A novel nomogram to stratify quality of life among advanced cancer patients with spinal metastatic disease after examining demographics, dietary habits, therapeutic interventions, and mental health status

Abstract

Background It would be very helpful to stratify patients and direct patient selection if risk factors for quality of life were identified in a particular population. Nonetheless, it is still challenging to forecast the health-related quality of life among individuals with spinal metastases. The goal of this study was to stratify patient’s populations for whom the assessment of quality of life should be encouraged by developing and validating a nomogram to predict the quality of life among advanced cancer patients with spine metastases. Methods This study prospectively analyzed 208 advanced cancer patients with spine metastases, and collected their general characteristics, food preferences, addictions, comorbidities, therapeutic strategies, and mental health status. The functional assessment of cancer therapy-general (FACT-G) and hospital anxiety and depression scale (HADS) were used to assess quality of life and mental health, respectively. The complete cohort of patients was randomly divided into two groups: a training set and a validation set. Patients from the training set were conducted to train and develop a nomogram, while patients in the validation set were performed to internally validate the nomogram. The nomogram contained significant variables discovered using the least absolute shrinkage and selection operator (LASSO) approach in conjunction with 10-fold cross-validation. The nomogram’s predictive ability was assessed utilizing discrimination, calibration, and clinical usefulness. Internal validation was also completed using the bootstrap method after applying 500 iterations of procedures. A web calculator was also developed to promote clinical practice. Results Advance cancer patients with spinal metastases had an extremely low quality of life, as indicated by the average FACT-G score of just 60.32 ± 20.41. According to the LASSO and 10-fold cross-validation, Eastern Cooperative Oncology Group (ECOG) score, having an uncompleted life goal, preference for eating vegetables, chemotherapy, anxiety status, and depression status were selected as nomogram predictors. In the training set, the area under the receiver operating characteristic curve (AUROC) was 0.90 (95% CI: 0.84–0.96), while in the validation set, it was 0.85 (95% CI: 0.78–0.93). They were 0.50 (95% CI: 0.41–0.58) and 0.44 (95% CI: 0.33–0.56), respectively, for the discrimination slopes. The nomogram had favorable capacity to calibrate and was clinically useful, according to the calibration curve and decision curve analysis. When compared to patients in the low-risk group, patients in the high-risk group were above four times more likely to experience a poor quality of life (82.18% vs. 21.50%, P < 0.001). In comparison to patients in the low-risk group, patients in the high-risk group also exhibited significant higher levels of anxiety and depression. The webpage for the web calculator was https://starshiny.shinyapps.io/DynNomapp-lys/. Conclusions This study suggests a nomogram that can be applied as a practical clinical tool to forecast and categorize the quality of life among patients with spine metastases. Additionally, patients with poor quality of life experience more severe anxiety and depression. Effective interventions should be carried out as soon as possible, especially for patients in the high-risk group, to improve their quality of life and mental health condition.

Development and validation of a nomogram for suicide attempts in patients with first-episode drug-naïve major depressive disorder

“…It is particularly useful when there are a large number of predictors, and it is necessary to identify the most relevant variables while discarding any confounding variables. The LASSO achieves this by minimizing the coefficients of less relevant variables to zero, effectively excluding them from the model ( 22 ). This robust approach effectively selects statistically meaningful variables, resulting in the reduction of the initial 21 variables to a concise set of five: HAMD, HAMA, TSH, Log (TPOAb), and SBP ( Figures 2A, B ).…”

A New Treatment Strategy for Spinal Metastasis: The “Systemic Conditions, Effectiveness of Systemic Treatment, Neurology, and Oncology” Decision Framework System

“…1 It is estimated that 20%-40% of patients with cancer develop metastatic spinal tumors, and 20% of these patients develop metastatic epidural spinal cord compression (ESCC). 2 The incidence of metastatic spinal tumor is increasing due to advancements in oncological interventions, [3][4][5] such as the targeted therapy and immunotherapy. These interventions have dramatically improved the survival prognosis for metastatic patients.…”

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“…It is particularly useful when there are a large number of predictors, and it is necessary to identify the most relevant variables while discarding any confounding variables. The LASSO achieves this by minimizing the coefficients of less relevant variables to zero, effectively excluding them from the model (22). This robust approach effectively selects statistically meaningful variables, resulting in the reduction of the initial 21 variables to a concise set of five: HAMD, HAMA, TSH, Log (TPOAb), and SBP (Figures 2A, B).…”