背景:患者选择在阻塞排便综合征(ODS)和直肠脱垂(RP)手术中极为重要。这项研究使用机器学习方法评估了指导ODS和RP手术适应症的因素及其在我们决策过程中的特定作用。
方法:这是一项长期前瞻性观察性研究的回顾性分析,该研究对2010年1月至2021年12月在一个学术三级转诊中心接受了完整诊断检查的报告ODS症状的女性患者进行。临床,排便,和其他功能测试数据进行了评估。执行并测试了使用分类树模型的监督机器学习算法。
结果:共纳入400例患者。与接受手术的可能性明显更高相关的因素如下:作为症状,会阴夹板,肛门或阴道自我指位,外部RP的感觉,大便失禁和脏污的发作;作为体检特征,内部和外部RP的证据,直肠膨出,肠膨出,或前/中盆腔器官脱垂;作为排粪造影结果,肛门内和外部RP,直肠膨出,直肠膨出不完全排空,肠膨出,膀胱膨出,和结肠-子宫膨出.协同失调患者的手术指征较少,严重的焦虑和抑郁。所有这些因素都包含在监督机器学习算法中。该模型在测试数据集上显示出较高的准确性(79%,p<0.001)。
结论:症状评估和体格检查被证明是基础,但其他功能测试也应考虑。通过在其他ODS和RP中心采用机器学习模型,可以更容易,更可靠地确定和分享手术指征.
BACKGROUND: Patient selection is extremely important in obstructed defecation syndrome (ODS) and rectal prolapse (RP) surgery. This study assessed factors that guided the indications for ODS and RP surgery and their specific role in our decision-making process using a machine learning approach.
METHODS: This is a retrospective analysis of a long-term prospective observational study on female patients reporting symptoms of ODS who underwent a complete diagnostic workup from January 2010 to December 2021 at an academic tertiary referral center. Clinical, defecographic, and other functional tests data were assessed. A supervised machine learning algorithm using a classification tree model was performed and tested.
RESULTS: A total of 400 patients were included. The factors associated with a significantly higher probability of undergoing surgery were follows: as symptoms, perineal splinting, anal or vaginal self-digitations, sensation of external RP, episodes of fecal incontinence and soiling; as physical examination features, evidence of internal and external RP, rectocele, enterocele, or anterior/middle pelvic organs prolapse; as defecographic findings, intra-anal and external RP, rectocele, incomplete rectocele emptying, enterocele, cystocele, and colpo-hysterocele. Surgery was less indicated in patients with dyssynergia, severe anxiety and depression. All these factors were included in a supervised machine learning algorithm. The model showed high accuracy on the test dataset (79%, p < 0.001).
CONCLUSIONS: Symptoms assessment and physical examination proved to be fundamental, but other functional tests should also be considered. By adopting a machine learning model in further ODS and RP centers, indications for surgery could be more easily and reliably identified and shared.