The Evolution of Central Venous-to-arterial Carbon Dioxide Difference (PCO2 Gap) during Resuscitation Affects ICU Outcomes: A Prospective Observational Study
Anand M Tiwari, Hrishikesh S Vaidya, Sushma K Gurav, Abhijit M Deshmukh, Prasad B Suryawanshi, Upendrakumar S Kapse, Abhaya P Bhoyar, Piyush A Dhawad, Shameek Mukherjee
Citation Information :
Tiwari AM, Vaidya HS, Gurav SK, Deshmukh AM, Suryawanshi PB, Kapse US, Bhoyar AP, Dhawad PA, Mukherjee S. The Evolution of Central Venous-to-arterial Carbon Dioxide Difference (PCO2 Gap) during Resuscitation Affects ICU Outcomes: A Prospective Observational Study. Indian J Crit Care Med 2024; 28 (4):349-354.
Introduction: The usual methods of perfusion assessment in patients with shock, such as capillary refill time, skin mottling, and serial serum lactate measurements have many limitations. Veno-arterial difference in the partial pressure of carbon dioxide (PCO2 gap) is advocated being more reliable. We evaluated serial change in PCO2 gap during resuscitation in circulatory shock and its effect on ICU outcomes.
Materials and methods: This prospective observational study included 110 adults with circulatory shock. Patients were resuscitated as per current standards of care. We recorded invasive arterial pressure, urine output, cardiac index (CI), PCO2 gap at ICU admission at 6, 12, and 24 hours, and various patient outcomes.
Results: Significant decrease in PCO2 gap was observed at 6 h and was accompanied by improvement in serum lactate, mean arterial pressure, CI and urine output in (n = 61). We compared these patients with those in whom this decrease did not occur (n = 49). Mortality and ICU LOS was significantly lower in patients with low PCO2 gap, while more patients with high PCO2 gap required RRT.
Conclusion: We found that a persistently high PCO2 gap at 6 and 12 h following resuscitation in patients with shock of various etiologies, was associated with increased mortality, need for RRT and increased ICU LOS. High PCO2 gap had a moderate discriminative ability to predict mortality.
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