Parashos Sotirios A, Bloem Bastiaan R, Browner Nina M, Giladi Nir, Gurevich Tanya, Hausdorff Jeffrey M, He Ying, Lyons Kelly E, Mari Zoltan, Morgan John C, Post Bart, Schmidt Peter N, Wielinski Catherine L
Struthers Parkinson's Center (SAP, CLW), HealthPartners, Golden Valley, MN; Parkinson Center Nijmegen (BRB, BP), Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Department of Neurology, the Netherlands; Department of Neurology (NMB), University of North Carolina at Chapel Hill; Neurological Institute (NG, TG, JMH), Tel Aviv Sourasky Medical Center, Sackler School of Medicine, and Sagol School of Neuroscience, Tel-Aviv University, Israel; Department of Mathematics (YH), Clarkson University, Potsdam, NY; University of Kansas Medical Center Parkinson's Disease Center (KEL), Kansas City; Department of Neurology (ZM), Johns Hopkins University, Baltimore, MD, currently at Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV; Movement Disorders Program (JCM), NPF Center of Excellence, Department of Neurology, Medical College of Georgia, Augusta University; Parkinson's Foundation (PNS), Miami, FL; and Department of Biostatistics (SSW), University of Florida, Gainesville.
Neurol Clin Pract. 2018 Jun;8(3):214-222. doi: 10.1212/CPJ.0000000000000461.
We undertook this study to identify patients with Parkinson disease (PD) with no or rare falls who may progress to frequent falling by their next annual follow-up visit.
We analyzed data in the National Parkinson Foundation Quality Improvement Initiative database to identify factors predicting which patients with PD with no or rare falls at the baseline visit will report at least monthly falls at the annual follow-up visit. Multivariable models were constructed using logistic regression. Variables were introduced in 4 blocks: in the 1st block, variables present at or before the baseline visit were entered; in the 2nd, baseline visit assessments; in the 3rd, interventions implemented during baseline visit; and, in the 4th block, changes in comorbidities, living situation, and treatment between visits.
Of 3,795 eligible participants, 3,276 (86.3%) reported no or rare falls at baseline visit, and of them, 382 (11.7%) reported at least monthly falls at follow-up visit. Predictors included female sex, <90% diagnostic certainty, motor fluctuations, levodopa treatment, antidepressant treatment, prior deep brain stimulation (DBS), worse quality of life, Hoehn & Yahr stage 2 or 3, worse semantic fluency, and, between visits, addition of amantadine, referral to occupational therapy, social services, or DBS, new diagnoses of cancer or osteoarthritis, and increased emergency visits.
This large-scale analysis identified several predictors of progression to falling in PD. Such identifiers may help target patient subgroups for falls prevention intervention. Some factors are modifiable, offering opportunities for developing such interventions.
我们开展这项研究，旨在识别帕金森病（PD）患者中那些在基线时无跌倒或很少跌倒，但到下一次年度随访时可能进展为频繁跌倒的患者。
我们分析了国家帕金森基金会质量改进计划数据库中的数据，以确定哪些因素可预测在基线访视时无跌倒或很少跌倒的PD患者在年度随访时会报告至少每月跌倒一次。使用逻辑回归构建多变量模型。变量分4组引入：在第1组中，纳入基线访视时或之前存在的变量；在第2组中，纳入基线访视评估；在第3组中，纳入基线访视期间实施的干预措施；在第4组中，纳入访视期间合并症、生活状况和治疗的变化。
在3795名符合条件的参与者中，3276名（86.3%）在基线访视时报告无跌倒或很少跌倒，其中382名（11.7%）在随访时报告至少每月跌倒一次。预测因素包括女性、诊断确定性<90%、运动波动、左旋多巴治疗、抗抑郁治疗、既往脑深部电刺激（DBS）、生活质量较差、Hoehn & Yahr分期2或3期、语义流畅性较差，以及访视期间加用金刚烷胺、转诊至职业治疗、社会服务或DBS、新诊断癌症或骨关节炎，以及急诊就诊次数增加。
这项大规模分析确定了PD患者跌倒进展的几个预测因素。这些标识符可能有助于针对患者亚组进行跌倒预防干预。一些因素是可改变的，为制定此类干预措施提供了机会。
