Shock index as a predictor for short‐term mortality in helicopter emergency medical services: A registry study

Abstract

Background The value of shock‐index has been demonstrated in hospital triage, but few studies have evaluated its prehospital use. The aim of our study was to evaluate the association between shock‐index in prehospital critical care and short‐term mortality. Methods We analyzed data from the national helicopter emergency medical services database and the Population Register Centre. The shock‐index was calculated from the patients' first measured parameters. The primary outcome measure was 1‐ and 30‐day mortality. Results A total of 22 433 patients were included. The 1‐day mortality was 7.5% and 30‐day mortality was 16%. The median shock‐index was 0.68 (0.55/0.84) for survivors and 0.67 (0.49/0.93) for non‐survivors (P = .316) at 30‐days. Association between shock‐index and mortality followed a U‐shaped curve in trauma (shock‐index < 0.5: odds ratio 2.5 [95% confidence interval 1.8‐3.4], shock‐index > 1.3: odds ratio 4.4 [2.7‐7.2] at 30 days). Patients with neurological emergencies with a low shock‐index had an increased risk of mortality (shock‐index < 0.5: odds ratio 1.8 [1.5‐2.3]) whereas patients treated after successful resuscitation from out‐of‐hospital cardiac arrest, a higher shock‐index was associated with higher mortality (shock‐index > 1.3: odds ratio 3.5 [2.3‐5.4). The association was similar for all ages, but older patients had higher mortality in each shock‐index category. Conclusion The shock‐index is associated with short time mortality in most critical patient categories in the prehospital setting. However, the marked overlap of shock‐index in survivors and non‐survivors in all patient categories limits its predictive value.

Association between physician's case volume in prehospital advanced trauma care and 30-day mortality: A registry-based analysis of 4,032 patients

“…The SI was categorized into five categories based on the earlier observation of a U-shape association between SI and mortality. 20 The ICISS was not included in the model because of the large amount of missing data. However, a multivariate logistic regression analysis including the ICISS was performed as a sensitivity analysis.…”

“…In this multivariate analysis, we used a base 10 logarithm of the number of trauma cases in the preceding 12 months because of the presumably different effect of a single additional case in a low versus a high case volume. The SI was categorized into five categories based on the earlier observation of a U-shape association between SI and mortality 20 . The ICISS was not included in the model because of the large amount of missing data.…”

“…As a secondary analysis to assess differences in practice patterns by case volume, the patients were divided into three groups according to the treating physician's prehospital trauma case volume: low (0-10 cases per 12 months), intermediate (11)(12)(13)(14)(15)(16)(17)(18)(19)(20) cases per 12 months), and high (>20 cases per 12 months). The categorization was done before the analyses by visually looking at the distribution of patients according to case volume to define three separate categories.…”

Standardised data collection in prehospital critical care: a comparison of medical problem categories and discharge diagnoses

“…In comparison to other European services, there is even more divergence in reporting of medical problems [ 7 , 8 ]. The categories in the consensus template are widely used in research reports, for example in studies regarding airway management [ 9 ] and physiological disturbances [ 10 ] in prehospital critical care. However, the differences in reporting make it challenging to compare these services.…”

Response to: Long-Term Mortality in Patients Transferred by Emergency Medical Services: Prospective Cohort Study

“…Data to support the prediction of short-term clinical outcomes using EMS data are weak. [2][3][4] The prediction of long-term outcomes using those same data, which the authors attempt, is even more challenging. This is further compounded by confounders such as multiple hypothesis testing, non-modifiable risk factors, differential resource allocation based on response to initial therapy, and goals of care which may not be evident on initial presentation.…”