Zusammenfassung
Die Sepsis und damit verbundene Komplikationen sind eine große Herausforderung für die Intensivmedizin. Trotz vieler Fortschritte in der antibiotischen Therapie gehört die Sepsis zu den häufigsten Krankheitsentitäten auf Intensivstationen und ist als Haupttodesursache kritisch kranker Patienten beschrieben. Persistiert die Sepsis, kommt es zu einer Störung der Immunität bis zur Immunsuppression. Unspezifische Frühzeichen erschweren oftmals eine schnelle Diagnose. Der rasche Beginn einer adäquaten Therapie ist allerdings für die Prognose von besonderer Bedeutung. Scoring-Systeme dienen der initialen Evaluation, werden aber in Bezug auf Verlaufs- und Therapie-Monitoring sowie Vorhersagekraft für die Sterblichkeit kontrovers diskutiert. Biomarker gelten hierfür als ergänzender Ansatz.
Abstract
Sepsis and related complications are a challenge for intensive care medicine. Despite many advances in antibiotic therapy sepsis remains one of the most common diseases of patients in intensive care units and is designated as the main cause of death in critically ill patients. Persisting sepsis leads to impaired immunity, resulting in immunosuppression. Unspecific predictive signs complicate an early diagnosis; however, an early initiation of adequate therapy is of crucial importance for the prognosis. Scoring systems can be applied for the initial evaluation but are controversially discussed concerning the monitoring of disease progression and therapy as well as outcome prediction. Biomarkers are considered as a complementary approach.
Abbreviations
- ACCP:
-
American College of Chest Physicians
- ADM:
-
Adrenomedullin
- APACHE:
-
Acute Physiology and Chronic Health Evaluation
- ARDS:
-
„acute respiratory distress syndrome“
- AUROCC:
-
„area under the receiver operating characteristic curve“
- CARS:
-
„compensatory anti-inflammatory response syndrome“
- cCK-18:
-
„caspase-cleaved CK-18“
- CCL3/4:
-
„C-C chemokine ligand 3/4“
- CD14:
-
„cluster-of-differentiation 14“
- CD64:
-
„cluster-of-differentiation 64“
- CK-18:
-
Zytokeratin-18
- CLI:
-
Clot-Lyse-Index
- CRP:
-
C-reaktives Protein
- GCS:
-
Glasgow Coma Scale
- GM-CSF:
-
„granulocyte-macrophage colony-stimulating factor“
- GZMB:
-
„granzyme B“
- hFABP:
-
„heart type fatty acid binding protein“
- HRV:
-
„heart rate variability“
- hs-cTnT:
-
„high sensitivity cardiac troponin T“
- HSPA1B:
-
„heat shock protein 70kD 1B“
- IL-6/8:
-
Interleukin-6/8
- IFN-γ:
-
Interferon-γ
- IPS:
-
Infection Probability Score
- LBP:
-
„LPS-binding protein“
- LPS:
-
Lipopolysaccharid
- MCF:
-
„maximal clot firmness“
- MFI:
-
„median fluorescence intensity“
- mHLA-DR:
-
„monocyte human leukocyte antigen class DR“
- MIF:
-
„macrophage migration inhibitory factor“
- MOF:
-
„multiple organ failure“
- PCT:
-
Prokalzitonin
- MR-proADM:
-
„midregional-pro-adrenomedullin“
- ROC:
-
„receiver operating characteristic“
- SCCM:
-
Society of Critical Care Medicine
- sCD14-ST:
-
„soluble cluster of differentiation 14 subtype“
- SIIS:
-
sepsisinduzierte Immunsuppression
- SIRS:
-
„systemic inflammatory response syndrome“
- SOFA:
-
Sequential Organ Failure Assessment
- sTRAIL:
-
„soluble tumor necrosis factor related apoptosis inducing ligand“
- sTREM-1:
-
„soluble triggering receptor expressed on myeloid cells-1“
- suPAR:
-
„soluble urokinase-type plasminogen activator receptor“
- TLR-4:
-
„toll-like receptor 4“
- TNF-α:
-
„tumor necrosis factor α“
- TRAIL:
-
„tumor necrosis factor related apoptosis inducing ligand“
- TREM-1:
-
„triggering receptor expressed on myeloid cells-1“
- uPAR:
-
„urokinase-type plasminogen activator receptor“
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Interessenkonflikt. B. H. Siegler, S. Weiterer, C. Lichtenstern, D. Stumpp, T. Brenner, S. Hofer, M. A. Weigand und F. Uhle geben an, dass kein Interessenkonflikt besteht. Der Beitrag enthält keine Studien an Menschen oder Tieren.
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Anhang
Dilemma der Kennzahlen
In wissenschaftlichen Publikationen zum Thema Biomarker findet man regelhaft die entsprechenden Angaben zur Sensitivität und Spezifität des Parameters, oftmals auch in grafischer Form einer „Receiver-operating-characteristic“-Kurve (ROC-Kurve, Sensitivität gegen 1-Spezifität). Doch sind diese Angaben das, was ein klinisch tätiger Arzt im Alltag benötigt? Sensitivität und Spezifität sind Kennzahlen für die Güte des Tests bzw. des untersuchten Parameters 2 Zustände, z. B. krank und gesund, voneinander zu unterscheiden. Viel wichtiger in der täglichen Anwendung ist jedoch das Wissen, mit welcher Wahrscheinlichkeit ein Patient mit positivem bzw. negativem Testergebnis tatsächlich erkrankt (positiver prädiktiver Wert; richtig-positives Ergebnis unter allen positiven Tests) bzw. wirklich gesund ist (negativer prädiktiver Wert; richtig-negatives Ergebnis unter allen negativen Tests; Abb. 5). Im Gegensatz zu Sensitivität und Spezifität beziehen sich diese Angaben somit auf eine „gemischte“ Gruppe von Kranken und Gesunden und sind kollektiv-bezogene Kennzahlen. Auf Basis dieser Eigenschaft sind sie, neben Spezifität und Sensitivität, nach dem Satz von Bayes maßgeblich von der Prävalenz der Erkrankung im untersuchten Kollektiv abhängig [10]. Je höher die Prävalenz, desto größer die Vorhersagekraft des Tests. Dies bedeutet für die klinische Anwendung:
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Bei niedriger Prävalenz der Erkrankung sollte vor Hinzuziehung des Tests eine Anreicherung der Prävalenz der Population auf der Basis klinischer Parameter erfolgen.
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Die Aussagekraft des Tests variiert zwischen verschiedenen Kollektiven und muss für jeden einzelnen Bereich (Notaufnahme, Intensivstation) auf der Basis der tatsächlichen Prävalenz evaluiert werden.
Anhand des folgenden Fallbeispiels soll dieser Sachverhalt verdeutlicht werden:
Frau H. wird mit unklarer Gesamtsymptomatik in die zentrale Notaufnahme eingeliefert. Routinemäßig wird jeder neu aufgenommene Patient verdachtsunabhängig mithilfe eines Schnelltests (Sensitivität und Spezifität jeweils 0,95) auf das Vorhandensein einer Sepsis gescreent, die mit einer Prävalenz von 0,05 auftritt (5 von 100 Patienten). Aus den Kennzahlen des Tests und der Prävalenz von Sepsis in dieser distinkten Notaufnahme errechnet sich ein positiv-prädiktiver Wert von 0,679 und ein negativ-prädiktiver Wert von 0,994, d. h. ein positives Testergebnis von Frau H. deutet mit knapp 68 %iger Wahrscheinlichkeit auf das Vorliegen einer Sepsis hin, während ein negatives Testergebnis eine Sepsis zu 99,4 % ausschließt.
Im gleichen Krankenhaus wird Herr M. auf der interdisziplinären Intensivstation nach einer großen viszeral-chirurgischen Operation behandelt. Aufgrund eines persistierenden SIRS mit hohem Fieber und erhöhten Entzündungswerten wird bei ausstehender mikrobiologischer Testung bei Herrn M. ebenfalls ein Schnelltest durchgeführt. Durch die Berücksichtigung des klinischen Bildes kommt es zu einer starken „Anreicherung“ der Prävalenz auf 0,7 (70 von 100 Patienten). Dies resultiert in veränderten Vorhersagewerten. Ein positives Testergebnis beruht in diesem Fall zu 97,8 % auf dem Vorhandensein einer Sepsis, während ein negatives Testergebnis eine Sepsis nur noch zu 89,1 % ausschließt.
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Siegler, B., Weiterer, S., Lichtenstern, C. et al. Einsatz von Biomarkern in der Sepsis. Anaesthesist 63, 678–690 (2014). https://doi.org/10.1007/s00101-014-2347-2
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DOI: https://doi.org/10.1007/s00101-014-2347-2