Predicting nonsurgical treatment outcomes in lumbar disc herniation: leveraging sparse electronic health records for patient phenotyping
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Published on: August 7, 2018
Ye-Seul Lee1, Yoon Jae Lee1, In-Hyuk Ha1
1Jaseng Spine and Joint Research Institute, Jaseng Medical Foundation, Seoul, Republic of Korea.
Objective:
Electronic health records (EHRs) offer a wealth of patient data but often fail to capture the dynamic progression of symptoms, particularly in non-continuously monitored conditions like lumbar disc herniation (LDH). This study aimed to enhance the prediction of nonsurgical treatment outcomes for LDH using latent class trajectory modeling (LCTM) combined with machine learning, leveraging sparse EHRs to inform patient care.
Materials And Methods:
The EHRs of 6,732 patients (2017-2021) were obtained and divided into training (2017-2019) and prediction (2020-2021) datasets. LCTM identified symptom progression trajectories using the Oswestry Disability Index (ODI), which were incorporated into machine learning models as patient phenotypes. Predictions of achieving the minimum clinically important difference (MCID) in ODI at discharge were compared between a baseline naïve model and a combined model utilizing phenotypes as intermediate variables.
Results And Discussion:
Three distinct trajectories were identified, reflecting varied recovery patterns. Patient characteristics were classified into three phenotypes. Incorporating these phenotypes into the predictive model improved the area under the receiver operating characteristic curve (AUROC) from 0.78 in the naïve model to 0.82 in the combined model. Precision, recall, and F1 scores also improved with the combined model, underscoring its robustness.
Conclusion:
This data-driven approach enhanced interpretability and prediction accuracy, demonstrating the potential to guide clinical decision-making. By addressing data sparsity in EHRs and minimizing temporal data leakage, the integration of LCTM and machine learning enables robust prediction models for treatment outcomes, facilitating personalized care and improved outcomes in nonsurgical LDH treatment. In clinical settings with sparse longitudinal data, this approach facilitates the development of robust prediction models for treatment outcomes, enabling personalized care strategies and achieving better outcomes in nonsurgical LDH treatment.
