处方非甾体抗炎药(NSAIDs)和老年骨关节炎癌症幸存者抑郁发生率:机器学习分析。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231165161
Nazneen Fatima Shaikh, Chan Shen, Traci LeMasters, Nilanjana Dwibedi, Amit Ladani, Usha Sambamoorthi
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引用次数: 0

摘要

目的:本研究考察了处方非甾体抗炎药作为抑郁症发生的主要预测因素之一,并评估了老年骨关节炎癌症幸存者之间的关联方向。方法:本研究采用回顾性队列研究(N = 14,992),纳入了发生癌症(乳腺癌、前列腺癌、结直肠癌或非霍奇金淋巴瘤)和骨关节炎的老年人。我们使用了2006年至2016年研究期间相关的监测、流行病学和最终结果-医疗保险数据的纵向数据,包括12个月的基线和12个月的随访期。在基线期评估累积的非甾体抗炎药天数,在随访期评估抑郁事件。利用训练数据集,通过10倍重复分层交叉验证和超参数调优,建立了极端梯度增强(XGBoost)模型。从训练数据中选择的最终模型在应用于测试数据时表现出高性能(准确率:0.82,召回率:0.75,精度:0.75)。SHapley加性解释(SHAP)用于解释XGBoost模型的输出。结果:超过50%的研究队列至少有一种非甾体抗炎药处方。近13%的人被诊断为偶发性抑郁症,前列腺癌的发病率为7.4%,结肠直肠癌的发病率为17.0%。在nsaid累计用药90和120天时观察到最高的抑郁发生率为25%。累计服用非甾体抗炎药天数是老年OA和癌症患者发生抑郁的第六大预测因子。年龄、教育程度、护理碎片化、多种药物治疗和邮政编码水平贫困是事件抑郁症的前5个预测因素。结论:总体而言,每8名患有癌症和OA的老年人中就有1人被诊断为偶发性抑郁症。累计服用非甾体抗炎药天数是与抑郁事件总体正相关的第六大预测因子。然而,随着非甾体抗炎药使用日数的增加,这种关联变得复杂和多样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.

Objectives: This study examined prescription NSAIDs as one of the leading predictors of incident depression and assessed the direction of the association among older cancer survivors with osteoarthritis.

Methods: This study used a retrospective cohort (N = 14, 992) of older adults with incident cancer (breast, prostate, colorectal cancers, or non-Hodgkin's lymphoma) and osteoarthritis. We used the longitudinal data from the linked Surveillance, Epidemiology, and End Results -Medicare data for the study period from 2006 through 2016, with a 12-month baseline and 12-month follow-up period. Cumulative NSAIDs days was assessed during the baseline period and incident depression was assessed during the follow-up period. An eXtreme Gradient Boosting (XGBoost) model was built with 10-fold repeated stratified cross-validation and hyperparameter tuning using the training dataset. The final model selected from the training data demonstrated high performance (Accuracy: 0.82, Recall: 0.75, Precision: 0.75) when applied to the test data. SHapley Additive exPlanations (SHAP) was used to interpret the output from the XGBoost model.

Results: Over 50% of the study cohort had at least one prescption of NSAIDs. Nearly 13% of the cohort were diagnosed with incident depression, with the rates ranging between 7.4% for prostate cancer and 17.0% for colorectal cancer. The highest incident depression rate of 25% was observed at 90 and 120 cumulative NSAIDs days thresholds. Cumulative NSAIDs days was the sixth leading predictor of incident depression among older adults with OA and cancer. Age, education, care fragmentation, polypharmacy, and zip code level poverty were the top 5 predictors of incident depression.

Conclusion: Overall, 1 in 8 older adults with cancer and OA were diagnosed with incident depression. Cumulative NSAIDs days was the sixth leading predictor with an overall positive association with incident depression. However, the association was complex and varied by the cumulative NSAIDs days.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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