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  • Writer's pictureOzzie Paez

Blood Pressure Drugs: Too many choices and too little time

Pharmaceutical companies point to the growth of blood pressure drugs as a sign of progress in combating hypertension, the “silent killer”. There are currently more than sixty hypertension drugs broadly divided into eleven classes: 1. ACE inhibitors (angiotensin-converting enzyme inhibitors); 2. Aldosterone Receptor Antagonists; 3. Alpha Blockers; 4. Alpha-Beta Blockers; 5. 6. Beta Blockers; 7. Calcium Channel Blockers; 8. Central Antagonists; 9. Direct Renin Inhibitors; 10. Diuretics, and 11. Vasodilators.


Most hypertensive patients are prescribed two and up to five different drugs in a variety of doses, as clinicians strive to find effective drug-dosage combinations. To be practical, they must quickly find patient-specific drug-dose combination(s) that lower individual patient blood pressure below 140/80 mmHg. The longer the process takes, the higher the odds of patients becoming noncompliant by quitting diagnostic and treatment processes. Based on the literature and discussions with clinicians, we assume that clinicians can find effective drug-dosage combinations within twelve months of diagnosis for 40-60% of patients. The balance of their patients will require lengthier, more extensive efforts involving a variety of tests, multiple blood pressure profiles, and evaluations of different drugs and dosage combinations.


This paper explores the challenges faced by clinicians attempting to find an effective patient-specific drug dosage treatment by quantifying the number of available drugs and dose combinations. We developed models based on the number of available blood pressure drugs and the number of drug classes and typical dosages. This strategy made explicit decision-making barriers to identifying specific high efficacy drugs-dosage combinations. It also illuminates their impact on patient engagement and compliance.


Objectives and limitations

The objective of this study was to broadly quantify the implications of the number of available blood pressure drugs and drug categories on treatment strategy development and drug-dosage selection. The investigation did not consider the decision-making processes of specific clinicians and groups of clinicians. The focus is the implications of treatment options set size on doctors’ ability to select patient-specific high-efficacy treatments.


The treatment of hypertension follows a process starting with one or two prescription drugs. Dosage levels are adjusted, and the effects (efficacy) are measured and compared against the recommended blood pressure range. Doctors introduce additional drugs when the patient's blood pressure remains undesirably high despite treatment based on fewer drugs. Hypertension treatment protocols usually employ one to three drugs in different dosage levels. Patients with resistive and refractive hypertension are treated with four or more drugs, often at maximum tolerable doses.


Viewed from a decision-making perspective, finding drug and dosage selections with proven efficacy becomes increasingly difficult as the number of options increases. The process, based on educated trial and error, is aggravated by limited physiological measures. Specifically, doctors only have access to output variables, i.e., systolic and diastolic blood pressure. The effects of treatment on the underlying drivers of blood pressure (systemic/peripheral vascular resistance and cardiac output) are not measured by legacy cuff-based instruments, i.e., sphygmomanometers. Thus, doctors have limited information on how individual patients respond to drugs that target either or both factors.


Models and calculations


We applied combinatorics to calculate the number of treatment combinations given an initial set of drugs and doses. The approximate number of blood pressure drugs is known and the estimated average doses per drug varied from two to five based on current practices. The number of combinations was calculated using the binomial coefficient equation:


s = n! / ((n-k)! * k!)


Where s is the number of potential solutions, n is the number of available drugs or drug-doses, and k is the number of drugs in a treatment, or the number of drugs multiplied by the average number of doses. For example, if four drugs are prescribed and the average number of doses per drug is three, then k is four times three, or twelve.


We used this method to develop models based on disease definitions and prescription practices. The first models used the number of available blood pressure drugs and treatment protocols documented in the literature and clinician input. Dosage was not considered in this model because the number of potential solutions (millions) rendered it useless.


A second model was based on the number of drug classes (11), and drug dosages (2-3) commonly used by doctors in developing treatment strategies. Two additional models were developed based on documented definitions for resistive hypertension and refractive hypertension. Treatments for these conditions rely on a minimum of four drugs, each available in three to four doses.


The models also calculate the amount of time required for all drug and drug-dosage combinations to be prescribed and evaluated. It assumes that patients return for a reevaluation after three weeks on a new prescription. These calculations help to frame the process within time constraints and their impacts on clinicians, patient engagement, and patient compliance.


Hypertension Models


The first model used 63 drugs as the set of options and then calculated the number of combinations for each of two to five drugs. It then calculated the time (weeks and years) required for a doctor to prescribe and evaluate them. The time for four and five drug combinations was too large to offer useful insights.

Models based on Drug Classes and Dosage


The following models are based on drug classes (11) and an average of two and three doses per drug resulting in (n) 22 and 33 class doses, respectively. This modeling strategy was considered more realistic based on current practices reported in the literature and described by clinicians. The model estimated the number of combinations for treatments relying on two and three drugs. The results of our calculations are shown below:


The following calculations are based on an average of two doses per drug.

The following calculations are based on an average of three doses per drug.

Models of Resistive and Refractory Hypertension


The number of available drug-dose combinations has implications for the diagnosis and treatment of resistive and refractory hypertension. Unlike other conditions, their diagnoses are based on the classes and the number of drug-doses used to reduce and control patient blood pressure within established norms. Specifically, resistive hypertension is defined as:

· The patient is taking three different blood pressure medications at their maximally tolerated doses.

· One of the blood pressure medications is a diuretic.

· The patient’s blood pressure exceeds recommended levels (usually 130/80 mmHg),

· OR: The patient requires four or more medications to control their blood pressure[1].


Refractory hypertension is defined as "uncontrolled blood pressure despite the use of ≥5 antihypertensive agents of different classes, including a long-acting thiazide-like diuretic and an MR (mineralocorticoid receptor) antagonist, at maximal or maximally tolerated doses.[2]


Resistive and refractory hypertension calculations

We modeled resistive hypertension based on an average of four drugs and four doses available for each drug. These parameters were selected based on the literature and other discussions given that physicians often adjust dosages in their search for tolerable and effective drugs and dosages. Refractive hypertension was similarly modeled and included up to six different drugs with an average of our different doses.

Resistive hypertension calculations

Refractive hypertension calculations


Implications

While the number of drug dosage combinations can be calculated, the number of solutions (combinations with high efficacy) in each set of combinations is not knowable. For clinicians, the challenge of finding high-efficacy drug and dosage combinations is akin to finding the right needle(s) in a bucket of needles. Time and patient compliance are the key constraints since patient-specific treatments must be identified quickly enough to secure patient collaboration. Patients are the ultimate boundary condition since noncompliance increases with increasing time, inconvenience, and costs.


Discussion

The literature and conversations with clinicians suggest that many are confident of their ability to find effective drug-dosage combinations within a practical period of time, i.e., three months to a year. Performance numbers reported in peer-reviewed journals suggest that there is much overconfidence in these beliefs. Our experiences and continuing research across fields including medicine suggest that experts frequently overestimate their capacity to accurately predict outcomes including the efficacy of their solutions (treatments).


Researchers and practitioners including Daniel Kahneman (psychology, economics, decision-making), Friedrich Hayek (economics, decision-making), Thomas Sowell (economics and decision-making), Philip Tetlock (psychology), and Nate Silver (statistics and probability) have documented the limits of expert predictions and experts’ overconfidence in their predictive abilities. “Overconfidence is one of the best documented cognitive biases. In particular, judgments of one’s ability to make precise predictions, even from limited information, are notoriously overconfident… Wherever there is prediction, there is ignorance, and more of it than you think[3].


Published research and studies by agencies including the Centers for Disease Control and Prevention (CDC) have documented the intractably low efficacy of hypertension treatment strategies and clinical predictive accuracy. Gaps in applicable knowledge are also reflected in conflicting analyses. For example, despite the often quoted “Only about 1 in 4 adults (24%) with hypertension have their condition under control[4],” published research documents greater variability in outcomes based on treatment strategies and patient-specific factors:


Despite the development of new antihypertensive medications and a better understanding of the physiology of high BP, control rates remain low. Data available from the National Health and Nutrition Examination Survey 2003–2004 show that two out of three patients with hypertension had uncontrolled BP, and control rates decreased according to age. In the same study, control rates were 71.7, 63.5 and 50.0% among treated hypertensive patients aged 18–39, 40–59 and ⩾60 years, respectively[5].


The published prevalence of resistant hypertension reflects broader gaps in knowledge resulting and even lower diagnostic and treatment predictive accuracy: “Estimates of the prevalence of resistant hypertension have varied widely, with reported figures ranging from 2% to 40%.[6]” That’s barely better than a coin toss.


Conclusions


The results of our investigation suggest that the number of drug-dosage combinations makes it impractical for clinicians to quickly identify effective treatment strategies for patients whose blood pressure remains uncontrolled after initial treatment. The challenge is aggravated by legacy cuff-based blood pressure measuring devices that do not resolve the underlying drivers of blood pressure: systemic vascular resistance and cardiac output. This limitation blinds clinicians to the effects and interplay of blood pressure drugs beyond the output variables, blood pressure, and heart rate. Tools based on legacy blood pressure measuring technologies do not provide the information clinicians need to quickly identify effective drug-dosage combinations from large sets with hundreds to thousands of options.


Studies of expert decision-making suggest that clinicians frequently overestimate their predictive accuracy in selecting effective blood pressure drug-dosage combinations. Overconfidence is not unique or exclusive to medicine. Researchers have similarly documented expert predictive overconfidence across fields, practices, and industries. In the context of hypertension, impractically large sets of drug-dosage combinations and intractably low efficacy rates suggest that patient outcomes will not improve until better clinical technologies and decision-support tools become available to help clinicians quickly identify patient-specific treatment options with a high probability of success.


References

[1] Pathan MK, Cohen DL. Resistant Hypertension: Where are We Now and Where Do We Go from Here? Integr Blood Press Control. 2020 Aug 5;13:83-93. doi: 10.2147/IBPC.S223334. PMID: 32801854; PMCID: PMC7415451. [2] Maria Czarina Acelajado, Zachary H. Hughes, Suzanne Oparil, David A. Calhoun, Treatment of Resistant and Refractory Hypertension, March 28, 2019, AHA Journals, https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.118.312156# [3] Daniel Kahneman, Olivier Sibony, Cass R. Sunstein, Noise–A flaw in human judgment, p. 142, Kindle Edition, May 2021, Little, Brown Spark. [4] Facts about hypertension, Centers for Disease Control and Prevention, accessed May 29, 2023, https://www.cdc.gov/bloodpressure/facts.htm# [5] Pimenta, E., Stowasser, M. Uncontrolled hypertension: beyond pharmacological treatment. Hypertens Res 32, 729–731 (2009). https://doi.org/10.1038/hr.2009.108 [6] Pathan MK, Cohen DL. Resistant Hypertension: Where are We Now and Where Do We Go from Here? Integr Blood Press Control. 2020 Aug 5;13:83-93. doi: 10.2147/IBPC.S223334. PMID: 32801854; PMCID: PMC7415451.

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