Predicting Neutropenia Risk in Breast Cancer Patients from Pre- Chemotherapy Characteristics

Q3 Computer Science Open Bioinformatics Journal Pub Date : 2015-01-13 DOI:10.2174/1875036201408010016
S. Lawal, M. Korenberg, Natalia M. Pittman, M. Mates
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Abstract

A previous study (Pittman, Hopman, Mates) of breast cancer patients undergoing curative chemotherapy (CT) found that the third most common reason for emergency department (ER) visits and hospital admission (HA) was febrile neutropenia. Factors associated with ER visits and HA included (1) stage of the cancer, (2) size of tumor, (3) adjuvant versus neo-adjuvant CT ("adjuvance"), and (4) number of CT cycles. We hypothesized that a statistically-significant pre- dictor of neutropenia could be built based on some of these factors, so that risk of neutropenia predicted for a patient feel- ing unwell during CT could be used in weighing need to visit the ER. The number of CT cycles was not used as a factor so that the predictor could calculate the neutropenia risk for a patient before the first CT cycle. Different models were built corresponding to different pre-chemotherapy factors or combinations of factors. The single factor yielding the best classification accuracy was tumor size (Mathews' correlation coefficient � = +0.18, Fisher's exact two-tailed probability P < 0.0374). The odds ratio of developing febrile neutropenia for the predicted high-risk group compared to the predicted low-risk group was 5.1875. Combining tumor size with adjuvance yielded a slightly more accurate predictor (Mathews' correlation coefficient � = +0.19, Fisher's exact two-tailed probability P < 0.0331, odds ratio = 5.5093). Based on the ob- served odds ratios, we conclude that a simple predictor of neutropenia may have value in deciding whether to recommend an ER visit. The predictor is sufficiently fast that it can run conveniently as an Applet on a mobile computing device.
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从化疗前特征预测乳腺癌患者中性粒细胞减少的风险
Pittman, Hopman, Mates先前对接受治疗性化疗(CT)的乳腺癌患者的研究发现,急诊科(ER)就诊和住院(HA)的第三大常见原因是发热性中性粒细胞减少症。与ER就诊和HA相关的因素包括(1)癌症分期,(2)肿瘤大小,(3)辅助与新辅助CT(“辅助”),(4)CT周期数。我们假设,基于这些因素,可以建立一个具有统计学意义的中性粒细胞减少的预测指标,因此,当患者在CT期间感觉不适时,预测的中性粒细胞减少的风险可以用来衡量是否需要去急诊室。CT周期的数量没有被用作一个因素,因此预测者可以在第一个CT周期之前计算患者中性粒细胞减少的风险。针对不同的化疗前因素或因素组合建立不同的模型。产生最佳分类准确度的单因素是肿瘤大小(Mathews相关系数= +0.18,Fisher精确双侧概率P < 0.0374)。预测高危组与预测低危组发生发热性中性粒细胞减少的比值比为5.1875。肿瘤大小与佐剂相结合的预测结果更为准确(Mathews相关系数= +0.19,Fisher精确双侧概率P < 0.0331,优势比= 5.5093)。根据观察到的优势比,我们得出结论,中性粒细胞减少症的简单预测因子可能对决定是否推荐急诊室就诊有价值。这个预测器足够快,可以作为Applet在移动计算设备上方便地运行。
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来源期刊
Open Bioinformatics Journal
Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
期刊介绍: The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.
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