Lundon Dara J, Kelly Brian D, Nair Sujit, Bolton Damien M, Patel Gopi, Reich David, Tewari Ashutosh
Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States.
Department of Urology, Austin Health, Melbourne, VIC, Australia.
Front Med (Lausanne). 2021 Apr 29;8:563465. doi: 10.3389/fmed.2021.563465. eCollection 2021.
Detecting and isolating cases of COVID-19 are amongst the key elements listed by the WHO to reduce transmission. This approach has been reported to reduce those symptomatic with COVID-19 in the population by over 90%. Testing is part of a strategy that will save lives. Testing everyone maybe ideal, but it is not practical. A risk tool based on patient demographics and clinical parameters has the potential to help identify patients most likely to test negative for SARS-CoV-2. If effective it could be used to aide clinical decision making and reduce the testing burden. At the time of this analysis, a total of 9,516 patients with symptoms suggestive of Covid-19, were assessed and tested at Mount Sinai Institutions in New York. Patient demographics, clinical parameters and test results were collected. A robust prediction pipeline was used to develop a risk tool to predict the likelihood of a positive test for Covid-19. The risk tool was analyzed in a holdout dataset from the cohort and its discriminative ability, calibration and net benefit assessed. Over 48% of those tested in this cohort, had a positive result. The derived model had an AUC of 0.77, provided reliable risk prediction, and demonstrated a superior net benefit than a strategy of testing everybody. When a risk cut-off of 70% was applied, the model had a negative predictive value of 96%. Such a tool could be used to help aide but not replace clinical decision making and conserve vital resources needed to effectively tackle this pandemic.
检测和隔离新冠病毒病例是世界卫生组织列出的减少传播的关键要素之一。据报道，这种方法可使人群中出现新冠病毒症状的人数减少90%以上。检测是一项拯救生命的战略的一部分。对每个人进行检测可能是理想的，但并不实际。一种基于患者人口统计学和临床参数的风险工具有可能帮助识别最有可能新冠病毒检测呈阴性的患者。如果有效，它可用于辅助临床决策并减轻检测负担。在进行这项分析时，纽约西奈山医院共评估并检测了9516名有新冠病毒症状的患者。收集了患者的人口统计学、临床参数和检测结果。使用强大的预测流程开发了一种风险工具，以预测新冠病毒检测呈阳性的可能性。在该队列的一个验证数据集中对该风险工具进行了分析，并评估了其区分能力、校准和净效益。该队列中超过48%的检测者结果呈阳性。所推导的模型曲线下面积为0.77，提供了可靠的风险预测，并且显示出比检测每个人的策略更高的净效益。当应用70%的风险临界值时，该模型的阴性预测值为96%。这样一种工具可用于辅助但不能取代临床决策，并节省有效应对这一疫情所需的重要资源。
