Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 16:2022:3050199.
doi: 10.1155/2022/3050199. eCollection 2022.

Establishment and Validation for Predicting the Death of Multiple Myeloma among Whites

Jian Tao  1 , Ling Wang  1 , Liyu Zhang  1 , Zheyun Gu  1 , Xiaodan Zhou  1
Affiliations

Establishment and Validation for Predicting the Death of Multiple Myeloma among Whites

Jian Tao et al. Evid Based Complement Alternat Med. .

Retraction in

Abstract

The prognosis of multiple myeloma (MM) patients was poor in white-American patients as compared to black-American patients. This study aimed to predict the death of MM patients in whites based on the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. A total of 28,912 white MM patients were included in this study. Data were randomly divided into a training set and a test set (7 : 3). The random forest and 5-fold cross-validation were used for developing a prediction model. The performance of the model was determined by calculating the area under the curve (AUC) with 95% confidence interval (CI). MM patients in the death group had older age, higher proportion of tumor distant metastasis, bone marrow as the disease site, receiving radiotherapy, and lower proportion of receiving chemotherapy than that in the survival group (all P < 0.001). The AUC of the random forest model in the training set and testing set was 0.741 (95% CI, 0.740-0.741) and 0.703 (95% CI, 0.703-0.704), respectively. In addition, the AUC of the age-based model was 0.688 (95% CI, 0.688-0.689) in the testing set. The results of the DeLong test indicated that the random forest model had better predictive effect than the age-based model (Z = 7.023, P < 0.001). Further validation was performed based on age and marital status. The results presented that the random forest model was robust in different age and marital status. The random forest model had a good performance to predict the death risk of MM patients in whites.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
The flow chart of patients screening. All white patients with MM included in the study were randomly divided into a training set and a test set in a ratio of 7 : 3 for the model establishment and validation, respectively.
Figure 2
Figure 2
Variable importance of random forest model for predicting the risk of death in white multiple myeloma (MM) patients. The variable importance of random forests indicates which variables contributed the most to the final model.
Figure 3
Figure 3
Performance and evaluation of random forest model in the training set. (a) Receiver operator characteristic (ROC) curves; (b) calibration curves.
Figure 4
Figure 4
Performance and evaluation of random forest model in the testing set. (a) Receiver operator characteristic (ROC) curves; (b) calibration curves.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Moreau P., San Miguel J., Sonneveld P., et al. Multiple myeloma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology . 2013;24(6):vi133–7. - PubMed
    1. Terpos E., Zamagni E., Lentzsch S., et al. Treatment of multiple myeloma-related bone disease: recommendations from the bone working group of the international myeloma working group. The Lancet Oncology . 2021;22:e119–30. - PubMed
    1. Cowan A. J., Allen C., Barac A., et al. Global burden of multiple myeloma: a systematic analysis for the global burden of disease study 2016. JAMA Oncology . 2018;4(9):1221–1227. doi: 10.1001/jamaoncol.2018.2128. - DOI - PMC - PubMed
    1. Howlader N. A., Noone A. M. SEER Cancer Statistics Review . Bethesda, MA, USA: National Cancer Institute; 2017.
    1. Henley S. J., Ward E. M., Scott S., et al. Annual report to the nation on the status of cancer, part I: National cancer statistics. Cancer . 2020;126(10):2225–2249. doi: 10.1002/cncr.32802. - DOI - PMC - PubMed

Publication types

LinkOut - more resources