Evaluating Outcomes in Patients with Overactive Bladder within an Integrated Healthcare Delivery System Using a Treatment Patterns Analyzer

September 2016 Vol 9, No 6 - Clinical
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BACKGROUND: Overactive bladder (OAB) is a relatively common disease that has been linked to a variety of comorbidities, reductions in quality of life, and increased healthcare costs. Antimuscarinic agents are the standard of care among pharmacologic treatments for OAB, but these drugs are linked to high levels of anticholinergic burden, especially in the elderly.

OBJECTIVE: To demonstrate how efficient data analysis can be used to identify gaps in care as a result of improvement strategies for OAB within an integrated healthcare delivery system setting.

METHODS: We developed an OAB treatment patterns analyzer, a clinical outcomes software analysis program, to identify gaps in care, high anticholinergic burden, and potential quality improvement initiatives. Deidentified pharmacy and medical claims data from an integrated delivery network were imported into the OAB treatment patterns analyzer. Patients with a diagnosis of OAB who were continuously enrolled in the network between January 1, 2009, and December 31, 2013, were identified and were imported into the analyzer. The analyzer used National Drug Code; International Classification of Diseases, Ninth Edition, Clinical Modification; Current Procedural Terminology; and UB-92 codes to measure treatment patterns, comorbid conditions, anticholinergic burden, concomitant use with anticholinesterases, costs, and healthcare resource utilization.

RESULTS: Of 157,710 members in the integrated delivery network population, 7309 patients met the study eligibility criteria. Of patients taking medications for OAB, 85% were nonadherent and 73% discontinued treatment within 1 year. Among 1147 patients in the integrated healthcare delivery system who were using medications for OAB, 39 (3.4%) patients were concomitantly taking anticholinesterase drugs and an antimuscarinic agent. The per-month plan-paid cost per member was $318.67. Of all the patients with OAB within the population, the rates of all-cause office visits, emergency department visits, and hospitalizations were 81%, 6%, and 4%, respectively. The rate of clinically relevant anticholinergic burden was 16%, with higher rates among patients with dementia who were also receiving a branded (20%) or generic (24%) antimuscarinic drug.

CONCLUSION: In patients using medications for the treatment of OAB, the rates of medication persistence and adherence were poor. Antimuscarinic medications may place certain patient populations at risk for increased anticholinergic burden. Data included in the analyzer can be used to implement member-specific strategies to prevent poor outcomes and reduce associated healthcare costs and utilization.

Key Words: adherence, anticholinergic drugs, antimuscarinic drugs, drug utilization management, genitourinary system, healthcare utilization, overactive bladder, persistence, pharmacy practice, quality improvement

Am Health Drug Benefits.

Received January 11, 2016
Accepted in final form June 3, 2016

Disclosures are at end of text
Supplemental material online

The International Continence Society defines overactive bladder (OAB) as the presence of “urinary urgency, usually accompanied by frequency and nocturia, with or without urgency urinary incontinence, in the absence of urinary tract infection (UTI) or other obvious pathology.”1 Similarly, the American Urological Association (AUA) notes that OAB primarily consists of the 4 symptom components of urgency, urinary frequency, nocturia, and urge incontinence.2

OAB affects approximately 30 million American adults aged ≥40 years.3 The prevalence of OAB is correlated with increasing age and female sex.4,5 The disease has been linked to comorbidities such as dementia, falls and fractures, depression, skin infection, UTIs, obesity, diabetes, vulvovaginitis, and a reduction in quality of life.6-9 OAB is also associated with a significant utilization of healthcare services, with costs estimated to reach $26 billion annually (in 2004 US dollars), which is similar to the costs of depression or Alzheimer’s disease.10,11

Antimuscarinic medications have been available for several decades and are considered to be the standard of care for OAB; they are typically used after, or in combination with, behavioral therapy (eg, bladder training, bladder control strategies, pelvic floor muscle training, fluid management).2,12 Current treatment guidelines suggest that third-line treatment after the failure of behavioral therapies and an antimuscarinic agent or mirabeg­ron should include options such as neuromodulation and intradetrusor injection of onabotulinumtoxinA.2 Indwelling catheters and cystoplasty should only be considered as last-resort treatment options.2

Antimuscarinic drugs block the activity of muscarinic acetylcholine receptors on the bladder and elsewhere in the body; typical treatment options for OAB in the class include darifenacin, fesoterodine, oxybutynin, solifenacin, tolterodine, and trospium.13,14 These drugs are widely considered to have moderate efficacy in treating the symptoms of OAB13,14; however, antimuscarinic drugs with strong anticholinergic effects have the potential to increase a patient’s anticholinergic burden.15

The Anticholinergic Cognitive Burden scale is a practical tool that is used to assess the severity of negative anticholinergic effects on cognition in patients who are receiving anticholinergic drugs. The Anticholinergic Cognitive Burden scale is intended to provide clinicians with a simple, easy-to-use score that identifies the cumulative anticholinergic cognitive burden from medication use in older adults.15 Patients’ anticholinergic burden is based on the inherent properties of their medications. Anticholinergic drugs are assigned a score of 1, 2, or 3. Medications for the treatment of OAB with a score of 3, or a score of 2 in conjunction with any other anticholinergic medication with ≥1 overlapping days’ supply of the drug place patients into the positive anticholinergic burden group. Anticholinergic burden may have a negative impact on patients, specifically regarding cognitive function.15 In addition, antimuscarinic drugs are associated with a risk for drug interactions and drug–disease interactions,16 which can affect the patient’s comorbid conditions and lead to adverse events. Common anticholinergic side effects include dry mouth and constipation.17

More severe effects that can potentially be associated with untreated OAB and antimuscarinic agents include cognitive dysfunction and delirium.18-20 The National Committee for Quality Assurance (NCQA)’s Healthcare Effectiveness Data and Information Set (HEDIS) has also recognized the impact of these drug–disease interactions in the elderly, which has led to the development of a quality reporting measure to assess the use of potentially harmful medications, including anticholinergic drugs, in Medicare beneficiaries (aged ≥65 years) who have evidence of medical conditions such as a history of falls or dementia.21 Furthermore, anticholinesterases are often prescribed to patients with dementia to reduce cognitive or functional decline by increasing acetylcholine levels in the brain.7 Paradoxically, patients with dementia often have comorbid conditions, such as urinary incontinence, for which they may receive medications with anticholinergic properties, such as antimuscarinic agents.7 Anticholinergic agents and anticholinesterases have opposing pharmacologic effects; therefore, their simultaneous use for the treatment of dementia and urinary incontinence could reduce the effectiveness of one or both of the drugs.7

Given these potential adverse events, the use of antimuscarinic drugs should be closely evaluated and monitored. Patients aged ≥65 years are particularly susceptible to these adverse events, because they are more likely than younger patients to be prescribed these drugs, and they also often experience age-related changes in drug absorption and metabolism, which can exaggerate antimuscarinic properties.15

Furthermore, these side effects, as well as inadequate drug efficacy, poor patient education, and cost may lead to low persistence and adherence.12,18,22

Quality measures associated with a diagnosis of OAB and the safety of its treatment, such as urinary incontinence and anticholinergic burden in elderly patients, have been disseminated by HEDIS measures and the Centers for Medicare & Medicaid Services’ (CMS) Five-Star Quality Rating System Quality Measures for health plans and integrated healthcare delivery systems.23 These measures have been developed in response to the consequences of OAB and the potential concerns with the medications used to treat it, as well as the paucity of population-based disease management initiatives in OAB.

To support the implementation of quality measures related to the diagnosis and treatment of this condition, we developed an OAB treatment patterns analyzer to (1) identify gaps in care between recognized best practices and practice patterns in a health plan or an integrated healthcare delivery system setting; (2) provide customized population-level and patient-level measures of OAB treatment utilization (via National Drug Codes), comorbidities (via proxies such as International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes), safety (via proxies), and medication persistence (via prescription claims); and (3) identify opportunities to develop interventions that may promote quality improvement, and decrease healthcare resource utilization and costs.

The objective of this article is to demonstrate how efficient data analysis, using a clinical outcomes software analysis program (ie, the analyzer), can be used to identify gaps in care, as a result of improvement strategies for OAB within an integrated healthcare delivery system setting.

OAB Treatment Patterns Analyzer Design

The OAB treatment patterns analyzer was designed to identify gaps in care between recognized best practices and practice patterns in a health plan population through retrospective pharmacy and medical claims analysis. To begin the analysis, raw claims were sorted by unique patient identification information based on ICD-9-CM codes of interest (see Appendix at the end of this article). All pharmacy and medical claims were then pulled for the identified patients. Duplicate and nonfinalized claims were excluded from the analysis. Continuous enrollment was required for the duration of the study period.

After the data were uploaded, using the analyzer we performed validation testing on the pharmacy and medical claims data. The data were then analyzed according to the main parameters listed below, which were specifically chosen based on their influence on outcomes in patients with OAB. The complete analysis was then available for export and deidentification.

Although the analyzer did not validate each plan data set specifically, the data set was extensively validated via a retrospective database analysis using a 5% sample of the IMS PharMetrics claims data set. This 5% sample contained pharmacy and medical claims from July 2010 to June 2012; these claims were representative of claims for the commercially insured US population.

Study Parameters

Deidentified pharmacy and medical claims were obtained from a midsize integrated healthcare delivery system (approximately 500,000 members) and were mapped into the analyzer as described above.

All patients aged ≥18 years with ≥1 primary or secondary diagnoses of OAB during the study period (based on the presence of ICD-9-CM codes 596.x, 788.3x, 788.41, or 788.43) were included in the study sample.

The study period for each patient was 12 months postindex follow-up (defined as the first date of OAB diagnosis for each patient). Continuous enrollment in the integrated delivery network was required from the index date of OAB diagnosis through a 1-year outcomes assessment. Patients diagnosed with OAB during the study period between January 1, 2009, and December 31, 2013, were eligible to be enrolled in the study.

Patients were assigned to an OAB medication category based on the index medication, which was defined as the first prescription for an OAB treatment after the study’s start date (Figure 1).

Figure 1

The antimuscarinic medications evaluated in the analyzer included darifenacin (Enablex), fesoterodine (Toviaz), oxybutynin (Ditropan, Ditropan XR, Gelnique, Oxytrol Patch), solifenacin (VESIcare), tolterodine (Detrol, Detrol LA), and trospium (Sanctura, Sanctura XR), as well as the generic antimuscarinic drugs oxybutynin, tolterodine, and trospium. Of note, the use of mirabegron was not captured, because the drug was not available at the beginning of the study. Mirabegron utilization might have been present after the index therapy or after a first switch.

Patients could also be assigned to a group that did not receive pharmacotherapy if there was no evidence of a patient taking an OAB medication. We did not include any third-line therapies for OAB, such as onabotulinumtoxinA or neuromodulation therapy in the analyzer; therefore, patients with evidence of having received these therapies are currently included in the group that did not receive pharmacologic therapy. All medical and pharmacy claims for every date of service within the range of the study period were included in the analysis.

Medication Use Patterns

The analyzer measured medication persistence according to the rate of discontinuation and the average time to discontinuation for all patients receiving OAB medications. Discontinuation was evaluated by the percentage of patients who stopped taking their index OAB medication, which was defined as a gap of at least 45 days in medication supply. Discontinuation rate was defined as the number of patients who discontinued the index OAB medication divided by the number of patients in the index medication category.

The time to OAB medication discontinuation (ie, the average length of therapy) was defined as the sum of the date of index medication discontinuation minus the date of first prescription of an OAB medication, divided by the total number of patients who have discontinued the OAB medication.

The average time patients took to switch from their index medication (in days) and the index OAB medication switching rate was defined as the number of patients who switched from the index OAB medication divided by the total number of patients in the index medication category.

Class switching rate was calculated as the number of drug-level switching events in class category divided by the total number of drug-level switching events.

The class switching calculation was performed when patients were switching from a branded to a generic antimuscarinic drug, from a generic to a branded antimuscarinic drug, within branded antimuscarinic drugs, or within generic antimuscarinic drugs.

Furthermore, in-class switching was separated from the drug class switches, by calculating as the number of switches from one specified class to another specified class category divided by the total number of switches within the drug class.

To determine a switch, at least 45 days had to have passed from the date of the index OAB medication claim days’ supply before the new prescription was filled. The goal was to exclude patients who had multiple medications for OAB filled within a short period but who did not switch from their index medication. Primary nonadherence (ie, not filling a prescription for any reason) is not captured in claims data and is therefore not included in this analysis.

Outcome Measures

Treatment changes (ie, switching and persistence), comorbid conditions, medication adherence, anticholinergic burden, concomitant use with an anticholinesterase, pharmacy and medical costs, and healthcare resource utilization were compared for all patients in the sample and were stratified based on their index treatment. The analyzer also recorded the presence of comorbid conditions, including dementia, depression, skin infection, UTI, vulvovaginitis, obesity, and diabetes, based on ICD-9-CM codes (see Appendix at the end of this article, for ICD-9-CM codes used in the analyzer). These comorbidities were evaluated based on their documented association with OAB.

Darkow and colleagues compared the prevalence rates of the comorbidities in patients with and without OAB.8 These findings, along with the findings from a study by Brown and colleagues, demonstrate that OAB significantly increases a patient’s risk for the specific comorbidities listed above, which explains their inclusion in the analysis.8,9

Medication adherence was measured as the proportion of days covered (PDC), which was defined as the period covered (ie, allotted days supply) by a medication claim.24 The analyzer evaluated measures of the PDC in 2 ways. The overall rate of the PDC was calculated as the number of days of any OAB medication, divided by the number of days in the study period, multiplied by 100 to achieve the PDC percentage.

The percentage of patients achieving an acceptable level of the PDC (≥80%) was calculated as the number of patients meeting a PDC of ≥80%, divided by the total number of patients using an OAB medication, multiplied by 100.

We chose the threshold of 80% for the PDC because there is “reasonable likelihood” that a medication is achieving its intended benefits at this level of use, based on published literature.24 The Pharmacy Quality Alliance endorses the PDC as the preferred method for analyzing medication adherence.24 Medication possession ratio (MPR) does not account for medication discontinuation or switching between medications in the same class. Both scenarios are applicable to the analysis of OAB and would result in falsely inflated MPR values. Thus, we chose PDC because it produces the most accurate measure of adherence.24

Anticholinergic Burden

Anticholinergic burden was also calculated within the study population. Medications with anticholinergic properties were given a score of 1, 2, or 3, where 1 indicates possible anticholinergic activity and 2 and 3 indicate definite anticholinergic activity. A score of 3 represents more pronounced anticholinergic activity than a score of 2.

To establish whether a patient had anticholinergic burden, patients with OAB who were taking an anticholinergic medication with a score of 3 or were receiving an anticholinergic medication with a score of 2 in conjunction with any other anticholinergic medication (defined as ≥1 days of overlapped use during the follow-up period) were considered to have clinically relevant anticholinergic burden.25 The details of anticholinergic burden scoring are detailed in the Aging Brain Program guidance.25 Using the analyzer, we also performed a subgroup analysis of anticholinergic burden among patients with dementia that was based on their additional risk for anticholinergic burden.

Healthcare Costs

We calculated the overall and average healthcare costs for members receiving an OAB treatment by cost per utilizing member per month and for all members using cost per member per month. Cost was defined as the total plan-paid costs and does not include the member-paid costs. Healthcare resource utilization was assessed in terms of the percentage of patients with a claim for an office visit, emergency department visit, or hospitalization. OAB-specific visits and all-cause visits were assessed, and the subgroups were assessed by the type of index medication for OAB.


Of 157,710 members in the integrated healthcare delivery system population, 7309 patients met the study inclusion criteria of having a diagnosis code for OAB. Table 1 details demographic information about the patient population. The majority (71%) of patients were women, and 20% had comorbid conditions of interest. A total of 6162 (84%) patients had not taken a medication for OAB within the observation period, and the remaining 1147 (16%) patients had received an antimuscarinic drug.

Table 1

Of the 1147 patients taking a medication for OAB in the integrated healthcare delivery system cohort, the overall PDC rate was 39% (Table 2); only 176 (15%) of the patients achieved a PDC of ≥80%. Similarly, in the PharMetrics cohort, the overall rate of PDC was 43%, and 1266 (20%) of patients had a PDC of ≥80%.

Table 2

Table 3 provides information regarding medication discontinuation. In the study population cohort, patients using generic antimuscarinic drugs were more likely to discontinue therapy than patients using branded antimuscarinic drugs (77% vs 67%, respectively), and the average time to treatment discontinuation was shorter for patients using a generic antimuscarinic drug than for patients receiving a branded antimuscarinic drug (68 days vs 87 days, respectively).

Table 3

A total of 95 (8%) of the 1147 patients in the study cohort switched from their index OAB medication, with a higher rate of switching among patients who received a branded antimuscarinic drug than among those who received a generic antimuscarinic drug (14% vs 5%, respectively). The mean time to switching from index medication was 168 days (161 days for a branded medication and 180 days for a generic medication; Table 3). Additional details on therapy switching are available in Figures 2 and 3.

Figure 2

Figure 3

The overall rate of anticholinergic burden in patients who had evidence of OAB medication use was 16%. A subgroup analysis showed higher rates of clinically relevant anticholinergic burden in patients with dementia who were also receiving a branded antimuscarinic drug (20%) or a generic antimuscarinic drug (24%; Table 4). The concomitant use of an anticholinesterase and an antimuscarinic drug was present in 3.4% of patients taking an OAB medication (Table 5).

Table 4

Table 5

The total healthcare cost per utilizing member per month was $318.67, and the total per-member per-month healthcare cost was $14.77. Table 6 lists the costs by medical and pharmacy components. Claims for OAB-specific office visits, emergency department visits, and hospitalizations were present for 78%, 2%, and 1% of the population, respectively (Figure 4). Claims for all-cause office visits, emergency department visits, and hospitalizations were present for 81%, 6%, and 4% of the population, respectively.

Table 6

Figure 4


This study revealed low levels of persistence, low levels of adherence, and low levels of switching from index therapies. The evaluation of persistence data indicated that patients who received branded antimuscarinic drugs tended to have lower levels of discontinuation and longer time to discontinuation than patients receiving generic antimuscarinic drugs; however, patients receiving branded antimuscarinic drugs also showed higher rates of switching and slightly faster time to switching than patients receiving generic antimuscarinic drugs. In 4 previously conducted studies, the rates of patients who failed to refill their initial prescription for an OAB medication ranged from 35% to 83%.26-29 The discontinuation rates observed in our study were on the higher end of that range, potentially because of the longer period in which a patient could discontinue therapy. Collectively, these data highlight a lack of persistence with OAB medications.

Our analysis showed that a relatively low, but meaningful, percentage of patients had evidence of clinically relevant anticholinergic burden. Patients who received an antimuscarinic drug and were diagnosed with dementia or were concomitantly using other anticholinergic drugs also have an increased risk for harmful anticholinergic burden–related outcomes.

Reducing the use of high-risk medications in the elderly is already a focus of the CMS’s Five-Star Quality Rating System Quality Measures and the NCQA’s HEDIS measures for the Medicare Advantage population. Anticholinergic burden is one of the focal areas of this effort, which is primarily based on the Beers Criteria.16,30

Methods to identify and reduce anticholinergic burden, and in turn the risk for falls, would be of particular importance to organizations that are sensitive to high readmission rates in their elderly population. Safe medication use in elderly patients is a high priority, especially considering that this population frequently has a higher overall medication burden.21,23

After identifying with the OAB analyzer individual patients with relevant anticholinergic burden, health plans can focus on implementing targeted interventions with the goal of eliminating this burden and mitigating risks such as the use of lower doses, switching to treatments in the category with a lower-risk anticholinergic burden, or trying alternative therapeutic approaches.2


This analysis did not adjust for baseline severity of illness or for patient demographics that may affect outcomes (eg, socioeconomic status, ethnicity, or regional influences). It should be noted that the clinical scenario presented to the physician at the point of service (eg, medical history, concomitant disease states) may not be identical to patient-level assessments derived from claims analysis. In addition, other conditions may be relevant to patients with OAB who are receiving pharmacotherapy that may be burdensome but are difficult to quantify within a database analysis (eg, dry mouth, constipation, and UTIs), because these types of side effects are likely underreported within claims data. This analysis uses retrospective medical and pharmacy claims data to provide a cross-sectional snapshot of healthcare resource utilization patterns in patients taking medications for OAB and therefore cannot adjust or fully capture these types of data.

Since the completion of our analysis in August 2013, certain medications that were originally only available as branded therapies have become available as generic. The National Drug Codes of these generic alternatives were not available at the time of this analysis. A future analysis of more recent data could incorporate a review of treatment patterns of these generic options to evaluate whether treatment patterns have been affected by their entry into the market. In addition, third-line therapies for OAB, such as onabotulinumtoxinA or neuromodulation therapy, were not evaluated in this analyzer. Although unlikely, as a result of minimal utilization (data not shown), some patients might have been treated with one of these therapies and would not be captured in the analyzer, which would falsely elevate the number of patients diagnosed with OAB who were not receiving pharmacotherapy.

As stated before, the use of the currently available beta3-adrenergic medication mirabegron was not captured during the time period that was evaluated. The utilization of mirabegron may have been present, but it was not evaluated as an index therapy or a first switch. As an alternative, an evaluation of the overall cross-section utilization of therapies could provide complementary data to this analysis, which was focused on the initiation and line of therapy, and could be beneficial to understanding other aspects of OAB medication utilization.

Although this study focused on anticholinergic burden as a measure of potentially inappropriate medication use in older adults, it did not evaluate medication use based on the Beers Criteria, which are often used to evaluate prescribing patterns in patients aged ≥65 years.16 This study analyzed claims for commercially insured patients, who are typically aged <65 years. Therefore, we did not evaluate the population in relation to the Beers Criteria. Comparing anticholinergic burden findings with the Beers Criteria may provide context for improving the quality of care in patients with OAB.

The analyzer used in this study was specifically created for OAB and is not translatable for use in other diseases.


The results of this analysis suggest that the majority of patients with OAB who received pharmacologic treatment during this study period did not persist with these medications, thereby perpetuating an area of unmet need. Patients in this population may be exposed to elevated anticholinergic burden, which can be particularly challenging for an elderly population. Although deidentified for the purposes of this analysis, the integrated healthcare delivery system, independently, can reidentify members who are eligible for intervention in pursuit of quality improvement opportunities to prevent poor outcomes and associated healthcare costs and utilization. As reimbursement continues to be based on adherence to quality metrics, having a consistent and systematic method for data analysis in OAB may allow health plans to evaluate quality of care in patients with OAB. Further research is needed to determine the utilization of newer therapies, including beta3-adrenergic agonists, and to elucidate patient-specific factors (eg, comorbidities or socioeconomic status) that affect medication selection and persistence.

Author Disclosure Statement
Dr Ng is an employee of Astellas Pharmaceutical Global Development. Dr McCart, Mr Klein, and Dr Campbell are employees of Xcenda. Dr Schoenhaus has provided consulting services for Astellas. Dr Berner was an employee of Astellas Scientific and Medical Affairs at the time of this research.

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Figure 1

Stakeholder Perspective
Implications of Big Data Analysis in the Real-World Setting: The Case of Overactive Bladder Treatment

Big data analysis holds a breakthrough potential for understanding the clinical relevance of therapies in real-world settings that are void of strict inclusion or exclusion criteria, frequent physician examination, and controlled daily protocols that apply to clinical trials. As data sets become larger, and the limitations become isolated, outcomes-based analyses can shed new light on clinical treatment pathways and can ideally become the basis for new prospective clinical trials.

In this issue of the journal, Ng and colleagues analyzed treatment outcomes with antimuscarinic agents in patients with overactive bladder1; and they clearly identified the limitations associated with this drug class using big data, such as patient demographics, clinical presentation, and the underreporting of comorbidities.

PAYERS: These findings have important implications for payers in terms of outcomes, as well as the associated healthcare costs. With only 15% of patients in the study reaching a proportion of days covered of ≥80%; an 85% nonadherence rate; and a 73% treatment discontinuation rate within 1 year, payers may consider implementing guidelines related to the use of antimuscarinic agents.

In addition, it may be worthwhile to understand the use of branded versus generic antimuscarinic drugs, considering the higher switching rates (14% vs 5%, respectively) and the shorter time to switching (161 days vs 180 days, respectively) associated with branded drugs versus generics; however, generic antimuscarinic drugs led to an increase in treatment discontinuation compared with branded drugs (77% vs 67%, respectively).1 It would also be of interest to explore whether the clinical performance, an increase in side effects, and comorbidities vary between the branded and the generic drugs in this class.

PROVIDERS: Providers may also find much interest in the outcomes associated with antimuscarinic agents discussed by Ng and colleagues, including the low persistence rate, as expressed by a high discontinuation rate (73% within 1 year), a high nonadherence rate (85%), and increased risk for anticholinergic burden. Such outcomes may influence the provider’s timing of follow-up care and the reporting of comorbidities and adverse events. Moreover, alternative therapies have recently been introduced for overactive bladder and can be incorporated into the physician’s considerations of a treatment plan.

Furthermore, providers will benefit from data analysis as data sets become bigger and as models become more correlated to individual traits.

PATIENTS: With providers benefiting from data analysis, and as analytics transition from descriptive to prescriptive to predictive, patients will benefit from cohort matching. For patients with overactive bladder, there is the potential for individual factors such as age, sex, and cognitive evaluation to be correlated to clinical outcomes and affect their individual treatment plan.

1. Ng DB, McCart M, Klein C, et al. Evaluating outcomes in patients with overactive bladder within an integrated healthcare delivery system using a treatment patterns analyzer. Am Health Drug Benefits. 2016;9(6):343-353.

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