Machine learning prediction of COVID-19 mortality in cancer patients
Abstract only 1558 Background: COVID-19 is a challenge for clinical decision-making in cancer patients and the allocation of healthcare resources. An accurate prognosis prediction to effectively triage patients is needed, especially in the community oncology practice. Methods:Nationwide cohort from...
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| Published in: | Journal of clinical oncology Vol. 39; no. 15_suppl; p. 1558 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
20-05-2021
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| Online Access: | Get full text |
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| Summary: | Abstract only 1558 Background: COVID-19 is a challenge for clinical decision-making in cancer patients and the allocation of healthcare resources. An accurate prognosis prediction to effectively triage patients is needed, especially in the community oncology practice. Methods:Nationwide cohort from Oncoclínicas Brazil was used to validate previously developed multivariable logistic regression (mLR) model (Ferrari et al, JCO GO 2021) and to construct a machine learning Random Forest (RF) algorithm as predictor of 30-day mortality after SARS-CoV-2 detection by RT-PCR in cancer patients diagnosed in an outpatient setting. To find the most important baseline clinical determinants of early COVID-19-related death via Gini index, a RF with 100,000 trees was trained in 75% of the dataset, and the performance was assessed in the remaining 25%. We then compared the accuracy of different models in terms of sensitivity, specificity and area under the receiver operating characteristics curves (AUC). Results:From March to December 2020, 533 patients with COVID-19 were prospectively registered in the database. Median age was 60 years (19-93) and 67% were female. Most frequent cancers were breast in 34%, hematological in 16%, and gastrointestinal in 15%. Comorbidities were common (52%), as was current/former smoking history (17%). Most patients were on active systemic therapy or radiotherapy (84%) in the advanced or metastatic disease setting (55%). The overall mortality rate was 15% (CI95% 12%-18%). We validated the original mLR model trained in the first 198 patients: management in a non-curative setting (odds ratio [OR] 3.7), age ≥ 60 years (OR 2.3), and current/former smoking (OR 1.9) were significant predictors of death in the expanded cohort. Presence of comorbidities (OR 1.9) also defined poor outcome in the updated mLR model, which yielded low sensitivity (74%), specificity (68%) and AUC (0.78). With RF modeling, the most significant predictors of 30-day death after COVID-19 (in decreasing order) were older age, treatment of advanced or metastatic disease, tumor type (respiratory tract, brain and unknown primary cancers had higher mortality), COVID-related symptom burden at baseline evaluation and treatment regimen (immunotherapy combinations had higher mortality). The RF model demonstrated high sensitivity (89%), specificity (88%) and AUC (0.96). Conclusions:The results highlight the possibility that machine learning algorithms are able to predict early mortality after COVID-19 in cancer patients with high accuracy. The proposed prediction model may be helpful in the prompt identification of high-risk patients based on clinical features alone, without having to wait for the results of additional tests such as laboratory or radiologic studies. It can also help prioritize medical resources and redefine vaccination strategies. A web-based mortality risk calculator will be created for clinical decision support. |
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| ISSN: | 0732-183X 1527-7755 |
| DOI: | 10.1200/JCO.2021.39.15_suppl.1558 |