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Tytuł pozycji:

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.

Tytuł:
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.
Autorzy:
Vetrugno G; Risk Management Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. .
Laurenti P
Franceschi F
Foti F
D'Ambrosio F
Cicconi M
LA Milia DI
Di Pumpo M
Carini E
Pascucci D
Boccia S
Pastorino R
Damiani G
De-Giorgio F
Oliva A
Nicolotti N
Cambieri A
Ghisellini R
Murri R
Sabatelli G
Musolino M
Gasbarrini A
Corporate Authors:
Gemelli-Against-Covid Group
Źródło:
European review for medical and pharmacological sciences [Eur Rev Med Pharmacol Sci] 2021 Mar; Vol. 25 (6), pp. 2785-2794.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: Rome : Verduci, [1997-
MeSH Terms:
Algorithms*
Decision Trees*
COVID-19/*diagnosis
COVID-19/*therapy
Home Care Services/*statistics & numerical data
Hospitalization/*statistics & numerical data
Aged ; COVID-19/epidemiology ; COVID-19/virology ; COVID-19 Testing ; Cohort Studies ; Decision Making, Computer-Assisted ; Female ; Follow-Up Studies ; Humans ; Italy/epidemiology ; Machine Learning ; Male ; Monitoring, Physiologic ; Prognosis ; Retrospective Studies ; SARS-CoV-2/isolation & purification
Contributed Indexing:
Investigator: V Abbate; N Acampora; G Addolorato; F Agostini; ME Ainora; K Akacha; E Amato; F Andreani; G Andriollo; MG Annetta; BE Annicchiarico; M Antonelli; G Antonucci; GM Anzellotti; A Armuzzi; F Baldi; I Barattucci; C Barillaro; F Barone; RDA Bellantone; A Bellieni; G Bello; A Benicchi; F Benvenuto; L Berardini; F Berloco; R Bernabei; A Bianchi; DG Biasucci; LM Biasucci; S Bibbò; A Bini; A Bisanti; F Biscetti; MG Bocci; N Bonadia; F Bongiovanni; A Borghetti; G Bosco; S Bosello; V Bove; G Bramato; V Brandi; T Bruni; C Bruno; D Bruno; MC Bungaro; A Buonomo; L Burzo; A Calabrese; MR Calvello; C Cambise; G Cammà; M Candelli; G Canistro; A Cantanale; G Capalbo; L Capaldi; E Capone; E Capristo; L Carbone; S Cardone; S Carelli; A Carfì; A Carnicelli; C Caruso; FA Casciaro; L Catalano; P Cattani; R Cauda; AL Cecchini; L Cerrito; M Cesarano; A Chiarito; R Cianci; S Cicchinelli; A Ciccullo; M Cicetti; F Ciciarello; A Cingolani; MC Cipriani; ML Consalvo; G Coppola; GM Corbo; A Corsello; F Costante; M Costanzi; M Covino; D Crupi; SL Cutuli; S D'Addio; A D'Alessandro; ME D'Alfonso; E D'Angelo; F D'Aversa; F Damiano; GM De Berardinis; T De Cunzo; DK De Gaetano; G De Luca; G De Matteis; G De Pascale; P De Santis; M De Siena; F De Vito; V Del Gatto; P Del Giacomo; F Del Zompo; AM Dell'Anna; D Della Polla; L Di Gialleonardo; S Di Giambenedetto; R Di Luca; L Di Maurizio; M Di Muro; A Dusina; D Eleuteri; A Esperide; D Fachechi; D Faliero; C Falsiroli; M Fantoni; A Fedele; D Feliciani; C Ferrante; G Ferrone; R Festa; MC Fiore; A Flex; E Forte; A Francesconi; L Franza; B Funaro; M Fuorlo; D Fusco; M Gabrielli; E Gaetani; C Galletta; A Gallo; G Gambassi; M Garcovich; I Gasparrini; S Gelli; A Giampietro; L Gigante; G Giuliano; G Giuliano; B Giupponi; E Gremese; DL Grieco; M Guerrera; V Guglielmi; C Guidone; A Gullì; A Iaconelli; A Iafrati; G Ianiro; A Iaquinta; M Impagnatiello; R Inchingolo; E Intini; R Iorio; IM Izzi; T Jovanovic; C Kadhim; R La Macchia; F Landi; G Landi; R Landi; R Landolfi; M Leo; PM Leone; L Levantesi; A Liguori; R Liperoti; MM Lizzio; MR Lo Monaco; P Locantore; F Lombardi; G Lombardi; L Lopetuso; V Loria; AR Losito; BPL Mothanje; F Macagno; N Macerola; G Maggi; G Maiuro; F Mancarella; F Mangiola; A Manno; D Marchesini; GM Maresca; G Marrone; I Martis; AM Martone; E Marzetti; C Mattana; MV Matteo; R Maviglia; A Mazzarella; C Memoli; L Miele; A Migneco; I Mignini; A Milani; D Milardi; M Montalto; G Montemurro; F Monti; L Montini; TC Morena; V Mora; C Morretta; D Moschese; CA Murace; M Murdolo; M Napoli; E Nardella; G Natalello; D Natalini; SM Navarra; A Nesci; A Nicoletti; R Nicoletti; TF Nicoletti; R Nicolò; EC Nista; E Nuzzo; M Oggiano; V Ojetti; FC Pagano; G Paiano; C Pais; F Pallavicini; A Palombo; F Paolillo; A Papa; D Papanice; LG Papparella; M Paratore; G Parrinello; G Pasciuto; P Pasculli; G Pecorini; S Perniola; E Pero; L Petricca; M Petrucci; C Picarelli; A Piccioni; A Piccolo; E Piervincenzi; G Pignataro; R Pignataro; G Pintaudi; L Pisapia; M Pizzoferrato; F Pizzolante; R Pola; C Policola; M Pompili; F Pontecorvi; V Pontecorvi; FR Ponziani; V Popolla; E Porceddu; A Porfidia; LM Porro; A Potenza; F Pozzana; G Privitera; D Pugliese; G Pulcini; S Racco; F Raffaelli; V Ramunno; GL Rapaccini; L Richeldi; E Rinninella; S Rocchi; B Romanò; S Romano; F Rosa; L Rossi; R Rossi; E Rossini; E Rota; F Rovedi; C Rubino; G Rumi; A Russo; L Sabia; A Salerno; M Sali; S Salini; L Salvatore; D Samori; C Sandroni; M Sanguinetti; R Santangelo; L Santarelli; P Santini; D Santolamazza; A Santoliquido; F Santopaolo; MC Santoro; F Sardeo; C Sarnari; A Saviano; L Saviano; F Scaldaferri; R Scarascia; T Schepis; F Schiavello; G Scoppettuolo; D Sedda; F Sessa; L Sestito; C Settanni; M Siciliano; V Siciliano; R Sicuranza; B Simeoni; J Simonetti; A Smargiassi; PM Soave; C Sonnino; D Staiti; C Stella; L Stella; E Stival; E Taddei; R Talerico; E Tamburello; E Tamburrini; ES Tanzarella; E Tarascio; C Tarli; A Tersali; P Tilli; J Timpano; E Torelli; F Torrini; M Tosato; A Tosoni; L Tricoli; M Tritto; M Tumbarello; AM Tummolo; MS Vallecoccia; F Valletta; F Varone; F Vassalli; G Ventura; L Verardi; L Vetrone; E Visconti; F Visconti; A Viviani; R Zaccaria; C Zaccone; C Zanza; L Zelano; L Zileri Dal Verme; G Zuccalà
Entry Date(s):
Date Created: 20210408 Date Completed: 20210513 Latest Revision: 20210513
Update Code:
20240104
DOI:
10.26355/eurrev_202103_25440
PMID:
33829463
Czasopismo naukowe
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 G2 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 G2 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.

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