2017
DOI: 10.1186/s13049-017-0461-2
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Impact of systolic blood pressure limits on the diagnostic value of triage algorithms

Abstract: BackgroundMajor incidents are characterized by a lack of resources compared to an overwhelming number of casualties, requiring a prioritization of medical treatment. Triage algorithms are an essential tool for prioritizing the urgency of treatment for patients, but the evidence to support one over another is very limited. We determined the influence of blood pressure limits on the diagnostic value of triage algorithms, considering if pulse should be palpated centrally or peripherally.MethodsWe used a database … Show more

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Cited by 17 publications
(24 citation statements)
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“…We categorized age into three groups: < 20 years, 20-64 years, and ≥ 65 years. We defined shock as a systolic blood pressure of < 80 mmHg on hospital arrival [18]. To assess concomitant injuries, we mapped AIScoded injuries to the following categories: head/neck, thorax, pelvis/extremities, and intra-abdominal organs (including the liver, spleen, pancreas, and gastrointestinal tract).…”
Section: Variablesmentioning
confidence: 99%
“…We categorized age into three groups: < 20 years, 20-64 years, and ≥ 65 years. We defined shock as a systolic blood pressure of < 80 mmHg on hospital arrival [18]. To assess concomitant injuries, we mapped AIScoded injuries to the following categories: head/neck, thorax, pelvis/extremities, and intra-abdominal organs (including the liver, spleen, pancreas, and gastrointestinal tract).…”
Section: Variablesmentioning
confidence: 99%
“…We de ned shock as a systolic blood pressure of <80 mmHg on hospital arrival. 18 To assess concomitant injuries, we mapped AIS-coded injuries to the following categories: head/neck, thorax, pelvis/extremities, and intra-abdominal organs (including the liver, spleen, pancreas, and gastrointestinal tract). Factors associated with mortality were assessed by a multivariable logistic regression analysis and adjusted odds ratios (ORs) and 95% con dence intervals (CIs) were calculated.…”
Section: Variablesmentioning
confidence: 99%
“…In aktuellen Studien wurde die Qualität verschiedener in Deutschland gebräuchlicher Vorsichtungsalgorithmen hinsichtlich der korrekten Identifizierung der SK I untersucht. Die Algorithmen ASAV [5] und mSTaRT [6] erzielten dabei die besten Ergebnisse hinsichtlich Sensitivität und Spezifitätalso ob möglichst alle vital bedrohten Patienten als SK I erkannt und keine leichter Verletzten als SK I triagiert wurden [7,8] Eine weitere Fehlerquelle steckt diesbezüglich im zeitigen Aussortieren der gehfähigen Patienten. Wenn gehfähige Patienten ganz am Anfang des Algorithmus als "grün" aussortiert werden, können in dieser Gruppe noch bis zu 28 % "rote" Patienten stecken.…”
Section: Qualität Verfügbarer Vorsichtungsalgorithmenunclassified
“…Wenn gehfähige Patienten ganz am Anfang des Algorithmus als "grün" aussortiert werden, können in dieser Gruppe noch bis zu 28 % "rote" Patienten stecken. Daher ist eine zeitnahe Nachsichtung der "grünen" Patienten unbedingt erforderlich [8]. Dies wird auch im aktuellen mSTaRT-Algorithmus berücksichtigt [12].…”
Section: Qualität Verfügbarer Vorsichtungsalgorithmenunclassified
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