Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study
| Title: | Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study |
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| Authors: | Vetrugno, G; Laurenti, P; Franceschi, F; Boccia, S; Damiani, G; Oliva, A; Cambieri, A; Murri, R; Gasbarrini, A; Miele, L; G. Addolorato; M. E. Ainora; M. G. Annetta; M. Antonelli; R. D. A. Bellantone; F. Berloco; L. M. Biasucci; F. Biscetti; R. Cauda; R. Cianci; A. Cingolani; M. Covino; S. L. Cutuli; M. E. D’Alfonso; F. Damiano; G. De Matteis; G. De Pascale; M. De Siena; F. De Vito; P. Del Giacomo; A. M. Dell’Anna; D. Della Polla; S. Di Giambenedetto; L. Di Maurizio; A. Fedele; D. Feliciani; G. Ferrone; A. Flex; L. Franza; B. Funaro; E. Gaetani; M. Garcovich; S. Gelli; L. Gigante; G. Giuliano; E. Gremese; D. L. Grieco; V. Guglielmi; C. Guidone; G. Ianiro; M. Impagnatiello; R. Inchingolo; E. Intini; R. Iorio; I. M. Izzi; T. Jovanovic; F. Landi; P. M. Leone; R. Liperoti; E. Marzetti; L. Miele; L. Montini; R. Pola; F. R. Ponziani; L. Richeldi |
| Contributors: | Vetrugno, Giuseppe; Laurenti, Patrizia; Franceschi, Francesco; Foti, F; D'Ambrosio, F; Cicconi, M; La Milia, D I; Di Pumpo, M; Carini, E; Pascucci, D; Boccia, Stefania; Pastorino, R; Damiani, Gianfranco; De-Giorgio, F; Oliva, Antonio; Nicolotti, N; Cambieri, Andrea; Ghisellini, R; Murri, Rita; Sabatelli, G; Musolino, M; Gasbarrini, Antonio; Montini, L; Miele, Luca; Group, Gemelli-Against-Covid; Rome; Abbate, Italy V.; Acampora, N.; Addolorato, Giovanni; Agostini, F.; Ainora, Maria Elena; Akacha, K.; Amato, E.; Andreani, F.; An- driollo, G.; Annetta, Maria Giuseppina; Annicchiarico, B. E.; Antonelli, Massimo; Antonucci, G.; Anzellotti, G. M.; Armuzzi, A.; Baldi, F.; Barattucci, I.; Barillaro, C.; Barone, F.; Bellantone, Rocco Domenico Alfonso; Bellieni, A.; Bello, G.; Benicchi, A.; Benvenuto, F.; Berardini, L.; Berloco, Filippo; Bernabei, R.; Bianchi, A.; Biasucci, D. G.; Biasucci, Luigi Marzio; Bibbò, S.; Bini, A.; Bisanti, A.; Biscetti, Federico; Bocci, M. G.; Bonadia, N.; Bongiovanni, F.; Borghetti, A.; Bosco, G.; Bosello, S.; Bove, V.; Bramato, G.; Brandi, V.; Bruni, T.; Bruno, C.; Bruno, D.; Bungaro, M. C.; Buonomo, A.; Burzo, L.; Calabrese, A.; Calvello, M. R.; Cambise, C.; Cammà, G.; Candelli, M.; Canistro, G.; Cantanale, A.; Capalbo, G.; Capaldi, L.; Capone, E.; Ca- pristo, E.; Carbone, L.; Cardone, S.; Carelli, S.; Carfì, A.; Carnicelli, A.; Caruso, C.; Casciaro, F. A.; Catalano, L.; Cat- tani, P.; Cauda, Roberto; Cecchini, A. L.; Cerrito, L.; Cesarano, M.; Chiarito, A. |
| Publisher Information: | VERDUCI PUBLISHER |
| Publication Year: | 2021 |
| Collection: | Università Cattolica del Sacro Cuore: PubliCatt |
| Subject Terms: | COVID-19; SARS-CoV-2; General practitioners; Primary health care; Machine learning; Community-based care; Aged; COVID-19 Testing; Cohort Studies; Decision Making; Computer-Assisted; Female; Follow-Up Studies; Home Care Services; Hospitalization; Humans; Italy; Male; Monitoring; Physiologic; Prognosis; Retrospective Studies; Algorithms; Decision Trees; Settore MED/09 - MEDICINA INTERNA |
| Description: | OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring.PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity.RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G(2) value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G(2) and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated.CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized. |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/33829463; info:eu-repo/semantics/altIdentifier/wos/WOS:000634876000043; volume:25; issue:6; firstpage:2785; lastpage:2794; numberofpages:10; issueyear:2021; journal:EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES; https://hdl.handle.net/10807/215073 |
| DOI: | 10.26355/eurrev_202103_25440 |
| Availability: | https://hdl.handle.net/10807/215073; https://doi.org/10.26355/eurrev_202103_25440 |
| Rights: | info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Accession Number: | edsbas.E76C7595 |
| Database: | BASE |