Decision thresholds for machine learning pre‐operative risk assessment

We found the work on pre‐operative risk assessment by Kotze et al. compelling as it offers a substantial contribution to closing the research–practice gap in NHS pre‐operative care [1]. We would like to draw attention to a minor aspect that could benefit from further refinement: the use of a uniform decision threshold (0.2) for risk stratification across all surgical subspecialities, which might limit the clinical applicability of the model in real‐world settings.

The validity of risk thresholds depends inherently on alignment with the baseline risk of specific patient populations and surgical procedures [2], an element not fully addressed in the current study. The NHS England 2024 mandate further emphasises that risk assessment strategies should be tailored to specific clinical contexts [3], yet the authors applied a consistent threshold despite notable variations in risk profiles across surgical disciplines. For example, elective laparoscopic cholecystectomy is a low‐complexity general surgical procedure with a 30‐day mortality rate of approximately 0.1%, whereas emergency neurosurgery for traumatic brain injury exceeds 10% [4]. Employing the same 0.2 threshold may lead to unintended overestimation of low‐risk situations and vice versa. This is a concern also echoed in the Getting It Right First Time (GIRFT) guidance, which advocates for speciality‐specific care pathways to ground risk assessment in clinical reality.

In keeping with the work from Oliver et al. [5], we would propose two practical adjustments. First, leveraging the inherent customisability of the Smart PreOp system to develop speciality‐tailored thresholds using real‐world subspecialty data. Second, integrating surgical complexity metrics into the threshold framework. These modest refinements would reinforce the clinical relevance of the model without compromising its core strengths, better reflecting frontline clinical needs and optimising patient allocation to appropriate care pathways. We believe that such adjustments would further enhance the translational value of the study, solidifying its role as a practical tool for pre‐operative care in the NHS.