Can outcome prediction data change patient outcomes and organizational outcomes?

Rothen, Hans Ulrich; Takala, Jukka

doi:10.1097/mcc.0b013e32830864e9

Cited by 15 publications

(18 citation statements)

References 69 publications

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“…A second model tries to guide ICU patient discharge by discerning risk factors associated with ICU readmission or death after ICU discharge [28]. However, we are left with the question, can predictive models be accurate and flexible enough to be used to direct ICU activity for individual patients and provide economic benefits [29]?…”

Section: Rationing Icu Carementioning

confidence: 99%

Can the costs of critical care be controlled?

Halpern

2009

Current Opinion in Critical Care

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Section: Rationing Icu Carementioning

confidence: 99%

Can the costs of critical care be controlled?

Halpern

2009

Current Opinion in Critical Care

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“…Severity of illness scoring systems is paramount for the evaluation of quality of care of critically ill and trauma patients [1-3]. The purpose of the present study was to evaluate the usefulness of the Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II) and Sequential Organ Failure Assessment (SOFA) scores in predicting long-term outcome of patients with moderate-to-severe traumatic brain injury (TBI).…”

Section: Introductionmentioning

confidence: 99%

Predicting 6-month mortality of patients with traumatic brain injury: usefulness of common severity scores

et al. 2014

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“…19,23 Comparing the observed outcome with the expected outcome has been shown to be a feasible method for improving quality of trauma and intensive care. 22,[136][137][138][139] For example, the Trauma Audit & Research Network (TARN) in the UK annually presents casemix adjusted survival rates (oft en referred to as SMR for Standardized Mortality Rate) from their participating hospitals publicly at https://www.tarn.ac.uk/. Public comparisons of adjusted survival rates are, however, not without problems and it is important to know the limitation of such comparisons.…”

Section: Quality Auditsmentioning

confidence: 99%

Prognostic models in traumatic brain injury

Raj

2015

Acta Anaesthesiol Scand

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Background Prognostic models are important tools for heterogeneity adjustment in traumatic brain injury (TBI). Prognoses after TBI have been particularly challenging to predict, with limited availability of robust prognostic models. TBI patients are by definition trauma patients, and often treated in the intensive care unit (ICU). Several prognostic models for ICU and trauma patients have been developed, although their applicability in patients with TBI is uncertain. Recently, however, some new prognostic models specifically designed for patients with TBI were introduced. Still, the optimal type of prognostic model in TBI remains unknown. Aim This aims to investigate the applicability of different types of prognostic models in patients with TBI and to develop novel models with enhanced performance to previous models, focusing on long‐ term outcome prediction. Methods Four patient databases of patients with TBI treated in the ICU were used to validate three TBI specific models, two computerized tomography (CT) scoring systems, one trauma scoring system, and three intensive care scoring systems. Models were validated by assessing their discrimination using area under the curve (AUC), calibration, and explanatory variation. Logistic regression was used for model customization and development. Models were internally validated using a resample bootstrap technique or a split‐sample technique. Primary outcome was 6‐month mortality and unfavorable neurological outcome by the Glasgow Outcome Scale. 30‐day in‐hospital mortality was used for the trauma scoring system. Results Study populations ranged from 342 to 9915 patients. The TBI models showed the best performance with AUCs between 0.80 and 0.85, followed by the intensive care scoring systems and the CT scores with AUCs between 0.68 to 0.80 and 0.63 to 0.70, respectively. Most models showed poor calibration, although good calibration was achieved following customization. The trauma scoring system exhibited modest to good discrimination (AUC 0.76–0.89) for short‐term mortality prediction but poor calibration. Several new prognostic models, with statistically significant superior performance to previous models were created, among them a combined TBI‐ICU model (‘IMPACT‐APACHE’) and a novel CT scoring system (‘The Helsinki CT score’). Using a TBI‐specific model, based on admission characteristics, up to 40% of the patient's final long‐term outcome could be predicted. Conclusion The TBI models showed superior predictive performance to the intensive care and trauma scoring systems, showing that TBI patients are a highly specific population in the trauma and ICU setting. Thus, the use of a TBI‐specific model is advocated in the setting of TBI. The newly proposed models were found to be significant improvements over previous models, but require external validation to show generalizability.

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Can outcome prediction data change patient outcomes and organizational outcomes?

Abstract: Outcome assessment models provide an important framework for benchmarking. They may help the individual ICU to spot appropriate fields of action, plan and initiate quality improvement projects, and monitor the consequences of such activity.

Cited by 15 publications

References 69 publications

Can the costs of critical care be controlled?

Can the costs of critical care be controlled?

Predicting 6-month mortality of patients with traumatic brain injury: usefulness of common severity scores

Prognostic models in traumatic brain injury

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