Lundon Dara J, Kelly Brian D, Shukla Devki, Bolton Damien M, Wiklund Peter, Tewari Ash
Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY 10029, USA.
Department of Urology, Austin Health, Melbourne, VIC 3084, Australia.
J Clin Med. 2020 Aug 30;9(9):2799. doi: 10.3390/jcm9092799.
Treatment decisions for both early and advanced genitourinary (GU) malignancies take into account the risk of dying from the malignancy as well as the risk of death due to other causes such as other co-morbidities. COVID-19 is a new additional and immediate risk to a patient's morbidity and mortality and there is a need for an accurate assessment as to the potential impact on of this syndrome on GU cancer patients. The aim of this work was to develop a risk tool to identify GU cancer patients at risk of diagnosis, hospitalization, intubation, and mortality from COVID-19. A retrospective case showed a series of GU cancer patients screened for COVID-19 across the Mount Sinai Health System (MSHS). Four hundred eighty-four had a GU malignancy and 149 tested positive for SARS-CoV-2. Demographic and clinical variables of >38,000 patients were available in the institutional database and were utilized to develop decision aides to predict a positive SARS-CoV-2 test, as well as COVID-19-related hospitalization, intubation, and death. A risk tool was developed using a combination of machine learning methods and utilized BMI, temperature, heart rate, respiratory rate, blood pressure, and oxygen saturation. The risk tool for predicting a diagnosis of SARS-CoV-2 had an AUC of 0.83, predicting hospitalization for management of COVID-19 had an AUC of 0.95, predicting patients requiring intubation had an AUC of 0.97, and for predicting COVID-19-related death, the risk tool had an AUC of 0.79. The models had an acceptable calibration and provided a superior net benefit over other common strategies across the entire range of threshold probabilities.
早期和晚期泌尿生殖系统（GU）恶性肿瘤的治疗决策需要考虑死于恶性肿瘤的风险以及因其他原因（如其他合并症）导致的死亡风险。新型冠状病毒肺炎（COVID-19）是对患者发病率和死亡率的又一个新的直接风险，因此有必要准确评估这种综合征对GU癌症患者的潜在影响。这项工作的目的是开发一种风险工具，以识别有感染COVID-19、诊断、住院、插管和死亡风险的GU癌症患者。一项回顾性病例研究展示了一系列在西奈山医疗系统（MSHS）接受COVID-19筛查的GU癌症患者。484例患有GU恶性肿瘤，149例SARS-CoV-2检测呈阳性。机构数据库中提供了超过38000名患者的人口统计学和临床变量，并用于开发决策辅助工具，以预测SARS-CoV-2检测呈阳性以及与COVID-19相关的住院、插管和死亡情况。利用机器学习方法组合开发了一种风险工具，并使用了体重指数、体温、心率、呼吸频率、血压和血氧饱和度。预测SARS-CoV-2诊断的风险工具的曲线下面积（AUC）为0.83，预测COVID-19管理住院的AUC为0.95，预测需要插管的患者的AUC为0.97，预测与COVID-19相关死亡的风险工具的AUC为0.79。这些模型具有可接受的校准度，并且在整个阈值概率范围内提供了优于其他常见策略的净效益。
