Association of automated data collection and data completeness with outcomes of intensive care. A new customised model for outcome prediction

Reinikainen, Matti; Mussalo, Petteri; Hovilehto, Seppo; Uusaro, Ari; Varpula, Tero; Kari, A.; Pettilä, Ville

doi:10.1111/j.1399-6576.2012.02669.x

Cited by 58 publications

(62 citation statements)

References 21 publications

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“…The Consortium database collects data from every ICU admission from all general adult ICUs in Finnish hospitals. The information on clinical characteristics, severity of illness, and outcome is validated in each ICU by local physicians [9]. We searched the database by using the ICD-10 codes for epilepsy, SE, and convulsions (G40.X, G41.X, R56.8) as well as the Acute Physiology and Chronic Health Evaluation (APACHE) II [10] diagnostic group "seizure" to identify all patients treated in an ICU for seizure disorders and included the patients that had been treated in the ICU for at least 48 h (minimum duration of treatment for patients treated with general anesthesia in ICUs).…”

Section: Methodsmentioning

confidence: 99%

Incidence and mortality of super-refractory status epilepticus in adults

Kantanen¹,

Reinikainen²,

Parviainen³

et al. 2015

Epilepsy & Behavior

101

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Section: Methodsmentioning

confidence: 99%

Incidence and mortality of super-refractory status epilepticus in adults

Kantanen¹,

Reinikainen²,

Parviainen³

et al. 2015

Epilepsy & Behavior

101

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“…During the last decades new administrative approaches (e.g., Lean [40] and Triple-Aim [41]), new treatment methodologies (e.g., a gene therapy) and new technologies are leveraged to both operational and leadership purposes [42,43]. At the same time, the outsourcing and subcontracting have increased [44] to even out the public-sector service demand and resource fluctuation.…”

Section: Discussionmentioning

confidence: 99%

“…It is possible to say that because of the project the intensive care results have smoothly improved. [43]  In Sweden, the clinical quality assessment is advanced. The government administrates the national quality registries (QRs) of individual clinical data to review, analyze and improve the healthcare delivery.…”

Section: Discussionmentioning

confidence: 99%

Common controls driven conceptual leadership framework

Mussalo¹,

Hotti²,

Kirjanen³

et al. 2018

FinJeHeW

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The forthcoming social welfare and healthcare reform in Finland with its organizational, financing and steering changes challenges the leadership. All service systems levels of the social welfare and healthcare have to achieve performance objectives whilst at the same time also meeting conformance requirements. However, there are hundreds authority documents (e.g., best practices, guidelines, regulations and standards) the common controls of which are adapted partly manually and partly by leveraging automation in organizations. Leaders review and develop their practices around performance and conformity (i.e., conformance or compliance) within frameworks that are mainly the sets of principles. However, the common controls affect into the main tasks of the governance (i.e., direct, evaluate and monitor). Therefore, we construct a conceptual leadership framework to highlight the meaning of the common controls and the meaning of criteria for performance and conformity. The constructed framework contains the terms (e.g., a control objective, decision criteria, event, insight, and transaction) that are mainly defined in the glossaries of the authority documents. The terms are used to find out terms and definitions for the leadership framework to figure out cognitive meanings for the concepts of the common controls driven leadership.

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“…Data from patient monitors, laboratory systems, and ventilators are automatically collected. 200 Specially trained ICU personnel manually enter specifi c types of other data, such as co-morbidities and outcomes. All data are stored in a central database maintained by Tieto Healthcare & Welfare Ltd. (Kuopio, Finland).…”

Section: Intensive Care Scoring Systems (Iv)mentioning

confidence: 99%

“…200 Th e primary outcome was six-month mortality, with a secondary outcome of hospital mortality. Patients with missing outcome data were excluded (n=897).…”

Section: Intensive Care Scoring Systems (Iv)mentioning

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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Association of automated data collection and data completeness with outcomes of intensive care. A new customised model for outcome prediction

Abstract: The use of a CIS and improving data completeness do decrease severity-adjusted mortality rates. However, this explains only one fifth of the improvement in measured outcomes of intensive care in Finland.

Cited by 58 publications

References 21 publications

Incidence and mortality of super-refractory status epilepticus in adults

Incidence and mortality of super-refractory status epilepticus in adults

Common controls driven conceptual leadership framework

Prognostic models in traumatic brain injury

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