Personalized treatment and P4 medicine

The prevalence⁸ of obstructive sleep apnea and its association with potentially serious consequences and comorbidities⁶ makes it ripe for the adoption of the P4 medicine approach. Based on targeted prediction, prevention, personalization and patient participation, P4 medicine could expand screening and diagnosis so OSA could be treated earlier and more effectively.

The challenge of prediction: who is at risk, and what is the risk?

A notable idiosyncrasy of OSA is that it is not easy to confidently predict who has it. Screening instruments¹ like the Berlin or STOP-BANG questionnaires use information about BMI, age, gender and symptoms to assess the likelihood that a patient has OSA, but they are relatively blunt instruments.

For example, there is increasing evidence that prevailing beliefs about the relationship between gender and OSA are inaccurate¹⁵ and that OSA might be over-diagnosed among the elderly.⁵ Existing screening methods may be missing patients with clinically relevant disease while sending others for unnecessary testing and—potentially—unnecessary treatment.

At the testing stage, accurate prediction is undermined by an over-reliance on the apnea-hypopnea index (AHI), a metric deeply embedded in research and clinical practice as the main parameter of OSA diagnosis and classification.^5,6 While an AHI score of ≥ 30 indicates an elevated risk of systemic hypertension,⁵ in other respects AHI is poorly associated with OSA symptomatology and outcomes. It is not an intrinsic marker of the pathophysiological profile of OSA.⁵ AHI may, in fact, overestimate the prevalence of OSA in the general population⁵ while underdiagnosing prevalence and severity in other patient groups, particularly women.^15,16

An opportunity to rethink diagnosis

P4 medicine provides an opportunity to revisit current approaches to OSA screening and diagnosis. The 4P medicine approach pioneered by Dr. Leroy Hood, offers a means to: Predict who will develop disease and co-morbidities; Prevent rather than react to disease; Personalize diagnosis and treatment; have patients Participate in their own care.¹ In the future, it is likely that diagnostic biomarkers, morbidity biomarkers and indicators of treatment response, particularly in asymptomatic subjects,^1,5 may make it possible to predict with accuracy which patients have OSA that requires treatment. In the meantime, however, it should be possible to refine existing screening instruments, for example by incorporating insights from patient phenotyping studies, based on the cluster analysis of real-life data,^21,22 that are shedding light on the risk burden of different patient profiles.

When it comes to diagnosis, alternative markers like hypoxic burden (HB), delta heart rate (DHR) and pulse wave amplitude drop (PWAD) do exist, although their current use is predominately for research. OSA-specific HB⁶, for instance, uses standard polysomnography (PSG) to simultaneously capture three dimensions of OSA, namely the frequency, depth and duration of respiratory event-related desaturation signals, whereas AHI captures frequency only. HB appears to consistently improve risk determination for both fatal and non-fatal cardiovascular events. It is a good example of a predictive test that can be used alongside existing testing methodologies and metrics to provide more nuanced information about the risk profile of an individual patient and link diagnostic decisions to decisions to treat.

The challenge of prevention: who should be treated, and when is treatment effective?

The prevention of symptomatic disease through treatment is hampered by the heterogeneous presentation of OSA: significant variations exist between patients in terms of symptoms, comorbidities, consequences and treatment outcomes. For example, women are less likely to be diagnosed with and treated for OSA and are more likely to be diagnosed with ‘mild’ OSA, even though research shows that women with mild OSA suffer a higher symptomatic burden and receive a greater benefit from treatment than men with the same diagnosis.⁷ Does this reflect a true, gender-based difference in treatment response or an issue with existing methods of diagnosis and disease classification?

OSA also has documented but poorly understood associations with a number of serious conditions and consequences.⁵ In fact, patients with OSA have an increased incidence of major cardiovascular events^6,9,10,11 and a mortality rate almost two times higher than the general public.¹² Research shows OSA treatment can relieve OSA symptoms, significantly improve quality of life and improve outcomes for a range of conditions, such as atrial fibrillation,¹⁸ type 2 diabetes¹⁹ and depression.²⁰ Research into the relationship between PAP therapy – the standard treatment for OSA – and some cardiovascular outcomes has, however, historically been less persuasive.^14,17

An opportunity to rethink treatment

It is likely that at least some of these inconsistencies and variations are due to the almost exclusive reliance on AHI to characterize OSA and measure treatment response. This myopic approach may also have skewed the results of clinical trials into OSA for decades,^5,6 with a corresponding impact on our understanding of clinical risk and treatment efficacy. Broadening the view to include other factors, such as symptomology and comorbidities, can incorporate key principles of P4 medicine.

Fortunately, access to real-world data is now enabling researchers to shine a light on how OSA and OSA therapies affect patient health and outcomes. For example, a recent meta-analysis incorporating data from more than 1.1 million patients found that PAP therapy significantly reduces all-cause and cardiovascular mortality in patients with OSA and identified that the reduction in all-cause mortality risk increased as nightly PAP usage increased.¹³ Disease prevention is easier when there is clear evidence to support interventions.

A P4 mindset is also being used to refine decisions to treat. Sweden has developed guidelines for OSA treatment³ based on factors likely to influence outcomes, notably OSA-related symptoms, cardiometabolic co-morbidities, AHI and age. This holistic approach results in a matrix with five levels of indication to treat, from very weak to very strong. In parallel, European experts have explored a modified Baveno classification,⁴ a multi-component grading system based on retrospective analysis of the pan-European ESADA patient cohort. It combines symptoms, AHI, co-morbidities and cardiovascular risk to provide a weak, intermediate or strong treatment indication. The Baveno and Swedish examples show that disease prediction does not have to be high tech to be useful for disease prevention.

From a P4 perspective, it is interesting to note that the Swedish guidelines emphasize patient involvement in the decision to treat, and view standardization and personalization as complementary aims. Standardization of guidelines ensures equitable access to diagnosis and treatment, as does the personalization of treatment decisions through informed discussion. The guidelines also draw attention to the need to consider the potential for harm caused by under- and over-treatment – driven largely by inadequate diagnostic tools – and the potential to correct this.³

Conclusion

One size fits all isn’t the right answer for the diagnosis and treatment of complex, heterogeneous conditions like OSA. The P4 approach to medicine provides a pathway to develop new, evidence-based methods for understanding how OSA – and OSA treatment – affect patient health and outcomes. The knowledge needed to predict OSA and personalize treatment is fast being acquired, the understanding required to prevent disease is complex but not out of reach. Together, these elements have the potential to support clinicians to deliver more accurate and effective care and to reduce the impact of co-morbidities on patients’ quality of life and overall health. The shift towards preventive care can start here, with the conscious adoption of a P4-based approach to OSA.