Article Figures & Data
Figures
- Fig 1.
Cumulative number of COVID-19 risk prediction tools without and with public involvement, number of global deaths, and iterations of patient and public engagement, January 2020–February 2021.
Tables
Men Alive at 1 year (N=1,882,198) Dead at 1 year (N=21,879) Women Alive at 1 year (N=1,930,850) Dead at 1 year (N=27,085) Overall (N=3,862,012) Age (years) (mean (SD)) 46.92 (15.26) 74.84 (14.27) 49.13 (17.30) 81.52 (13.00) 48.43 (16.68) Age group (%) 30–55 1,349,987 (71.7) 2,250 (10.3) 1,285,788 (66.6) 1,446 (5.3) 2,639,471 (68.3) 56–60 139,682 (7.4) 1,008 (4.6) 137,218 (7.1) 619 (2.3) 278,527 (7.2) 61–65 111,619 (5.9) 1,334 (6.1) 112,800 (5.8) 852 (3.1) 226,605 (5.9) 66–70 89,041 (4.7) 1,807 (8.3) 94,658 (4.9) 1,171 (4.3) 186,677 (4.8) 71–75 74,606 (4.0) 2,600 (11.9) 87,939 (4.6) 1,972 (7.3) 167,117 (4.3) 76–80 59,196 (3.1) 3,697 (16.9) 84,038 (4.4) 3,411 (12.6) 150,342 (3.9) 81–85 33,503 (1.8) 3,801 (17.4) 60,756 (3.1) 4,646 (17.2) 102,706 (2.7) 86+ 24,564 (1.3) 5,382 (24.6) 67,653 (3.5) 12,968 (47.9) 110,567 (2.9) No moderate- or high-risk conditions, n (%) 1,425,196 (75.7) 5,659 (25.9) 1,293,381 (67.0) 7,555 (27.9) 2,731,791 (70.7) Moderate-risk conditions, n (%) 0 1,460,614 (77.6) 7,082 (32.4) 1,349,096 (69.9) 9,390 (34.7) 2,826,182 (73.2) 1 235,951 (12.5) 4,477 (20.5) 373,152 (19.3) 6,028 (22.3) 619,608 (16.0) 2 or more 185,633 (9.9) 10,320 (47.2) 208,602 (10.8) 11,667 (43.1) 416,222 (10.8) High-risk conditions, n (%) 0 1,775,464 (94.3) 14,172 (64.8) 1,774,970 (91.9) 18,381 (67.9) 3,582,987 (92.8) 1 88,343 (4.7) 4,880 (22.3) 130,372 (6.8) 5,889 (21.7) 229,484 (5.9) 2 or more 18,391 (1.0) 2,827 (12.9) 25,508 (1.3) 2,815 (10.4) 49,541 (1.3) - Box 1.
Examples of hypothetical patient-led conversations with clinicians informed by OurRisk.CoV
Scenario 1
66-year-old man. Maths teacher in primary school. Heart failure and type 2 diabetes.OurRisk.CoV: 7.4% (95% CI: 6.6–8.9%) risk of 1-year mortality at baseline This information could be used to guide a discussion about mortality risk at baseline and the role of other markers of severity, eg echocardiography, where data are not available at present. For that, joining up data from the National Heart Failure Registry (www.bsh.org.uk/resources/national-heart-failure-audit/) and the Diabetes Audit (digital.nhs.uk/data-and-information/clinical-audits-and-registries/national-diabetes-audit with other sources of NHS health data would be necessary. OurRiskCoV 7.9%(7–8.7%) projected risk of 1-year mortality during pandemic This could be used to discuss the importance of secondary prevention during the pandemic, while also following government COVID-19 guidance regarding social distancing, wearing masks in public, closed areas (www.gov.uk/coronavirus). Scenario 2
33-year-old female. Has leukaemia and asthma, currently not on treatment. Works as a business executive, being encouraged to go back to the office. She may need chemotherapy in the next year or two, according to specialists.OurRisk.CoV (patient): 3.0% (0.6–5.3%) risk of 1-year mortality at baseline; 3.4% (0.7–6.1%) risk of 1-year mortality during pandemic If put in ‘Chemotherapy’ instead of ‘Cancer (leukaemia)’: 3.8% (2.9–4.6%) risk of 1-year mortality at baseline; 4.1% (3.2–5%) risk of 1-year mortality during pandemic Could use to discuss indirect effects of pandemic and cancer services, eg delayed chemotherapy during the pandemic. What this score does not include are data from specific cancer registries like NCRAS (National Cancer Registration and Analysis): (www.ncin.org.uk/cancer_type_and_topic_specific_work/cancer_type_specific_work/haematological_cancers/) Scenario 3 (Beyond COVID-19)
72-year-old man deciding on whether he should have a coronary bypass surgery after the COVID-19 pandemic, based on a perioperative mortality risk of 1% (EUROSCORE II: www.euroscore.org/calc.html). Stable angina for many years but now has worsening chest pain. Has diabetes.OurRisk.CoV (patient): 6.3% (5.9–6.6%) risk of 1-year mortality at baseline Could use to discuss difference between baseline risk and perioperative risk which is shorter term and calculated differently using other scores and tools. Summary
What is known? Several risk prediction tools have been developed during the pandemic, but the input of, and their value to, patients and public in their development is unknown. What is the question? Have patients and public been involved in existing mortality risk tools, and, can mortality risk information for people with diseases considered ‘high risk’ for COVID-19 be developed with patients? What was found? Our systematic review shows lack of public and patient engagement in COVID-19 risk tools to-date and lack of mortality risk information designed for patients with underlying conditions. Throughout the pandemic, we demonstrate sustained patient and public interest and engagement in developing a risk information tool during and beyond the pandemic. We show feasibility and utility of a single online portal for mortality information for a wide range of conditions, informed by patients and public. What is the implication for practice now? The Montgomery ruling58 places a duty on doctors to provide ‘all material risks’ when consenting patients, but new ways to generate and communicate reliable risk information are required, with wide application beyond COVID-19. There is a role for charities, patient organisations and patients to come together in order to articulate a framework of understanding demands for better risk information across disease silos.
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‘What is the risk to me from COVID-19?’: Public involvement in providing mortality risk information for people with ‘high-risk’ conditions for COVID-19 (OurRisk.CoV)
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