development of a new warning tool that differs from the conventional viewpoint is needed.

To identify critical patients who seem non-severe in the ED, vital sign measurements are indispensable during the triage process, although using a single evaluation is associated with limitations. In this context, changes in vital signs are known to predict mortality among trauma patients, although we are not aware of any previous reports regarding non-trauma patients. Thus, the present observational cohort study evaluated the predictive ability of changing vital signs of non-trauma patients between prehospital and in-hospital and revealed that some of these factors could predict in-hospital mortality. Furthermore, clinically meaningful cut-off points were identified for ΔGCS, |ΔSBP|, |ΔPP|, and |ΔSI|. The cut-off point of 0.3 for |ΔSI| is very useful in clinical settings because it conveys a slight change but could be critical (e.g., changing from HR 65 and SBP 130 to HR 88 and SBP 110). In addition, although attention has only been paid to dropping SBP or rising SI, we found that the reverse trends were no less important.

 

Source:

http://doi.org/10.1371/journal.pone.0211580

 

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