Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19.

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706.

Incidence of COVID-19 in outpatients with cancer receiving active treatment in the context of a pandemic: An Andalusian cohort study.

BACKGROUND: Leading scientific societies have recommended delaying and/or suspending active cancer treatment during the COVID-19 pandemic. Nevertheless, data on this novel infection in patients with a diagnosis of cancer receiving active treatment are scarce and it is unknown if these recommendations could have repercussions on future progress of the disease. The main objective of this study is to learn the COVID-19 incidence rate in outpatients with cancer receiving active treatment. METHODS: This work is a retrospective cohort study that included all patients with a diagnosis of cancer who received active cancer treatment in two Andalusian hospitals between February 26 and May 13, 2020. Variables regarding the patient, tumor, and development of COVID-19 were collected. A descriptive analysis was performed and the cumulative incidence of COVID-19 in these patients was evaluated. RESULTS: A total of 673 patients were included. The median age was 62 years. There was a low rate of comorbidity and 12.1% had an ECOG >2. Breast cancer was the most common cancer (41%), followed by colorectal and lung cancer. Stage IV cancer was reported in 52.7% of patients. The most common treatment was chemotherapy (53.9%). Treatment was delayed or suspended in 6% of patients. Only three patients developed COVID-19. The cumulative incidence was 0.44% and one person died due to infection. CONCLUSIONS: In the present retrospective cohort study we found a low incidence of COVID-19 infection in patients with cancer receiving active treatment in an outpatient setting. The sociodemographic factors of Andalusia may explain why these results differ from those presented by other colleagues in Spain, but raise questions about whether universal recommendations may put the benefits of antineoplastic therapy at risk.