Pre-hospital qSOFA as a predictor of sepsis and mortality

Shu, Eileen; Tallman, Crystal Ives; Frye, William W.; Boyajian, Jonathan; Farshidpour, Leyla; Young, Megann; Campagne, Danielle

doi:10.1016/j.ajem.2018.09.025

Cited by 21 publications

(14 citation statements)

References 19 publications

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“…Shu E et al have evaluated the prehospital qSOFA score of patients who were brought by EMS personnel and analyzed the prognostic value of the prehospital qSOFA score among 428 patients diagnosed with infection. They showed that an increase in prehospital qSOFA score was associated with in-hospital mortality (positive likelihood ratio 3.99, 95% CI: 2.21–7.21) [25]. In both of these studies, no covariate adjustments were made.…”

Section: Discussionmentioning

confidence: 99%

Use of prehospital qSOFA in predicting in-hospital mortality in patients with suspected infection: A retrospective cohort study

et al. 2019

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Background The quick sequential organ failure assessment (qSOFA) score has recently been introduced to the emergency department (ED) and wards, and it predicted a higher number of deaths among patients with sepsis compared with baseline risk. However, studies about the application of the qSOFA score are limited in prehospital settings. Thus, this study aimed to assess the performance of prehospital qSOFA score in predicting the risk of mortality among patients with infection. Methods This single center, retrospective cohort study was conducted in a Japanese tertiary care teaching hospital between April 2016 and March 2017. We enrolled all consecutive adult patients transported to the hospital by ambulance and admitted to the ED due to a suspected infection. We calculated the prehospital qSOFA score using the first vital sign obtained at the scene by emergency medical service (EMS) providers. The primary outcome was in-hospital mortality. The Cox proportional hazards model was used to assess the association between prehospital qSOFA positivity and in-hospital mortality. Results Among the 925 patients admitted to the ED due to a suspected infection, 51.1% (473/925) were prehospital qSOFA-positive and 48.9% (452/925) were prehospital qSOFA-negative. The in-hospital mortality rates were 14.0% (66/473) in prehospital qSOFA-positive patients and 6.0% (27/452) in prehospital qSOFA-negative patients. The Cox proportional hazard regression model revealed a strong association between prehospital qSOFA score and in-hospital mortality (adjusted hazard ratio: 2.41, 95% confidence interval: 1.51–3.98; p <0.01). Conclusions Among the patients with suspected infection who were admitted at the ED, a strong association was observed between the prehospital qSOFA score and in-hospital mortality. In order to use this score in clinical practice, future study is necessary to evaluate how infection is suspected in the prehospital arena.

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Section: Discussionmentioning

confidence: 99%

Use of prehospital qSOFA in predicting in-hospital mortality in patients with suspected infection: A retrospective cohort study

et al. 2019

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“…In addition, tools for accurately predicting the prognosis and treatment are important to communicate between the prehospital EMS technician and hospital medical staff [6,7]. However, most triage tools in prehospital situations were developed for trauma patients only, and there is no generalized tool that covers all EMS situations [2,25]. Although the conventional triage tools of EDs have been applied to predict the need for critical care at prehospital situations [21,26,27], they showed an unsatisfactory performance in predicting prognosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods: We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results: The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). Conclusions: The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

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“…Additional studies are needed to articulate the optimal screening paradigm across the care continuum. At this point in time, we recommend a stepwise screening approach to patients with potential sepsis, which can begin in the pre-hospital setting if presenting by EMS 16 , 17 , 21 or from a physician’s office or urgent care center, can integrate patient vital signs obtained from home devices, be augmented at triage, and continue to incorporate new data throughout the ED stay. This optimal screening paradigm needs to acknowledge that in initial screening sensitivity is more important than specificity to optimize capture of potential patients.…”

Section: Discussionmentioning

confidence: 99%

“…Large administrative data analyzed by the SEPSIS-3 authors suggested that a qSOFA score of ≥ 2 would rapidly identify non-ICU patients “more likely to have poor outcomes typical of sepsis,” defined as in-hospital mortality > 10%, with an area under the receiver operating characteristic (AUROC) curve of 0.81 (compared to 0.76 for the SIRS criteria; p = 0.01) 1 , 15 . The authors concluded that the new definitions should “facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis 1 .” Since this assertion in 2016, numerous authors have analyzed the usefulness of qSOFA in retrospective and prospective cohorts at different points in the care continuum from pre-hospital 16 , 17 to initial triage 18 – 20 to the period of ED management 20 , 21 to in-patient wards and the ICU 15 ; have looked at it as a screening tool for all patients presenting to the ED 22 or for those with suspected infection 23 , 24 ; have investigated dynamic changes in qSOFA during ED stay 20 , 25 ; have analyzed its accuracy as a predictor of ICU admission, length of stay, and in-hospital mortality 26 ; have tried to improve the performance of qSOFA by add various biomarkers including lactate 27 , 28 , procalcitonin 29 , monocyte distribution width 30 , and CRP combined with mid-regional proadrenomedullin 31 or vital sign measures including heart rate variability 32 , EtCO 2 33 , and shock index 19 ; have examined its utility in high and low resource settings 27 , 29 , 34 ; and have compared it to other scoring systems including SIRS, MEWS, NEWS, and conventional SOFA 35 , 36 . All of these studies provide important clinical information and have various limitations mainly related to the data sets used, the presence or absence of serial qSOFA values, the clinical setting where the studies were performed, and the overall mortality of the cohorts.…”

Section: Introductionmentioning

confidence: 99%

The sensitivity of qSOFA calculated at triage and during emergency department treatment to rapidly identify sepsis patients

Perman

Mikkelsen

Goyal

et al. 2020

Sci Rep

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The quick sequential organ failure assessment (qSOFA) score has been proposed as a means to rapidly identify adult patients with suspected infection, in pre-hospital, Emergency Department (ED), or general hospital ward locations, who are in a high-risk category with increased likelihood of “poor outcomes:” a greater than 10% chance of dying or an increased likelihood of spending 3 or more days in the ICU. This score is intended to replace the use of systemic inflammatory response syndrome (SIRS) criteria as a screening tool; however, its role in ED screening and identification has yet to be fully elucidated. In this retrospective observational study, we explored the performance of triage qSOFA (tqSOFA), maximum qSOFA, and first initial serum lactate (> 3 mmol/L) at predicting in-hospital mortality and compared these results to those for the initial SIRS criteria obtained in triage. A total of 2859 sepsis cases were included and the in-hospital mortality rate was 14.4%. The sensitivity of tqSOFA ≥ 2 and maximum qSOFA ≥ 2 to predict in-hospital mortality were 33% and 69%, respectively. For comparison, the triage SIRS criteria and the initial lactate > 3 mmol/L had sensitivities of 82% and 65%, respectively. These results demonstrate that in a large ED sepsis database the earliest measurement of end organ impairment, tqSOFA, performed poorly at identifying patients at increased risk of mortality and maximum qSOFA did not significantly outperform initial serum lactate levels.

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Pre-hospital qSOFA as a predictor of sepsis and mortality

Cited by 21 publications

References 19 publications

Use of prehospital qSOFA in predicting in-hospital mortality in patients with suspected infection: A retrospective cohort study

Use of prehospital qSOFA in predicting in-hospital mortality in patients with suspected infection: A retrospective cohort study

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

The sensitivity of qSOFA calculated at triage and during emergency department treatment to rapidly identify sepsis patients

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