Title: Developing a new early warning score to more accurately predict which hospital patients are in need of immediate care, using novel dynamic prediction methods
Funder: Department of Health and Social Care

Abstract: Background Evidence suggests that serious events in hospital patients, such as death, cardiac arrest and ICU admission are preceded by clinical deterioration, which is in turn preceded by changes in physiological parameters such as blood pressure, temperature and pulse. Many of these serious events could thus be preventable. A recent review estimated that English hospitals annually suffer 12,000 preventable deaths. Reported causes include poor patient monitoring and failure to recognise signs of deterioration. Hospitals need tools to help staff monitor and identify patients at increased risk of deterioration to prevent unnecessary serious events. In 1997, the Early Warning Score (EWS) was proposed as a solution. Many adaptations have followed. EWSs are simple algorithms allocating points for each physiological vital sign weighted according to the degree of departure from normal' which are then summed to get a total score. EWSs are usually implemented alongside traditional paper-based observation charts. When the score reaches a predefined threshold, an action is triggered, such as increased monitoring frequency or calling more experienced staff. Historically EWSs have been developed ad-hoc and devoid of any conventional statistical methods, and therefore have many associated problems and shortcomings. For example, only the most current signs are typically used in EWSs and previous vital signs are ignored, therefore any trend (in deterioration) is ignored, the correlation between vital signs is not taken into account and existing studies have largely failed to identify whether additional (routinely collected) information could be used in the model. To assess the performance of EWSs, they should be evaluated on independent datasets, but this rarely happens. Electronic EWS systems have recently been developed, and their use is set to increase as a response to a government mandate that the NHS be fully electronic by 2018. As these electronic systems become more widely available there are opportunities for more research: they allow more sophisticated models to be implemented; and allow easy collection of a large number of vital sign observation for data analysis. These electronic systems offer great potential, but without a concerted effort, basic algorithms, developed with the restrictions of paper-based use in mind, will continue to predominate. Thus, there is now a need for robust and novel statistical methods to be investigated and implemented. I will do this using robust and unbiased methods, taking into account the full complexity of the problem. I will use dynamic prediction models which are a novel class of models that account for the multiple observations within a patient to be included in the prediction, thereby making a more efficient use of all available data and allowing any trends in vital signs to be fully captured. Research Plan The aim of this research is to investigate alternative methods for developing EWSs and build (and evaluate) a new model that will more accurately predict which patients are in need of immediate intervention. The objectives are as follows: Conduct a systematic review of current EWSs, and examine the methodology used in their development and validation, as well as how well they perform. Develop and evaluate a basic model comparable to current EWSs, but using recommended statistical methods for prediction modelling. Conduct a systematic review to identify methods that will allow for past as well as current vital signs to inform a patient's risk of deterioration, called dynamic prediction methods. Develop a dynamic prediction model, informed by the results of objective 3, which will include longitudinal patient data to estimate their predicted risk of deterioration. Investigate and apply methods to deal with treatment interventions which confound the relationship between vital signs and patient outcomes.

Lay abstract: Background In hospital there are often signs of a patient becoming sicker several hours before they have a serious event such as the heart stopping (cardiac arrest) or death. Even before this sickness, changes can be seen in a patient's vital sign' measurements such as blood pressure, temperature and pulse. This means that, using these vital signs, it should be possible to predict which patients are getting sicker, and therefore prevent the serious events. In English hospitals 12,000 deaths are reported annually, which could be prevented. Reasons why they were not prevented include poor monitoring of patients and failure to notice changes in their vital signs. Hospitals need tools to help staff identify which patients are getting sicker in order to treat them quickly and prevent serious events. Such a tool was developed about 20 years ago, and there have been several different versions of it since then. These tools are known as Early Warning Scores. Each vital sign measurement such as blood pressure, pulse and temperature is given points depending on how different they are from what would normally be expected. The individual points are added up to get a total score. Early Warning Scores are usually calculated using the observations written on paper-based bedside charts. When the score reaches a certain value the nurse is to take appropriate action, such as more frequent monitoring or calling more experienced staff. There are several problems with the way Early Warning Scores have been developed. Good statistical techniques exist to develop a tool for predicting patient outcomes. However these have not generally been used in the past for the creation of Early Warning Scores, meaning that they do not perform as well as they could. In addition, the current Early Warning Scores do not include factors such as age and disease type (or severity), yet they are intended to be used across all types of patients, meaning that the predicted risks are unlikely to be accurate for all patients. Finally, and importantly, because current Early Warning Scores only take into account the most recent set of patient readings and ignore the previous ones, they will not notice a patient who is getting sicker as soon as they could do. Understandably, there are often errors made when calculating the Early Warning Score using paper based charts, so increasingly tablet computers are used to calculate the scores automatically based on the readings entered by a nurse. The government are encouraging the NHS to make all their paper-based systems electronic by 2018, and therefore we expect Early Warning Scores to be calculated electronically in most hospitals by then. This creates an opportunity for more research. One benefit is that the Early Warning Score could be made more complex, because it does not need to be calculated manually by a nurse any more. Another benefit is that all data entered using electronic systems is kept, and can be used for data analysis to develop better Early Warning Scores. There is a clear need for better Early Warning Scores, using appropriate and innovative methods. The Increased use of electronic systems now makes this possible. I will make make key advances by using appropriate statistical methods to take account of the full complexity of the problem. This will include using patients past readings as well as their current ones to improve identification of at risk patients. The new Early Warning Scores could help clinical staff by minimising false alarms, and better identifying patients most at risk of serious events. They have the potential to save many lives Research Plan The aim of this research is to look into alternative methods for developing Early Waring Scores and create and test a new model that will more accurately predict which patients are in need of immediate care. The project objectives are as follows: Review all current Early Warning Scores, including how they were developed and how well they perform. Create and test a tool similar to current Early Warning Scores, but using recommended statistical techniques. Review methods in the scientific literature that allow patients' risk of deterioration to be predicted incorporating past, as well as current readings. Create and test a new Early Warning Score that can use past as well as current patient readings. Based on the findings of objective three. To investigate methods that will help to better understand the relationship between vital signs and patient outcomes, when the patients receive medication in the meantime.

Research activity codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: GENERIC HEALTH RELEVANCE