This is the concluding article in my series on respiratory dysfunction in post-surgical patients. Here are highlights from the preceding articles:
- “Improving the Safety of Post-Surgical Care,” introduced the concept that, although the current approach to physiologic threshold monitoring works well in the OR, it is unreliable on post-surgical floors.
- “Pulse Oximetry False Alarms on Post-Surgical Floors,” explored in more depth why the threshold for triggering a pulse oximetry alarm should vary depending on the site of care (OR vs post-surgical floor).
- “Detecting Deadly Post-Surgical Respiratory Dysfunction,” reviewed the pattern, called Type I, of respiratory compromise that characterizes the conditions not related to opioid use.
- “The Risk of Opioids in Post-surgical Settings,” examined the two different patterns of respiratory dysfunction (Types II and III) that can occur when opioids are used in post-surgical settings.
Having examined the logic behind early detection of each of these three common deadly patterns of respiratory dysfunction that place post-surgical patients at risk, let’s now look at the detection challenges they present. We will specifically explore how we can monitor all floor patients being treated with opioids for each of the three patterns continuously using one electronic surveillance technology with one threshold alarm trigger.
The good news is that much of it has already been done with excellent published outcomes that will be discussed in detail below. It is important to note, however, that the strategy that made this possible preceded the articulation of the three pattern cognitive framework that now makes it clear why any single oximetric threshold alarm trigger can’t simultaneously detect all three pattern types either early enough or specifically enough. We also know that certain alarm thresholds used today lead to poor monitoring behaviors because of the alarm fatigue that ensues when all opioid-treated patients are being monitored simultaneously.
So, are we facing an unsolvable paradox? The answer is “no.” But, before I show you how we can use one electronic surveillance technology with one threshold alarm trigger to monitor all post-surgical patients, let’s first review the best way to detect each of the three patterns.
Type 1 Respiratory Dysfunction
If you review my prior post, you will see that there is no oximetric threshold alarm capable of detecting the Type I pattern early. By the time a patient’s SPO2 begins declining, the condition is advanced and worsening by the minute. The most reliable early alarms are the explicit signs and symptoms of dyspnea. Therefore, optimal early detection of the most prevalent pattern (Type I) requires an alert clinician who listens and responds immediately to any complaints of shortness of breath. The clinician must act on these findings aggressively with an immediate and thorough evaluation.
Type II Respiratory Dysfunction
Early detection of the less common Type II pattern of respiratory dysfunction likewise requires a knowledgeable, observant clinician. While an occasional professional may still argue that floor nurses can capably provide safe care without using some continuous electronic monitoring, most experts contend this not to be the case where parenteral opioids are involved.
I believe, along with the Anesthesia Patient Safety Foundation and many others [References 1-4 below], that the copious competing responsibilities encroaching on post-surgical nursing time as well as the unpredictability of these patient populations make risks associated with intermittent monitoring or even individual condition-based monitoring indefensible and obsolete.
The Type II pattern can be duplicitous! It doesn’t always coincide with progressively slower breathing. While reasonably early detection of Type II patterns could, in theory, be achieved by using a 90% oximetric threshold alarm, it’s only the case with patients that have otherwise normal lungs and are breathing either room air or supplemental oxygen at no more than a 27% Fraction of Inspired Oxygen (FIO2). This does not pertain to therapeutic oxygen ordered for known lung disease.
More importantly, continuous monitoring of all patients receiving parenteral opioids simultaneously often fails because of the negative consequences from alarm fatigue when 90% oximetric thresholds are used. This happens because of the plenitude of innocent sleep apneas that constantly trigger alarms set at this threshold. Once again, astute clinicians become the most reliable early detection monitors of Type II respiratory dysfunction. They need only to pay attention and take action on any progressive downward oxygen saturation (SPO2) drifts into the low nineties in “sleeping” patients. (Remember, the same FIO2 27% restrictions apply for this to work.)
And then there’s our Type III pattern
Even though obstructive sleep apnea is common, with an estimated 80 million Americans now believed to have moderate to severe forms of it, there is no reliable way to predict preoperatively who is at risk for a Type III event. Although relatively rare, it can appear and take a life in less than 10 minutes.
A recent study demonstrated that 25% of a pre-surgical patient population tested with polysomnography and found not to have OSA, converted to having moderate to severe sleep apnea associated with opioids immediately following their surgery. For anyone to still assume that OSA patients can be reliably identified and prepared for preoperatively is terribly misguided.
Furthermore, there is some evidence that this pattern is impervious to sedation scale risk assessment that has been the mainstay of nursing opioid management for years. Type III has to be caught in the act, and that depends on both a reliable form of universal continuous electronic surveillance and a reliable safety net alarm.
The Dartmouth experience
This conundrum was partially solved at Dartmouth’s Hitchcock Medical Center in 2007 when Andreas Taenzer launched the first successful universal post-surgical electronic surveillance on an orthopedic unit using a continuous pulse oximetry protocol. This new approach to detecting unrecognized postoperative deterioration was a departure from the concept of optimized individual care to optimized population care. It was necessary because of the well-documented failure to identify individual patients at risk for adverse events.
Dartmouth Hitchcock Medical Center closely examined the failures associated with individual condition monitoring. Taenzer and his team selected continuous pulse oximetry over end-tidal CO2 monitoring because of its much more reliable patient acceptance. And, they further demonstrated the direct negative effects on nursing surveillance behaviors created because of alarm fatigue when a 90% SPO2 threshold alarm was being used.
Then, they did what no one else had the courage to do. They increased the threshold by dropping the SPO2 alarm threshold value to 80% with a 15-second audio alarm delay at the bedside and an additional 15-second delay for pager annunciation resulting in an average of 2 alarms per patient per 12-hour shift. They, then, began seeing amazing results:
- Length of stay decreased from 3.68 for all patients to 3.20 days for patients who did not have ICU transfers associated with their care.
- Rescue events decreased from 3.4 to 1.2 per 1,000 patient discharges.
- ICU transfers declined from 5.6 to 2.9 per 1,000 patient days, over one year equating to a decrease from 54 to 28 transfers.
The researchers concluded that universal continuous pulse oximetry surveillance could improve outcomes in this setting. They further speculated that these gains could hold true for other settings as well.
Because of this experience, all medical and surgical patients at Dartmouth are now mandated to be continuously monitored with pulse oximetry when they aren’t being directly observed by health care providers. The program has been shown to be cost-effective, primarily from cost savings due to decreased ICU transfer rates. And, just as impressively, there have been no opioid-related deaths or anoxic brain injuries since this program was instituted regardless having the same opioid usage and same number of opioid reversals.
Putting it all together
I believe the monitoring caveats taken from the three pattern cognitive framework further enriches what Dartmouth began in 2007. Think of the 80% alarm threshold safety net now as the optimal threshold for detecting the Type III pattern. Add to this, clinical recognition of early SPO2 downward drifting as the optimal early alarm for detecting the Type II pattern and the patient’s first sign or symptom of dyspnea for appropriate Type I pattern detection.