Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning?

Abstract

The routine introduction of novel anti-inflammatory therapies into the management algorithms of patients with Crohn’s disease over the last 2 decades has not substantially changed the likelihood of ultimate surgery. Rather it has delayed the operative need and altered the presentation phenotype. The prospect of complications continues to remain high in this modern era but depending upon the cohort assessed, it remains difficult to make strict comparisons between individual specialist centres. Those patients who present rather late after their diagnosis with a septic complication like an intra-abdominal abscess and a penetrating/fistulizing pattern of disease are more likely to have a complicated course particularly if they have clinical features such as difficult percutaneous access to the collection or multilocularity both of which can make preoperative drainage unsuccessful. Equally, those cases with extensive adhesions where an initial laparoscopic approach needs open conversion and where there is an extended operative time, unsurprisingly will suffer more significant complications that impact their length of hospital stay. The need for a protective stoma also introduces its own derivative costs, utilizing a range of health resources as well as resulting in important alterations in quality of life outcomes. Having established the parameters of the problem can the statistical analysis of the available data identify high-risk cases, promote the notion of centralization of specialist services or improve the allocation of disease-specific health expenditure?

Keywords: Crohn’s disease; Postoperative complications; Clavien-Dindo; Machine learning; Outcome

Core Tip: Significant postoperative complications continue to be a challenge in those who come to operation for Crohn’s disease. Modern management with immunosuppressive treatment has only significantly delayed surgery rather than prevented the need for operation. Machine learning provides new algorithms that supersede logistic regression of prognostic outcome factors in retrospective analyses. Multi-institutional prospective studies are required to better identify those patients where major complications are likely and where there will be a requirement for postoperative critical care and higher health care expenditure.