The spine journal : official journal of the North American Spine Society · 2018
Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling.
Оценочное исследованиеСтатьяНазвание и аннотация: Перевод в очереди
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Staartjes Victor E, de Wispelaere Marlies P, Vandertop William Peter, Schröder Marc L
Аннотация
BACKGROUND CONTEXT: There is considerable variability in patient-reported outcome measures following surgery for lumbar disc herniation. Individualized prediction tools that are derived from center- or even surgeon-specific data could provide valuable insights for shared decision-making.
PURPOSE: To evaluate the feasibility of deriving robust deep learning-based predictive analytics from single-center, single-surgeon data.
STUDY DESIGN: Derivation of predictive models from a prospective registry.
PATIENT SAMPLE: Patients who underwent single-level tubular microdiscectomy for lumbar disc herniation.
OUTCOME MEASURES: Numeric rating scales for leg and back pain severity and Oswestry Disability Index scores at 12 months postoperatively.
METHODS: Data were derived from a prospective registry. We trained deep neural network-based and logistic regression-based prediction models for patient-reported outcome measures. The primary endpoint was achievement of the minimum clinically important difference (MCID) in numeric rating scales and Oswestry Disability Index, defined as a 30% or greater improvement from baseline. Univariate predictors of MCID were also identified using conventional statistics.
RESULTS: A total of 422 patients were included (mean [SD] age: 48.5 [11.5] years; 207 [49%] female). After 1 year, 337 (80%), 219 (52%), and 337 (80%) patients reported a clinically relevant improvement in leg pain, back pain, and functional disability, respectively. The deep learning models predicted MCID with high area-under-the-curve of 0.87, 0.90, and 0.84, as well as accuracy of 85%, 87%, and 75%. The regression models provided inferior performance measures for each of the outcomes.
CONCLUSIONS: Our study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counselling and shared decision-making.
PURPOSE: To evaluate the feasibility of deriving robust deep learning-based predictive analytics from single-center, single-surgeon data.
STUDY DESIGN: Derivation of predictive models from a prospective registry.
PATIENT SAMPLE: Patients who underwent single-level tubular microdiscectomy for lumbar disc herniation.
OUTCOME MEASURES: Numeric rating scales for leg and back pain severity and Oswestry Disability Index scores at 12 months postoperatively.
METHODS: Data were derived from a prospective registry. We trained deep neural network-based and logistic regression-based prediction models for patient-reported outcome measures. The primary endpoint was achievement of the minimum clinically important difference (MCID) in numeric rating scales and Oswestry Disability Index, defined as a 30% or greater improvement from baseline. Univariate predictors of MCID were also identified using conventional statistics.
RESULTS: A total of 422 patients were included (mean [SD] age: 48.5 [11.5] years; 207 [49%] female). After 1 year, 337 (80%), 219 (52%), and 337 (80%) patients reported a clinically relevant improvement in leg pain, back pain, and functional disability, respectively. The deep learning models predicted MCID with high area-under-the-curve of 0.87, 0.90, and 0.84, as well as accuracy of 85%, 87%, and 75%. The regression models provided inferior performance measures for each of the outcomes.
CONCLUSIONS: Our study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counselling and shared decision-making.