Prevention and treatment of sepsis-induced acute kidney injury: an update
Patrick M. Honoré; Rita Jacobs; Inne Hendrickx; Sean M. Bagshaw; Olivier Joannès-Boyau; Willem Boer; Elisabeth De Waele; Viola Van Gorp · 2015 · Annals of Intensive Care
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Sepsis-induced acute kidney injury (SAKI) remains an important challenge in critical care medicine. We reviewed current available evidence on prevention and treatment of SAKI with focus on some recent advances and developments. Prevention of SAKI starts with early and ample fluid resuscitation preferentially with crystalloid solutions. Balanced crystalloids have no proven superior benefit. Renal function can be evaluated by measuring lactate clearance rate, renal Doppler, or central venous oxygenation monitoring. Assuring sufficiently high central venous oxygenation most optimally prevents SAK
Abstract by Patrick M. Honoré; Rita Jacobs; Inne Hendrickx; Sean M. Bagshaw; Olivier Joannès-Boyau; Willem Boer; Elisabeth De Waele; Viola Van Gorp, Annals of Intensive Care (2015) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke
Negative / Null Result ReportDuodenal Infusion of Donor Feces for Recurrent Clostridium difficile
Negative / Null Result ReportStenting versus Endarterectomy for Treatment of Carotid-Artery Stenosis
Negative / Null Result ReportEffects of Combination Lipid Therapy in Type 2 Diabetes Mellitus
Replication FailureA Randomized Trial of Bevacizumab for Newly Diagnosed Glioblastoma
Negative / Null Result ReportSpironolactone for Heart Failure with Preserved Ejection Fraction
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1186/s13613-015-0095-3