Multidetector CT Imaging Biomarkers as Predictors of Prognosis in Shock: Updates and Future Directions.
Tullio ValenteGiorgio BocchiniCandida MassimoGaetano ReaRoberta LietoSalvatore GuarinoEmanuele MutoAhmad Abu-OmarMariano ScaglioneGiacomo SicaPublished in: Diagnostics (Basel, Switzerland) (2023)
A severe mismatch between the supply and demand of oxygen is the common sequela of all types of shock, which present a mortality of up to 80%. Various organs play a protective role in shock and contribute to whole-body homeostasis. The ever-increasing number of multidetector CT examinations in severely ill and sometimes unstable patients leads to more frequently encountered findings leading to imminent death, together called "hypovolemic shock complex". Features on CT include dense opacification of the right heart and major systemic veins, venous layering of contrast material and blood, densely opacified parenchyma in the right hepatic lobe, decreased enhancement of the abdominal organ, a dense pulmonary artery, contrast pooling in dependent lungs, and contrast stasis in pulmonary veins. These findings are biomarkers and prognostic indicators of paramount importance which stratify risk and improve patient outcomes. In this review, we illustrate the various CT patterns in shock and review the spectrum and prognostic significance of thoraco-abdominal vascular and visceral alarming signs of impending death with the intention of increasing awareness among radiologists and radiographers to prepare for immediate resuscitation when required.
Keyphrases
- contrast enhanced
- computed tomography
- dual energy
- pulmonary artery
- image quality
- pulmonary hypertension
- magnetic resonance
- magnetic resonance imaging
- end stage renal disease
- positron emission tomography
- coronary artery
- chronic kidney disease
- newly diagnosed
- heart failure
- pulmonary arterial hypertension
- ejection fraction
- high resolution
- peritoneal dialysis
- prognostic factors
- insulin resistance
- inferior vena cava
- cardiac arrest
- type diabetes
- cardiovascular disease
- atrial fibrillation
- metabolic syndrome
- coronary artery disease
- deep learning
- machine learning
- patient reported