Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: We investigated the clinical efficacy of anabolic agents compared with bisphosphonates (BPs) for the incidence of new osteoporotic vertebral fracture (OVF) and fracture healing of OVF in the patients with OVF via meta-analyses of randomized controlled trials (RCTs). Methods: Electronic databases, including PubMed, Embase, and Cochrane Library were searched for published RCTs till December 2022. The RCTs that recruited participants with osteoporosis at high-/very high-risk of fracture (a history of osteoporotic vertebral or hip fracture) or fresh OVF were included in this study. We assessed the risk of bias on every included RCTs, estimated relative risk (RR) for the incidence of new OVF and fracture healing of OVF, and overall certainty of evidence. Meta-analyses were performed by Cochrane review manager (RevMan) ver. 5.3. Cochrane risk of bias 2.0 and GRADEpro/GDT were applied for evaluating methodological quality and overall certainty of evidence, respectively. Results: Five hundred eighteen studies were screened, and finally 6 eligible RCTs were included in the analysis. In the patients with prevalent OVF, anabolic agents significantly reduced the incidence of new OVF (teriparatide and romosozumab vs. alendronate and risedronate [RR, 0.57; 95% confidence interval, 0.45–0.71; p < 0.00001; high-certainty of evidence]; teriparatide vs. risedronate [RR, 0.50; 95% confidence interval, 0.37–0.68; p < 0.0001; high-certainty of evidence]). However, there was no evidence of teriparatide compared to alendronate in fracture healing of OVF (RR, 1.23; 95% confidence interval, 0.95–1.60; p = 0.12; low-certainty of evidence). Conclusion In the patients with prevalent OVF, anabolic agents showed a significant superiority for preventing new OVF than BPs, with no significant evidence for promoting fracture healing of OVF. However, considering small number of RCTs in this study, additional studies with large-scale data are required to obtain more robust evidences.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: We investigated the clinical efficacy of anabolic agents compared with bisphosphonates (BPs) for the incidence of new osteoporotic vertebral fracture (OVF) and fracture healing of OVF in the patients with OVF via meta-analyses of randomized controlled trials (RCTs). Methods: Electronic databases, including PubMed, Embase, and Cochrane Library were searched for published RCTs till December 2022. The RCTs that recruited participants with osteoporosis at high-/very high-risk of fracture (a history of osteoporotic vertebral or hip fracture) or fresh OVF were included in this study. We assessed the risk of bias on every included RCTs, estimated relative risk (RR) for the incidence of new OVF and fracture healing of OVF, and overall certainty of evidence. Meta-analyses were performed by Cochrane review manager (RevMan) ver. 5.3. Cochrane risk of bias 2.0 and GRADEpro/GDT were applied for evaluating methodological quality and overall certainty of evidence, respectively. Results: Five hundred eighteen studies were screened, and finally 6 eligible RCTs were included in the analysis. In the patients with prevalent OVF, anabolic agents significantly reduced the incidence of new OVF (teriparatide and romosozumab vs. alendronate and risedronate [RR, 0.57; 95% confidence interval, 0.45–0.71; p < 0.00001; high-certainty of evidence]; teriparatide vs. risedronate [RR, 0.50; 95% confidence interval, 0.37–0.68; p < 0.0001; high-certainty of evidence]). However, there was no evidence of teriparatide compared to alendronate in fracture healing of OVF (RR, 1.23; 95% confidence interval, 0.95–1.60; p = 0.12; low-certainty of evidence). Conclusion In the patients with prevalent OVF, anabolic agents showed a significant superiority for preventing new OVF than BPs, with no significant evidence for promoting fracture healing of OVF. However, considering small number of RCTs in this study, additional studies with large-scale data are required to obtain more robust evidences.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: We investigated the clinical efficacy of anabolic agents compared with bisphosphonates (BPs) for the incidence of new osteoporotic vertebral fracture (OVF) and fracture healing of OVF in the patients with OVF via meta-analyses of randomized controlled trials (RCTs). Methods: Electronic databases, including PubMed, Embase, and Cochrane Library were searched for published RCTs till December 2022. The RCTs that recruited participants with osteoporosis at high-/very high-risk of fracture (a history of osteoporotic vertebral or hip fracture) or fresh OVF were included in this study. We assessed the risk of bias on every included RCTs, estimated relative risk (RR) for the incidence of new OVF and fracture healing of OVF, and overall certainty of evidence. Meta-analyses were performed by Cochrane review manager (RevMan) ver. 5.3. Cochrane risk of bias 2.0 and GRADEpro/GDT were applied for evaluating methodological quality and overall certainty of evidence, respectively. Results: Five hundred eighteen studies were screened, and finally 6 eligible RCTs were included in the analysis. In the patients with prevalent OVF, anabolic agents significantly reduced the incidence of new OVF (teriparatide and romosozumab vs. alendronate and risedronate [RR, 0.57; 95% confidence interval, 0.45–0.71; p < 0.00001; high-certainty of evidence]; teriparatide vs. risedronate [RR, 0.50; 95% confidence interval, 0.37–0.68; p < 0.0001; high-certainty of evidence]). However, there was no evidence of teriparatide compared to alendronate in fracture healing of OVF (RR, 1.23; 95% confidence interval, 0.95–1.60; p = 0.12; low-certainty of evidence). Conclusion In the patients with prevalent OVF, anabolic agents showed a significant superiority for preventing new OVF than BPs, with no significant evidence for promoting fracture healing of OVF. However, considering small number of RCTs in this study, additional studies with large-scale data are required to obtain more robust evidences.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: We investigated the clinical efficacy of anabolic agents compared with bisphosphonates (BPs) for the incidence of new osteoporotic vertebral fracture (OVF) and fracture healing of OVF in the patients with OVF via meta-analyses of randomized controlled trials (RCTs). Methods: Electronic databases, including PubMed, Embase, and Cochrane Library were searched for published RCTs till December 2022. The RCTs that recruited participants with osteoporosis at high-/very high-risk of fracture (a history of osteoporotic vertebral or hip fracture) or fresh OVF were included in this study. We assessed the risk of bias on every included RCTs, estimated relative risk (RR) for the incidence of new OVF and fracture healing of OVF, and overall certainty of evidence. Meta-analyses were performed by Cochrane review manager (RevMan) ver. 5.3. Cochrane risk of bias 2.0 and GRADEpro/GDT were applied for evaluating methodological quality and overall certainty of evidence, respectively. Results: Five hundred eighteen studies were screened, and finally 6 eligible RCTs were included in the analysis. In the patients with prevalent OVF, anabolic agents significantly reduced the incidence of new OVF (teriparatide and romosozumab vs. alendronate and risedronate [RR, 0.57; 95% confidence interval, 0.45–0.71; p < 0.00001; high-certainty of evidence]; teriparatide vs. risedronate [RR, 0.50; 95% confidence interval, 0.37–0.68; p < 0.0001; high-certainty of evidence]). However, there was no evidence of teriparatide compared to alendronate in fracture healing of OVF (RR, 1.23; 95% confidence interval, 0.95–1.60; p = 0.12; low-certainty of evidence). Conclusion In the patients with prevalent OVF, anabolic agents showed a significant superiority for preventing new OVF than BPs, with no significant evidence for promoting fracture healing of OVF. However, considering small number of RCTs in this study, additional studies with large-scale data are required to obtain more robust evidences.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: We investigated the clinical efficacy of anabolic agents compared with bisphosphonates (BPs) for the incidence of new osteoporotic vertebral fracture (OVF) and fracture healing of OVF in the patients with OVF via meta-analyses of randomized controlled trials (RCTs). Methods: Electronic databases, including PubMed, Embase, and Cochrane Library were searched for published RCTs till December 2022. The RCTs that recruited participants with osteoporosis at high-/very high-risk of fracture (a history of osteoporotic vertebral or hip fracture) or fresh OVF were included in this study. We assessed the risk of bias on every included RCTs, estimated relative risk (RR) for the incidence of new OVF and fracture healing of OVF, and overall certainty of evidence. Meta-analyses were performed by Cochrane review manager (RevMan) ver. 5.3. Cochrane risk of bias 2.0 and GRADEpro/GDT were applied for evaluating methodological quality and overall certainty of evidence, respectively. Results: Five hundred eighteen studies were screened, and finally 6 eligible RCTs were included in the analysis. In the patients with prevalent OVF, anabolic agents significantly reduced the incidence of new OVF (teriparatide and romosozumab vs. alendronate and risedronate [RR, 0.57; 95% confidence interval, 0.45–0.71; p < 0.00001; high-certainty of evidence]; teriparatide vs. risedronate [RR, 0.50; 95% confidence interval, 0.37–0.68; p < 0.0001; high-certainty of evidence]). However, there was no evidence of teriparatide compared to alendronate in fracture healing of OVF (RR, 1.23; 95% confidence interval, 0.95–1.60; p = 0.12; low-certainty of evidence). Conclusion In the patients with prevalent OVF, anabolic agents showed a significant superiority for preventing new OVF than BPs, with no significant evidence for promoting fracture healing of OVF. However, considering small number of RCTs in this study, additional studies with large-scale data are required to obtain more robust evidences.