Abstract：
BACKGROUND: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP).
METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity.
RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing.
CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.
METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity.
RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing.
CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.
摘要：
背景：临床预测模型(CPM)，例如SCOAP-CERTAIN工具，可以通过提供结果的定量估计来提高腰椎融合手术的决策，帮助外科医生评估每个患者的潜在益处和风险。在CPM中，外部验证对于评估初始数据集之外的可泛化性至关重要。这确保了在不同人群中的表现，结果的可靠性和现实世界的适用性。因此,我们在外部验证了奥斯威西残疾指数(ODI)改善的可预测性工具，背部和腿部疼痛(血压，LP）。
方法：获得来自多中心注册的前瞻性和回顾性数据。作为结果指标，选择ODI的最小临床重要变化，在腰椎融合治疗退行性疾病后12个月，BP和LP的数字评定量表（NRS）降低≥15分和≥2分。我们通过计算辨别和校准指标，如截距，斜坡,Brier分数,预期/观察到的比率，Hosmer-Lemeshow(HL),AUC,敏感性和特异性。
结果：我们包括1115例患者，平均年龄60.8±12.5岁。对于12个月的ODI，曲线下面积(AUC)为0.70,校准截距和斜率分别为1.01和0.84.对于NRSBP，AUC为0.72,校准截距为0.97,斜率为0.87。对于NRSLP，AUC为0.70,校准截距为0.04,斜率为0.72。敏感性范围为0.63至0.96，而特异性范围为0.15至0.68。基于HL测试，发现所有三个模型都缺乏拟合。
结论：利用来自跨国注册管理机构的数据，我们在外部验证了SCOAP-CERTAIN预测工具。该模型证明了对预测概率的公平区分和校准，在临床实践中应用时需要谨慎。我们建议未来的CPM专注于预测该患者人群的长期预后，强调稳健校准和全面报告的重要性。
方法：获得来自多中心注册的前瞻性和回顾性数据。作为结果指标，选择ODI的最小临床重要变化，在腰椎融合治疗退行性疾病后12个月，BP和LP的数字评定量表（NRS）降低≥15分和≥2分。我们通过计算辨别和校准指标，如截距，斜坡,Brier分数,预期/观察到的比率，Hosmer-Lemeshow(HL),AUC,敏感性和特异性。
结果：我们包括1115例患者，平均年龄60.8±12.5岁。对于12个月的ODI，曲线下面积(AUC)为0.70,校准截距和斜率分别为1.01和0.84.对于NRSBP，AUC为0.72,校准截距为0.97,斜率为0.87。对于NRSLP，AUC为0.70,校准截距为0.04,斜率为0.72。敏感性范围为0.63至0.96，而特异性范围为0.15至0.68。基于HL测试，发现所有三个模型都缺乏拟合。
结论：利用来自跨国注册管理机构的数据，我们在外部验证了SCOAP-CERTAIN预测工具。该模型证明了对预测概率的公平区分和校准，在临床实践中应用时需要谨慎。我们建议未来的CPM专注于预测该患者人群的长期预后，强调稳健校准和全面报告的重要性。
