Identifying the patient at risk of acute kidney injury: a predictive scoring system for the development of acute kidney injury in acute medical patients.

BACKGROUND: Acute kidney injury (AKI) in hospitalized patients has significant implications in terms of morbidity and mortality, length of hospital stay and associated costs. To date, no interventions are proven to prevent the development of AKI but this is hampered in part by the lack of early recognition of patients at risk. We aimed to determine whether a simple system could be devised from both physiological and demographic data in order to identify individuals at increased risk from the development of inpatient AKI. METHOD: Our observational, population-based single-centred study took place in an 870-bed associated university hospital. All patients admitted to the acute medical admissions unit on the Worthing site of the Western Sussex Hospitals Trust during the study period were included. RESULTS: Multivariate logistic regression analysis demonstrated that age, respiratory rate and disturbed consciousness together with a history of chronic kidney disease, diabetes mellitus, congestive cardiac failure and liver disease were associated with an increased risk of developing AKI within 7 days of admission. We derived a simple scoring system to identify acute medical patients at greater risk of developing AKI. CONCLUSIONS: The incidence of AKI complicating inpatient admissions remains high, however with the application of the derived AKI prediction score it is hoped that early recognition will translate to improved outcomes.

Evaluation of facial electromyographic pain responses in healthy participants.

Aim: Assessing pain perception through self-reports may not be possible in some patients, for example, sedated. Our group considered if facial electromyography (fEMG) could provide a useful alternative, by testing on healthy participants subjected to experimental pain. Materials & methods: Activity of four facial muscles was recorded using fEMG alongside self-reported pain scores and physiological parameters. Results: The pain stimulus elicited significant activity on all facial muscles of interest as well as increases in heart rate. Activity from two of the facial muscles correlated significantly against pain intensity. Conclusion: Pain perception can be assessed through fEMG on healthy participants. We believe that this model would be valuable to clinicians that need to diagnose pain perception in circumstances where verbal reporting is not possible.

Objectively measuring pain using facial expression: is the technology finally ready?

Currently, clinicians observe pain-related behaviors and use patient self-report measures in order to determine pain severity. This paper reviews the evidence when facial expression is used as a measure of pain. We review the literature reporting the relevance of facial expression as a diagnostic measure, which facial movements are indicative of pain, and whether such movements can be reliably used to measure pain. We conclude that although the technology for objective pain measurement is not yet ready for use in clinical settings, the potential benefits to patients in improved pain management, combined with the advances being made in sensor technology and artificial intelligence, provide opportunities for research and innovation.