使用意大利管理数据库开发老年癌症实体瘤患者的多发病预后评分

Matteo Franchi, Federico Rea, Claudia Santucci, Carlo La Vecchia, Paolo Boffetta, Giovanni Corrao
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引用次数: 3

摘要

通过使用基于人口的意大利管理数据库,开发并验证癌症多发病率评分(CMS)对老年实体瘤患者死亡率的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Developing a multimorbidity prognostic score in elderly patients with solid cancer using administrative databases from Italy

Developing a multimorbidity prognostic score in elderly patients with solid cancer using administrative databases from Italy

Aims

To develop and to validate a Cancer Multimorbidity Score (CMS) predictive of mortality in elderly patients affected by solid tumor, by using population-based administrative Italian databases.

Methods

Through administrative databases of Lombardy Region (Northern Italy), a cohort of patients aged ≥65 years with a new diagnosis of solid tumor during the period 2009–2014 was identified. Sixty-one conditions and diseases, measured from hospital inpatient diagnosis and outpatient drug prescription within 2 years before cancer diagnosis in a training set randomly including 70% of the cohort patients were tested to predict 5-year mortality using a Cox regression model. Regression coefficients were used for assigning a weight to the predictive conditions, selected by the LASSO method. Weights were summed up in order to produce an aggregate score (the CMS). CMS performance was evaluated on a validation set, including the remaining 30% of the cohort patients, in terms of discrimination and calibration.

Results

The study cohort included 148,242 cancer patients. Thirty conditions were selected as independent predictors of 5-year mortality and were included in the computation of the CMS. The area under the receiving operating characteristics curve was 0.68, becoming 0.71 when considering 1-year mortality as outcome and reaching values of 0.74 and 0.81 when focusing on patients with breast and prostate cancer, respectively. A strong increasing trend in mortality was observed with increasing CMS value.

Conclusions

CMS represents a new useful tool for identifying high-risk elderly cancer patients in everyday clinical practice, as well as for risk adjustment in clinical and epidemiological studies.

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