Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study

被引:4
作者
Reuveni, Miri Mizrahi [1 ]
Kertes, Jennifer [1 ]
Ben David, Shirley Shapiro [1 ,2 ]
Shahar, Arnon [1 ]
Shamir-Stein, Naama [1 ]
Rosen, Keren [1 ]
Liran, Ori [1 ]
Bar-Yishay, Mattan [1 ]
Adler, Limor [1 ,2 ]
机构
[1] Maccabi Healthcare Serv, Hlth Div, IL-6812509 Tel Aviv, Israel
[2] Tel Aviv Univ, Sackler Fac Med, Dept Family Med, IL-6997801 Tel Aviv, Israel
关键词
COVID-19; risk stratification model; mortality; hospitalization; ISRAEL;
D O I
10.3390/biomedicines11030767
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: This is a retrospective cohort study that was conducted in Israel's second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model. Results: A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8-115.9), immunosuppression (OR-4.8, 95% CI 3.4-6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53-129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4-0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2-0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%. Conclusions: This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant.
引用
收藏
页数:9
相关论文
共 18 条
[1]  
[Anonymous], UND RISK CDC
[2]   Developing a COVID-19 mortality risk prediction model when individual-level data are not available [J].
Barda, Noam ;
Riesel, Dan ;
Akriv, Amichay ;
Levy, Joseph ;
Finkel, Uriah ;
Yona, Gal ;
Greenfeld, Daniel ;
Sheiba, Shimon ;
Somer, Jonathan ;
Bachmat, Eitan ;
Rothblum, Guy N. ;
Shalit, Uri ;
Netzer, Doron ;
Balicer, Ran ;
Dagan, Noa .
NATURE COMMUNICATIONS, 2020, 11 (01)
[3]   Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19 [J].
Chu, Kelly ;
Alharahsheh, Batool ;
Garg, Naveen ;
Guha, Payal .
BMJ HEALTH & CARE INFORMATICS, 2021, 28 (01)
[4]   Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study [J].
Corrao, Giovanni ;
Rea, Federico ;
Carle, Flavia ;
Scondotto, Salvatore ;
Allotta, Alessandra ;
Lepore, Vito ;
D'Ettorre, Antonio ;
Tanzarella, Cinzia ;
Vittori, Patrizia ;
Abena, Sabrina ;
Iommi, Marica ;
Spazzafumo, Liana ;
Ercolanoni, Michele ;
Blaco, Roberto ;
Carbone, Simona ;
Giordani, Cristina ;
Manfellotto, Dario ;
Galli, Massimo ;
Mancia, Giuseppe .
BMJ OPEN, 2021, 11 (11)
[5]   Winter of Omicron-The Evolving COVID-19 Pandemic [J].
del Rio, Carlos ;
Omer, Saad B. ;
Malani, Preeti N. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2022, 327 (04) :319-320
[6]   The mystery of COVID-19 reinfections: A global systematic review and meta-analysis [J].
Dhillon, Rubaid Azhar ;
Qamar, Mohammad Aadil ;
Gilani, Jaleed Ahmed ;
Irfan, Omar ;
Waqar, Usama ;
Sajid, Mir Ibrahim ;
Mahmood, Syed Faisal .
ANNALS OF MEDICINE AND SURGERY, 2021, 72
[7]   A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination [J].
Experton, Bettina ;
Tetteh, Hassan A. ;
Lurie, Nicole ;
Walker, Peter ;
Elena, Adrien ;
Hein, Christopher S. ;
Schwendiman, Blake ;
Vincent, Justin L. ;
Burrow, Christopher R. .
BIOLOGY-BASEL, 2021, 10 (11)
[8]   Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study [J].
Hippisley-Cox, Julia ;
Coupland, Carol A. C. ;
Mehta, Nisha ;
Keogh, Ruth H. ;
Diaz-Ordaz, Karla ;
Khunti, Kamlesh ;
Lyons, Ronan A. ;
Kee, Frank ;
Sheikh, Aziz ;
Rahman, Shamim ;
Valabhji, Jonathan ;
Harrison, Ewen M. ;
Sellen, Peter ;
Haq, Nazmus ;
Semple, Malcolm G. ;
Johnson, Peter W. M. ;
Hayward, Andrew ;
Nguyen-Van-Tam, Jonathan S. .
BMJ-BRITISH MEDICAL JOURNAL, 2021, 374
[9]   Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization [J].
Ioannou, George N. ;
Green, Pamela ;
Fan, Vincent S. ;
Dominitz, Jason A. ;
O'Hare, Ann M. ;
Backus, Lisa I. ;
Locke, Emily ;
Eastment, McKenna C. ;
Osborne, Thomas F. ;
Ioannou, Nikolas G. ;
Berry, Kristin .
JAMA NETWORK OPEN, 2021, 4 (04) :E214347
[10]   A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received [J].
Israel, Ariel ;
Schaffer, Alejandro A. ;
Merzon, Eugene ;
Green, Ilan ;
Magen, Eli ;
Golan-Cohen, Avivit ;
Vinker, Shlomo ;
Ruppin, Eytan .
MICROORGANISMS, 2022, 10 (06)