Computational physiological models for hemodynamic management in critical care: a systematic literature review focusing on model design, credibility and clinical readiness

Background:

Hemodynamic instability is a highly prevalent, complex and life-threatening condition in critically ill patients. Its multifactorial nature and patient-specific variability challenge standardised treatment approaches. Computational physiological models (CPMs) offer a promising solution by simulating cardiovascular dynamics to guide individualised hemodynamic management. This systematic review evaluates the current landscape of cardiovascular CPMs, focusing on their design, credibility, and clinical readiness. 

Methods:

A systematic search was conducted in MEDLINE ALL, Embase, Scopus, and Web of Science. Original research articles describing zero-dimensional, closed-loop cardiovascular models with (potential) applications in critical care were included. Data were extracted on context of use, model design, and validation. Model credibility was assessed using a risk-based framework and clinical readiness using a nine-level technology maturity scale. 

Results: 

Out of 10,704 screened articles, 183 were included. Direct clinical applications were described in 50% of these studies, including diagnosis, decision support, and closed-loop control. Fluid management was the most common application domain (30%). Personalisation of model parameters was reported in 25% of the articles. While 66% of the articles presented model validation, only 21% achieved moderate credibility scores. Reporting of model characteristics was consistently (100%) insufficient. Most models (75%) were at clinical readiness level 3-4 (model prototyping and development), with four studies reaching clinical testing (level 6-8).

Conclusion: 

A substantial body of cardiovascular CPMs exists with promising prospects for relevant applications in critical care, while a large part is currently confined to pre-clinical research settings. Advancing clinical integration requires leveraging existing models, improving transparency in verification and validation, and establishing robust personalisation strategies. Trial registration PROSPERO - CRD42022300137, registered on February 11, 2022.