Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure

Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study

Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-III database and artificial intelligence (AI), we aimed to increase diagnostic precision, focusing on Bayesian network classifiers (BNCs) and comparing them with other AI methods. Data from 5970 adults, including 950 patients with cardiogenic shock, 1946 patients with septic shock, and 3074 patients with sepsis, were extracted for this study. Of the original 51 variables included in the data records, 12 were selected for constructing the predictive model. The data were divided into training and validation sets at an 80:20 ratio, and the performance of the BNCs was evaluated and compared with that of other AI models, such as the one rule classifier (OneR), classification and regression tree (CART), and an artificial neural network (ANN), in terms of accuracy, sensitivity, specificity, precision, and F1-score. The BNCs exhibited an accuracy of 87.6% to 91.5%. The CART model demonstrated a notable 91.6% accuracy when only three decision levels were used, whereas the intricate ANN model reached 90.5% accuracy. Both the BNCs and the CART model allowed clear interpretation of the predictions. BNCs have the potential to be valuable tools in diagnostic tasks, with an accuracy, sensitivity, and precision comparable, in some cases, to those of ANNs while demonstrating superior interpretability.

Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management

Cardiogenic shock (CS) is a complex clinical syndrome with various etiologies and clinical presentations. Despite advances in therapeutic options, mortality remains high, and clinical trials in the field are complicated in part by the heterogeneity of CS patients. More individualized targeted therapeutic approaches might improve outcomes in CS, but their implementation remains challenging. The present review discusses current and emerging machine learning-based approaches, including unsupervised and supervised learning methods that use real-world clinical data to individualize therapeutic strategies for CS patients. We will discuss the rationale for each approach, potential advantages and disadvantages, and how these strategies can inform clinical trial design and management decisions.

Application of Cardiogenic Shock Working Group-defined Society for Cardiovascular Angiography and Interventions (CSWG-SCAI) Staging of Cardiogenic Shock to the Medical Information Mart for Intensive Care IV (MIMIC-IV) database

BACKGROUND

The optimal parameters for defining stages of cardiogenic shock (CS) are not yet known. The Cardiogenic Shock Working Group-defined Society for Cardiovascular Angiography and Interventions (CSWG-SCAI) staging of CS was developed to provide simple and specific parameters for risk-stratifying patients.

OBJECTIVES

The purpose of this study was to test whether the Cardiogenic Shock Working Group-defined Society for Cardiovascular Angiography and Interventions (CSWG-SCAI) staging is associated with in-hospital mortality, using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

METHODS

We utilized the open-access MIMIC-IV database, which includes >300,000 patients admitted between 2008 and 2019. We extracted the clinical profile of patients admitted with CS and stratified them into different SCAI stages at admission based on the CSWG criteria. We then tested the association between in-hospital mortality and parameters of hypotension, hypoperfusion, and overall CSWG-SCAI stage.

RESULTS

Of the 2463 patients, CS was predominantly caused by heart failure (HF; 54.7 %) or myocardial infarction (MI; 26.3 %). Mortality was 37.5 % for the total cohort, 32.7 % for patients with HF, and 40 % for patients with MI (p < 0.001). Mortality was higher among patients with mean arterial pressure < 65 mmHg, lactate >2 mmol/L, ALT >200 IU/L, pH ≤ 7.2, and more than one drug/device support at baseline. Increasing CSWG-SCAI stages at baseline and maximum CSWG-SCAI stage achieved were significantly associated with in-hospital mortality (p < 0.05).

CONCLUSIONS

The CSWG-SCAI stages are significantly associated with in-hospital mortality and may be used to identify hospitalized patients at risk of worsening cardiogenic shock severity.

CONDENSED ABSTRACT

We analyzed data from 2463 patients with cardiogenic shock using the MIMIC-IV database to investigate the relationship between the Cardiogenic Shock Working Group-defined Society for Cardiovascular Angiography and Interventions (CSWG-SCAI) staging and in-hospital mortality. The main causes of cardiogenic shock were heart failure (54.7 %) and myocardial infarction (26.3 %). The overall mortality rate was 37.5 %, with a higher rate among patients with myocardial infarction (40 %) compared to those with heart failure (32.7 %). Mean arterial pressure < 65 mmHg, lactate >2 mmol/L, ALT >200 IU/L, and pH ≤ 7.2 were significantly associated with mortality. Increasing CSWG-SCAI stages at baseline and maximum achieved stages were strongly associated with higher mortality (p < 0.05). Therefore, the CSWG-SCAI staging system can be used to risk-stratify patients with cardiogenic shock.

Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.

Early Prediction of Cardiogenic Shock Using Machine Learning

Cardiogenic shock (CS) is a severe condition with in-hospital mortality of up to 50%. Patients who develop CS may have previous cardiac history, but that may not always be the case, adding to the challenges in optimally identifying and managing these patients. Patients may present to a medical facility with CS or develop CS while in the emergency department (ED), in a general inpatient ward (WARD) or in the critical care unit (CC). While different clinical pathways for management exist once CS is recognized, there are challenges in identifying the patients in a timely manner, in all settings, in a timeframe that will allow proper management. We therefore developed and evaluated retrospectively a machine learning model based on the XGBoost (XGB) algorithm which runs automatically on patient data from the electronic health record (EHR). The algorithm was trained on 8 years of de-identified data (from 2010 to 2017) collected from a large regional healthcare system. The input variables include demographics, vital signs, laboratory values, some orders, and specific pre-existing diagnoses. The model was designed to make predictions 2 h prior to the need of first CS intervention (inotrope, vasopressor, or mechanical circulatory support). The algorithm achieves an overall area under curve (AUC) of 0.87 (0.81 in CC, 0.84 in ED, 0.97 in WARD), which is considered useful for clinical use. The algorithm can be refined based on specific elements defining patient subpopulations, for example presence of acute myocardial infarction (AMI) or congestive heart failure (CHF), further increasing its precision when a patient has these conditions. The top-contributing risk factors learned by the model are consistent with existing clinical findings. Our conclusion is that a useful machine learning model can be used to predict the development of CS. This manuscript describes the main steps of the development process and our results.