Association of triage time Shock Index, Modified Shock Index, and Age Shock Index with mortality in Emergency Severity Index level 2 patients

Torabi, Mehdi; Mirafzal, Amirhossein; Rastegari, Azam; Sadeghkhani, Neda

doi:10.1016/j.ajem.2015.09.014

Cited by 44 publications

(33 citation statements)

References 29 publications

(33 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…Similar data were also obtained by other researchers. In particular, one study showed that the low sensitivity and specificity of the stress index as a prognostic index of mortality allows using it as an auxiliary means only 55 ; the same applies to high-frequency heart rate variability parameters-they can potentially be used as an auxiliary diagnostic method in combination with other methods. 46 In regards to the data provided in literature and data obtained during this study, it was inexpedient to determine the class of the functional dependency between age and heart rate variability indices or the specific type and parameters of the said dependency.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predictors of aging based on the analysis of heart rate variability

Botsva

Naishtetik

Khimion

et al. 2017

Pacing Clinical Electrophis

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The use of neural network computations and the modification of the algorithm through the construction of individual training samples for different age intervals, and the creation of individual ensembles of classification neural networks, therefore achieved a prediction of the age of examined persons based on the values of their time and frequency domain heart rate variability indices, with 87% accuracy for women and 85% accuracy for men in the 66-85 years age interval and at least 85% for age groups across the entire sample.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predictors of aging based on the analysis of heart rate variability

Botsva

Naishtetik

Khimion

et al. 2017

Pacing Clinical Electrophis

View full text Add to dashboard Cite

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“…Patients who died on hospital arrival or at the accident scene were not included in the study. Detailed patient information retrieved from the Trauma Registry System of our institution included the following: age; sex; SBP and HR assessed by the nursing staff upon arrival at the triage desk of the ED; SI, defined as the ratio of HR/SBP; MSI, defined as HR divided by MAP = (2 × DBP + SBP) ÷ 3 [ 18 , 23 ]; and Age SI, defined as age multiplied by SI, which accounts for the age of the patient in addition to the factors addressed by SI [ 20 , 23 ]. The pre-existing comorbidities and chronic diseases noted included diabetes mellitus (DM), hypertension (HTN), coronary artery disease (CAD), congestive heart failure (CHF), cerebrovascular accident (CVA), end-stage renal disease (ESRD) and abnormal bodyweight as defined by the World Health Organization [ 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Massive Transfusion in Trauma Patients with Shock Index, Modified Shock Index, and Age Shock Index

Rau

Kuo

et al. 2016

IJERPH

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Objectives: The shock index (SI) and its derivations, the modified shock index (MSI) and the age shock index (Age SI), have been used to identify trauma patients with unstable hemodynamic status. The aim of this study was to evaluate their use in predicting the requirement for massive transfusion (MT) in trauma patients upon arrival at the hospital. Participants: A patient receiving transfusion of 10 or more units of packed red blood cells or whole blood within 24 h of arrival at the emergency department was defined as having received MT. Detailed data of 2490 patients hospitalized for trauma between 1 January 2009, and 31 December 2014, who had received blood transfusion within 24 h of arrival at the emergency department, were retrieved from the Trauma Registry System of a level I regional trauma center. These included 99 patients who received MT and 2391 patients who did not. Patients with incomplete registration data were excluded from the study. The two-sided Fisher exact test or Pearson chi-square test were used to compare categorical data. The unpaired Student t-test was used to analyze normally distributed continuous data, and the Mann-Whitney U-test was used to compare non-normally distributed data. Parameters including systolic blood pressure (SBP), heart rate (HR), hemoglobin level (Hb), base deficit (BD), SI, MSI, and Age SI that could provide cut-off points for predicting the patients’ probability of receiving MT were identified by the development of specific receiver operating characteristic (ROC) curves. High accuracy was defined as an area under the curve (AUC) of more than 0.9, moderate accuracy was defined as an AUC between 0.9 and 0.7, and low accuracy was defined as an AUC less than 0.7. Results: In addition to a significantly higher Injury Severity Score (ISS) and worse outcome, the patients requiring MT presented with a significantly higher HR and lower SBP, Hb, and BD, as well as significantly increased SI, MSI, and Age SI. Among these, only four parameters (SBP, BD, SI, and MSI) had a discriminating power of moderate accuracy (AUC > 0.7) as would be expected. A SI of 0.95 and a MSI of 1.15 were identified as the cut-off points for predicting the requirement of MT, with an AUC of 0.760 (sensitivity: 0.563 and specificity: 0.876) and 0.756 (sensitivity: 0.615 and specificity: 0.823), respectively. However, in the groups of patients with comorbidities such as hypertension, diabetes mellitus, or coronary artery disease, the discriminating power of these three indices in predicting the requirement of MT was compromised. Conclusions: This study reveals that the SI is moderately accurate in predicting the need for MT. However, this predictive power may be compromised in patients with HTN, DM or CAD. Moreover, the more complex calculations of MSI and Age SI failed to provide better discriminating power than the SI.

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“…Comparison with Previous Models. We compared the XGBoost model with severity scores Shock Index (SI), Modified Shock Index (MSI), and Aged Shock Index (ASI) 9,11 where SI = HR/SBP; MSI = HR/(2/3 × DBP + 1/3 × SBP); ASI = Age × SI.…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…The ESI relies heavily on provider judgment which can lead to inaccuracy and misclassification. 9,10 Differentiating between levels 2 and 3 is a challenging task, 11 and ESI level 3 is assigned to a largely diverse ill patient group. 12 Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications because of flexibility in handling large datasets from electronic medical records (EMR).…”

Section: Introductionmentioning

confidence: 99%