For decades, Obstructive Sleep Apnea (OSA) has been diagnosed and treated using a surprisingly small slice of the information collected during sleep studies. In his PhD research Luca Cerina shows that sleep apnea is far more complex than a single severity score suggests, and that better, more personalized treatment becomes possible when we use richer data, smarter metrics, and simpler but high-quality home measurements. In short, the future of sleep apnea care lies in understanding the patient, not just the number.
Since sleep apnea was first described in the 1970s, research has shown that Sleep Disordered Breathing (SDB) is not one uniform condition but a broad spectrum of different patterns and patient types. People may experience full breathing pauses or partial reductions in breathing, sometimes mainly during dream sleep, sometimes mostly when lying on their back, sometimes with severe daytime sleepiness and sometimes with almost no obvious symptoms at all. These different patterns are not just technical details; they are linked to very different health risks, long-term outcomes, and responses to treatment. Two patients with the same diagnosis may therefore need very different therapeutic approaches.
The problem with a single number
Despite this complexity, the main measure used in diagnosis today is still the Apnea-Hypopnea Index (AHI), a number that simply counts how many breathing disruptions occur per hour of sleep. This number is useful, but it also reduces a complex night of sleep, breathing patterns, sleep stages, and body position into a single value. As a result, a large amount of valuable physiological information is collected but never fully used, and important differences between patients can remain hidden.
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