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通过机器学习识别有错过心理肿瘤治疗风险的癌症患者。

Towards identifying cancer patients at risk to miss out on psycho-oncological treatment via machine learning.

机构信息

Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland.

出版信息

Eur J Cancer Care (Engl). 2022 Mar;31(2):e13555. doi: 10.1111/ecc.13555. Epub 2022 Feb 9.

DOI:10.1111/ecc.13555
PMID:35137480
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9286797/
Abstract

OBJECTIVE

In routine oncological treatment settings, psychological distress, including mental disorders, is overlooked in 30% to 50% of patients. High workload and a constant need to optimise time and costs require a quick and easy method to identify patients likely to miss out on psychological support.

METHODS

Using machine learning, factors associated with no consultation with a clinical psychologist or psychiatrist were identified between 2011 and 2019 in 7,318 oncological patients in a large cancer treatment centre. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting.

RESULTS

Patients were least likely to receive psycho-oncological (i.e., psychiatric/psychotherapeutic) treatment when they were not formally screened for distress, had inpatient treatment for less than 28 days, had no psychiatric diagnosis, were aged 65 or older, had skin cancer or were not being discussed in a tumour board. The final validated model was optimised to maximise sensitivity at 85.9% and achieved an area under the curve (AUC) of 0.75, a balanced accuracy of 68.5% and specificity of 51.2%.

CONCLUSION

Beyond conventional screening tools, results might contribute to identify patients at risk to be neglected in terms of referral to psycho-oncology within routine oncological care.

摘要

目的

在常规肿瘤治疗环境中,30%至 50%的患者存在心理困扰,包括精神障碍,但这些问题往往被忽视。由于工作量大且需要不断优化时间和成本,因此需要一种快速简便的方法来识别可能错过心理支持的患者。

方法

使用机器学习,在 2011 年至 2019 年期间,在一家大型癌症治疗中心的 7318 名肿瘤患者中,确定了与未咨询临床心理学家或精神科医生相关的因素。根据统计相关性对参数进行分层排序。采用嵌套重采样和交叉验证来避免过度拟合。

结果

当患者未进行痛苦筛查、住院治疗时间少于 28 天、无精神科诊断、年龄在 65 岁及以上、患有皮肤癌或未在肿瘤委员会上讨论时,他们接受心理肿瘤学(即精神科/心理治疗)治疗的可能性最小。最终验证模型被优化为最大程度地提高 85.9%的敏感性,曲线下面积(AUC)为 0.75,平衡准确性为 68.5%,特异性为 51.2%。

结论

除了常规筛查工具外,这些结果可能有助于识别在常规肿瘤护理中被忽视的有风险的患者,以向心理肿瘤学转诊。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/5557936fae9e/ECC-31-0-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/e4eb8cea073f/ECC-31-0-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/20fe20688f01/ECC-31-0-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/5557936fae9e/ECC-31-0-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/e4eb8cea073f/ECC-31-0-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/20fe20688f01/ECC-31-0-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27a/9286797/5557936fae9e/ECC-31-0-g002.jpg

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