Assessment of Variation in Ambulatory Cardiac Monitoring Among Commercially Insured Patients

ABSTRACT

Objectives: Ambulatory cardiac monitors (ACMs) enable heart rhythm monitoring for various durations, including Holter monitors (0-48 hours), long-term continuous monitors (LTCMs; 3-14 days), and external ambulatory event monitors (AEMs; up to 30 days). These devices detect intermittent or asymptomatic arrhythmias that might go unnoticed with a standard electrocardiogram. Previous research has explored variations in ACM use among Medicare beneficiaries. This study assessed the incidence of clinical and economic outcomes among commercially insured patients who had never had an arrhythmia diagnosis and received their first ACM.

Study Design: Retrospective cohort study using a large commercial claims database focused on patients without prior arrhythmia diagnoses who received their first ACM between 2016 and 2023.

Methods: Outcomes included new arrhythmia diagnoses, repeat ACM testing, cardiovascular (CV) events, and health care resource use and costs. Results were stratified by major ACM manufacturers using National Provider Identifiers. To minimize confounding, inverse probability of treatment weighting was used to balance covariates, and adjusted regression models were used to evaluate outcomes during follow-up.

Results: Of 428,707 patients meeting inclusion criteria, 36% used LTCMs, 36% used Holter monitors, and 27% used external AEMs. Adjusted analyses showed that a certain LTCM brand was associated with higher odds of a new arrhythmia diagnosis, fewer retests (except vs AEMs), lower odds of CV events, and less follow-up health care resource use and costs than other ACM types and manufacturers.

Conclusions: Clinical and economic outcomes can vary by ACM type among commercially insured patients. A specific LTCM manufacturer demonstrated superior performance, with greater diagnoses of arrhythmia, fewer repeat tests, and fewer CV events compared with other ACM types and manufacturers.

Am J Manag Care. 2026;32(2):In Press

Takeaway Points

This study evaluated clinical and economic outcomes among commercially insured patients who had never had an arrhythmia diagnosis and received their first ambulatory cardiac monitor (ACM). Findings revealed variations in arrhythmia diagnosis, repeat testing, cardiovascular events, health care resource utilization, and costs, with one monitor demonstrating superior performance.

• These findings provide critical real-world evidence informing managed care decision makers on the comparative effectiveness of ACMs on costs, quality, and patient outcomes.
• The results add to existing literature on the comparative effectiveness of ACMs among Medicare beneficiaries and expand the purview to commercially insured populations.
• These data support the integration of high-performing ACMs into value-based care models to improve patient outcomes, optimize resources, and reduce expenditures.

Arrhythmias are a significant public health concern, contributing to increased morbidity, mortality, and health care costs worldwide.1-4 Early and accurate detection of arrhythmias is crucial for initiating appropriate interventions to prevent adverse cardiovascular (CV) events, particularly in cases of silent or subclinical arrhythmias such as stroke, heart failure, and sudden cardiac death.5-7 For example, although recent trials have clearly demonstrated that patients with subclinical atrial fibrillation (AF) are at increased risk of stroke, the real incidence of this form of AF remains unclear.8

Ambulatory cardiac monitors (ACMs) play a pivotal role in diagnosing arrhythmia by enabling continuous cardiac rhythm assessment in outpatient settings.9-11 Traditional monitoring modalities may have limitations such as shorter monitoring durations, episodic collection of data, patient adherence issues, poor device designs, unreliable autodetection algorithms, and difficult-to-interpret end-of-wear reports.12 The advent of long-term continuous monitors (LTCMs) offers extended monitoring periods of continuous heartbeat collection with improved patient comfort and adherence, potentially increasing diagnostic yield.13 Previous study findings suggest that extended monitoring durations enhance arrhythmia detection rates,14 but comprehensive analyses comparing the impact of different ACM modalities and brands on clinical and economic outcomes are limited.15

A recent study among Medicare fee-for-service populations showed variation in diagnostic yield, resulting retesting, and health care utilization across ACM modalities.16 However, it has yet to be seen whether these differences remain in a commercially insured population that is younger, healthier, and more likely to be employed; has lower rates of chronic conditions; differs in care-seeking behavior; and is more likely to receive shorter-duration monitors. The primary objective of this study was to assess and compare the incidence of diagnosed arrhythmias, retesting rates, select CV events, and health care resource use (HCRU) and costs by ACM modalities and LTCM manufacturers among commercially insured patients who had never had an arrhythmia diagnosis. By leveraging a large national claims database, this study aimed to provide real-world evidence on the clinical and economic outcomes associated with different ACMs.

METHODS

To understand utilization trends and outcomes of patients monitored by ACMs, we examined national pharmacy and medical closed claims data between January 1, 2016, and March 1, 2024. The data were of patients who resided in the US and had medical and pharmacy benefits through a large commercial insurance provider. Claims data were used to identify a cohort of patients who had never had a diagnosis of arrythmia or conduction disorders and received an ACM for the first time. The index date was the technical code usually submitted at the end of wear. Individuals were included if they were 18 years or older, were continuously enrolled for at least 12 months prior to and following the index date, and had a Current Procedural Terminology code for 1 ACM type and, if available, a National Provider Identifier to determine the manufacturer.

Baseline demographic and clinical characteristics were summarized using descriptive statistics. Continuous variables were reported as medians with IQRs, whereas categorical variables were summarized using frequencies and percentages. Baseline differences among cohorts were adjusted using inverse probability of treatment weighting (IPTW). This approach accounted for baseline differences in the probability of receiving treatment (ie, prescription of an ACM) to provide comparable estimates of the direct effect of treatment on arrhythmia diagnosis, retesting, CV events, HCRU, and all-cause costs.

IPTW was implemented using a generalized linear model to address potential confounding and balance baseline covariates across ACM types, enhancing the robustness of the analysis. The IPTW approach estimated the average treatment effect (ATE) in the study population under the hypothetical scenario that all patients were offered ACMs. Treatment probabilities were estimated using logistic regression based on covariates that influenced both treatment selection and outcomes. Individual patient weights were calculated as the inverse of these treatment probabilities to equalize the distribution of covariates across treatment groups. Covariate balance was evaluated using standardized mean differences (SMDs), with an SMD of less than 0.10 indicating successful covariate balance, ensuring comparability for effect estimation.

Two separate IPTW models were developed for this study: one for ACM types and a second for LTCM manufacturers. Baseline covariates included in the IPTW models were patient demographics (age, sex, geographic region, urbanicity, race/ethnicity), Social Vulnerability Index score, and comorbidities (anxiety/depression, diabetes, dyslipidemia, hypertension, acute and chronic cerebral ischemia, congestive heart failure, valvular heart disease, pulmonary disease, smoking status, anticoagulant use with or without pulmonary embolism, obesity, obstructive sleep apnea, thyroid dysfunction, syncope, moderate to severe liver disease, myocardial infarction, mild liver disease, renal disease, use of heart rate control drugs).

IPTW-adjusted regression models were performed to assess the ATE of ACM types and LTCM manufacturers on outcomes (ie, arrhythmia diagnosis at 90 days; retests at 180 days; CV events at 365 days [cardiac arrest, myocardial infarction, arterial embolism and thrombosis, embolic stroke, systemic embolism, coronary heart disease, chronic obstructive pulmonary disease, acute and chronic cerebral ischemia, heart failure]; emergency department visits at 365 days; inpatient hospital stays at 365 days; 30-day inpatient hospital readmissions; inpatient hospital length of stay [LOS]; and all-cause medical and pharmacy costs at 365 days). For categorical outcomes, a logistic regression was used; for follow-up inpatient LOS, a Poisson regression; and for follow-up cost regressions, a γ distribution and a log-link.

RESULTS

Study Populations

Among patients included in the study (N = 428,707), 36% had an LTCM, 36% had Holter monitoring, and 27% had an external AEM at index. Among those patients who had LTCM monitoring at index (n = 154,694), approximately 62% used iRhythm’s LTCM whereas the remaining 38% used a non-iRhythm LTCM. Utilization of LTCMs grew steadily from 11% in 2017 to 56% in 2023. Accordingly, the proportion of patients in this study monitored with a Holter monitor (55.5% in 2017 to 20.6% in 2023) or an external AEM (from 33.9% in 2017 to 23.4% in 2024) declined over time. Overall, patients were predominantly aged 45 to 64 years (55.3%), White (59.3%), female (62.6%), of low social vulnerability (34.6%), located in the South Atlantic (20.1%) and East North Central (16.3%) regions, and from urban core areas (66.7%) (Table 1 and Table 2). ACM cohorts were similar in age (median [IQR], 47-49 [35-57] years) and age-adjusted Charlson Comorbidity Index scores (median [IQR], 1 [0-2]), indicating few serious comorbidities at baseline. The most common predisposing comorbidities were anxiety disorders (~20%), dyslipidemia (~20%), and hypertension (~34%), and most heart rate control medications were β-blockers used to help control hypertension. Although demographic characteristics and comorbidities were comparable across LTCM manufacturers, there were regional variations likely due to vendor contracting and preferences by providers and health systems.

Covariate Balance

Baseline characteristics among cohorts were assessed for balance prior to adjustment using SMDs, with an SMD of less than 0.10 considered indicative of adequate balance. Notable imbalances were observed across certain geographic regions (eg, Pacific and West North Central census divisions) (eAppendix Figure [eAppendix available at ajmc.com]). Additionally, several covariates exhibited marginal imbalances, remaining within the acceptable range but suggesting potential residual confounding. These imbalances were addressed through IPTW, where propensity scores were derived from logistic regression models determining the probability of receiving an ACM, adjusting for predisposing characteristics. After IPTW, the distribution of baseline covariates across treatment groups was substantially improved, with all SMDs reduced to below 0.01, achieving a high degree of comparability between cohorts. The improved balance mitigated potential confounding and differences in baseline characteristics among the cohorts, thereby strengthening the validity of the estimated ATE.

Arrhythmia Diagnosis

iRhythm LTCM–monitored patients had the most clinical encounters for an arrhythmia diagnosis (26.5%) at 90 days from index, compared with patients with non-iRhythm LTCMs (18.4%), Holter monitors (14.7%), and external AEMs (17.0%). The time in days to diagnosis for an arrhythmia was shortest for patients with iRhythm LTCMs (median [IQR], 9 [3-25]), compared with those with non-iRhythm LTCMs (21 [10-36]), Holter monitors (12 [5-29]), and external AEMs (30 [15-43]). At the manufacturer level, iRhythm LTCM patients also had the most clinical encounters for an arrhythmia diagnosis (Bardy, 24.2%; Biotelemetry, 16.8%; Preventice, 17.0%; unclassified LTCM manufacturers, 17.3%) as well as the shortest median (IQR) time in days to diagnosis (Bardy, 13 [5-31]; Biotelemetry, 23 [13-38]; Preventice, 25 [15-39]; unclassified LTCM manufacturers, 20 [9-36]). In adjusted analysis after accounting for variation in baseline demographics, socioeconomics, and comorbidities, patients using iRhythm LTCMs had significantly higher odds of having a clinical encounter for an arrhythmia diagnosis within 90 days of the index date than patients with other ACM types and other LTCM manufacturers (Table 3). These findings were consistent for AF/flutter, bradyarrhythmia, conduction disorders, supraventricular tachycardia, and ventricular dysrhythmia (P < .05) (eAppendix Table).

Retesting

Retests were significantly lower in those monitored with iRhythm LTCMs (3.1%) than in those with other ACM types (non-iRhythm LTCMs, 5.6%; Holter, 6.2%) and other LTCM manufacturers (Bardy, 4.3%; Biotelemetry, 4.1%; Preventice, 3.8%; unclassified LTCM manufacturers, 9.1%), but higher compared with those with external AEMs (2.8%). Among those with a retest, iRhythm LTCM–monitored patients had a retest after a median (IQR) of 76 (34-125) days, compared with 38 (18-95) days for patients with non-iRhythm LTCMs, 52 (27-99) days for those with Holter monitors, and 78 (42-124) days for those with external AEMs. Compared with iRhythm LTCM–monitored patients, the median (IQR) time in days to first retest was shorter among those with other LTCM manufacturers (Bardy, 69 [28-114]; Biotelemetry, 62 [33-116]; Preventice, 69 [34-124]; unclassified LTCM manufacturers, 21 [14-53]). In adjusted analysis after accounting for variations in baseline demographics, socioeconomics, and comorbidities, the odds of retesting within 180 days of the index date remained lower for iRhythm LTCMs compared with non-iRhythm LTCMs and Holter monitors, but slightly more than external AEMs (Table 3).

CV-Related Events

Approximately 11.2% of iRhythm LTCM–monitored patients had a CV event within a year following monitoring, compared with 13.3% for patients with non-iRhythm LTCMs, 10.5% for those with Holter monitors, and 15% for those with external AEMs. iRhythm LTCM–monitored patients had a significantly longer median (IQR) time in days to a CV event (54 [15-165]) compared with those with non-iRhythm LTCMs (38 [9-129]), Holter monitors (34 [6-131]), or external AEMs (38 [9-127]). At the manufacturer level, iRhythm LTCM–monitored patients also had a lower rate of CV events within a year following monitoring compared with patients monitored by non-iRhythm LTCM manufacturers: Bardy (12.8%), Biotelemetry (13.5%), Preventice (12.6%), and unclassified LTCM manufacturers (13.7%). iRhythm LTCM–monitored patients had a significantly longer median (IQR) time in days to a CV event compared with those with Bardy (53 [16-167]), Preventice (42 [9-134]), Biotelemetry (32 [6-117]), or unclassified LTCM manufacturers (37 [8-120]). In adjusted analysis after accounting for variation in baseline demographics, socioeconomics, and comorbidities, the odds of having a CV event within 365 days of the index date remained significantly lower with iRhythm LTCMs compared with non-iRhythm LTCMs and external AEMs and vs other LTCM manufacturers (Table 3).

HCRU

In the year prior to the index date, between 24% and 30% of ACM-monitored patients had an emergency department visit, and 9% to 16% had a hospital stay (median [IQR], 1 [1-1] stay and 5 [2-5] days LOS). Among those hospitalized, 8% to 11% had a rehospitalization within 30 days of discharge. In the year following the index date, between 16% and 20% of ACM-monitored patients had an emergency department visit, and 10% to 13% had a hospital stay (median [IQR], 1 [1-1] stay and 3 [2-5] days LOS). Among those hospitalized, 11% to 12% had a rehospitalization within 30 days of discharge. Across LTCM manufacturers, the baseline and follow-up HCRU followed similar patterns and ranges as the broader ACM types. Sustained in adjusted analysis accounting for differences in baseline demographics, socioeconomics, comorbidities, and baseline HCRU and costs, the odds of a patient having an emergency department visit within 365 days, an inpatient hospital stay within 365 days, or an inpatient hospital readmission within 30 days were significantly lower for those with iRhythm LTCMs vs other ACM types and some non-iRhythm LTCM manufacturers. Also in adjusted analysis, the mean inpatient hospital LOS was significantly lower in the iRhythm LTCM cohort compared with the Holter and external AEM cohorts (Table 4).

Health Care Costs

In the year prior to the index date, patients with iRhythm LTCMs had greater median (IQR) all-cause health care costs per patient per year ($6568 [$3262-$13,599]) compared with patients with non-iRhythm LTCMs ($5553 [$2572-$12,076]), Holter monitors ($4952 [$2321-$10,647]), or external AEMs ($6370 [$2924-$14,082]). However, in the year following the index date, patients with iRhythm LTCMs had the lowest median (IQR) all-cause health care costs ($6426 [$2796-$15,227]) vs those with non-iRhythm LTCMs ($6779 [$3317-$15,703]), external AEMs ($7274 [$3306-$16,956]), or Holter monitors ($6677 [$3284-$14,558]). Similarly, patients with iRhythm LTCMs had greater median (IQR) all-cause health care costs for the baseline period vs those with other LTCM manufacturers (Bardy, $5875 [$2810-$12,462]; Biotelemetry, $5068 [$2347-$11,288]; Preventice, $5512 [$2506-$12,079]) but less than those with unclassified LTCM manufacturers ($5981 [$2739-$12,796]). However, in the year following the index date, patients with iRhythm LTCMs had lower median (IQR) all-cause health care costs than those with Preventice ($6828 [$3206-$16,339]) or unclassified LTCM manufacturers ($7270 [$3391-$16,681]) but slightly higher than those with Bardy ($6326 [$2794-$15,401]) or Biotelemetry ($6453 [$3076-$14,814]).

In adjusted analysis accounting for baseline demographics, socioeconomics, comorbidities, HCRU, and costs, the median follow-up all-cause total health care costs were significantly lower in the iRhythm LTCM cohort than in patients with other ACM types and some LTCM manufacturers. Compared with iRhythm LTCM–monitored patients, the differences in estimated median total health care costs were greater for patients monitored using non-iRhythm LTCMs, Holter monitors, or external AEMs. Consistent with differences in the estimates of costs within the broader ACM population, the differences in estimated median follow-up total health care costs were greater for patients monitored with Bardy, Biotelemetry, Preventice, or unclassified LTCM manufacturers compared with iRhythm LTCM–monitored patients (Table 4).

DISCUSSION

ACMs are commonly used by clinicians to evaluate treatment responses, confirm indications of arrhythmia, and detect previously undiagnosed arrhythmias. For patients without a prior diagnosis of arrhythmia or conduction disorders, selecting a device that provides the most accurate and comprehensive data is crucial for ensuring timely and effective treatment. Our study aimed to evaluate the patterns of ACM use among commercially insured, arrhythmia-naive patients and determine whether the findings were consistent with prior research conducted in Medicare populations.

We observed significant variations in the likelihood of receiving a new arrhythmia diagnosis, retesting, CV events, as well as HCRU and costs across different ACM types, particularly within the LTCM modality. Specifically, iRhythm’s LTCM device was associated with a higher diagnostic yield at 90 days, a lower likelihood of retesting at 180 days (except vs external AEM), lower CV events, and lower HCRU and costs compared with other ACM types and other LTCM manufacturers. These findings are consistent with those of prior studies demonstrating the advantages of extended continuous monitoring over shorter-duration devices in improving diagnostic yield up to and optimized at 14 days,14 as well as findings showing that LTCMs are more effective than other modalities at helping clinicians diagnose costly arrhythmias and reduce retests, HCRU, and costs among Medicare populations.16

The higher rates of arrhythmia diagnosis with iRhythm’s LTCM may be attributed to its extended continuous monitoring capability, user-friendly design, advanced detection algorithms, and the comprehensive nature of the end-of-review reports. These factors likely enhance both patient adherence and data quality, which helps optimize diagnostic yield.17 The shorter time to diagnosis observed in the iRhythm cohort may provide additional clinical benefits by enabling more timely interventions and potentially preventing adverse CV events. Lower retesting rates among iRhythm users may be a result of receiving an accurate and reliable diagnosis, which reduces the need for repeated monitoring and thus improves the patient experience while contributing to lower health care costs. Although the optimal therapeutic approach following the detection of subclinical arrhythmias, particularly AF, continues to be evaluated, their identification presents an important opportunity to initiate clinician-patient discussions focused on more precise assessment of individual CV risk and the implementation of risk-modification strategies.18

The reduced incidence of CV events and decreased HCRU observed in the iRhythm LTCM group suggest that early and accurate arrhythmia diagnosis may allow for prompt treatment and prevention of downstream complications. These findings are particularly relevant in demonstrating the economic value of iRhythm’s LTCM, as the associated lower costs further support the utility of this modality in the treatment of patients with suspected arrhythmias. Payers and managed care organizations prioritize all-cause HCRU because it is a key metric for assessing cost-effectiveness, guides reimbursement decisions, and captures the total burden associated with ACM use, including additional testing, medication adjustments, and arrhythmia-linked resource use for other major comorbidities and their interventions.19

Limitations

This study has several limitations that warrant careful consideration. Because the analysis was not based on randomized trial data, it is possible that the findings reflect undetected confounding. The retrospective nature of the claims analysis introduces potential biases, as coding inaccuracies and omissions are inherent in administrative data sets. Additionally, the use of claims data limits our ability to assess clinical details not captured in billing codes, such as arrhythmia severity or patient adherence to monitoring devices.20 Our analysis was restricted to a commercially insured population, which may limit the generalizability of our findings to other populations, such as those covered by government insurance or the uninsured. Although we used IPTW to balance covariates and reduce confounding, the selection of ACMs was not randomized, and unmeasured factors such as vendor contracts and provider preferences may have influenced device choice, introducing bias. We also did not assess long-term clinical outcomes beyond 1 year, leaving the sustained benefits of different ACM modalities unclear. This is an important area for future research, as noted in previous literature.16

CONCLUSIONS

Our study demonstrated that among a commercially insured population, the use of LTCMs has steadily increased from 2017 to 2023, making it the most prescribed option compared with other Holter monitors and external AEMs. Additionally, this study showed that iRhythm’s LTCM was associated with a higher likelihood of arrhythmia diagnosis within a shorter time frame, a reduced need for retesting in most cases, and lower HCRU and costs. These findings underscore both the clinical and economic advantages of iRhythm’s LTCM in routine arrhythmia monitoring, suggesting that its use may lead to improved patient outcomes and more efficient management of health care resources.

Acknowledgments

The authors thank Laura Anatale-Tardiff, Sunil Swami, Vijaya Henry, and Caroline Margiotta for their assistance.

Author Affiliations: Eversana (PR), Overland Park, KS; Blue Health Intelligence (HC), Chicago, IL; iRhythm Technologies (EMH, KB, BW), San Francisco, CA.

Source of Funding: iRhythm Technologies.

Author Disclosures: Dr Russo has been paid advisory board member for iRhythm Technologies, which manufactures the Zio LTCM. Drs Hendrickson, Boyle, and Wright are employed by and own stock in iRhythm Technologies. Dr Coetzer reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (PR, HC, EMH, KB, BW); analysis and interpretation of data (PR, HC); drafting of the manuscript (PR); critical revision of the manuscript for important intellectual content (PR, HC); obtaining funding (EMH, KB, BW); administrative, technical, or logistic support (EMH, KB, BW); and supervision (HC).

Address Correspondence to: Pierantonio Russo, MD, Eversana, 3 St Moritz Ln, Cherry Hill, NJ 08003. Email: parusso@eversana.com.

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