Identification of high-risk COVID-19 patients using machine learning

Quiroz-Juárez, Mario A.; Gómez, Armando Torres; Hoyo-Ulloa, Irma; León‐Montiel, Roberto de J.; U’Ren, Alfred B.

doi:10.1371/journal.pone.0257234

Cited by 50 publications

(73 citation statements)

References 46 publications

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“…Here we describe an innovative healthcare strategy to optimize the management system of COVID-19 patients while sparing resources for the care of patients with non-COVID-19-related conditions. Similar models have previously been proposed ( 18 , 19 ). MMCOs were designed to fill the gap of care delivery to COVID-19 patients with clinical features that are neither too mild to be managed by the GP in a home-based setting nor too severe to require ED admission or hospitalization.…”

Section: Discussionsupporting

confidence: 56%

A Pilot Study of the Efficacy and Economical Sustainability of Acute Coronavirus Disease 2019 Patient Management in an Outpatient Setting

Lorenzo

Montagna

Bossi³

et al. 2022

Front. Med.

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ObjectiveTo report a preliminary experience of outpatient management of patients with Coronavirus disease 2019 (COVID-19) through an innovative approach of healthcare delivery.Patients and MethodsPatients evaluated at the Mild-to-Moderate COVID-19 Outpatient clinics (MMCOs) of San Raffaele University Hospital and Luigi Sacco University Hospital in Milan, Italy, from 1 October 2020 to 31 October 2021 were included. Patients were referred by general practitioners (GPs), Emergency Department (ED) physicians or hospital specialists (HS) in case of moderate COVID-19. A classification and regression tree (CART) model predicting ED referral by MMCO physicians was developed to aid GPs identify those deserving immediate ED admission. Cost-effectiveness analysis was also performed.ResultsA total of 660 patients were included. The majority (70%) was referred by GPs, 21% by the ED and 9% by HS. Patients referred by GPs had more severe disease as assessed by peripheral oxygen saturation (SpO2), ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2), C-reactive protein (CRP) levels and interstitial involvement at lung ultrasound. Among them, 18% were addressed to the ED following MMCO assessment. CART analysis identified three independent predictors, namely home-measured SpO2, age and body mass index (BMI), that robustly divide patients into risk groups of COVID-19 severity. Home-measured SpO2 < 95% and BMI ≥ 33 Kg/m2 defined the high-risk group. The model yielded an accuracy (95% CI) of 83 (77–88)%. Outpatient management of COVID-19 patients allowed the national healthcare system to spare 1,490,422.05 € when compared with inpatient care.ConclusionMild-to-moderate COVID-19 outpatient clinics were effective and sustainable in managing COVID-19 patients and allowed to alleviate pressure on EDs and hospital wards, favoring effort redirection toward non-COVID-19 patients.

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Section: Discussionsupporting

confidence: 56%

A Pilot Study of the Efficacy and Economical Sustainability of Acute Coronavirus Disease 2019 Patient Management in an Outpatient Setting

Lorenzo

Montagna

Bossi³

et al. 2022

Front. Med.

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“…A deep learning model achieved high sensitivity (90%) and high specificity (96%)in the detection of COVID-19 using chest CT with an area under the curve of 0.96 and an average time for each CT scan of 4,51 s [53] . In addition, an AI algorithm can identify patients who developed severe COVID-19 symptoms [55] , [56] . Quiroz-Juarez has presented a study for the early identification of high-risk patients among those exposed to the SARS-COV-2 virus, using a supervised artificial neural network [55] .…”

Section: Role Of the Aimentioning

confidence: 99%

“…In addition, an AI algorithm can identify patients who developed severe COVID-19 symptoms [55] , [56] . Quiroz-Juarez has presented a study for the early identification of high-risk patients among those exposed to the SARS-COV-2 virus, using a supervised artificial neural network [55] . The machine learning models were trained using comorbidities, patient demographic data, as well as recent COVID-19-related medical information.…”

Section: Role Of the Aimentioning

confidence: 99%

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Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Cau

Faa

Nardi

et al. 2022

European Journal of Radiology

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SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

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“…To overcome this situation, clustering or unsupervised machine learning approaches bring an opportunity to extract relevant information by identifying patterns, clusters, or profiles within large volumes of data. Although some machine learning or similar approaches have been implemented to investigate clinical symptoms, risk factors, and other parameters related to COVID-19 (Dixon et al 2021 ; Han et al 2020 ; Sudre et al 2021 ; Tong et al 2020 ; Kim et al 2020 ; Fu et al 2020 ; Alballa and Al-Turaiki 2021 ; Oshinubi et al 2021 , 2022 ; Quiroz-Juárez et al 2021 ; Zoabi et al 2021 ; Li et al 2020 ), to our knowledge, none has been formally reported from Costa Rican cases.…”

Section: Introductionmentioning

confidence: 99%

Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

et al. 2022

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The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00058-x.

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Identification of high-risk COVID-19 patients using machine learning

Cited by 50 publications

References 46 publications

A Pilot Study of the Efficacy and Economical Sustainability of Acute Coronavirus Disease 2019 Patient Management in an Outpatient Setting

A Pilot Study of the Efficacy and Economical Sustainability of Acute Coronavirus Disease 2019 Patient Management in an Outpatient Setting

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

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