Lin Simon, Nateqi Jama, Weingartner-Ortner Rafael, Gruarin Stefanie, Marling Hannes, Pilgram Vinzenz, Lagler Florian B, Aigner Elmar, Martin Alistair G
Science Department, Symptoma GmbH, Vienna, Austria.
Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria.
Front Neurol. 2023 Apr 21;14:1108222. doi: 10.3389/fneur.2023.1108222. eCollection 2023.
We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs.
We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated.
Of the 104 patients flagged by the algorithms, generalist physicians found five "diagnosed," 10 "suspected," and seven patients with "reduced suspicion." After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively.
Our study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients.
我们回顾性筛查了350,116份电子健康记录（EHR），以识别疑似庞贝病患者。利用这些疑似患者，我们随后描述了他们的表型特征，并估计了EHR所覆盖的相应人群中的患病率。
我们将Symptoma基于人工智能的罕见病患者识别方法应用于由“萨尔茨堡大学医院”诊所集团提供的回顾性匿名EHR。在1个月内，人工智能筛查了来自五家医院的350,116份可追溯15年的EHR，104名患者被标记为可能患有庞贝病。标记的患者由全科医生和专科医生进行人工审查和评估，以确定他们患庞贝病的可能性，并据此评估算法的性能。
在算法标记的104名患者中，全科医生发现5名“确诊”、10名“疑似”和7名“疑似程度降低”的患者。在庞贝病专科医生提供反馈后，19名患者在临床上仍有可能患有庞贝病，人工智能的特异性为18.27%。根据其余可能的患者估计，大萨尔茨堡地区（包括德国巴伐利亚州、奥地利施蒂利亚州和上奥地利州）的庞贝病患病率为每18,427人中有1人。确定了症状出现年龄约在1岁以上或以下的患者队列的表型，分别对应婴儿型庞贝病（IOPD）和晚发型庞贝病（LOPD）。
我们的研究表明，Symptoma基于人工智能且利用回顾性EHR识别罕见病患者的方法是可行的。通过算法对整个EHR人群进行筛查，医生平均只需人工审查5.47名患者就能找到一名疑似患者。这种效率至关重要，因为庞贝病虽然罕见，但却是一种会逐渐使人衰弱但可治疗的神经肌肉疾病。因此，我们展示了该方法的效率以及可扩展解决方案在系统识别罕见病患者方面的潜力。因此，应鼓励类似地实施这种方法，以改善所有罕见病患者的护理。
