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ABSTRACT Background Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. The ability of risk prediction tools to correctly identify those at high risk of PI (prognostic accuracy) and to have a clinically significant impact on patient management and outcomes (effectiveness) is not clear. We aimed to evaluate the prognostic accuracy and clinical effectiveness of risk prediction tools for PI, and to identify gaps in the literature. Methods and Findings The umbrella review was conducted according to Cochrane guidance. MEDLINE, Embase, CINAHL, EPISTEMONIKOS, Google Scholar and reference lists were searched to identify relevant systematic reviews. Methodological quality was assessed using adapted AMSTAR-2 criteria. Results were described narratively. We identified 19 reviews that assessed prognostic accuracy and 11 that assessed clinical effectiveness of risk prediction tools for PI. The 19 reviews of prognostic accuracy evaluated 70 tools (39 scales and 31 machine learning models), with the Braden, Norton, Waterlow, Cubbin-Jackson scales (and modifications thereof) the most evaluated tools. Meta-analyses from a focused set of included reviews showed that the scales had sensitivities and specificities ranging from 53%-97% and 46%-84%, respectively. Only 2/19 reviews performed appropriate statistical synthesis and quality assessment. Two reviews assessing machine learning based algorithms reported high prognostic accuracy estimates, but some of which were sourced from the same data within which the models were developed, leading to potentially overoptimistic results. Two randomised trials assessing the effect of PI risk assessment tools (within the full test-intervention-outcome pathway) on the incidence of PIs were identified from the 11 systematic reviews of clinical effectiveness; both were included in a Cochrane review and assessed as high risk of bias. Both trials found no evidence of an effect on PI incidence. Conclusions Available systematic reviews suggest a lack of high-quality evidence for the accuracy of risk prediction tools for PI and limited reliable evidence for their use leading to a reduction in incidence of PI. Further research is needed to establish the clinical effectiveness of appropriately developed and validated risk prediction tools for PI. Author Summary Why was this study done? Pressure injuries (PIs) are injuries to and below the skin caused by prolonged pressure, especially on bony areas, and people who spend extensive periods in a bed or chair are particularly vulnerable. The majority of pressure injuries are preventable if appropriate preventive measures are put into place, but it is crucial to conduct risk stratification of individuals in order to appropriately allocate preventive measures. Numerous tools that give patients a score (or probability) to signify their risk of developing a PI exist. However, there is a lack of clarity on how accurate the risk scores are, and how effective the scores are at improving patient outcomes (the clinical effectiveness) when patient management is subsequently changed for patients classified as high-risk. What did the researchers do and find? We conducted an umbrella review (an overview of existing systematic reviews), identifying 26 systematic reviews which included 70 risk prediction tools. Of these 70 risk prediction tools, 31 were developed using machine learning methods, while the remainder were derived from statistical modelling and/or clinical expertise. Risk prediction tools demonstrated moderate to high accuracy, as measured by a variety of metrics. However, there were concerns regarding the quality of both the systematic reviews, and the primary studies included in these reviews, as reported by the systematic review authors. There were only two randomised controlled trials that investigated the clinical effectiveness of risk prediction tools and subsequent changes in PI management, and neither trial found that use of the tools had an impact on the incidence of PIs. What do these findings mean? Whilst an abundance of risk prediction tools exists, it is unclear how accurate they are due to poor quality evidence and poor reporting, so it is difficult to recommend a particular tool/tools. Even if the tools are shown to be accurate, they are not useful unless they lead to improvement in patient outcomes. There is very limited evidence to determine whether the tools are clinically effective and the evidence that does exist suggests that the tools did not lead to improved patient outcomes. More research into the clinical effectiveness of appropriately developed and evaluated tools, when they are adopted within the clinical pathway, is needed.
Hillier et al. (Wed,) studied this question.