Development and temporal validation of a prognostic model for 1-year clinical outcome after decompression surgery for lumbar disc herniation.

Staub, Lukas P; Aghayev, Emin; Skrivankova, Veronika; Lord, Sarah J; Haschtmann, Daniel; Mannion, Anne F (2020). Development and temporal validation of a prognostic model for 1-year clinical outcome after decompression surgery for lumbar disc herniation. European spine journal, 29(7), pp. 1742-1751. Springer 10.1007/s00586-020-06351-5

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PURPOSE

Surgeons need tools to provide individualised estimates of surgical outcomes and the uncertainty surrounding these, to convey realistic expectations to the patient. This study developed and validated prognostic models for patients undergoing surgical treatment of lumbar disc herniation, to predict outcomes 1 year after surgery, and implemented these models in an online prediction tool.

METHODS

Using the data of 1244 patients from a large spine unit, LASSO and linear regression models were fitted with 90% upper prediction limits, to predict scores on the Core Outcome Measures Index, and back and leg pain. Candidate predictors included sociodemographic factors, baseline symptoms, medical history, and surgeon characteristics. Temporal validation was conducted on 364 more recent patients at the same unit, by examining the proportion of observed outcomes exceeding the threshold of the 90% upper prediction limit (UPL), and by calculating mean bias and other calibration measures.

RESULTS

Poorer outcome was predicted by obesity, previous spine surgery, and having basic obligatory (rather than private) insurance. In the validation data, fewer than 12% of outcomes were above the 90% UPL. Calibration plots for the model validation showed values for mean bias < 0.5 score points and regression slopes close to 1.

CONCLUSION

While the model accuracy was good overall, the prediction intervals indicated considerable predictive uncertainty on the individual level. Implementation studies will assess the clinical usefulness of the online tool. Updating the models with additional predictors may improve the accuracy and precision of outcome predictions. These slides can be retrieved under Electronic Supplementary Material.

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