Indispensable tools of critical care management: blood gas analyzers


Gozgoz H., KÜME T., GÖKMEN A. N.

SCANDINAVIAN JOURNAL OF CLINICAL & LABORATORY INVESTIGATION, cilt.86, sa.3, ss.209-223, 2026 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 86 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1080/00365513.2026.2659585
  • Dergi Adı: SCANDINAVIAN JOURNAL OF CLINICAL & LABORATORY INVESTIGATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CINAHL, EMBASE
  • Sayfa Sayıları: ss.209-223
  • Anahtar Kelimeler: biosensing techniques, Blood gas analysis, blood specimen collection, oximetry, point-of-care systems
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Blood gas analysis remains one of the most indispensable diagnostic tools in critical care, yet increasing automation often obscures its analytical foundations and limitations. This review provides an integrated perspective by tracing the evolution of blood gas testing from the 1952 polio epidemic to contemporary microfluidic biosensor platforms. Core physical principles governing gas behavior are summarized, and the operational mechanisms of potentiometric (pH and pCO2) and amperometric (pO2) sensors are explained alongside the essential role of optical co-oximetry in identifying dyshemoglobinemias. Major sources of pre-analytical error-such as leukocyte larceny, heparin dilution effects, and syringe permeability-are critically examined, emphasizing the need for meticulous sample handling and appropriate temperature correction. Acid-base interpretation frameworks, including the Boston, Copenhagen, and Stewart physicochemical approaches, are compared to highlight their respective clinical utilities. While Henderson-Hasselbalch-based methods support rapid bedside decision-making, the Stewart model provides enhanced diagnostic clarity in complex metabolic disturbances. Understanding sensor limitations and pre-analytical vulnerabilities remains as crucial as interpreting the numerical results themselves. Future integration of artificial intelligence-based decision support and emerging in-vivo monitoring technologies may further improve diagnostic accuracy and therapeutic turnaround time in critical care.