Critical Care Department, King´s College Hospital, London, United Kingdom.
Hospital Virgen de la Salud, Servicio de Anestesia y Reanimación, Surgical Intensive Care Unit, Complejo Hospitalario de Toledo, Toledo, Spain.
PLoS One. 2021 Mar 10;16(3):e0248264. doi: 10.1371/journal.pone.0248264. eCollection 2021.
Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context.
We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population.
The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant.
The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient.
床边即时动脉血气（ABG）检测是一种血液测量测试，也是一种有用的诊断工具，可辅助治疗，从而改善临床结果。然而，数值报告的测试结果使得快速解读变得困难或容易产生解读偏差。动脉血气算法（ABG-a）是一种新的数字诊断解决方案，可为临床医生提供实时解释初步数据的功能，包括安全特性、氧合、酸碱失衡和肾谱。本研究的主要目的是在临床环境中，针对酸碱失衡的解读，在资深经验丰富的临床医生中对算法进行临床验证。
我们进行了一项前瞻性的国际多中心观察性横断面研究。符合严格采样标准的 346 个样本组和 64 名住院患者有资格进行 ABG。使用 Cohen's kappa 指数评估一致性，使用灵敏度、特异性、效率或总体准确率以及阳性预测值（PPV）和阴性预测值（NPV）评估算法在研究人群中的流行率的诊断准确性。
临床医生和 ABG-a 对酸碱失衡的解读结果之间的一致性率为 84.3%，Cohen's kappa 系数为 0.81；95%置信区间为 0.77 至 0.86；p < 0.001。对于检测正常酸碱状态的准确性，该算法的灵敏度为 90.0%（95%置信区间 79.9 至 95.3），特异性为 97.2%（95%置信区间 94.5 至 98.6），总体准确率为 95.9%（95%置信区间 93.3 至 97.6）。对于四种简单的酸碱失衡，呼吸性碱中毒：灵敏度为 91.2%（77.0 至 97.0），特异性为 100.0%（98.8 至 100.0），总体准确率为 99.1%（97.5 至 99.7）；呼吸性酸中毒：灵敏度为 61.1%（38.6 至 79.7），特异性为 100.0%（98.8 至 100.0），总体准确率为 98.0%（95.9 至 99.0）；代谢性酸中毒：灵敏度为 75.8%（59.0 至 87.2），特异性为 99.7%（98.2 至 99.9），总体准确率为 97.4%（95.1 至 98.6）；代谢性碱中毒：灵敏度为 72.2%（56.0 至 84.2），特异性为 95.5%（92.5 至 97.3），总体准确率为 93.0%（88.8 至 95.3）；对于四种复杂的酸碱失衡，呼吸性和代谢性碱中毒、呼吸性和代谢性酸中毒、呼吸性碱中毒和代谢性酸中毒、呼吸性酸中毒和代谢性碱中毒，灵敏度、特异性和总体准确率也很高。对于正常酸碱状态，该算法的阳性预测值为 87.1%（95%置信区间 76.6 至 93.3）%，阴性预测值为 97.9%（95%置信区间 95.4 至 99.0）%，流行率为 17.4%（95%置信区间 13.8 至 21.8）%。对于四种简单的酸碱失衡和四种复杂的酸碱失衡，PPV 和 NPV 也具有统计学意义。
ABG-a 在临床环境中与资深经验丰富的临床医生在酸碱失衡方面表现出非常高的一致性和诊断准确性。该方法还可以在床边日常工作中提供即时的细化和深入的复杂分析，临床医生可以在床边获得。ABG-a 方法还可以通过检查即将发生的危及生命的情况、分析结果的内部一致性、患者的氧合和肾功能状态，减少人为错误的发生。
