Risk modelling for quality improvement in the critically ill

Background

When a patient becomes very sick, it is usually an emergency. The patient will not be able to choose where they are treated. We must therefore make sure that all hospitals provide good care. When comparing hospitals, we need to take account of the different patients they treat; if one hospital has more very sick patients, we expect the death rate to be higher. To do this, we use risk prediction models. These models take information about the patient from early in their care and make a prediction of their probable outcome.

Design

We used information about patients who were admitted to an intensive care unit or had a heart attack in hospital, including how sick each patient was (e.g. blood pressure or heart rhythm). We used statistical techniques to fill in missing information and to estimate curves to relate this information to the patients’ outcomes.

Results

We produced new risk prediction models to predict outcomes for patients who were admitted to an intensive care unit or had a heart attack. We showed that the new models work well when they are used to predict the outcomes of different patients.

Conclusion

The new models can be used to compare outcomes for patients who were admitted to an intensive care unit or who had a heart attack in different hospitals.



Who is leading the project?

Professor David Harrison, ICNARC

This project is funded by the National Institute for Health Research (NIHR) – Health Services and Delivery Research (HS&DR) Programme (Project: 09/2000/65)

Publications

Bedford JP, Ferrando-Vivas P, Redfern O, Rajappan K, Harrison DA, Watkinson PJ, Doidge JC. New-onset atrial fibrillation in intensive care: epidemiology and outcomes. Eur Heart J Acute Cardiovasc Care 2022; 11(8):620-8. http://dx.doi.org/10.1093/ehjacc/zuac080

Bedford JP, Ferrando-Vivas P, Redfern O, Rajappan K, Harrison DA, Watkinson PJ, Doidge JC. New-onset atrial fibrillation in intensive care: epidemiology and outcomes. Eur Heart J Acute Cardiovasc Care 2022; 11(8):620-8. http://dx.doi.org/10.1093/ehjacc/zuac080

Bedford JP, Johnson A, Redfern O, Gerry S, Doidge J, Harrison D, Rajappan K, Rowan K, Young JD, Mouncey P, Watkinson PJ. Comparative effectiveness of common treatments for new-onset atrial fibrillation within the ICU: Accounting for physiological status. J Crit Care 2022; 67:149-56. http://dx.doi.org/10.1016/j.jcrc.2021.11.005

Ferrando-Vivas P, Shankar-Hari M, Thomas K, Doidge JC, Caskey FJ, Forni L, Harris S, Ostermann M, Gornik I, Holman N, Lone N, Young B, Jenkins D, Webb S, Nolan JP, Soar J, Rowan KM, Harrison DA. Improving risk prediction model quality in the critically ill: data linkage study. Health Soc Care Deliv Res 2022; 10(39). http://dx.doi.org/10.3310/eqab4594

Shankar-Hari M, Rubenfeld GD, Ferrando-Vivas P, Harrison DA, Rowan K. Development, Validation, and Clinical Utility Assessment of a Prognostic Score for 1-Year Unplanned Rehospitalization or Death of Adult Sepsis Survivors. JAMA Netw Open 2020; 3(9):e2013580. http://dx.doi.org/10.1001/jamanetworkopen.2020.13580

Shankar-Hari M, Harrison DA, Ferrando-Vivas P, Rubenfeld GD, Rowan K. Risk Factors at Index Hospitalization Associated With Longer-term Mortality in Adult Sepsis Survivors. JAMA Netw Open 2019; 2(5):e194900. http://dx.doi.org/10.1001/jamanetworkopen.2019.4900

Ferrando-Vivas P, Jones A, Rowan KM, Harrison DA. Development and validation of the new ICNARC model for prediction of acute hospital mortality in adult critical care. J Crit Care 2017; 38:335-9. http://dx.doi.org/10.1016/j.jcrc.2016.11.031