Predicting postoperative quality-of-life outcomes in patients with metastatic spine disease: who benefits?

Clinical Characteristics, Surgical Outcomes, and Risk Factors for Emergency Surgery in Patients With Spinal Metastases: A Prospective Cohort Study

OBJECTIVE

To elucidate the patient characteristics and outcomes of emergency surgery for spinal metastases and identify risk factors for emergency surgery.

METHODS

We prospectively analyzed 216 patients with spinal metastases who underwent palliative surgery from 2015 to 2020. The Eastern Cooperative Oncology Group performance status, Barthel index, EuroQol-5 dimension (EQ5D), and neurological function were assessed at surgery and at 1, 3, and 6 months postoperatively. Multivariate analysis was performed to identify risk factors for emergency surgery.

RESULTS

In total, 146 patients underwent nonemergency surgery and 70 patients underwent emergency surgery within 48 hours of diagnosis of a surgical indication. After propensity score matching, we compared 61 patients each who underwent nonemergency and emergency surgery. Regardless of matching, the median performance status and the mean Barthel index and EQ5D score showed a tendency toward worse outcomes in the emergency than nonemergency group both preoperatively and 1 month postoperatively, although the surgery greatly improved these values in both groups. The median survival time tended to be shorter in the emergency than nonemergency group. The multivariate analysis showed that lesions located at T3-10 (p = 0.002; odds ratio [OR], 2.92; 95% confidence interval [CI], 1.48-5.75) and Frankel grades A-C (p < 0.001; OR, 4.91; 95% CI, 2.45-9.86) were independent risk factors for emergency surgery.

CONCLUSION

Among patients with spinal metastases, preoperative and postoperative subjective health values and postoperative survival are poorer in emergency than nonemergency surgery. Close attention to patients with T3-10 metastases is required to avoid poor outcomes after emergency surgery.

Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery

STUDY DESIGN

Retrospective cohort study.

PURPOSE

This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis.

OVERVIEW OF LITERATURE

Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals.

METHODS

This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score.

RESULTS

In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88).

CONCLUSIONS

Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

Background

Health-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC.

Methods

A total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram's performance with and without the consideration of comorbidities.

Results

Four variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074-0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004-0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities.

Conclusions

This study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.

Evaluation of different scoring systems for spinal metastases based on a Chinese cohort

INTRODUCTIONS

The spine is one of the most common sites of metastasis for malignancies. This study aimed to compare the predictive performance of seven commonly used prognostic scoring systems for surgically treated spine metastases. It is expected to assist surgeons in selecting appropriate scoring systems to support clinical decision-making and better inform patients.

METHODS

We performed a retrospective study involving 268 surgically treated patients with spine metastases between 2017 and 2020 at a single regional oncology center in China. The revised Tokuhashi, Tomita, modified Bauer, revised Katagiri, van der Linden, Skeletal Oncology Research Group (SORG) nomogram, and SORG machine-learning (ML) scoring systems were externally validated. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate sensitivity and specificity at different postoperative time points. The actual survival time was compared with the reference survival time provided in the original publication.

RESULTS

In the present study, the median survival was 16.6 months. The SORG ML scoring system demonstrated the highest accuracy in predicting 90-day (AUC: 0.743) and 1-year survival (AUC: 0.787). The revised Katagiri demonstrated the highest accuracy (AUC: 0.761) in predicting 180-day survival. The revised Katagiri demonstrated the highest accuracy (AUC: 0.779) in predicting 2-year survival. Based on this series, the actual life expectancy was underestimated compared with the original reference survival time.

CONCLUSIONS

None of the scoring systems can perform optimally at all time points and for all pathology types, and the reference survival times provided in the original study need to be updated. A cautious awareness of the underestimation by these models is of paramount importance in relation to current patients.