Functional Outcomes for Patients With Congenital Anorectal Malformations: A Systematic Review and Evidence-based Guideline From the APSA Outcomes and Evidence Based Practice Committee

Objective: Consensus on functional outcomes for anorectal malformations (ARM) is hindered by the heterogeneity of the available literature. Optimal patient counseling includes discussion of short- and long-term outcomes for bowel and urinary continence, sexual and psychosocial function, transitional care, and quality of life. This systematic review examines and summarizes the current literature available related to functional outcomes for children with ARM. Methods: The American Pediatric Surgical Association Outcomes and Evidence Based Practice Committee drafted consensus-based questions regarding anorectal malformations. Pertinent articles from 1985 to 2021 were reviewed. Results: More than 10,843 publications were reviewed with 109 being included in the final recommendations. Recommendations are primarily based on C-D levels of evidence. Continence and constipation rates were higher in patients with perineal fistula and rectovestibular fistula, although symptoms tended to improve as patients got older. Urological anomalies are common and longer term urologic surveillance protocols for patients with ARM need to be further outlined. Sexual and psycho- social issues are common, but ARM patients can have a good quality of life when gastrointestinal symptoms are minimized. Many of the problems associated with ARM can persist into adulthood, supporting structured care plans as patients transition to adult care. Conclusions: Evidence to support best practices and achieve optimal outcomes for patients with ARM is lacking for many aspects of care. Multi-institutional registries have begun to address management and prognosis for these patients. Prospective and comparative studies are needed to improve care and provide consensus guidelines for this complex patient population. Level of evidence: 4. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.