Quality of Life and Mental Health Status Among Cancer Patients With Metastatic Spinal Disease

Liu, Yaosheng; Cao, Xuyong; Shi, Xiaoyi; Lei, Mingxing; Qin, Haifeng

doi:10.3389/fpubh.2022.916004

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…The predominance of hepatitis B virus infection is also reported by El-Kass [18] in Egypt, Nartey et al [19] in Ghana and Somé et al [16] in Burkina Faso. A low prevalence of viral hepatitis B virus infection in liver cancer is reported by Giannitrapani et al [20] in Italy, Endo et al [21] in Japan and Lui Yet al [22] in China.…”

Section: Discussionmentioning

confidence: 95%

Profile of people living with cancer followed in the palliative care unit of Parakou, Benin in 2021

ALASSANI¹,

DOVONOU²,

SAKE³

et al. 2023

World J. Adv. Res. Rev.

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Introduction: Cancer is a global public health problem. This study focused on the the profile of people living with cancer in Parakou. Study method: This was a cross-sectional, descriptive and analytical study which enrolled people living with cancer followed in the palliative heath care of the internal medicine unit at the Regional University Health Center of Borgou who had given their consent. The variables of the study were: sociodemographic data, nutritional status, comorbidities, circumstances of cancer screening and their location. Data analysis was performed using Epi Data 3.1 software. Results: The study population consisted of 264 people with cancer. A male predominance was observed with a sex ratio of 2.66. The average age was 47.51 years ±13.20. Alcohol consumption was observed in 15.2%. Arterial hypertension was found in 63.6% of subjects. On the nutritional level, malnutrition and weight loss were observed respectively in 68.2% and 98.1% of patients. Abdominal pain, asthenia and weight loss were the main circumstances for cancer screening and were present in 37.1%, 27.6% and 16.7% of participants respectively. Liver cancer was found in the majority (72%) of the participants. Viral hepatitis B was the most common cause with 98.3% Conclusion: cancer affects young adults and is often detected at an advanced stage. The main site is the liver and infection with the hepatitis B virus is found in most of the cases.

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Section: Discussionmentioning

confidence: 95%

Profile of people living with cancer followed in the palliative care unit of Parakou, Benin in 2021

ALASSANI¹,

DOVONOU²,

SAKE³

et al. 2023

World J. Adv. Res. Rev.

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“…Metastatic spinal disease is a common complication of cancer, with a reported incidence of 70% 1 , 2 . It poses significant challenges for patients and clinicians due to its detrimental effects on neurological function and quality of life 3 , 4 . The disease is characterized by the spread of cancer cells to the vertebral column, leading to spinal instability, compression of neural elements, pain, and neurological deficits 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis

Cui,

Shi,

Qin

et al. 2024

International Journal of Surgery

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Background: Identification of patients with high risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence brings a promising opportunity to develop accurate prediction models. Methods: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study’s model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. Results: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95%CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95%CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/. By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts (P<0.001), denoting that the AI platform obviously outperformed the individual medical experts. Conclusions: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

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“…Numerous studies have focused on interventions with the aims of relieving pain, promoting neurological recovery, controlling local tumors, maintaining spinal stability, and improving quality of life for patients with metastatic spinal tumors. 6 After a randomized trial showing that decompressive surgery combined with radiotherapy is superior to radiotherapy alone for patient's functional outcome, 7 this approach thus became widely accepted for treating patients with metastatic spinal cord compression. 8 Nonetheless, this therapeutic strategy is only suitable for selected patients.…”

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confidence: 99%

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

Cao,

Jiang,

Zhang

et al. 2023

Neurosurgery

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BACKGROUND AND OBJECTIVES: Treating metastatic spinal tumors poses a significant challenge because there are currently no universally applied guidelines for managing spinal metastases. This study aims to propose a new decision framework for the 12-point epidural spinal cord compression grading system to treat patients with metastatic spinal tumors and investigate its clinical effectiveness in a multicenter analysis. METHODS: This study analyzed 940 patients with metastatic spinal tumors between December 2017 and March 2023. The study provided the clinical evidence for the systemic conditions, effectiveness of systemic treatment, neurology, and oncology (SENO) decision framework among spine metastases. The SENO decision framework was launched in January 2021 in our hospitals, classifying patients into 2 groups: The non-SENO group (n = 489) consisted of patients treated between December 2017 and January 2021, while the SENO group (n = 451) comprised patients treated from January 2021 to March 2023. RESULTS: Patients in the SENO group were more likely to receive minimally invasive surgery (67.85% vs 58.69%) and less chance of receiving spinal cord circular decompression surgery (14.41% vs 24.74%) than patients in the non-SENO group (P < .001). Furthermore, patients in the SENO group experienced fewer perioperative complications (9.09% vs 15.34%, P = .004), incurred lower hospitalization costs (P < .001), had shorter length of hospitalization (P < .001), and received systematic treatments for tumors earlier (P < .001). As a result, patients in the SENO group (329.00 [95% CI: 292.06-365.94] days) demonstrated significantly improved survival outcomes compared with those in the non-SENO group (279.00 [95% CI: 256.91-301.09], days) (P < .001). At 3 months postdischarge, patients in the SENO group reported greater improvements in their quality of life, encompassing physical, social, emotional, and functional well-being, when compared with patients in the non-SENO group. CONCLUSION: The SENO decision framework is a promising approach for treating patients with metastatic spinal tumors.

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Quality of Life and Mental Health Status Among Cancer Patients With Metastatic Spinal Disease

Cited by 9 publications

References 40 publications

Profile of people living with cancer followed in the palliative care unit of Parakou, Benin in 2021

Profile of people living with cancer followed in the palliative care unit of Parakou, Benin in 2021

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis

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

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