Shock index, modified shock index, and age shock index for prediction of mortality in Emergency Severity Index level 3

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

doi:10.1016/j.ajem.2016.07.017

Cited by 52 publications

(34 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In other words, the same threshold might not be sensitive as to the aged population. Several studies have used age modified SI (age×SI) as a better predictor to offset the disadvantage of SI [ 24 – 26 ]. In our data, the breakpoint of SI was set at 0.93 as an ideal threshold for exclusion of developing CS.…”

Section: Discussionmentioning

confidence: 99%

The value of shock index in prediction of cardiogenic shock developed during primary percutaneous coronary intervention

Wei

Bai

Dai

et al. 2018

BMC Cardiovasc Disord

View full text Add to dashboard Cite

BackgroundShock index(SI) is a conventional predictive marker for haemodynamic state. Its breakpoint varies by different conditions according to previous studies. The current study was performed to evaluate the capability of SI in prediction of cardiogenic shock(CS) developed during primary percutaneous coronary intervention (pPCI).MethodsTotal 870 patients of ST segment elevation myocardial infarction(STEMI) who were haemodynamic stable before pPCI were involved in the study. In this cohort, 625 consecutive patients composed analysis series and 245 consecutive patients composed validation series. Multivariate regression analysis was used to evaluate whether SI was a significant predictor of developed CS and Hosmer-Lemeshow test was used to assess the goodness of model fitness. Receiver-operating characteristics (ROC) analysis was used to compare the predictive capability of SI with other predictors. The sensitivity, specificity, accuracy, positive and negative predictive values of SI at different cutoff values was compared to identify a best breakpoint.ResultsIn the analysis series, SI and Killips classification were identified as independent predictors. ROC analysis demonstrated the diagnostic capability of SI was superior to pre-procedural systolic blood pressure(SBP) or heart rate(HR) alone (0.8113 vs 0.7582, P = 0.04 and 0.8113 vs 0.7111, P < 0.001). The diagnostic capability of SI was equivalent to that of combination of SBP, HR and Killips claasification(0.8133 vs 0.8137, P = 0.97). SI had a high specificity and low sensitivity. When the cutoff value was set at 0.93, the positive predictive value, negative predictive value and diagnostic accuracy was 42.6%, 95.1% and 87.4% respectively. In validation series, the area under ROC curve was 0.8245, which was similar to that in the analysis series. The positive predictive value, negative predictive value and diagnostic accuracy at the cutoff value of 0.93 was 53.8%, 93.2% and 88.9% respectively.ConclusionsSI has a high predictive accuracy for developing CS during pPCI in STEMI patients. It is an excellent exclusion diagnosis index rather than confirmative diagnosis index.

show abstract

Section: Discussionmentioning

confidence: 99%

The value of shock index in prediction of cardiogenic shock developed during primary percutaneous coronary intervention

Wei

Bai

Dai

et al. 2018

BMC Cardiovasc Disord

View full text Add to dashboard Cite

show abstract

“…(2017) suggested temperature as an indicator of septic shock and concluded that fever slows the process. Modified shock index (MSI) has emerged as an early non-invasive measure, which is calculated by dividing HR by MAP (Jayaprakash, Gajic, Frank, & Smischney, 2018;Torabi, Moeinaddini, Mirafzal, Rastegari, & Sadeghkhani, 2016…”

Section: Features Selectionmentioning

confidence: 99%

“…DBP and Albumin (Holder et al, 2016) 8. MSI (Jayaprakash et al, 2018) 9. ageSI, Age, SBP, and Gender (Torabi et al, 2016) Second step. Used the Cox proportional hazards model to obtain the coefficients (Li, Zhou, Choubey, & Sievenpiper, 2007), based on the nine sets listed in the first step.…”

Section: Data Cleanup and Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Methods for Septic Shock Prediction

Darwiche

Mukherjee

2018

Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality

View full text Add to dashboard Cite

“…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%

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

Klug

Barash

Bechler³

et al. 2019

J GEN INTERN MED

View full text Add to dashboard Cite

Shock index, modified shock index, and age shock index for prediction of mortality in Emergency Severity Index level 3

Cited by 52 publications

References 25 publications

The value of shock index in prediction of cardiogenic shock developed during primary percutaneous coronary intervention

The value of shock index in prediction of cardiogenic shock developed during primary percutaneous coronary intervention

Machine Learning Methods for Septic Shock Prediction

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

Contact Info

Product

Resources

About