The sensitivity of room-air pulse oximetry in the detection of hypercapnia

The Use of Pulse Oximetry to Determine Hypoxemia in Acute Exacerbations of COPD

BACKGROUND

There is little evidence that the guideline-recommended oxygen saturation of 92% is the best cut-off point for detecting hypoxemia in COPD exacerbations.

OBJECTIVE

To detect and validate pulse oximetry oxygen saturation cut-off values likely to detect hypoxemia in patients with aeCOPD, to explore the correlation between oxygen saturation measured by pulse oximetry and hypoxemia or hypercapnic respiratory failure.

METHODOLOGY

Cross-sectional study nested in the IRYSS-COPD study with 2,181 episodes of aeCOPD recruited between 2008 and 2010 in 16 hospitals belonging to the Spanish Public Health System. Data collected include determination of oxygen saturation by pulse oximetry upon arrival in the emergency department (ED), first arterial blood gasometry values, sociodemographic information, background medical history and clinical variables upon ED arrival. Logistic regression models were performed using as the dependent variables hypoxemia (PaO2 < 60 mmHg) and hypercapnic respiratory failure (PaO2 < 60 mmHg and PaCO2 > 45). Optimal cut-off points were calculated.

RESULTS

The correlation coefficient between oxygen saturation and pO2 measured by arterial blood gasometry was 0.89. The area under the curve (AUC) for the hypoxemia model was 0.97 (0.96-0.98) and the optimal cut-off point for hypoxemia was an oxygen saturation of 90%. The AUC for hypercapnic respiratory failure was 0.90 (0.87-0.92) and the optimal cut-off point was an oxygen saturation of 88%.

CONCLUSIONS

Our results support current recommendations for ordering blood gasometry based on pulse oximetry oxygen saturation cut-offs for hypoxemia. We also provide easy to use formulae to calculate pO2 from oxygen saturation measured by pulse oximetry.

Pulse oximetry in pediatric practice

The introduction of pulse oximetry in clinical practice has allowed for simple, noninvasive, and reasonably accurate estimation of arterial oxygen saturation. Pulse oximetry is routinely used in the emergency department, the pediatric ward, and in pediatric intensive and perioperative care. However, clinically relevant principles and inherent limitations of the method are not always well understood by health care professionals caring for children. The calculation of the percentage of arterial oxyhemoglobin is based on the distinct characteristics of light absorption in the red and infrared spectra by oxygenated versus deoxygenated hemoglobin and takes advantage of the variation in light absorption caused by the pulsatility of arterial blood. Computation of oxygen saturation is achieved with the use of calibration algorithms. Safe use of pulse oximetry requires knowledge of its limitations, which include motion artifacts, poor perfusion at the site of measurement, irregular rhythms, ambient light or electromagnetic interference, skin pigmentation, nail polish, calibration assumptions, probe positioning, time lag in detecting hypoxic events, venous pulsation, intravenous dyes, and presence of abnormal hemoglobin molecules. In this review we describe the physiologic principles and limitations of pulse oximetry, discuss normal values, and highlight its importance in common pediatric diseases, in which the principle mechanism of hypoxemia is ventilation/perfusion mismatch (eg, asthma exacerbation, acute bronchiolitis, pneumonia) versus hypoventilation (eg, laryngotracheitis, vocal cord dysfunction, foreign-body aspiration in the larynx or trachea). Additional technologic advancements in pulse oximetry and its incorporation into evidence-based clinical algorithms will improve the efficiency of the method in daily pediatric practice.

Patterns of unexpected in-hospital deaths: a root cause analysis

BACKGROUND

Respiratory alarm monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected.

METHODS

The Pubmed database was searched for articles relating to methods for triggering rapid response teams and respiratory alarms and these were contrasted with the fundamental timed pathophysiologic patterns of death which evolve due to sepsis, congestive heart failure, pulmonary embolism, hypoventilation, narcotic overdose, and sleep apnea.

RESULTS

In contrast to the simplicity of the numeric threshold breach method of generating alerts, the actual patterns of evolving death are complex and do not share common features until near death. On hospital general floors, unexpected clinical instability leading to death often progresses along three distinct patterns which can be designated as Types I, II and III. Type I is a pattern comprised of hyperventilation compensated respiratory failure typical of congestive heart failure and sepsis. Here, early hyperventilation and respiratory alkalosis can conceal the onset of instability. Type II is the pattern of classic CO2 narcosis. Type III occurs only during sleep and is a pattern of ventilation and SPO2 cycling caused by instability of ventilation and/or upper airway control followed by precipitous and fatal oxygen desaturation if arousal failure is induced by narcotics and/or sedation.

CONCLUSION

The traditional threshold breach method of detecting instability on hospital wards was not scientifically derived; explaining the failure of threshold based monitoring and rapid response team activation in randomized trials. Furthermore, the thresholds themselves are arbitrary and capricious. There are three common fundamental pathophysiologic patterns of unexpected hospital death. These patterns are too complex for early detection by any unifying numeric threshold. New methods and technologies which detect and identify the actual patterns of evolving death should be investigated.