Patro Ankita, Lawrence Patrick J, Tamati Terrin N, Ning Xia, Moberly Aaron C
Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
These authors are co-first authors of this work.
Ear Hear. 2024 Sep 6;46(2):543-9. doi: 10.1097/AUD.0000000000001593.
To use machine learning and a battery of measures for preoperative prediction of speech recognition and quality of life (QOL) outcomes after cochlear implant (CI) surgery.
Demographic, audiologic, cognitive-linguistic, and QOL predictors were collected from 30 postlingually deaf adults before CI surgery. K-means clustering separated patients into groups. Reliable change index scores were computed for speech recognition and QOL from pre-CI to 6 months post-CI, and group differences were determined.
Clustering yielded three groups with differences in reliable change index for sentence recognition. One group demonstrated low baseline sentence recognition and only small improvements post-CI, suggesting a group "at risk" for limited benefits. This group showed lower pre-CI scores on verbal learning and memory and lack of musical training.
Preoperative assessments can prognosticate CI recipients' postoperative performance and identify individuals at risk for experiencing poor sentence recognition outcomes, which may help guide counseling and rehabilitation.
运用机器学习和一系列测量方法对人工耳蜗（CI）植入术后的言语识别和生活质量（QOL）结果进行术前预测。
在CI植入手术前，收集了30名语后聋成人的人口统计学、听力学、认知语言和QOL预测指标。采用K均值聚类将患者分组。计算了CI术前至CI术后6个月言语识别和QOL的可靠变化指数得分，并确定了组间差异。
聚类产生了三组，在句子识别的可靠变化指数方面存在差异。一组表现出较低的基线句子识别率，CI术后改善较小，表明该组“受益有限风险较高”。该组在CI术前的言语学习和记忆得分较低，且缺乏音乐训练。
术前评估可以预测CI接受者的术后表现，并识别出句子识别结果较差风险较高的个体，这可能有助于指导咨询和康复。
