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16th November 2021
A model which accurately predicts delayed transfer of care (DTOC) has been developed with only eight pieces of data routinely collected from patients upon admission to hospital. This was the finding of a retrospective study by a team from University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, UK. Within a hospital setting it is necessary to ensure not only that patients receive appropriate clinical care but that they can be discharged either back home or to another setting, in a timely manner. A DTOC has become associated with the term ‘bed blocking” and a symbol of inefficiency within the national health service, occurring when a medically fit person is unable to go home or to another clinical setting and thus still occupies a hospital bed. In fact, national data for England shows that in February 2020 there were 155,700 total delayed days, of which 103,000 were in acute care, amounting to 5,370 people delayed per day. Furthermore, a report from the Department of Health in the UK estimated that in 2014-15 the cost due to discharge delay among patients over 65 years of age was £820 million.
With such enormous costs associated with DTOC, the Stoke-on-Trent team, sought to explore whether it was possible to identify the specific risk factors associated with DTOC among those patients admitted to hospital following attendance at an emergency department. They hypothesised that the capacity to predict which patients were more likely to experience a delayed transfer could enable earlier discharge planning.
The team turned to routinely collected data within the hospital including age, gender, ethnicity, national early warning score (NEWS), arrival by ambulance, the Glasgow admission prediction (GAP) score and an index of multiple deprivation (IMD) for their DTOC analysis. Using data on all adult patients admitted through the emergency department between January 2018 and December 2020, the team randomised these patients into a training and a validation dataset. Using the above and other variables, the team created a predictive model that included only statistically significant variables. The final model was assessed using the area under the receiver operating curve (AUC).
There were a total of 132,311 admissions over the 3-year period which were available for the delayed transfer of care analysis. The cohort had an overall mean age of 63 years (52% female) and over 90% were Caucasian. Initially, 10 variables were included in the predictive model, of which eight remained statistically significant: age, gender, ethnicity, GAP score, IMD, NEWS, arrival by ambulance, admitted in the last 12 months. Using all eight variables, the predictive DTOC model achieved a sensitivity of 0.77 (95% CI 0.75 – 0.78) and a specificity of 0.70 (95% CI 0.69 – 0.70) with an overall accuracy of 70%.
In their discussion, the authors noted that for example, patients arriving by ambulance were 13 times more likely to experience a DTOC. From a policy perspective, they suggested that use of the model would enable targeting of potential delayed patients for more proactive support.
They concluded that future studies should examine the potential effect of other factors and which together with machine learning, could improve the accuracy of prediction.
Davy A et al. A predictive model for identifying patients at risk of delayed transfer of care: a retrospective, cross-sectional study of routinely collected data. Int J Qual Health Care 2021
8th November 2021
Levels of three amino acids, glycine, phenylalanine and valine measured upon admission to an ED in patients admitted with dyspnoea are strongly predictive of 90-day mortality. This was the conclusion of a study by a team from the Department of Clinical Sciences, Lund University, Malmo, Sweden. Dyspnoea is a common presentation in an ED with one study of over 3,000 patients, finding that 5.2% of ED presentations, 11.4% of ward admissions and 19.9% of intensive care unit admissions were due to dyspnoea. There are a number of underlying conditions which can cause dyspnoea which presents as either an impaired ventilation or increased ventilatory demand, or some cases, both. Irrespective of the underlying cause, in patients with dyspnoea there is the release of stress hormones and metabolic changes, one of which is the induction of a catabolic state and insulin resistance.
Interestingly, some previous work has shown that the elevation of a combination of three amino acids could be used to successfully predict future diabetes. Based on these observations, the Lund University team hypothesised that the insulin resistance induced by stress in those with acute dyspnoea, would also alter levels of certain amino acids and that these alterations might be of valve in the assessment of dyspnoea severity and possibly even predictive of dyspnoea mortality.
In an effort to examine their hypothesis, the researchers retrospectively analysed patient data for those admitted to an ED with acute dyspnoea between 2013 and 2015. Plasma levels of nine amino acids were measured and Cox proportional hazard models used to explore the relationship between the level of these amino acids and the risk of 90-day mortality, which served as the primary endpoint for the study.
Data were analysed for a total of 663 patients with a mean age of 71.5 years (53.4% female), of whom 61% were admitted to a ward and 20.1% required intensive care treatment. Overall, 12% of patients died during the 90-day follow-up period. Only three amino acids of the original nine measured, demonstrated a significant association with 90-day mortality. These were glycine (hazard ratio, HR = 1.32, 95% CI 1.08 – 1.62, p < 0.001), phenylalanine (HR = 1.53) and valine (HR = 0.61).
Next, the researchers created an amino acid mortality risk score (AMRS) which was divided into quartiles and they found that in quartile 1, the 90-day mortality was 2.4% whereas it increased massively to 26.5% in quartile 4.
Commenting on these findings, the authors suggested that changes in the levels of these three amino acids, measured during presentation at the ED, were able to strongly predict 90-mortality in patients with acute dyspnoea, irrespective of the underlying cause. They concluded that a score using just these three amino acids could be used as a guide in risk assessment and to support decision-making to establish an appropriate level of care for patients presenting to an ED with acute dyspnoea.
Wiklund K et al. Amino acids predict prognosis in patients with acute dyspnea. BMC Emerg Med 2021