Emergency Medical Admission

The Problems with Risk Prediction during an Emergency Medical Admission Using Laboratory Data – Evidence from Potassium

Background: The prediction of clinical outcomes using biochemical markers is an important tool.

Methods: We calculated a risk score for all emergency admissions 2002-2017. We related potassium and mortality in a multivariable fractional polynomial model. We investigated the potassium distribution and relationship of potassium to mortality over time.

Results: There were 106,586 admissions in 54,928 patients. Mortality was higher for those with an admission potassium above the median – 6.1% vs 4.6% (p<0.001), OR 1.07 (95%CI: 1.06, 1.09). There was a progressive increase in mortality from the lowest – 8.9% (95%CI: 8.3%, 9.4%) to highest potassium decile – 14.2% (95%CI: 13.5%, 14.8%). The frequency of admission hypokalaemia and the mortality at any given potassium decreased over time. Conclusion: Admission potassium predicts mortality.

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Relationship of the clinical acuity & complexity to the outcome of an emergency medical admission

Abstract

Background: An Illness Severity and Co-morbidity composite score can predict 30-day mortality outcome.

Methods: We computed a summary risk score (RS) for emergency medical admissions and used cluster analysis to define four subsets

Results: Four cluster groups were defined. Cluster 1 – RS 7 points (IQR 5, 8) Cluster 2 - 9 (IQR 8, 11), Cluster 3 - 12 (IQR 11, 13) and Cluster 4 - 14 (IQR 13, 15). Clusters predicted 30-day in hospital mortality OR 1.86 (95%CI: 1.82, 1.92); respective rates 1.4% (95% CI: 1.3%, 1.6%), 3.4% (95% CI: 3.1%, 3.6%), 7.8% (95% CI: 7.5%, 8.1%) and 16.5% (95% CI: 15.7%, 17.2%).

Conclusion: Cluster grouping of Risk Score was age related; strongest outcome determinant was Acute Illness Severity

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Does admission via an Acute Medical Unit influence hospital mortality? 12 years’ experience in a large Dublin Hospital

Abstract

Background: Following an emergency medical admission, patients may be admitted an acute medical assessment unit (AMAU) or directly into a ward. An AMAU provides a structured environment for their initial assessment and treatment.

Methods: All emergency admissions (66,933 episodes in 36,271 patients) to an Irish hospital over an 12-year period (2002-2013) were studied with 30-day in-hospital mortality as the outcome measure. Univariate Odds Ratios, by initial patient allocation, and the fully adjusted Odds Ratios were calculated, using a validated logistic regression model.

Results: Patients, by design, were intended to be admitted initially to the AMAU (<= 5 day stay). Capacity constraints dictated that only 39.8% of patients were so admitted; the remainder bypassed the AMAU to a ward (60.2%). All patients remained under the care of the admitting consultant/team. We computed the risk profile for each group, using a multiple variable validated model of 30-day in-hospital mortality; the model indicated the same risk profile between these groups. The univariate OR of an in-hospital death by day 30 for a patient initially allocated to the AMAU, compared with an initial ward allocation was 0.76 (95% CI: 0.71, 0.82- p<0.001). The fully adjusted risk for patients was 0.67 (95% CI: 0.62, 0.73- p<0.001). Conclusion: Patients, with equivalent mortality risk, allocated initially to AMAU or a more traditional ward, appeared to have substantially different outcomes.

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