With today’s sleep apnea treatments, data is everywhere.
CPAP machines track nightly usage, mask leaks, and residual AHI. Oral appliances, neurostimulators, and other therapies increasingly come with their own dashboards. On paper, it might seem like clinicians already have everything they need to know whether treatment is working.
So why are some sleep physicians insisting on home sleep testing (HST) to monitor therapy effectiveness—sometimes even when CPAP data looks “perfect”?
The answer, they say, is that device-reported numbers don’t always tell the full story of what’s happening during sleep.
When CPAP Data Says “Success,” but Patients Still Feel Miserable
Sleep physician Ernesto Eusebio, MD, ran into a puzzling case with one of his obstructive sleep apnea patients. She had completed an in-lab CPAP titration, then tried autoPAP when fixed pressure wasn’t enough. Her CPAP device showed a residual AHI below 5—typically considered well controlled.
But she still felt awful.
Rather than dismissing her symptoms, Eusebio sent her home with a PPG-based home sleep test (ensoHST) designed for multi-night monitoring. The results were eye-opening: the HST detected numerous residual breathing events that the CPAP machine simply wasn’t reporting.
After adjusting her therapy using the HST data, her symptoms finally resolved.
“That’s when we realized what remote monitoring at home can show us,” Eusebio says.
He’s since seen similar scenarios—rare, but meaningful—where CPAP data alone failed to capture ongoing sleep-disordered breathing.
Why Some Physicians Are Expanding HST Beyond Diagnosis
Traditionally, home sleep tests have been used mainly to diagnose sleep apnea. But a growing number of clinicians are now using them to monitor therapy, especially in specific situations:
- While patients are waiting for an in-lab study and want to begin treatment sooner
- During titration of non-CPAP therapies, such as Inspire neurostimulation
- When patients feel better and decline to return to the lab but still need objective confirmation
In many Inspire patients, for example, multi-night HST data showing low residual AHI has eliminated the need for repeated in-lab visits.
What Home Sleep Testing Reveals That CPAP Can’t
Pulmonologist and sleep physician Sahil Chopra, MD, says longitudinal home sleep testing has fundamentally changed how he evaluates treatment success.
At his practice, Empower Sleep, patients often complete one to two weeks of baseline HST, followed by ongoing nightly monitoring during therapy.
What the data reveals surprises many clinicians.
“CPAP improves sleep quality in about a third of patients,” Chopra says. “Another third see no meaningful change. And in about a third, their sleep actually gets worse.”
CPAP adherence alone doesn’t guarantee healthy sleep. Home sleep testing allows clinicians to see changes in sleep architecture, fragmentation, and physiological stress—factors CPAP machines don’t measure.
For some patients, CPAP increases arousals due to endotypic traits like a low arousal threshold. Without HST data, these patients might be told their therapy is working when it’s not.
The Importance of Heart Rate and Physiology
Another critical metric missing from CPAP data is heart rate behavior during sleep.
“When heart rate dipping is absent, that’s a predictor of cardiovascular mortality risk,” Chopra explains.
HST-derived heart rate trends help clinicians assess whether therapy is truly normalizing physiology—or whether a referral to cardiology might be necessary.
“CPAP gives us three very limited metrics: usage, leak, and residual AHI,” Chopra says. “Home sleep testing shows us the health of sleep itself.”
Why Standardized Data Matters When Therapies Change
In Canada, where in-lab capacity is limited, Elaina Zebroff, RRT, co-owner of My Sleep Services in Calgary, uses HST not just for diagnosis but throughout therapy.
“When patients are diagnosed using home sleep testing, it makes sense to continue using those same metrics to evaluate treatment,” she says.
Her clinic places every new CPAP patient on 30 days of HST monitoring, followed by repeat monitoring at three, six, or twelve months, depending on progress.
This consistency allows for apples-to-apples comparisons, especially when patients switch therapies—something CPAP data alone can’t reliably provide.
“The algorithms across devices are different,” says sleep physician Dimi Barot, MD. “Standardizing your data source improves the quality of your clinical decisions.”
AHI Discrepancies That Raise Red Flags
One of the most striking findings clinicians report is a mismatch between CPAP-reported AHI and HST-measured AHI.
When discrepancies appear, the HST almost always shows more residual apnea.
“It’s not a small difference,” Chopra says. “Some patients labeled ‘well controlled’ on CPAP are actually still having moderate or even severe sleep apnea.”
Zebroff agrees. “We don’t use CPAP-reported AHI to guide clinical decisions,” she says. “Waveforms, flow limitation, and HST data matter far more.”
Beyond Apnea: Behavior, Environment, and Feedback Loops
Long-term HST also reveals how lifestyle factors influence sleep.
Changes in bedtime, alcohol intake, meals, and sleep position all show up in the data. This creates a powerful feedback loop that helps patients understand what improves—or worsens—their sleep.
For patients exploring CPAP alternatives or adjuncts like nasal strips or side-sleeping aids, nightly data shows what actually works.
“The more data you have,” Chopra says, “the more precise the recommendations become.”
The Tradeoffs: More Insight, More Logistics
Using HST for therapy monitoring isn’t without challenges. Device shipping, data review, and patient education can add complexity.
Some practices outsource logistics. Others dedicate staff to data management. Empower Sleep even built AI-powered software to flag patients who need intervention.
Still, many clinicians believe the effort is worth it.
Does HST Replace the Sleep Lab? Not Quite.
Even the strongest advocates caution against abandoning in-lab studies entirely.
Eusebio still relies heavily on lab-based testing and wishes there were more large-scale validation studies for HST in therapy monitoring. His approach is pragmatic: use multi-night data, and when possible, confirm findings in the lab.
“We trust lab studies where patients sleep for 20 minutes,” he says. “But home testing lets us see what happens night after night, in real life.”
The Bottom Line
Home sleep testing isn’t replacing CPAP data—or the sleep lab—but it is changing how clinicians define success.
By revealing residual events, sleep quality changes, cardiovascular signals, and real-world physiology, HST helps answer a critical question CPAP data alone often can’t:
Is the patient actually sleeping better?
For a growing number of sleep physicians, that question makes all the difference.
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