Page 116 - 《中国药房》2025年5期

et al. Real-world burden of chemotherapy-induced myelo‐ [18] ERNSTER V L. Nested case-control studies[J]. Prev Med，
suppression in patients with small cell lung cancer：a retro‐ 1994，23（5）：587-590.
spective analysis of electronic medical data from commu‐ [19] CAO W，CHEN H D，YU Y W，et al. Changing profiles of
nity cancer care providers[J]. J Med Econ，2022，25（1）： cancer burden worldwide and in China：a secondary analy‐
108-118. sis of the global cancer statistics 2020[J]. Chin Med J
[ 9 ] MOONS K G M，DE GROOT J A H，BOUWMEESTER （Engl），2021，134（7）：783-791.
W，et al. Critical appraisal and data extraction for syste- [20] NATTINO G，PENNELL M L，LEMESHOW S. Assessing
matic reviews of prediction modelling studies：the the goodness of fit of logistic regression models in large
CHARMS checklist[J]. PLoS Med，2014，11（10）： samples：a modification of the Hosmer-Lemeshow test[J].
e1001744. Biometrics，2020，76（2）：549-560.
[10] WOLFF R F，MOONS K G M，RILEY R D，et al. [21] PARK S H，HAN K. Methodologic guide for evaluating
PROBAST：a tool to assess the risk of bias and applica- clinical performance and effect of artificial intelligence
bility of prediction model studies[J]. Ann Intern Med， technology for medical diagnosis and prediction[J]. Radio-
2019，170（1）：51-58. logy，2018，286（3）：800-809.
[11] DRANITSARIS G，RAYSON D，VINCENT M，et al. [22] TALARI K，GOYAL M. Retrospective studies：utility and
Identifying patients at high risk for neutropenic complica‐ caveats[J]. J R Coll Physicians Edinb，2020，50（4）：
tions during chemotherapy for metastatic breast cancer 398-402.
with doxorubicin or pegylated liposomal doxorubicin：the [23] OGUNDIMU E O，ALTMAN D G，COLLINS G S.
development of a prediction model[J]. Am J Clin Oncol， Adequate sample size for developing prediction models is
2008，31（4）：369-374. not simply related to events per variable[J]. J Clin Epide‐
[12] JENKINS P，SCAIFE J，FREEMAN S. Validation of a miol，2016，76：175-182.
predictive model that identifies patients at high risk of de‐ [24] COLLINS G S，OGUNDIMU E O，COOK J A，et al.
veloping febrile neutropaenia following chemotherapy for Quantifying the impact of different approaches for hand-
breast cancer[J]. Ann Oncol，2012，23（7）：1766-1771. ling continuous predictors on the performance of a prog‐
[13] CHO B J，KIM K M，BILEGSAIKHAN S E，et al. Ma‐ nostic model[J]. Stat Med，2016，35（23）：4124-4135.
chine learning improves the prediction of febrile neutrope‐ [25] PESKOE S B，ARTERBURN D，COLEMAN K J，et al.
nia in Korean inpatients undergoing chemotherapy for Adjusting for selection bias due to missing data in elec‐
breast cancer[J]. Sci Rep，2020，10（1）：14803. tronic health records-based research[J]. Stat Methods Med
[14] CHEN K，ZHANG X L，DENG H R，et al. Clinical predic‐ Res，2021，30（10）：2221-2238.
tive models for chemotherapy-induced febrile neutropenia [26] LEON RAPOPORT B，GARCIA-MORILLO M，FONT
in breast cancer patients：a validation study[J]. PLoS One， C，et al. A prospective，real-world，multinational study of
2014，9（6）：e96413. febrile neutropenia （FN） occurrence in oncology patients
[15] 李亚玲，饶真真，胡保玲，等. 乳腺癌化疗患者骨髓抑制 receiving chemotherapy with intermediate risk of FN：a
风险预测模型的构建与验证[J]. 军事护理，2024，41（2）： MASCC Neutropenia，Infection，and Myelosuppression
6-10. Study Group initiative[J]. Support Care Cancer，2023，31
LI Y L，RAO Z Z，HU B L，et al. Construction and valida‐ （12）：628.
tion of a risk prediction model for myelosuppression in [27] DAI Y，LIU M B，LEI L，et al. Prognostic significance of
breast cancer chemotherapy patients[J]. Mil Nurs，2024， preoperative prognostic nutritional index in ovarian can‐
41（2）：6-10. cer：a systematic review and meta-analysis[J]. Medicine
[16] 黄家良，夏坤健，郭伟，等. 三阴性乳腺癌化疗致重度骨 （Baltimore），2020，99（38）：e21840.
髓抑制的危险因素分析及其预测模型的构建[J]. 中山大 [28] CHEN L，BAI P，KONG X Y，et al. Prognostic nutritional
学学报（医学科学版），2023，44（5）：886-892. index （PNI） in patients with breast cancer treated with
HUANG J L，XIA K J，GUO W，et al. Risk factors and neoadjuvant chemotherapy as a useful prognostic indicator
predictive model for severe myelosuppression due to che‐ [J]. Front Cell Dev Biol，2021，9：656741.
motherapy in triple-negative breast cancer[J]. J Sun Yat [29] ZHOU Y L，LIU Y D，ZHANG Y，et al. Identifying non-
Sen Univ Med Sci，2023，44（5）：886-892. linear association between maternal free thyroxine and
[17] CHANG L L，SCHNEIDER S M，CHIANG S C，et al. risk of preterm delivery by a machine learning model[J].
Implementing an evidence-based risk assessment tool to Front Endocrinol （Lausanne），2022，13：817595.
predict chemotherapy-induced neutropenia in women with （收稿日期：2024-08-29 修回日期：2025-01-07）
breast cancer[J]. Cancer Nurs，2013，36（3）：198-205. （编辑：刘明伟）

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