BACKGROUND: Healthcare systems are under prominent stress due to the COVID-19 pandemic. A fast and simple triage is mandatory to screen patients who will benefit from early hospitalization, from those that can be managed as outpatients. There is a lack of all-comers scores, and no score has been proposed for western-world population. AIMS: To develop a fast-track risk score valid for every COVID-19 patient at diagnosis. METHODS: Single-center, retrospective study based on all the inhabitants of a healthcare area. Logistic regression was used to identify simple and wide-available risk factors for adverse events (death, intensive care admission, invasive mechanical ventilation, bleeding > BARC3, acute renal injury, respiratory insufficiency, myocardial infarction, acute heart failure, pulmonary emboli, or stroke). RESULTS: Of the total healthcare area population, 447.979 inhabitants, 965 patients (0.22%), were diagnosed with COVID-19. A total of 124 patients (12.85%) experienced adverse events. The novel SODA score (based on sex, peripheral O2 saturation, presence of diabetes, and age) demonstrated good accuracy for adverse events prediction (area under ROC curve 0.858, CI: 0.82-0.98). A cut-off value of ≤2 points identifies patients with low risk (positive predictive value [PPV] for absence of events: 98.9%) and a cut-off of ≥5 points, high-risk patients (PPV 58.8% for adverse events). CONCLUSIONS: This quick and easy score allows fast-track triage at the moment of diagnosis for COVID-19 using four simple variables: age, sex, SpO2, and diabetes. SODA score could improve preventive measures taken at diagnosis in high-risk patients and also relieve resources by identifying very low-risk patients.
BACKGROUND: Healthcare systems are under prominent stress due to the COVID-19 pandemic. A fast and simple triage is mandatory to screen patients who will benefit from early hospitalization, from those that can be managed as outpatients. There is a lack of all-comers scores, and no score has been proposed for western-world population. AIMS: To develop a fast-track risk score valid for every COVID-19 patient at diagnosis. METHODS: Single-center, retrospective study based on all the inhabitants of a healthcare area. Logistic regression was used to identify simple and wide-available risk factors for adverse events (death, intensive care admission, invasive mechanical ventilation, bleeding > BARC3, acute renal injury, respiratory insufficiency, myocardial infarction, acute heart failure, pulmonary emboli, or stroke). RESULTS: Of the total healthcare area population, 447.979 inhabitants, 965 patients (0.22%), were diagnosed with COVID-19. A total of 124 patients (12.85%) experienced adverse events. The novel SODA score (based on sex, peripheral O2 saturation, presence of diabetes, and age) demonstrated good accuracy for adverse events prediction (area under ROC curve 0.858, CI: 0.82-0.98). A cut-off value of ≤2 points identifies patients with low risk (positive predictive value [PPV] for absence of events: 98.9%) and a cut-off of ≥5 points, high-risk patients (PPV 58.8% for adverse events). CONCLUSIONS: This quick and easy score allows fast-track triage at the moment of diagnosis for COVID-19 using four simple variables: age, sex, SpO2, and diabetes. SODA score could improve preventive measures taken at diagnosis in high-risk patients and also relieve resources by identifying very low-risk patients.
Authors: A Pieter Kappetein; Stuart J Head; Philippe Généreux; Nicolo Piazza; Nicolas M van Mieghem; Eugene H Blackstone; Thomas G Brott; David J Cohen; Donald E Cutlip; Gerrit-Anne van Es; Rebecca T Hahn; Ajay J Kirtane; Mitchell W Krucoff; Susheel Kodali; Michael J Mack; Roxana Mehran; Josep Rodés-Cabau; Pascal Vranckx; John G Webb; Stephan Windecker; Patrick W Serruys; Martin B Leon Journal: J Am Coll Cardiol Date: 2012-10-09 Impact factor: 24.094
Authors: Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng Journal: N Engl J Med Date: 2020-01-29 Impact factor: 176.079
Authors: Matteo Bassetti; Daniele Roberto Giacobbe; Paolo Bruzzi; Emanuela Barisione; Stefano Centanni; Nadia Castaldo; Silvia Corcione; Francesco Giuseppe De Rosa; Fabiano Di Marco; Andrea Gori; Andrea Gramegna; Guido Granata; Angelo Gratarola; Alberto Enrico Maraolo; Malgorzata Mikulska; Andrea Lombardi; Federico Pea; Nicola Petrosillo; Dejan Radovanovic; Pierachille Santus; Alessio Signori; Emanuela Sozio; Elena Tagliabue; Carlo Tascini; Carlo Vancheri; Antonio Vena; Pierluigi Viale; Francesco Blasi Journal: Infect Dis Ther Date: 2021-07-30
Authors: Ezat Rahimi; Mina Shahisavandi; Albert Cid Royo; Mohammad Azizi; Said El Bouhaddani; Naseh Sigari; Miriam Sturkenboom; Fariba Ahmadizar Journal: Front Microbiol Date: 2022-07-25 Impact factor: 6.064
Authors: Mark A Nyman; Thulasee Jose; Ivana T Croghan; Mark A Parkulo; Charles D Burger; Darrell R Schroeder; Ryan T Hurt; John C O'Horo Journal: J Prim Care Community Health Date: 2022 Jan-Dec