Physicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction

Feder, Shelli; Geda, Mary; Williams, Karin; Dodson, John A.; Nanna, Michael G.; Allore, Heather G.; Murphy, Terrence E.; Tinetti, Mary E.; Gill, Thomas M.; Chaudhry, Sarwat I.

doi:10.1016/j.hrtlng.2015.05.005

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…At present, the TIMI risk score is the most commonly used means to assess acute ACS prognosis in clinic, which is a summary of 15,000 cases of STEMI patients who underwent thrombolytic therapy, including historical data, hemodynamic parameters, electrocardiogram indicators, and clinical treatment and development. A number of studies have confirmed that TIMI scores are closely correlated with acute ACS cases, and have a good correlation with the long-term prognosis of the incidence of adverse events ( 14 , 15 ). In the present study, in the 4D-STI and TIMI risk score system, as the risk assessment score increased, the probability of occurrence of deaths and poor prognosis were substantially increased in the high- and low-risk groups.…”

Section: Discussionmentioning

confidence: 91%

Comparison of the predictive value of four-dimensional speckle tracking imaging risk classification and the TIMI system after STEMI reperfusion therapy

Wang

Yin

et al. 2018

Exp Ther Med

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The predictive value of four-dimensional speckle tracking imaging (4D-STI) risk classification and TIMI risk scores for the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) after reperfusion therapy were investigated. One hundred and twenty patients with STEMI after reperfusion therapy were involved. At 12 h after reperfusion therapy, 2nd and 3rd day, the three-dimensional longitudinal strain (LS), circumferential strain (CS) and radial strain (RS), area strain (AS), as well as other 4D-STI detection indicators, were collected. The patients were followed up for one year, and were divided into good prognosis group and poor prognosis group. LS, CS, RS and AS indicators were analyzed between these two groups. The ROC curve was drawn to establish the 4D-STI risk classification and its predictive value for poor prognosis and mortality were compared with TIMI risk scores. AS, LS and RS at 12 h after reperfusion treatment, and AS and RS at 2nd and 3rd day had a certain degree of prediction accuracy in STEMI patients in the poor prognosis group. In the 4D-STI and TIMI risk scores, the risk of death and adverse prognosis significantly increased as the risk scores increased (P<0.01). The 4D-STI risk score for predicting poor prognosis and mortality was greater than the TIMI risk score. 4D-STI risk scores are superior to TIMI risk scores in predicting poor prognosis and mortality in patients with STEMI after reperfusion therapy.

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Section: Discussionmentioning

confidence: 91%

Comparison of the predictive value of four-dimensional speckle tracking imaging risk classification and the TIMI system after STEMI reperfusion therapy

Wang

Yin

et al. 2018

Exp Ther Med

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“…The lack of weighting for the risk factors, while improving usability, decreased TIMI risk score discriminatory performance [ 6 , 67 ]. In addition, there are different scoring systems for STEMI and NSTEMI.…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, the TIMI Risk Score for patients with UA or NSTEMI is composed of seven equally weighted, binary variables [ 69 ]. Age, aspirin use during the previous seven days, coronary artery disease (CAD) risk factors, known CAD, recent anginal episodes; ST-segment alterations of at least 0.5mm on the ECG at the time of initial presentation, and elevation of serum cardiac markers [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

et al. 2022

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Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

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“…In conclusion, in the setting of evidence that SDM could improve outcomes including adherence and satisfaction with care, there has been a movement in the health care system to implement SDM across a wide range of practice settings. [29][30][31][32] Cardiologists in our study valued SDM in the setting of older adults hospitalized for NSTEMI, but they noted a lack of specific tools for implementation. On the other hand, patients often only perceived catheterization as a possible choice for treatment, and many would have liked to be more informed.…”

Section: Discussionmentioning

confidence: 99%

Patient and Cardiologist Perspectives on Shared Decision Making in the Treatment of Older Adults Hospitalized for Acute Myocardial Infarction

et al. 2020

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Background. Medical and interventional therapies for older adults with acute myocardial infarction (AMI) reduce mortality and improve outcomes in selected patients, but there are also risks associated with treatments. Shared decision making (SDM) may be useful in the management of such patients, but to date, patients’ and cardiologists’ perspectives on SDM in the setting of AMI remain poorly understood. Accordingly, we performed a qualitative study eliciting patients’ and cardiologists’ perceptions of SDM in this scenario. Methods. We conducted 20 in-depth, semistructured interviews with older patients (age ≥70) post-AMI and 20 interviews with cardiologists. The interviews were transcribed and analyzed using ATLAS.ti. Two investigators independently coded transcripts using the constant comparative method, and an integrative, team-based process was used to identify themes. Results. Six major themes emerged: 1) patients felt their only choice was to undergo an invasive procedure; 2) patients placed a high level of trust and gratitude toward physicians; 3) patients wanted to be more informed about the procedures they underwent; 4) for cardiologists, patients’ age was not a major contraindication to intervention, while cognitive impairment and functional limitation were; 5) while cardiologists intuitively understood the concept of SDM, interpretations varied; and 6) cardiologists considered SDM to be useful in the setting of non-ST elevated myocardial infarction (NSTEMI) but not ST-elevated myocardial infarction (STEMI). Conclusions. Patients viewed intervention as “the only choice,” whereas cardiologists saw a need for balancing risks and benefits in treating older adults post-NSTEMI. This discrepancy implies there is room to improve communication of risks and benefits to older patients. A decision aid informed by the needs of older adults could help to better convey patient-specific risk and increase choice awareness.

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Physicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction

Cited by 6 publications

References 30 publications

Comparison of the predictive value of four-dimensional speckle tracking imaging risk classification and the TIMI system after STEMI reperfusion therapy

Comparison of the predictive value of four-dimensional speckle tracking imaging risk classification and the TIMI system after STEMI reperfusion therapy

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

Patient and Cardiologist Perspectives on Shared Decision Making in the Treatment of Older Adults Hospitalized for Acute Myocardial Infarction

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