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Purpose We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models. Materials and Methods Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed. Results Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI): 0.614–0.934] and 0.728 (95% CI: 0.559–0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI: 0.20–1.22) after hospitalization and by 0.85 points (95% CI: 0.36–1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care ( p =0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI: 0.48–3.14) vs. -0.28 (95% CI: 1.00–0.43), p =0.007]. Conclusion Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.

Prediction of successful weaning from mechanical ventilation in advance to intubation can facilitate discussions regarding end-of-life care before unnecessary intubation. In this context, we aimed to develop a machine-learning-based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation. We used the Medical Information Mart for Intensive Care-IV database, including adult patients who underwent mechanical ventilation in intensive care at the Beth Israel Deaconess Medical Center, USA. Clinical and laboratory variables collected before or within 24 hours of intubation were used to develop machine-learning models that predict the probability of successful weaning within 14 days of ventilator support. Of 23,242 patients, 19,025 (81.9%) patients were successfully weaned from mechanical ventilation within 14 days. We selected 46 clinical and laboratory variables to create machine-learning models. The machine-learning-based ensemble voting classifier revealed the area under the receiver operating characteristic curve of 0.863 (95% confidence interval 0.855–0.870), which was significantly better than that of Sequential Organ Failure Assessment (0.588 [0.566–0.609]) and Simplified Acute Physiology Score II (0.749 [0.742–0.756]). The top features included lactate, anion gap, and prothrombin time. The model’s performance achieved a plateau with approximately the top 21 variables. We developed machine learning algorithms that can predict successful weaning from mechanical ventilation in advance to intubation in the intensive care unit. Our models can aid in appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.

COVID-19 has officially been declared a pandemic. The spread of the virus is placing extraordinary and sustained demands on public health systems. There is speculation that the differences in mortality rates between regions are a result of an abundance and availability of medical resources. The selection of patients for diagnosis and treatment is essential in a situation where medical resources are scarce. Military personnel are especially at risk of such an infectious disease, and patient selection with an evidence-based prognostic model is critical.

BACKGROUND Patients around the world do not have clear guidelines on the steps to be taken if COVID-19 infection is suspected. Many countries rely on self-assessment via the phone, but this only adds to the burden on the already overwhelmed healthcare system. In this study, we develop an algorithm that will help with the screening and provide patients with guidance. OBJECTIVE The aim of this study is to make decision making easier for the general public by developing a mobile application that will enable them to decide when to seek timely medical care. METHODS The algorithm was developed by consulting six physicians who are directly involved in the process of screening, diagnosis, and/or treatment of COVID-19 patients. The main focus in developing the algorithm was when to test the patient, under the limitation of laboratory capacity. The application was deployed on the web and designed to be mobile-friendly. Google Analytics was embedded and to collect usage data from March 1, 2020, to March 27, and the data were correlated with COVID-19 confirmed cases, screened cases, and death counts by the access location. RESULTS Epidemiological factors, fever, and symptoms were used in the algorithm. The application is deployed on the web, https://docl.org/ncov/. Total of 96,972 users assessed the application 128,673 times during the study period. Without any advertisement, almost half of the access was from outside of Korea. The number of people confirmed was highly correlated to the number of users (rs = 0.82, p-value < 0.0001). And the number of people deceased due to COVID19 was moderately correlated to the number of users. (rs = 0.77, p-value 0.0001). Even though the digital literacy of the 60s were half of that of the 50s, the user count was similar in our application. CONCLUSIONS Expert opinion-based algorithm and mobile application for patient screening and guidance can be beneficial in a circumstance where there is not enough information yet on the novel disease, and medical resource allocation is crucial.