RAIN: Risk Adjustment In Neurocritical care


Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. It has been recommended that patients with a severe TBI should be managed within a neuroscience centre. Currently, many (particularly those who do not require surgery) are neither treated in, nor transferred to one. A combination of geography, bed availability, local variation and clinical assessment of prognosis can often determine the location of critical care for adult patients with TBI.

Risk prediction models are tools which can be used to predict the risk of a specific outcome, such as severe disability. They could be used to assist healthcare professionals to make decisions about a patient’s care.  A number of risk prediction models for TBI exist. The aim of the RAIN Study was to consider available risk prediction models for TBI and to use the best model to evaluate the best location to treat adult patients with a TBI. It also considered whether transfer to a neurosciences centre would be more beneficial and cost-effective for the NHS.


The Risk Adjustment In Neurocritical care (RAIN) Study included prospective admissions following acute TBI to 67 UK adult critical care units during 2009-11. Data were collected on patients during their critical care stay including their characteristics (case-mix) on admission and survival. The patients were followed up six months after their initial TBI.


The study data set contained 3626 admissions. The most common causes of TBI were road traffic accidents (33%), falls (47%) and assault (12%). Patients were generally young (average age 45 years) and the majority (76%) were male.  Information was obtained for 81% of patients at six months. Overall, 61% of patients either did not survive or had severe disability at six months. The existing risk prediction model was fell below the level required to guide individual patient decision-making but are still useful in research.


The results of the study support current recommendations that all patients with a severe TBI would benefit from transfer to a neurosciences centre, regardless of the need for surgery. We recommend further research to improve risk prediction models.



Who led the study?

Professor David Harrison, ICNARC

The study was funded by the National Institute for Health Research – Health Technology Assessment Programme (Project: 07/37/29)


Menon DK, Rowan KM, Harrison DA. ICU Structure and Outcomes Following Traumatic Brain Injury. Crit Care Med 2019; 47(1):e68-e9. http://dx.doi.org/10.1097/ccm.0000000000003386

Grieve R, Sadique Z, Gomes M, Smith M, Lecky FE, Hutchinson PJ, Menon DK, Rowan KM, Harrison DA. An evaluation of the clinical and cost-effectiveness of alternative care locations for critically ill adult patients with acute traumatic brain injury. Br J Neurosurg 2016; 30(4):388-96. http://dx.doi.org/10.3109/02688697.2016.1161166

Kreif N, Grieve R, Diaz I, Harrison D. Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury. Health Econ 2015; 24(9):1213-28. http://dx.doi.org/10.1002/hec.3189

Harrison DA, Griggs KA, Prabhu G, Gomes M, Lecky FE, Hutchinson PJ, Menon DK, Rowan KM. External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom. J Neurotrauma 2015; 32(19):1522-37. http://dx.doi.org/10.1089/neu.2014.3628

Harrison DA, Prabhu G, Grieve R, Harvey SE, Sadique MZ, Gomes M, Griggs KA, Walmsley E, Smith M, Yeoman P, Lecky FE, Hutchinson PJ, Menon DK, Rowan KM. Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study. Health Technol Assess 2013; 17(23):vii-viii, 1-350. http://dx.doi.org/10.3310/hta17230

Menon D, Harrison D. Prognostic modelling in traumatic brain injury. BMJ 2008; 336(7641):397-8. http://dx.doi.org/10.1136/bmj.39461.616991.80