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Kosten als Instrument zur Effizienzbeurteilung intensivmedizinischer Funktionseinheiten

Cost analysis as a tool for assessing the efficacy of intensive care units

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Zusammenfassung

Hintergrund

Das „Krankenhausstrukturgesetz“ sieht vor, die Landeskrankenhausplanung künftig an Qualitätskriterien zu orientieren. Dabei soll auch die Effektivität der medizinischen Versorgung mittels Kosten-Nutzen-Analysen (KNA) bewertet werden. KNA intensivmedizinischer Funktionseinheiten benötigen zur Objektivierung eine Normierung (Adjustierung) der Kosten an die Ausgangssituation. Die vorliegende Studie wollte untersuchen, inwieweit Behandlungskosten auf patientenspezifische Ausgangsvariablen (u. a. Art und Schweregrad der Grunderkrankung) bezogen werden können.

Methodik

Kosten wurden von 2000–2004 auf 14 Intensivstationen in 9 deutschen Universitätskliniken mittels einer sog. Bottom-up-Methode ermittelt und mit demographischen Variablen bzw. mit Informationen zur Art (International Classification of Diseases [ICD]-10-Codes) und dem Schweregrad (intensivmedizinische Scores) der Grunderkrankung bei Aufnahme auf die Intensivstation zusammengeführt. Verschiedene statistische Modelle wurden zur Beschreibung der Kostendeterminanten untersucht.

Ergebnisse

Ausgewertet wurden 3803 Intensivpatienten. Die gesamten Kosten für die Therapie pro Patient lagen im Median bei 3199 € (Interquartilsabstand [IQR] 1768–6659 €). Die Prognosegüte war bei allen Modellen unzureichend und der geschätzte mittlere absolute Prognosefehler lag mindestens bei 3860 € (relativer Fehler 66 %; Extreme-gradient-boosting-Modell).

Schlussfolgerung

Mit den gegenwärtig verfügbaren Instrumenten (patientenspezifische Ausgangsvariablen) ist eine Normierung der Kosten und damit eine objektive KNA intensivmedizinischer Funktionseinheiten nicht durchführbar. Faktoren, die zum Zeitpunkt der Aufnahme unbekannt sind, scheinen für einen Großteil der anfallenden Kosten verantwortlich zu sein.

Abstract

Background

The German “Hospital Structure Act” intends to align the state hospital planning on quality criteria. Within this process cost-utility analyses (CUAs) shall be used to assess the efficacy of medical care. To be objective, CUAs of intensive care units (ICUs) require standardization (adjustment) of costs. The present study analyzed the extent to which treatment costs are related to patient-specific baseline variables (such as type and severity of the primary disease).

Methods

From 2000–2004, a bottom-up procedure was used to quantify total costs on 14 ICUs in nine German university hospitals. Results were combined with demographic data, and data indicating type (ICD-10 codes) and severity (ICU scoring systems) of the primary disease at ICU admission. Various statistical models were tested to identify that which best described the associations between baseline variables and costs.

Results

In all, 3803 critically ill patients could be examined. The median of treatment costs per patient was 3199 € (IQR 1768–6659 €). No model allowed an acceptably precise adjustment of costs; the estimated mean absolute prognostic error was at least 3860 € (mean relative prognostic error 66%), when we tested an Extreme Gradient Boosting Model.

Conclusion

Instruments which are currently available (cost adjustment based on patient-specific baseline variables) do not allow a standardization of costs, and an objective CUA of ICUs. Factors unknown at baseline may cause a large portion of treatment costs.

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Correspondence to W. H. Hartl.

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T. Maierhofer, F. Pfisterer, A. Bender, H. Küchenhoff, O. Moerer, H. Burchardi und W.H. Hartl geben an, dass kein Interessenkonflikt besteht.

Für die retrospektive anonymisierte Analyse der Daten bestanden keine Einwände von Seiten der Ethik-Kommissionen der jeweiligen medizinischen Fakultäten.

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Thomas Maierhofer und Florian Pfisterer haben zu gleichen Teilen zur Arbeit beigetragen.

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Maierhofer, T., Pfisterer, F., Bender, A. et al. Kosten als Instrument zur Effizienzbeurteilung intensivmedizinischer Funktionseinheiten. Med Klin Intensivmed Notfmed 113, 567–573 (2018). https://doi.org/10.1007/s00063-017-0315-8

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