Body composition predictors of mortality in patients undergoing surgery for long bone metastases

Groot, Olivier Q.; Bongers, Michiel E R; Buckless, Colleen; Twining, P.; Kapoor, Neal D; Janssen, Stein J.; Schwab, Joseph H.; Torriani, Martin; Bredella, Miriam A.

doi:10.1002/jso.26793

Cited by 7 publications

(9 citation statements)

References 46 publications

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“…These observations were consistent with a recent study by Groot et al. that found decreased PMA on CT was associated with a higher likelihood of mortality at 1 year (Hazard ratio, HR, 1.68; 95% CI, 1.08–2.61; p = 0.02) in 212 patients undergoing surgery for extremity metastases 28 . Although the authors concluded body composition measurements could be used as a novel imaging biomarker to supplement current survival prediction tools for patients with long‐bone metastases, they did not further analyze if predictions made by these tools could actually be improved when morphometric factors were considered.…”

Section: Discussionsupporting

confidence: 93%

“…[17][18][19][20][21][22][23]25 These observations were consistent with a recent study by Groot et al that found decreased PMA on CT was associated with a higher likelihood of mortality at 1 year (Hazard ratio, HR, 1.68; 95% CI, 1.08-2.61; p = 0.02) in 212 patients undergoing surgery for extremity metastases. 28 Although the authors concluded body composition measurements could be used as a novel imaging biomarker to supplement current survival prediction tools for patients with long-bone metastases, they did not further analyze if predictions made by these tools could actually be improved when morphometric factors were considered. Our study demonstrated that decreased PMA was still associated with higher 90-day and 1-year mortality after controlling for the predictions made by three extensively validated SPAs, implying that body composition metrics might capture some prognostic value not currently appreciated by these algorithms.…”

Section: Unadjustedmentioning

confidence: 99%

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Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long‐bone metastases

Lee,

Tseng,

Chang

et al. 2024

Cancer Medicine

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BackgroundPredictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG‐NG, and SORG‐MLA) improves their performance.MethodsOne hundred eighty‐five patients surgically treated for long‐bone metastases between 2014 and 2019 were divided into three PMA tertiles (small, medium, and large) based on their psoas size on CT. Kaplan–Meier, multivariable regression, and Cox proportional hazards analyses were employed to compare survival between tertiles and examine factors associated with mortality. Logistic regression analysis was used to assess whether incorporating adjusted PMA values enhanced the three SPAs' discriminatory abilities. The clinical utility of incorporating PMA into these SPAs was evaluated by decision curve analysis (DCA).ResultsPatients with small PMA had worse 90‐day and 1‐year survival after surgery (log‐rank test p < 0.001). Patients in the large PMA group had a higher chance of surviving 90 days (odds ratio, OR, 3.72, p = 0.02) and 1 year than those in the small PMA group (OR 3.28, p = 0.004). All three SPAs had increased AUC after incorporation of adjusted PMA. DCA indicated increased net benefits at threshold probabilities >0.5 after the addition of adjusted PMA to these SPAs.ConclusionsDecreased PMA on CT is associated with worse survival in surgically treated patients with extremity metastases, even after controlling for three contemporary SPAs. Physicians should consider the additional prognostic value of PMA on survival in patients undergoing consideration for operative management due to extremity metastases.

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Section: Discussionsupporting

confidence: 93%

Section: Unadjustedmentioning

confidence: 99%

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long‐bone metastases

Lee,

Tseng,

Chang

et al. 2024

Cancer Medicine

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“…In the entire cohort, 36.63% (43,305/118,227) of patients experienced early death. Other studies reported that the 3-month mortality rate was approximately 30% [ 30 - 32 ], which was consistent with this study. According to variables’ importance, this study found that chemotherapy, primary site, and liver metastasis were the top 3 important variables associated with early death.…”

Section: Discussionsupporting

confidence: 93%

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study

Lei,

Wu,

Zhang

et al. 2023

J Med Internet Res

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Background Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. Objective This study aimed to develop a machine learning–based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. Methods This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches—logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine—were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. Results In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was −0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. Conclusions This study develops a machine learning–based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

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“…Predictive models and clinical risk assessment tools continue to be an area of active research in metastatic bone disease 67,[73][74][75][76] . The Skeletal Oncology Research Group (SORG) machinelearning (ML) algorithms have been widely studied as tools to estimate survival in patients with extremity metastases.…”

Section: Predictive Modelsmentioning

confidence: 99%

“…Table III shows some recent studies evaluating patients with metastatic bone disease [66][67][68][69][70][71][72][73][74][75][76] .…”

Section: Metastatic Bone Tumorsmentioning

confidence: 99%

What’s New in Musculoskeletal Tumor Surgery

Gazendam

Ghert

2022

Journal of Bone and Joint Surgery

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Body composition predictors of mortality in patients undergoing surgery for long bone metastases

Cited by 7 publications

References 46 publications

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long‐bone metastases

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long‐bone metastases

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study

What’s New in Musculoskeletal Tumor Surgery

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