emergency medical admissions

Blood Culture and Troponin Testing in Suspected Bacteraemic Admissions – Example of Risk Stratification Based on Clinical Testing

Aim: To investigate the clinical predictive value of troponin (hscTnT) and blood culture testing.
Methods: We examined all medical admissions from 2011-2020. Prediction of 30-day in-hospital mortality, dependent on blood culture and hscTnT requests/results, was evaluated using multiple variable logistic regression. Length of stay was related to utilization of procedures/services with truncated Poisson regression.
Results: There were 77,566 admissions in 42,325 patients. With both blood cultures and hscTnT requested, 30-day in-hospital mortality increased to 20.9% (95%CI: 19.7, 22.1) vs 8.9% (95%CI: 8.5, 9.4) for blood cultures alone and 2.3% (95%CI: 2.2, 2.4) with neither. Blood culture 3.93 (95%CI: 3.50, 4.42) or hsTnT requests 4.58 (95%CI: 4.10, 5.14) were prognostic.
Conclusion: Blood culture and hscTnT requests and results predict worse outcomes.

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The Relationship between Resource Utilization, Clinical Risk and Hospital Costs for Emergency Medical Admissions

Abstract

There has been little study of the relationship between resource utilisation, clinical risks and hospital costs in acute medicine with the question remaining as to whether current funding models reflect patient acuity. We examined the relationship between resource use for investigations/allied professional and patient episode costs in all emergency medical admissions admitted to our institution during 2008-2013. Univariate estimates were compared with a multivariate model adjusted for major cost predictors. Interestingly, the model adjusted cost estimates changed considerably when compared with univariate analysis. We used both linear and non-linear (quantile regression) methods due to the highly skewed nature of hospital costs.

The data suggested that hospital episode costs were predictable and driven by objective measures of clinical complexity. The use of expensive investigations and healthcare professional time was secondary to the clinical acuity. Thus, cost was heavily weighted towards higher complexity, and lower resource utilisation associated with lower risk patient groups. However, the non-linear nature of the costings would caution against simple predictor models and non-linear techniques such as quantile regression may, as we have demonstrated, prove superior in defining the underlying relationships.

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Predicting outcomes in emergency medical admissions – role of laboratory data and co-morbidity

Abstract

Background: The utility of risk stratification following an emergency medical admission has been debated. We have examined the predictability of outcomes, from a database of all emergency admissions to St James’ Hospital, Dublin, over a six year period (2005-2010).

Methods: Analysis was performed using the hospital in-patient enquiry system, linked to the patient administration system and laboratory data. The utility of a fractional polynomial laboratory only model to predict 30-day in-hospital mortality was determined.

Results: The AUROC for the laboratory parameters to predict a 30 day death was 0.90 (95% CI 0.89, 0.90) in the 2002 – 2010 derivation dataset and was 0.88 (95% CI 0.86, 0.90) in the 2011 validation set. The addition of co-morbidity measures did not improve the model prediction (0.89 : 95% CI 0.88 – 0.89).

Conclusion: A fractional polynomial laboratory only model can reliably predict 30-day hospital mortality following an emergency medical admission, potentially allowing resources to be risk focused and patients to be prioritised.

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