Scholarlink

Objective <p>The postoperative recovery of patients with lumbar disc herniation (LDH) requires further study. This study aimed to establish and validate a predictive model for functional recovery in patients with LDH and explore associated risk factors.</p> Method <p>Patients with LDH undergoing PLIF admitted from January 1, 2018 to December 31, 2022 were included, and patient data were prospectively collected through follow-up. The training and validation cohorts were randomly assigned in a 7:3 ratio. To pool data variables LASSO regression was used. The pooled variables were subsequently included in binary logistic regression analyses, construct risk prediction models, and plot nomograms. Additionally, recovery prediction models and interactive web page calculators were developed using R Shiny.</p> Results <p>Overall, 1,097 patients with LDH following PLIF were included in this study. Regarding patients’ economic and functional scores, 927 (84.5%) received excellent scores. Key indicators significantly were screened. Multivariate analysis showed that age, season, occupation, HDL-C, smoking, weekly exercise time, and osteoporosis were independent risk factors for postoperative recovery. The C-index of the model was 0.776 (95% CI: 0.7312–0.8208) and 0.804 (95% CI: 0.7408–0.8673) for the training and validation cohorts, respectively. The H–L test showed good fitting of the model (all <i>P</i> &gt; 0.05). The DCA curve showed the best clinical efficacy when the threshold probability was in the ranges of 0–0.71 and 0.79–0.84. The interactive web calculator is accessed at <a href="https://postoperativerecoveryofldh.shinyapps.io/DynNomapp/">https://postoperativerecoveryofldh.shinyapps.io/DynNomapp/</a>.</p> Conclusion <p>The predictive tools derived from this study can provide realistic and personalized expectations of postoperative outcomes for patients undergoing lumbar spine surgery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and validation of a predictive model and tool for functional recovery in patients after postero-lateral interbody fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月10日

查看全文
  • Shuai Zhou,
  • Zhenbang Yang,
  • Wei Zhang,
  • Shihang Liu,
  • Qian Xiao,
  • Guangzhao Hou,
  • Rui Chen,
  • Nuoman Han,
  • Jiao Guo,
  • Miao Liang,
  • Qi Zhang,
  • Yingze Zhang,
  • Hongzhi Lv

摘要

Objective

The postoperative recovery of patients with lumbar disc herniation (LDH) requires further study. This study aimed to establish and validate a predictive model for functional recovery in patients with LDH and explore associated risk factors.

Method

Patients with LDH undergoing PLIF admitted from January 1, 2018 to December 31, 2022 were included, and patient data were prospectively collected through follow-up. The training and validation cohorts were randomly assigned in a 7:3 ratio. To pool data variables LASSO regression was used. The pooled variables were subsequently included in binary logistic regression analyses, construct risk prediction models, and plot nomograms. Additionally, recovery prediction models and interactive web page calculators were developed using R Shiny.

Results

Overall, 1,097 patients with LDH following PLIF were included in this study. Regarding patients’ economic and functional scores, 927 (84.5%) received excellent scores. Key indicators significantly were screened. Multivariate analysis showed that age, season, occupation, HDL-C, smoking, weekly exercise time, and osteoporosis were independent risk factors for postoperative recovery. The C-index of the model was 0.776 (95% CI: 0.7312–0.8208) and 0.804 (95% CI: 0.7408–0.8673) for the training and validation cohorts, respectively. The H–L test showed good fitting of the model (all P > 0.05). The DCA curve showed the best clinical efficacy when the threshold probability was in the ranges of 0–0.71 and 0.79–0.84. The interactive web calculator is accessed at https://postoperativerecoveryofldh.shinyapps.io/DynNomapp/.

Conclusion

The predictive tools derived from this study can provide realistic and personalized expectations of postoperative outcomes for patients undergoing lumbar spine surgery.