All Journals from Richard Conway
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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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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Background: Accurate efficient prognostication in acute medical admissions remains challenging.Methods: We constructed a Vital Sign based Risk Calculator using vital parameters and Major Disease Categories to predict 30-day in-hospital mortality using a multivariable fractional polynomial model.
Results: We evaluated 113,807 admissions in 58,126 patients. The Vital Sign based Risk Calculator predicted 30-day inhospital mortality to increase from 2 points - 3.6% (95%CI 3.4, 3.7) to 12 points - 14.8% (95%CI 14.0, 15.7). AUROC was 0.74 (95%CI 0.72, 0.74). The addition of illness severity and comorbidity data improved AUROC to 0.90 (95%CI 0.89, 0.90).
Conclusion: The Vital Sign based Risk Calculator is limited by its simplicity; inclusion of illness severity and comorbidity data improve prediction.
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Abstract
Background: There is concern that undue ED wait times may result in adverse outcomes.
Methods: We studied 30-day in-hospital mortality (2002-2017) for all medical admissions (106,586 episodes; 54,928 patients) focusing on clinical risk profile
Results: Comparing 2002-09 vs. 2010-17, median ED waits > 6 hours (hr) increased 10h (95% CI: 8,13) to 15h (95% CI: 9,19). 30-day mortality declined 6.2% to 4.9%- (RRR- 20.8%/ NNT- 78). 30-day-mortality by ED wait: - < 4hr 6.6% (95% CI: 6.3%, 6.9%), 4-8hr 4.8% (95% CI: 4.6%, 5.0%), 8-12hr 4.3% (95% CI: 4.1%, 4.5%) or >=12hr 4.2% (95% CI: 3.9%, 4.5%).
Conclusion: Admissions with shorter waits are overrepresented with high clinical acuity. Higher Risk Score patient with extended wait times had worse clinical outcomes.
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Background: Positive blood cultures predict mortality. The prognostic value of blood culture performance itself has not been fully defined.
Methods: We evaluated medical admissions from 2002-2017. We defined blood culture category as 1) no culture 2) negative culture 3) positive culture. We employed a multivariable logistic regression model to evaluate outcomes.
Results: We evaluated 78,568 blood cultures in 106,586 admissions. 30-day in-hospital mortality for no culture was 2.8% (95%CI 2.7, 2.9), culture negative 8.9% (95%CI 8.5, 9.3) and culture positive 16.7% (95%CI 15.5, 17.9). There was significant interaction between blood culture category and illness severity, OR 1.06 (95%CI 1.05, 1.08), and comorbidity, OR 1.09 (95%CI 1.09, 1.10).
Conclusion: Performance and results of blood cultures are independently associated with increased mortality.
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