Researchers Submit Patent Application, “Integrated Platform For Connecting Physiological Parameters Derived From Digital Health Data To Models Of Mortality, Morbidity, Life Expectancy And Lifestyle Interventions”, for Approval (USPTO 20190148020)

2019 MAY 31 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor DU PREEZ, Franco Bauer (Cumming, GA), filed on November 13, 2018, was made available online on May 16, 2019.

The patent’s assignee is LifeQ Global Limited (San Gwann, Montana, United States).

News editors obtained the following quote from the background information supplied by the inventors: “With recent advances in the quality of medication and healthcare, longevity of humans has increased significantly. However, this acquired longevity is not necessarily accompanied by acceptable health levels, but rather by multiple, and often complex, terminal health conditions. This co-occurrence of at least two chronic illnesses in an individual, termed multi-morbidity, with the prevalence amongst the elderly of any given nation usually exceeding 60%, does not only greatly diminish the quality of life for those affected, but has become one of the main challenges in, and burdens on, health-care and -insurance worldwide.

“The monitoring of population health is essential not only in terms of global economy, but also as a measure of quality of life of a nation. Various measures of health have been developed and improved upon, which lies beyond the scope of this discussion.

“In recent years, a growing body of research on how variation in physiological measurements may relate to long term outcomes, such as the risk of disease development and all-cause mortality, has emerged. In addition, a significant body of research exists that demonstrates the expected changes in physiological parameters in response to lifestyle choices such as performing aerobic exercise.

“In survival analysis, several techniques are demonstrated in the scientific literature to quantify the health risk associated with various physiological variations. One of the more commonly used techniques, Cox regression, is often used to express the logarithm of all-cause mortality risk as a function of age (or time) plus a linear combination of demographic and physiological risk factors. By using the specific values of physiological parameters for an individual in such population based models, it is possible to compute mortality or morbidity risks and to compute an expected life expectancy in years.

“Many studies, spanning across groups of up to millions of individuals, have been conducted to improve our understanding of the all-cause mortality and disease risk implications associated with variations in physiological parameter values. An example of such a parameter is the resting heart rate of a person, for which a dose dependent increase in the risk of all-cause mortality has been observed. The risk associated with such a physiological parameter is often expressed as the ratio of the risk that a person with said resting heart rate is exposed to, compared to the risk of an individual or population that holds a normative value for the parameter. This ratio of hazards (hazard for subject/hazard of individual or group with normative parameter value) is known as the hazard ratio and is available for many physiological parameters.

“A unit, such as all-cause mortality risk, cause-specific risk, morbidity risk, and the hazard ratio or life expectancy, mediates the expression of the consequences of lifestyle choices as a single number with a common understandable unit, for example, years of life expectancy gained or lost associated with a specific choice, such as commencing an exercise program, or, taking up smoking. This enables the direct comparison of these choices and optimization of various lifestyle choices in a numerical fashion. Similarly, methods exist for converting the survival analysis associated with life expectancy figures to a so called biological age by calculating the equivalent age of a reference population for which the all-cause mortality risk is equal to the all-cause mortality risk of a specific individual in question, i.e., the risk equivalent age.

“Research has also been performed to elucidate the positive correlation between changes in physiological parameters to lifestyle interventions exemplified by, but not limited to, diet and exercise regimes. An example of such a study is the response of overall aerobic fitness (as measured during a VO2max test) to exercise intervention. Depending on the frequency, duration, intensity and initial fitness levels of a participant, specific changes in VO2max have been observed. It is possible to capture such data in a model to extrapolate from past studies directly to the individual, whereby expected changes in VO2max value in response to a planned duration, intensity and frequency of exercise, may be provided. Similarly, it is possible to make predictions of planned weight loss values, based on estimates of an individual’s metabolic rate and diet and how weight loss would affect VO2max value, which is expressed in ml/min/kg of body mass. Changes in blood pressure values in response to changes in dietary sodium levels is another area that has been well studied and where a projection of expected changes at the hand of a low sodium diet may be provided, given an online diary and/or blood pressure measurements.

“Using body monitoring technology, it has also become possible to track the lifestyle changes implemented by users, in a passive way. By using a wrist worn wearable device equipped with one or more optical sensors as well as an accelerometer, the measuring of real-time heart rate and exercise activity is facilitated. Moreover, the passive tracking of the frequency, intensity and duration of exercise can be enabled which, in turn, could be used to compare measured behavior to planned behavior.

“Physiological parameters such as body mass, blood pressure and VO2max may be tracked either now or in the foreseeable future, by using body monitoring technology. For example, various connected Wi-Fi scale models exist that automatically upload the weight of a user to a cloud server. Regarding wearable technology, some devices include a sub-maximal exertion protocol which may be employed in conjunction with an exercise treadmill to obtain frequent estimation of the VO2max value of said user. Similarly, a connected sphygmomanometer and/or less intrusive body monitoring technologies may be used to measure blood pressure values in a more continuous fashion, after which said values may be communicated to a cloud server. The platform proposed herein has the necessary architecture for considering data from such external services via API (Application Programming Interface) calls or other relevant methods for sharing and accessing data and calculations in an anonymized, Health Insurance Portability and Accountability Act (HIPAA) compliant manner.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor’s summary information for this patent application: “Certain physiological parameters, exemplified by, but not limited to VO2max value, RHR, maximum heart rate and BMI are indicative of morbidity- and mortality risk. Embodiments of the claimed invention comprise methods by which data gathered from, for example, wearable devices, are used to track the value of these parameters to predict morbidity and mortality risk and derivatives thereof, such as life expectancy and biological age. In addition, lifestyle choices such as exercise can also be tracked to project how current lifestyle will affect said physiological parameters and also how that will affect morbidity- and mortality risk and derivatives thereof. Moreover, in the case of predicted life expectancy, the disclosure can produce a value in years, a single unit wherein the impact of different lifestyle choices can be expressed and compared against each other to make an optimal choice.”

The claims supplied by the inventors are:

“1. A system, comprising: a plurality of sensors configured to produce a plurality of signals related to a physiology of a first individual; a first device configured to receive the plurality of signals from the plurality of sensors and to generate a plurality of data streams based on the plurality of signals; and a second device different from the first device and configured to receive the plurality of data streams, wherein the second device is configured to be in communication with a cloud computing device, wherein at least one of the first device, the second device, or the cloud computing device is further configured to: derive a derivative of each of the plurality of data streams, derive a plurality of physiological parameters from the plurality of data streams, derive one or more morbidity or mortality associated parameters based on the plurality of physiological parameters, and transmit the morbidity or mortality associated parameters to one or more computing devices of the individual or a permitted third party.

“2. The system of claim 1, wherein the morbidity or mortality associated parameters are replaced with the plurality of physiological parameters in the context of ranges associated with increasing or decreasing levels of mortality.

“3. The system of claim 1, wherein the permitted third party is an insurance provider and the individual is a subscriber to the insurance provider, and wherein the morbidity or mortality associated parameters are used to determine at least one of a premium, a benefit, and a reward for the individual.

“4. The system of claim 1, wherein at least one of the first device and the second device is configured to: derive the derivative of each of the plurality of data streams; and transmit at least one of the plurality of data streams and the plurality of derivatives of the data streams to the cloud computing device.

“5. The system of claim 4, wherein at least one of the first device, the second device, or the cloud computing device is further configured to: derive a plurality of behavioral parameters from the plurality of data streams, wherein at least one of the morbidity associated parameters and the mortality associated parameters of the first individual is expressed as at least one of: (i) the plurality of physiological parameters or the plurality of behavioral parameters relative to a reference value of at least one of the plurality of physiological parameters or the plurality of behavioral parameters, the reference value for a particular physiological parameter or particular behavioral parameter being associated with a reference risk level for the particular physiological parameter or particular behavioral parameter, (ii) a mortality or morbidity hazard ratio with respect to a behavioral parameter or a physiological parameter, (iii) a relative risk with respect to a behavioral parameter or a physiological parameter, or (iv) a score expressing an ordering of an increasing or decreasing level of a mortality or morbidity risk.

“6. The system of claim 5, wherein at least one of the first device, the second device, or the cloud computing device is further configured to: determine an estimated combined mortality associated parameter or an estimated combined morbidity associated parameter based on at least one of the plurality of physiological parameters and the plurality of behavioral parameters, wherein the estimated combined mortality associated parameter or combined morbidity associated parameter is determined using at least one of a first model and a second model, wherein the first model incorporates and de-confounds at least two of the plurality of physiological parameters or the plurality of behavioral parameters, and wherein the second model incorporates and de-correlates at least a first one of a first subset of the plurality of physiological parameters or plurality of behavioral parameters with respect to a second one of a second subset of the plurality of physiological parameters or the plurality of behavioral parameters; determine the morbidity or mortality associated parameters based on different models, wherein the different models are based on the first and second subsets of the plurality of physiological parameters or the plurality of behavioral parameters; and combine the mortality or morbidity associated parameters produced by said separate models into the combined mortality parameter or the combined morbidity associated parameter.

“7. The system of claim 6, wherein the first individual and a second individual are each part of a social network having a lifestyle plan, and wherein at least one of the combined morbidity associated parameter, the combined mortality associated parameter, and the plurality of physiological parameters and behavioral parameters is used to evaluate progress on the lifestyle plan, to project the benefit of a change to the lifestyle plan, or to compute an alternative lifestyle plan.

“8. The system of claim 5, wherein the at least one of the first device and the second device is further configured to: adjust a parameter of a model for computing life expectancy of the first individual by adjusting the overall risk modification value of the first individual, and determine a life expectancy value for the individual based on the adjusted parameter, wherein the parameter determines an estimated chance of the first individual surviving to the life expectancy value using the morbidity risk to quantify a chance of disability of the first individual.

“9. The system of claim 6, wherein at least one of the first device, the second device, and the cloud computing device is further configured to: determine a second risk modification factor for a particular age of a second individual different from the first individual, wherein the age of the second individual is different from the age of the first individual, and wherein the second risk modification factor is based on an aging model to predict one or more age related changes of the plurality of physiological parameters.

“10. The system of claim 6, wherein at least one of the first device, the second device, and the cloud computing platform is further configured to determine a second risk modification factor based on genetic data of the individual.

“11. The system of claim 6, wherein at least one of the first device, the second device, and the cloud computing platform is further configured to: calculate an estimated benefit of a plurality of behavioral choices for the first individual from the plurality of data streams based on at least one of the combined morbidity associated parameter or the combined mortality associated parameter.

“12. The system of claim 5, wherein at least one of the first device, the second device, and the cloud computing platform is further configured to: calculate a projection of at least one of a first risk and a first benefit associated with a range of different planned lifestyle choices for the individual based on at least one of the combined morbidity associated parameter and the combined mortality associated parameter.

“13. The system of claim 5, wherein the at least one of the morbidity associated parameter or mortality associated parameter provides a score for the individual.

“14. The system of claim 5, wherein the at least one of the morbidity associated parameter or the mortality associated parameter is expressed as a biological age associated with a reference group for which the at least one of the morbidity associated parameter and mortality associated parameter would attain the same value.

“15. The system of claim 5, wherein at least one of the first device, the second device, and the cloud computing platform is further configured to: receive data from a consented third party for deriving at least one of the plurality of behavioral parameters, the plurality of physiological parameters, and a plurality of medical parameters; and using the received data to determine at least one of the plurality of mortality associated parameters, morbidity associated parameters, a combined mortality associated parameter, and a combined morbidity associated parameter.”

For additional information on this patent application, see: DU PREEZ, Franco Bauer. Integrated Platform For Connecting Physiological Parameters Derived From Digital Health Data To Models Of Mortality, Morbidity, Life Expectancy And Lifestyle Interventions. Filed November 13, 2018 and posted May 16, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190148020%22.PGNR.&OS=DN/20190148020&RS=DN/20190148020

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