I-Corps: A machine learning algorithm to predict recurrent disc herniation following microdiscectomy surgery

这个i-Corps项目的更广泛的影响/商业潜力是开发预测脊柱手术并发症的软件。全国每年有120万例脊柱手术，其中约25%(30万例)是微椎间盘切除术。微椎间盘切除术的主要目的是减轻神经压力，缓解背部疼痛。在脊柱手术中，微椎间盘切除术侵袭性较小，恢复时间较快，然而，5%-10%的微椎间盘切除术患者会发生再突出，导致额外的手术费用、较长的患者恢复时间和生产力损失。此外，超过1/3的再突出发生在初次手术后的3个月内。客观地说，这些患者从第一次手术中恢复了6周，但在几个月内再次突出，并回到手术台上进行第二次显微椎间盘切除术或脊柱融合术。这些翻修手术通常需要数月的进一步恢复时间和痛苦。这些二次手术对医院和保险公司来说也是一个挑战。再次入院会降低医院的质量评级，如果在90天内再次入院，保险公司往往不会报销。这项拟议的技术通过识别可能再次发生疝气的患者并允许进行风险调解，帮助减少手术再入院人数，节省患者不必要的痛苦，并降低医院成本。这个I-Corps项目是基于一种机器学习算法的开发，该算法使用手术前的数据来预测哪些患者可能在显微椎间盘切除术后复发。这项技术旨在降低并发症发生率，从而提高患者满意度，降低患者和医院的成本。在一家研究所的350名患者的队列中，机器学习算法能够正确识别98%的疝气患者有或没有复发的风险。目前正在将这项工作扩展到多个研究所。到目前为止，已收集了来自3个国家4个研究所的1077名患者的结果，结果表明，再突出患者的预测再突出率较高；然而，由于从X线片计算输入指标时医生间的差异，一些机构的总体正确分类百分比仍然较差。其目标是开发半自动图像分析软件来计算输入指标，从而在机构之间创造更多的一致性。这种一致性将使其有可能提供一种预测工具，能够汇编所有潜在的风险因素，并报告单一的统一风险概率，以便更好地了解护理决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of software to predict spine surgery complications. There are 1.2 million spine surgeries nationally per year and about 25% (300,000 cases) of these are microdiscectomies. The main goal of microdiscectomy is to take pressure off your nerves to relieve back pain. Among spinal surgeries, microdiscectomies are less invasive and have quicker recovery times, however, 5-10% of microdiscectomy patients reherniate leading to additional surgery costs, longer patient recovery times, and lost productivity. Additionally, over 1/3 of reherniations occur within 3 months of the initial operation. To put this in perspective, these patients spend 6 weeks recovering from their first operation only to reherniate within a few months and return to the operating table for a second microdiscectomy or a spinal fusion. These revision surgeries often require months of further recovery time and pain. These secondary surgeries are also challenging for hospitals and insurance companies. Re-admissions decrease hospital quality ratings, and are often not reimbursed by insurance companies if they happen within 90 days. The proposed technology helps support a reduction in surgical re-admittance by identifying patients likely to reherniate and allowing for risk mediation, saving patients unnecessary pain and reducing hospital costs. This I-Corps project is based on the development of a machine learning algorithm that uses presurgical data to predict which patients are likely to suffer from recurrent disc herniation following microdiscectomy surgery. This technology aims to reduce the complication rates, thereby increasing patient satisfaction and decreasing costs to the patient and hospital. The machine learning algorithm is capable of correctly identifying 98% of herniation patients as either at risk or not at risk of reherniation in a cohort of 350 patients from one institute. Expansion of this work to multiple institutes is underway. Results have been collected from 1077 patients from 4 institutes in 3 countries thus far, showing that predicted rates of reherniation are higher in reherniated patients; however, the overall percent correct classification is still poor in some institutes because of physician inter-evaluator variability in calculating input metrics from radiographs. The goal is to develop semi-automated image analysis software to calculate input metrics, creating more consistency among institutions. This consistency will make it possible to provide a predictive tool capable of compiling all of the potential risk factors for reherniation and to report a single unified probability of risk so that care decisions are better informed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.