Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares

Future for cardiogenic shock research

PURPOSE OF REVIEW

To discuss future research themes and study design in cardiogenic shock.

RECENT FINDINGS

Cardiogenic shock research faces multiple challenges, hindering progress in understanding and treating this life-threatening condition. Cardiogenic shock's heterogeneous nature poses challenges in patient selection for clinical trials, potentially leading to variability in treatment responses and outcomes. Ethical considerations arise due to the acuity and severity of the condition, posing challenges in obtaining informed consent and conducting randomized controlled trials where time to treatment is pivotal.

SUMMARY

This review discusses research in this area focusing on the importance of phenotyping patients with cardiogenic shock, based on artificial intelligence, machine learning, and unravel new molecular mechanisms using proteomics and metabolomics. Further, the future research focus in mechanical circulatory support and targeting inflammation is reviewed. Finally, newer trial designs including adaptive platform trials are discussed.

The Multicenter Collaborative to Enhance Biologic Understanding, Quality, and Outcomes in Cardiogenic Shock (VANQUISH Shock): Rationale and Design

BACKGROUND

Despite efforts to advance therapies in cardiogenic shock (CS), outcomes remain poor. This is likely due to several factors, including major gaps in our understanding of the pathophysiology, phenotyping of patients, and challenges with conducting adequately powered clinical studies. An unmet need exists for a comprehensive multicentre "all-comers" prospective registry to facilitate characterising contemporary presentation, treatment (in a device-agnostic fashion), and short- and intermediate-term outcomes and quality of life (QOL) of CS patients.

METHODS

The Multicenter Collaborative to Enhance Biological Understanding, Quality and Outcomes in Cardiogenic Shock (VANQUISH Shock) registry is a prospective observational registry that will study unrestricted adult patients with a primary diagnosis of CS at 4 North American centres with multidisciplinary shock programs. Both acute myocardial infarction (AMI-CS) and acute heart failure (HF-CS) etiologies will be included, and the registry will be device agnostic and widely inclusive. The primary end point will be survival at 30 days after hospital discharge. Secondary outcomes will include in-hospital adverse events and survival to 6 and 12 months. Patients will also undergo neurologic and health-related QOL assessments with the Cerebral Performance Category (CPC) and Short-Form 36 (SF-36) health survey tools before discharge and during follow-up. Serial biospecimens will facilitate biomarker studies.

CONCLUSIONS

The VANQUISH Shock registry provides a unique opportunity to study the pathophysiology, contemporary management, clinical course, and outcomes of CS. By capturing detailed and high-quality longitudinal data, the registry will address existing knowledge gaps and serve as a springboard for future mechanistic clinical studies to advance the field.

Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review

STUDY OBJECTIVE

Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research.

METHODS

We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers.

RESULTS

Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed.

CONCLUSION

This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.