Accurate prediction of postoperative complications in older patients: a long way to go

Abstract

We read with interest the Science Letter by Dubowitz et al. [1]. The application of biological instead of chronological age for the prediction of postoperative complications in patients is a promising advance, and we concur with the authors' perspective on the potential association between biological ageing and postoperative complications after cancer surgery. This study helps prompt surgeons and anaesthetists to reflect on their practice and identify high-risk patients, regardless of the method used.

However, in statistics, correlation is not a substitute for causal inference. Also, exclusive reliance on the PhenoAge model as a means of calculating biological age has considerable limitations. This model uses conventional clinical biomarkers (e.g. white blood cell count, blood glucose, lipids, creatinine, etc.) to calculate biological age [2]. In our opinion, these markers reflect primarily the macroscopic health of the body, but do not fully capture the ageing process at the cellular or molecular level. They are also susceptible to external factors (e.g. diet, lifestyle, environmental pollution, etc.) and, thus, may not reflect an individual's biological ageing accurately. In addition, PhenoAge modelling is based on data from specific populations, usually in the USA. In other races and regions, the performance of this model may not be the same. Therefore, when the PhenoAge model is applied in different races or regions, it may need to be re-based on a large sample size of data for optimisation or training.

Most importantly, the PhenoAge model simplifies the ageing process, which is a multifactorial and complex process involving interactions at multiple levels, such as gene expression; telomere shortening; immune system decline; and epigenetic changes [3, 4]. The model focuses mainly on a few clinical markers, but it fails to account for all the biological processes involved in the ageing process and, therefore, may overlook some subtle key roles of age-related molecular targets. Indeed, one of the primary challenges in predicting the incidence of postoperative complications in older patients accurately is the absence of dedicated peri-operative databases for this specific population. Such databases are essential for researchers to obtain sufficient, high-quality raw data.

In accordance with this objective, our research group is currently engaged in the establishment of the Perioperative Management and Outcome database, which is a large-scale, nationwide registry of older patients in China [5]. The prospective registry is expected to provide a rich dataset that will facilitate the evaluation of the quality of peri-operative care and, in turn, improve clinical care for older patients. Nevertheless, it has proven challenging to construct a database that encompasses multiple countries or regions and is based on participants from a diverse range of populations. It is anticipated that, in the future, multi-country and multi-regional collaborative research will become standard in this field of research.