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

Abstract

Background and Aim Cardiogenic shock (CS) withholds a significantly high mortality rate between 40% and 60% despite advances in diagnosis and medical/surgical intervention. To date, machine learning (ML) is being implemented to integrate numerous data to optimize early diagnostic predictions and suggest clinical courses. This systematic review summarizes the area under the curve (AUC) receiver operating characteristics (ROCs) accuracy for the early prediction of CS. Methods A systematic review was conducted within databases of PubMed, ScienceDirect, Clinical Key/MEDLINE, Embase, GoogleScholar, and Cochrane. Cohort studies that assessed the accuracy of early detection of CS using ML software were included. Data extraction was focused on AUC–ROC values directed towards the early detection of CS. Results A total of 943 studies were included for systematic review. From the reviewed studies, 2.2% (N = 21) evaluated patient outcomes, of which 14.3% (N = 3) were assessed. The collective patient cohort (N = 698) consisted of 314 (45.0%) females, with an average age and body mass index of 64.1 years and 28.1 kg/m2, respectively. Collectively, 159 (22.8%) mortalities were reported following early CS detection. Altogether, the AUC–ROC value was 0.82 (α = .05), deeming it of superb sensitivity and specificity. Conclusions From the present comprehensively gathered data, this study accounts the use of ML software for the early detection of CS in a clinical setting as a valid tool to predict patients at risk of CS. The complexity of ML and its parallel lack of clinical evidence implies that further prospective randomized control trials are needed to draw definitive conclusions before standardizing the use of these technologies. Brief Summary The catastrophic risk of developing CS continues to be a concern in the management of critical cardiac care. The use of ML predictive models have the potential to provide the accurate and necessary feedback for the early detection and proper management of CS. This systematic review summarizes the AUC–ROCs accuracy for the early prediction of CS.

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

“…A recent systematic review covers the field in further detail. 50 A key aspect is to define the outcome of interest, and most reports have focused on mortality, 14 but other end points, such as cognitive function and quality of life, could also be investigated with the VANQUISH Shock data set.…”

“…Several groups have employed a variety of sophisticated approaches including techniques such as LogitBoost (49) and Extreme Gradient boost algorithms (50)which facilitate computer detection of variables which associate with the outcome of interest. A recent systematic review covers the area in further detail (51). A key aspect is to define the outcome of interest, and most reports have focused on mortality (14)but other endpoints such as cognitive function and quality of life could also be investigated in addition with the VANQUISH dataset.…”

Papel Da Inteligência Artificial Na Predição De Eventos Cardíacos

“…Um estudo exemplificou o uso da IA para estratificar a resposta prognóstica aos betabloqueadores em pacientes com insuficiência cardíaca (IC) e fração de ejeção ventricular esquerda reduzida. A IA identificou pacientes sem resposta terapêutica, reduzindo o risco de morte e ilustrando como a IA pode guiar decisões terapêuticas personalizadas, melhorando prognósticos e evitando eventos adversos (Aleman, 2021).…”

“…As técnicas de IA têm impacto significativo no diagnóstico por imagem em cardiologia, abrangendo áreas como precisão diagnóstica, interpretação de imagens cardíacas, modelagem estatística da anatomia cardíaca e estratégias de tratamento personalizado. Essas técnicas são aplicadas com sucesso no diagnóstico de doenças cardiovasculares (DCVs), incluindo infarto do miocárdio, insuficiência cardíaca, síndromes coronarianas agudas e fibrilação atrial (Aleman, 2021).…”

Future for cardiogenic shock research

“…1. Machine learning algorithms can analyze large datasets, possibly thereby identifying new patterns and risk factors associated with cardiogenic shock onset, progression, and outcomes [9–11]. But to truly use the potential of artificial intelligence this should be applied to very large samples and not based on existing databases with preselected variables where artificial intelligence provide less new insight that cannot be obtained by conventional regression analyses [12 ▪ ].…”