Researchers Submit Patent Application, “Population-Based Medication Risk Stratification And Personalized Medication Risk Score”, for Approval (USPTO 20210375486): Patent Application

2021 DEC 16 (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 inventors CICALI, Brian (Gainesville, FL, US); MICHAUD, Veronique (Orlando, FL, US); TURGEON, Jacques (Philadelpia, PA, US), filed on October 31, 2018, was made available online on December 2, 2021.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “Medications are vital for the prevention and treatment of diseases, illnesses, disabilities and death. However due to the biologically active nature of medications they can also cause bodily harm, especially when multiple medications are taken simultaneously, a condition described as polypharmacy. Patients with polypharmacy are at a known risk for multi-drug interactions which can lead to adverse drug events responsible for negative changes in quality of life and even death. Due to this, the prediction of potential multi-drug interactions and adverse drug events are a major focus for drug developers, so much so that the FDA requires all drug-labels to report known interactions with other medications. Even still, multi-drug interactions and adverse drug events continue to cause health and financial issues for patients and their care providers. According to a report from the Centers for Medicare and Medicaid Services (CMS) in 2014, adverse drug events cause 125,000 hospitalizations, 1 million emergency department visits, 3.5 million doctor’s office visits and more than 100,000 deaths per year. With these numbers in mind, better methods to estimate the risk associated with drug intake are required.

“A plethora of clinical data has been generated concerning the causes of adverse drug events. One of these data sources is multi-drug interactions due to a hindered capacity to excrete or metabolize drugs. For instance, the FDA, CDER and US Department of Health and Human Services have published guidelines on how to study and report drug metabolism characteristics and drug interactions. Drug metabolism is the body’s form of defense that is responsible for excreting toxins, such as medications, out of the body. The guidelines mentioned above focus mostly on characterizing singular medication’s metabolic pathways within the body. These characterization results are then compared to other singular medication characterizations and conclusions are made. If two drugs share the same metabolic pathway then it is concluded that they interact with each other, a condition termed Competitive Inhibition. One drug to one drug interaction studies are then required to determine the extent of drug interaction through competitive inhibition for these two drugs. Even with this system in place, forty-five to fifty million adverse drug events are observed per year in the US. In fact, adverse drug events represent the fourth leading cause of death in the United States. A major reason for this is the current methodology for predicting multi-drug interactions is immensely complex when a patient is taking multiple medications at once. For example, if a patient is taking twelve medications due to multiple health issues, sixty-six singular drug-drug pairs need to be examined for interaction potential. Since there are only a few pathways that drugs are metabolized within the body, competitive inhibition and thus multi-drug interactions are unavoidable in these cases. One of the aspects of this presented invention is to take advantage of developed techniques to identify and then analyze these multi-drug interactions (Patent Application No. 62/111,707, filed on Feb. 4, 2015, entitled “Medication Risk Mitigation Matrix System and Method,” and patent application Ser. No. 15/008,555, filed on Jan. 28, 2016, entitled “Medication Risk Mitigation Matrix System and Method,” which are incorporated by reference herein in their entireties, and may be referred to hereinafter as “the ‘707 application” and “the ‘555 application”, respectively) and, in an innovative method not yet discovered within the industry, to attribute a quantitative risk score value to multi-drug interactions observed within a patient’s drug regimen.

“Adverse drug events are not only caused by competitive inhibition of metabolic pathways. For instance, there are multiple tools available to measure varying aspects of medication risk available that are outside of competitive inhibition. These tools include the Drug Burden Index, Sedative Load Model, Tool 3I for Medication Fall Risk, Opioid Risk Tool, and the Beers Criteria, to just name a few. There remains a need for systems that measure and stratify risk of the occurrence of adverse drug events due to a particular medication regimen.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “Embodiments of the invention described herein generally relate to a system and method for population based medication risk stratification and for generating a personalized medication risk score. In some embodiments, the system and method may pertain to the development of a software that relates pharmacological characteristics of medications and patient’s drug regimen data into algorithms that (1) enable identification of high-risk patients for adverse drug events within a population distribution, and (2) allow computation of a personalized medication risk score which provides personalized, evidence-based information for safer drug use to mitigate medication risks. Each part of these embodiments of the invention contributes to the recognition of the risk of drug-related adverse events and empowers a care provider to mitigate the harm arising from taking multiple medications, including prescription, over-the-counter, and herbal products.

“Embodiments of the invention described herein generate a Medication Risk Score for Drug-induced Long QT Syndrome and potentially lethal cardiac arrhythmias. The newly developed algorithms which are part of this invention utilize, in part, information from developed proprietary information (U.S. Provisional Application No. 62/338,704, filed May 19, 2016, entitled “Methods of Treatment Having Reduced Drug-Related Toxicity and Methods of Identifying Patient Harm Arising from Prescribed Medications,” and International Application No. PCT/US2017/033539, filed on May 19, 2017, entitled “Methods of Treatment Having Reduced Drug-Related Toxicity and Methods of Identifying The Likelihood of Patient Harm Arising from Prescribed Medications”, which are incorporated by reference herein in their entireties, and may be referred to hereinafter as “the ‘704 application” and “the ‘539 application”, respectively).

“In brief, embodiments of the invention described herein include algorithms that look at multiple factors that influence a medication regimen’s likelihood of causing a negative health effect. The following factors are used to drive the software’s algorithms to determine risk in respect to patient’s medication regimen:

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“The number of prescribed medications

“The indices of anticholinergic burden

“The indices of sedation effects

“The risk of QT-interval prolongation

“The Competitive Inhibition of the regimen

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“The combinatorial assessment of these individual risk factors provides a comprehensive approach to medication risk stratification at a population level as well as the possibility of personalized medication risk mitigation at the individual level by interpreting a

“Personalized Medication Risk Score. Hence, the output of this assessment is a quantitative score that can be used to measure and stratify risk of the occurrence of adverse drug events due to a particular medication regimen. This quantitative score also allows the identification of patients at higher risk for multi-drug interactions within a population, and thus require medication risk mitigation more so than others. This identification ability is of high importance for care providers who seek to know which of their patients require immediate attention. Once these patients are recognized, the software tool provides a personalized snapshot of the risk factors described above, empowering the provider to mitigate their medication risk accurately and efficiently.

“To ensure the accuracy of the invention described herein, the scoring mechanisms have been validated against literature and clinical cases. The software has been applied to various healthcare population settings numbering approximately 800,000 patients. In these applications various high-risk groupings were identified and criteria were generated for patients of the highest-risk through statistical aggregation. The tool has been found to typically identify the top 15 to 20 percent of high risk patients for each risk factor as well as identify the approximate top 5 to 15 percent of the population considered at highest risk. Not only were high risk members of the population identified, but the embodiments of the invention generated personalized medication risk score snapshots, which empower health professionals to generate recommendations which mitigate the established risks

“According to embodiments of the invention described herein, the advantages are obtained by using a method for estimating the risk of medication-related problems due to medication characteristics in accordance with a patient’s overall medication regimen. The invention allows for the creation of personalized, evidence-based recommendations for healthcare providers. Once determined using the invention, the risk of medication-related problems can be quantitatively compared to identify high-risk patients within a population.

“Concerning one aspect of the invention, the method utilizes a non-transitory computer readable storage media having program instructions stored in a memory device. The instructions are executable by a processor to direct the performance of operations to estimate patient medication-related risk. The program instructions for determining the medication-related risk scoring may include one or more of the following steps:

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“Importing a first data set comprising patient-specific drug regimens, converting the medication data into their respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning an aggregated risk score. According to an alternative embodiment, this step includes importing a first data set comprising patient-specific drug regimens, converting the medication data into their respective active ingredients, associating the respective active ingredients with their risk of one or more side effects (e.g., by utilizing data from the FDA Adverse Event Reporting System), and assigning an aggregated risk score.

“Importing a second data set comprising the indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning an aggregated risk score.

“Importing a third data set comprising the indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning an aggregated risk score.

“Importing a fourth data set comprising the indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning an aggregated risk score.

“Importing a fifth data set comprising the metabolic pathways and extent of metabolism for each active ingredient, associate the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning an aggregated risk score.

“

“The datasets outline above are processed by 5 pre-defined algorithms to calculate a patient-specific medication risk stratification score for each factor. The factor scores are then combined to determine a Personalized Medication Risk Score.

“Following the above operations, according to another aspect of the invention, personalized medication risk mitigation snapshots are generated for each medication regimen that is analyzed. The snapshot is generated by assembling the outputs of the processing instructions between each risk factor data set and algorithm. These snapshots provide a personalized overview of each patient’s medication-related risk. The snapshot empowers a healthcare professional to provide accurate, evidence based medication risk mitigation recommendations.

“In accordance with another aspect of the invention, each medication risk factor score and the total medication risk score of each medication regimen is compiled into a data set. The compiled data set is then statistically aggregated by risk factor score criteria into high and low risk groups based on literature and clinical observations. The high-risk groups are then analyzed by repeat string search to categorize those members who are included in all high-risk groups. Typically, the output is approximately 5 to 15 percent of the population, who are considered at most risk for medication related problems including, but not limited to, adverse drug events.

“In an embodiment, the invention includes a method of treating a patient who is identified as being at high risk for an adverse drug event, wherein the patient has been prescribed a drug regimen that includes at least a first drug and a second drug, the method includes one or more of the steps of:

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“(a) removing the first drug and/or the second drug from the patient’s drug regimen;

“(b) reordering which of the first drug and the second drug is taken first by the patient;

“© changing the timing of when the first drug and/or the second drug are taken by the patient;

“(d) changing time of day when the first drug and/or the second drug are taken by the patient;

“(e) replacing the first drug and/or the second drug of the patient’s drug regimen with one or more alternate drugs of the same class and/or category as the first drug and/or the second drug;

“(f) reducing the dosage of the first drug and/or the second drug from an initial dosage to a reduced dosage;

“(g) increasing the dosage of the first drug and/or the second drug from an initial dosage to an increased dosage;

“(h) performing a surgical procedure; and

“(i) adding at least a third drug to the patient’s drug regimen;

“wherein the one or more steps are provided to reduce the patient’s high risk of the adverse drug event.

“’

There is additional summary information. Please visit full patent to read further.”

The claims supplied by the inventors are:

“1. A non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor, perform a method comprising: calculating an aggregated risk factor score representative of each of two or more risk factors associated with a patient’s drug regimen, wherein the two or more risk factors are selected from the group consisting of: 1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, and 5) competitive inhibition of the active ingredients in the drug regimen; and combining the aggregated risk factor scores calculated for each of said two or more risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event.

“2. The non-transitory computer-readable medium according to claim 1, wherein the method comprises calculating the risk factor score representative of five or more risk factors associated with the patient’s drug regimen within a patient population, wherein the risk factors comprise: 1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, and 5) competitive inhibition of the active ingredients in the drug regimen.

“3. The non-transitory computer-readable medium according to claim 1, the method comprising combining the aggregated risk factor scores calculated for each of said two or more risk factors to further provide a data set representative of a patient population’s risk of an adverse drug event.

“4. The non-transitory computer-readable medium according to claim 1, the method comprising providing the quantitative personalized medication risk score as a visual representation of a relative risk of each of said risk factors with respect to each other.

“5. The non-transitory computer-readable medium according to claim 1, wherein calculating the aggregated risk factor score representative of the number of active ingredients in the drug regimen comprises importing a data set comprising patient-specific drug regimens, converting said data set into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the risk factor score representative of the number of active ingredients in the drug regimen.

“6. The non-transitory computer-readable medium according to claim 1, wherein calculating the aggregated risk factor score representative of the anticholinergic burden of the active ingredients in the drug regimen comprises importing a data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen.

“7. The non-transitory computer-readable medium according to claim 1, wherein calculating the aggregated risk factor score representative of the sedative burden of the active ingredients in the drug regimen comprises importing a data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen.

“8. The non-transitory computer-readable medium according to claim 1, wherein calculating the aggregated risk factor score representative of the QT-interval prolongation risk of the active ingredients in the drug regimen comprises importing a data set comprising indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen.

“9. The non-transitory computer-readable medium according to claim 1, wherein calculating the aggregated risk factor score representative of the competitive inhibition of the active ingredients in the drug regimen comprises importing a data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen.

“10. The non-transitory computer-readable medium according to claim 1, wherein calculating each of the aggregated risk factor scores comprises: importing a first data set comprising patient-specific drug regimens, converting said data set into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the aggregated risk factor score representative of the number of active ingredients in the drug regimen; importing a second data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen; importing a third data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen; importing a fourth data set comprising indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen; and importing a fifth data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen.

“11. A processor configured to implement the non-transitory computer-readable medium with instructions stored thereon according to claim 1.

“12. A client device comprising the processor of claim 11, a communication infrastructure, a memory, a user interface and a communication interface.

“13. A system comprising one or more computing devices, the one or more computing devices comprising one or more processors according to claim 11.

“14. A computer-implemented system for determining a patient’s risk of an adverse drug event based as least on the patient’s drug regimen comprising: a database containing two or more of the following data sets related to the patient’s risk factors: (1) number of active ingredients in the drug regimen, (2) anticholinergic burden of the active ingredients in the drug regimen, (3) sedative burden of the active ingredients in the drug regimen, (4) QT-interval prolongation risk of the active ingredients in the drug regimen, and (5) competitive inhibition of the active ingredients in the drug regimen; and a calculating module, which applies algorithms to said two or more data sets and calculates a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event.

“15. The system according to claim 14, wherein the calculating module calculates the quantitative personalized medication risk score based on aggregated risk factor scores representative of each of the two or more data sets.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent application, see: CICALI, Brian; MICHAUD, Veronique; TURGEON, Jacques. Population-Based Medication Risk Stratification And Personalized Medication Risk Score. Filed October 31, 2018 and posted December 2, 2021. Patent URL: https://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=%2220210375486%22.PGNR.&OS=DN/20210375486&RS=DN/20210375486

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