Mortality prediction algorithms for patients undergoing primary percutaneous coronary intervention

Hizoh, István; Domokos, Dominika; Banhegyi, Gyongyver; Becker, Dávid; Merkely, Béla; Ruzsa, Zoltán

doi:10.21037/jtd.2019.12.83

Cited by 13 publications

(11 citation statements)

References 45 publications

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“…The CADILLAC risk score was developed from a cohort of STEMI patients undergoing primary PCI [10] . Among various risk models, the CADILLAC score is well-balanced because it includes presentation characteristics, procedural data, and laboratory and imaging results [22] . Recently, Mitarai et al reported the good predictivity of "angiographic GRACE score" for inhospital mortality, which incorporates a location of culprit artery and TIMI flow grade in addition to the original GRACE risk score [25] , suggesting the importance of other information than presentation characteristics in risk stratification.…”

Section: Discussionmentioning

confidence: 99%

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Impact of CADILLAC and GRACE risk scores on short- and long-term clinical outcomes in patients with acute myocardial infarction

Sato

Saito

Matsumoto

et al. 2021

Journal of Cardiology

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Section: Discussionmentioning

confidence: 99%

“…In this study, we exclusively tested the CADILLAC and GRACE risk scores. Thus, the impact of different risk models such as TIMI and Zwolle risk scores which are not suitable to our dataset is unknown [22] .…”

Section: Discussionmentioning

confidence: 99%

Impact of CADILLAC and GRACE risk scores on short- and long-term clinical outcomes in patients with acute myocardial infarction

Sato

Saito

Matsumoto

et al. 2021

Journal of Cardiology

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“…Various algorithms have been developed to predict in-hospital outcomes. These include mortality prediction systems, such as the acute physiology and chronic health evaluation (APACHE) score, the simplified acute physiology score (SAPS), and the sequential organ failure assessment (SOFA) score [ 5 , 6 , 7 , 8 , 9 ], the duration of stay estimation based on electronic health record data [ 10 ], and outcomes prediction-specific to underlying medical conditions [ 11 , 12 ]. However, these algorithms are tailored for subjects admitted to the intensive care units or general medical emergency departments and are not optimized to predict outcomes at the time of admission to the cardiac care unit.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

et al. 2022

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Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

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“…Thus, they may contribute to an improved quality of care. Furthermore, risk models may be helpful in clinical trial design identifying patients with the needed risk profile thereby increasing statistical power or reducing sample size and costs [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…Also, 30-day risk-adjusted mortality is one of the quality indicators that is used to evaluate quality of care of STEMI patients [ 3 ]. As in many of the current risk estimation algorithms, heart rate and systolic blood pressure are key predictors of the GRACE 2.0 score [ 1 ]. Nevertheless, these parameters may also be altered by the applied medical treatment / circulatory support without concomitant improvement in microcirculatory failure / prognosis of the patient.…”

Section: Introductionmentioning

confidence: 99%

Admission lactate level and the GRACE 2.0 score are independent and additive predictors of 30-day mortality of STEMI patients treated with primary PCI—Results of a real-world registry

et al. 2022

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Background In many of the risk estimation algorithms for patients with ST-elevation myocardial infarction (STEMI), heart rate and systolic blood pressure are key predictors. Yet, these parameters may also be altered by the applied medical treatment / circulatory support without concomitant improvement in microcirculation. Therefore, we aimed to investigate whether venous lactate level, a well-known marker of microcirculatory failure, may have an added prognostic value on top of the conventional variables of the “Global Registry of Acute Coronary Events” (GRACE) 2.0 model for predicting 30-day all-cause mortality of STEMI patients treated with primary percutaneous coronary intervention (PCI). Methods In a prospective single-center registry study conducted from May 2020 through April 2021, we analyzed data of 323 cases. Venous blood gas analysis was performed in all patients at admission. Nested logistic regression models were built using the GRACE 2.0 score alone (base model) and with the addition of venous lactate level (expanded model) with 30-day all-cause mortality as primary outcome measure. Difference in model performance was analyzed by the likelihood ratio (LR) test and the integrated discrimination improvement (IDI). Independence of the predictors was evaluated by the variance inflation factor (VIF). Discrimination and calibration was characterized by the c-statistic and calibration intercept / slope, respectively. Results Addition of lactate level to the GRACE 2.0 score improved the predictions of 30-day mortality significantly as assessed by both LR test (LR Chi-square = 8.7967, p = 0.0030) and IDI (IDI = 0.0685, p = 0.0402), suggesting that the expanded model may have better predictive ability than the GRACE 2.0 score. Furthermore, the VIF was 1.1203, indicating that the measured lactate values were independent of the calculated GRACE 2.0 scores. Conclusions Our results suggest that admission venous lactate level and the GRACE 2.0 score may be independent and additive predictors of 30-day all-cause mortality of STEMI patients treated with primary PCI.

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Mortality prediction algorithms for patients undergoing primary percutaneous coronary intervention

Cited by 13 publications

References 45 publications

Impact of CADILLAC and GRACE risk scores on short- and long-term clinical outcomes in patients with acute myocardial infarction

Impact of CADILLAC and GRACE risk scores on short- and long-term clinical outcomes in patients with acute myocardial infarction

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

Admission lactate level and the GRACE 2.0 score are independent and additive predictors of 30-day mortality of STEMI patients treated with primary PCI—Results of a real-world registry

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