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

Frontiers in Endocrinology, 2023 · DOI: 10.3389/fendo.2023.1206840 · Published: September 1, 2023

Spinal Cord Injury Participation Oncology

Simple Explanation

This study created a tool to predict the health-related quality of life (HRQoL) after surgery for lung cancer patients who have metastatic spinal cord compression (MSCC). This condition severely affects well-being, and a reliable prediction method is needed. The prediction tool, called a nomogram, uses factors like the patient's physical condition (ECOG score), whether they receive targeted therapy, their anxiety levels, and the number of other health issues they have. It helps doctors estimate how well a patient will feel after surgery. By using this nomogram, healthcare providers can better understand which patients are at risk for having a poor quality of life after surgery and can then create treatment plans tailored to their specific needs.

Study Duration

April 2019 and November 2022

Participants

119 patients diagnosed with MSCC secondary to lung cancer

Evidence Level

Not specified

Key Findings

1
Four key variables were identified for predicting HRQoL: ECOG score, targeted therapy, anxiety scale, and number of comorbidities.
2
The nomogram demonstrated a good ability to distinguish between patients with good and poor HRQoL, as indicated by a C-index of 0.87 and a discrimination slope of 0.47.
3
Including the number of comorbidities in the nomogram improved its predictive performance, leading to more accurate risk stratification.

Research Summary

This study developed a nomogram to predict postoperative HRQoL in lung cancer patients with MSCC, using ECOG score, targeted therapy, anxiety score, and number of comorbidities as predictors. The nomogram showed favorable discriminative ability and consistency between predicted and observed probabilities of poor HRQoL. The nomogram can assist clinicians in risk stratification and personalized treatment planning for this specific patient population.

Practical Implications

Personalized Treatment Planning

The nomogram allows clinicians to develop treatment plans tailored to individual patients based on their predicted HRQoL outcomes.

Risk Stratification

The tool helps to identify and stratify patients at risk of experiencing suboptimal HRQoL after surgery, enabling timely interventions.

Improved Patient-Physician Communication

The visual representation of the prediction model facilitates patient understanding and participation in shared decision-making.

Study Limitations

1
The study had a limited sample size, restricting the ability to perform data splitting for internal validation.
2
The study relied on self-reported measures (FACT-G and HADS scales), which may introduce recall bias.
3
Certain variables, such as surgical margins and lymphatic metastasis, were not included in the evaluation, which might have enhanced predictive performance.

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