Performance of the streamlined quality outcomes database web-based calculator: internal and external validation.

Area of Science:

• Spine Surgery Outcomes Research
• Health Informatics
• Predictive Analytics in Medicine

Background:

• Web-based calculators are increasingly used to predict patient outcomes after lumbar spine surgery.
• Accurate validation of these predictive models is essential for clinical decision-making.

Purpose of the Study:

• To conduct an internal and external validation of the reduced Quality Outcomes Database web-based Calculator (QOD-Calc).

Main Methods:

• An observational longitudinal cohort study design was employed.
• Data from 24,755 Quality Outcomes Database (QOD) cases and 8,105 DaneSpine cases (elective lumbar spine surgery) were analyzed.
• The QOD-Calc's predictions for 'Any Improvement' and '30% Improvement' in Oswestry Disability Index (ODI), Numeric Rating Scales (NRS) for pain, and EuroQOL-5D (EQ-5D) were compared to 12-month postoperative data using receiver-operating characteristic analyses and calibration plots.

Main Results:

• QOD-Calc demonstrated acceptable to outstanding predictive ability (AUC: 0.694-0.874) for 'Any Improvement' in the QOD cohort.
• Predictive ability for '30% Improvement' was moderate to acceptable (AUC: 0.658-0.747) in the QOD cohort.
• In the DaneSpine cohort, QOD-Calc showed acceptable to exceptional ability for 'Any Improvement' (AUC: 0.669-0.734) and moderate to exceptional ability for '30% Improvement' (AUC: 0.619-0.862), with consistently lower AUCs than the QOD cohort.

Conclusions:

• QOD-Calc performs well in predicting outcomes for patient populations similar to its development cohort.
• Model performance was slightly diminished in a distinct, albeit more homogenous, population.
• The findings suggest that prediction models may require development tailored to specific population characteristics, potentially due to a low discrimination threshold where most cases show improvement.