To Sleep, Perchance to Dream

You would think that with all the emphasis on the need for a restful night's sleep, getting a night's rest while hospitalized would be a snap. You, of course, would be incorrect. One of the most consistently reported disruptions is being awoken to check your vital signs; evidently, a mirror to check for breathing is no longer sufficient. We need to check your blood pressure, pulse, temperature, and respiratory rate every 4 hours. That 4-hour interval is one of those rules passed from resident to intern, always part of the standard orders, and with no clinical rationale.

Can hospitalists, those doctors who now specialize in caring for hospitalized patients, safely reduce the frequency of vital signs, especially at night? Because we live in the digital age, the answer may lie with an algorithm “to identify patients who are at low risk for abnormal nighttime vital signs.” The authors refer to reducing the frequency from every 4 hours to every eight while patients are awake “sleep promotion vitals. (SPV).” The algorithm is embedded in the electronic health record of the hospital, one of the burgeoning algorithms that make up clinical decision support (CDS) tools. It should be easy-peasy [1]

While the researchers were interested in getting their patients more sleep, their real interest was in determining whether this treatment change reduced the delirium (disorientation) that many patients experience from the combination of being hospitalized, ill, woken up, and medicated. Once the algorithm was “trained” and let loose in the wild as it were, hospitalists were notified by clinical decision support that their patient was at low risk for medical instability and could benefit from sleep promotion vitals. Clinicians could agree by clicking a button or put off a decision for an hour or until the next day. In order not to be too bothersome to the clinician’s work, the algorithm was only activated when it was “90% sure” that the patient would not be harmed by being awoken to check their vital signs.

In addition to the outcome of delirium, as noted by the nurses, secondary outcomes included patient satisfaction and an increase in sleep opportunity – the time “between 11 pm and 6 am”  without interruptions by the staff. 1,930 nights of sleep involving 400 women and 566 men with a mean age of 53 were randomized, 964 acting as the control, 966 passing through clinical decision support. All patients were on med-surg floors, receiving routine, not critical care.

• There was no difference in the incidence of delirium between the groups
• Physicians “usually agreed” with the CDS recommendation, and as a result, the treatment group experienced 31% fewer sleep time interruptions. Put into other terms, this resulted in 23 more minutes of sleep during that 7-hour window.
• None of the treatment patients were unexpectedly transferred to the ICU or suffered cardiac arrest, so the algorithm was safely applied.
• Patient satisfaction surveys were completed by only 5% of the subjects, roughly 50 in each group. There was no greater or lesser patient satisfaction.

The researchers concluded

“The results of this randomized clinical trial suggest that a predictive algorithm, paired with targeted and informative CDS, can assist physicians in reducing overnight vital sign checks that disrupt sleep for hospitalized patients, with no adverse effects on measures of patient safety.”

They point out that algorithms that appear in the EHR are much easier to scale and provide than education and reinforcement. Let the computer system remind the clinicians to consider reducing vital signs – an easy solution to a computer system that requires and defaults to standardized vital signs on admission.  

You may also wonder why all of this effort resulted in so little extra sleep. The researchers note that 40% of the time, clinicians opted not to reduce vital sign frequency, but that still leaves a gap in reaching 31%. In roughly a third of cases, despite an order for reduced frequency, “busy patient-care assistants and nurses may check vital signs out of habit without noticing that the order has changed for some of the patients.” Not reviewing a patient's orders is, at least in my mind, a more severe process issue than reduced sleep. The researchers also point to room cleaning or noisy neighbors as other sources of sleep disruption. (UCSF, where the study was conducted, has received an average score from patients on quietness)

The accompanying editorial noted that delirium has many underlying factors, so that correcting sleep deprivation by itself may be insufficient. They note as I did the disconnect between physician “orders” and the actions by the staff; perhaps physician “suggestions” would be more appropriate.

[1] Nothing is easy when using EHRS. Here is the description by the authors.

“To implement the model for real-time use within our EHR system (EPIC, version 2019), we expressed the logistic model in the standard predictive model markup language, mapped the variables used by our model from the reporting database (Clarity) to equivalent versions in the live EHR database (Chronicles), and uploaded it into the Epic Cognitive Computing Platform for triggered implementation of the algorithm and integrated display of the results. To avoid alarm fatigue, we created a cutoff probability for the model's output that would trigger notification. The threshold of 0.9 (90% predicted likelihood of normal vital signs overnight) was chosen as a balance between safety (positive predictive value of 94%) and potential effect (desire to reduce sleep interruptions in a large proportion of hospitalized patients).”

The description alone makes me sleepy.

Source: Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients A Randomized Clinical Trial JAMA Internal Medicine DOI:10.1001/jamainternmed.2021.7387