Abstract:                                                                                                                                                                            Background:  The association between body mass index (BMI) and frailty in the context of leadless pacemaker (LPM) implantation remains underexplored.                                                                                   Objective: This study aims to investigate the relationship between BMI, frailty, and outcomes in patients who underwent LPM implantation.                                                                                                     Methods: Data from the National Inpatient Sample (NIS) from 2018-2021 was analyzed for patients who underwent LPM implantation. Patients were categorized into five BMI groups: underweight, normal weight, overweight, obese, and morbidly obese. Frailty was assessed using the Hospital Frailty Risk Score (HFRS).                                                                                                                                         Results: The study included a weighted cohort of 8,865 patients who underwent LPM implantation. Among frail patients (n=6120), mortality occurred in 5.0% of obese individuals compared to 7.5% in non-obese individuals (aOR: 0.763, 95% CI: 0.601-0.968). Frail, obese patients showed reduced risks of venous thromboembolism (aOR: 0.760, 95% CI: 0.565-0.943), the need for blood transfusions (aOR: 0.826, 95% CI: 0.690-0.988), and pericardial complications (aOR: 0.778, 95% CI: 0.564-0.925), but demonstrated elevated risks of renal complications (aOR: 1.284, 95% CI: 1.132-1.458) and device removal/revision (aOR: 2.121, 95% CI: 1.943-2.345).                                                                                                                                                                                                Conclusion: This study demonstrates that frail, obese patients who underwent LPM implantation, exhibited a lower risk of mortality, pericardial complications, venous thromboembolism (VTE), and the need for blood transfusion compared to non-obese individuals, despite facing a higher likelihood of renal complications and device-related issues. Keywords: BMI, Frailty, cardiovascular outcomes, leadless pacemaker, obesity, in-hospital mortality, National Inpatient Sample (NIS)Abbreviations: BMI: Body Mass Index; LPM: Leadless Pacemaker; NIS: National Inpatient Sample; TVP: Traditional Transvenous Pacemaker; HCUP: Healthcare Cost and Utilization Project; AHRQ: Agency for Healthcare Research and Quality; ICD-10: International Classification of Diseases, Tenth Revision; CM/PCS: Clinical Modification/Procedure Coding System; CIED: Cardiovascular Implantable Electronic Device; VTE: Venous Thromboembolism; CVA: Cerebrovascular Accident; HFRS: Hospital Frailty Risk Score; aOR: adjusted Odds Ratio; OR: Odds Ratio; CI: Confidence IntervalIntroduction:The advent of leadless pacemaker technology represents a significant advancement in the field of cardiac electrophysiology, offering a less invasive alternative to traditional transvenous pacemakers.1,2 LPMs, designed to be directly implanted into the right ventricle without the need for transvenous leads, mitigate many complications associated with conventional devices, such as infections, lead dislodgement, and venous thrombosis.3 Since their FDA approval in 2016, these devices have gained widespread acceptance, particularly among patients unsuitable for conventional pacemakers due to their enhanced safety and efficacy profiles.4 Patients requiring LPMs often present with a complex array of health issues, including multiple comorbidities and varying levels of frailty.5 Frailty, marked by decreased physiological reserve and heightened vulnerability to external stressors, is associated with higher rates of hospitalization, disability, and mortality.6 This condition poses significant challenges in cardiovascular treatments, impacting procedural outcomes and healthcare costs. Thus, accurately assessing frailty is crucial for optimizing patient outcomes in LPM implantations. However, the evaluation of outcomes following LPM implantation must also consider Body Mass Index (BMI), a prevalent health metric based on weight-to-height ratio. Although BMI is instrumental in assessing overall health risks across various conditions, its relationship with frailty, particularly in the context of cardiac interventions, is nuanced and not fully elucidated.7 Drawing on data from the National Inpatient Sample (NIS) spanning 2018 to 2021, this study explores how BMI interacts with frailty to affect clinical outcomes in patients receiving LPMs. By dissecting these dynamics, the research seeks to enhance our understanding of how frailty and BMI together impact patient care, aiming to refine clinical strategies and optimize outcomes in this vulnerable population.Methods:This study employed a retrospective cohort design using data from the National Inpatient Sample (NIS) spanning the years 2018 to 2021. The NIS database, maintained by the Healthcare Cost and Utilization Project (HCUP), represents the largest publicly available all-payer inpatient healthcare database in the United States, encompassing approximately 20% of all hospital discharges and providing a representative sample of the U.S. inpatient population. Patients who underwent leadless pacemaker implantation were identified by the ICD-10 procedure code 02HK3NZ. Patients with incomplete data on Body Mass Index (BMI), as well as those under 18, were excluded from the study. Patients were stratified into five BMI categories: Underweight (≤ 19.9), Normal weight (20-24.9), Overweight (25-29.9), Obese (30-34.9), and Morbidly Obese (≥ 35). Frailty was assessed using the Hospital Frailty Risk Score (HFRS), formulated by Gilbert et al.8 The HFRS applies an ICD-10 algorithm to administrative health data to derive a weighted frailty score between 0.1 and 7.1, based on 109 clinical indicators. Frailty status in patients was then grouped into three predefined risk levels: low risk for scores less than 5, intermediate risk for scores between 5 and 15, and high risk for scores exceeding 15. This categorization is based on established protocols in previous research utilizing the NIS database.9-11 Outcomes assessed were in-hospital mortality, removal/revision, mechanical complications, infection/inflammation, venous thromboembolism (VTE), pericardial complications, renal complications, bleeding complications, and the need for blood transfusion. Pericardial complications included non-infective acute pericarditis, non-inflammatory pericardial effusion, non-traumatic hemopericardium, cardiac tamponade, and unspecified pericardial complications. Renal complications included acute kidney injury. Bleeding complications covered periprocedural bleeding, post-procedural anemia, and hemoperitoneum/retroperitoneal bleeding. Additionally, a detailed analysis was conducted to assess frailty across BMI categories. For baseline demographics and comorbid conditions, selected comorbidities from the Elixhauser index were analyzed. Patient demographics and clinical characteristics were summarized using descriptive statistics. Continuous variables were presented as means with standard deviations, while categorical variables were summarized as frequencies and percentages. The statistical significance was evaluated using Pearson chi-square tests for categorical variables and ANOVA for continuous variables. In the assessment of primary and secondary outcomes, binary logistic regression models were developed. Additionally, frailty and obesity were dichotomized, classifying patients as frail (HFRS ≥ 5) or non-frail (HFRS < 5) and as obese (BMI ≥ 30) or non-obese (BMI < 30). This approach aligns with BMI and frailty categorizations used in previous studies.12,13 Adjusted (aORs) and crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for all regression models. Each model included adjustments for age, sex, and Elixhauser comorbidities, as detailed in Table 1. Data analysis was performed using SPSS software (version 26.0; IBM Corp, Armonk, NY). A p-value of less than 0.05 was considered statistically significant. All relevant ICD-10 PCS/CM codes used for statistical analysis and baseline demographics are provided in Supplemental Table 1 (S1).Results:In the analysis of clinical outcomes detailed in Table 2, the following results were observed within the frail patient cohort: Mortality was lower in obese patients (5.0%, n=200) compared to non-obese patients (7.5%, n=160) (aOR: 0.763, 95% CI: 0.601-0.968, p=0.026). The incidence of venous thromboembolism (VTE) was lower in obese patients (4.0%, n=160) compared to non-obese patients (7.3%, n=155) (aOR: 0.760, 95% CI: 0.565-0.943, p=0.007). The need for blood transfusions was lower in obese patients (10.6%, n=425) compared to their non-obese patients (14.2%, n=300) (aOR: 0.826, 95% CI: 0.690-0.988, p=0.036). Pericardial complications occurred less frequently in obese patients (4.4%, n=175) compared to non-obese patients (4.7%, n=100) (aOR: 0.778, 95% CI: 0.564-0.925, p=0.009). Bleeding complications showed no statistical significance (aOR: 0.947, 95% CI: 0.797-1.102, p=0.432). Renal complications were more prevalent in obese patients (47.1%, n=1885) compared to non-obese patients (37.5%, n=795) (aOR: 1.284, 95% CI: 1.132-1.458, p<0.001). Similarly, within the non-frail cohort, renal complications were higher in obese patients (10.9%, n=235) compared to non-obese patients (4.2%, n=25) (aOR: 1.837, 95% CI: 1.758-1.975, p<0.001). Furthermore, the prevalence of removal/revision was greater in obese patients (2.0%, n=80) than in non-obese patients (0.7%, n=15) (aOR: 2.121, 95% CI: 1.943-2.345, p<0.001). In the non-frail cohort, all secondary outcomes, except for renal complications, lacked statistical significance.In the dichotomized binary logistic regression model detailed in Table 3, frail patients comprised 78.1% (2,120) of the non-obese group and 65% (4,000) of the obese group (aOR: 0.591, 95% CI: 0.124-0.739, p = 0.001). Conversely, non-frail patients accounted for 21.9% (595) of the non-obese group and 35% (2,150) of the obese group.Mechanical complications and infection/inflammation were not reportable according to the National Inpatient Sample (NIS) dataset guidelines, which prohibit the reporting of data where the number of cases is less than 11, as mandated by the Healthcare Cost and Utilization Project (HCUP) standards. Discussion:                                                                                                                           In this nationally representative cohort, our findings are as follows: (I) Frail, obese patients who underwent leadless pacemaker implantation (LPM) exhibited a reduced risk of mortality. (II) Frail, obese patients exhibited a reduced risk of pericardial complications, venous thromboembolism (VTE), and the need for blood transfusion compared to non-obese patients. (III) Frail, obese patients demonstrated an elevated risk of renal complications and removal/revision compared to non-obese patients.Previous studies have consistently demonstrated that frailty increases the risk of complications associated with cardiac implantable electronic devices (CIEDs). Diaz et al. found that frailty is a significant predictor of mortality in patients who underwent LPM implantation, with the risk markedly increasing when the Hospital Frailty Risk Score (HFRS) exceeds five points.14 Moreover, frailty has been associated with higher rates of both cardiac and non-cardiac comorbidities, further elevating the overall risk of mortality.15 Regarding BMI, studies have typically demonstrated a linear correlation wherein higher BMI is associated with an increased risk of cardiovascular complications.16,17 However, an "obesity paradox" has been noted, suggesting that higher BMI may be linked to improved clinical outcomes. For example, a study by Almani et al., analyzing NIS data from 2016-2018, revealed that obese patients who underwent transvenous pacemaker (TVP) insertion exhibited lower inpatient mortality rates compared to their non-obese counterparts.18 Furthermore, research by Attanasio et al. indicated that obese patients (BMI > 30) experienced significantly fewer major complications compared to non-obese patients (BMI < 30) during CIED implantation.19The clinical implications of frailty and BMI are profound, serving as strong predictors of mortality and major complications.20,21 Research exploring the interplay between BMI and frailty is extensive. For instance, Jayanama et al. examined a cohort of 29,937 middle-aged to older adults, discovering that overweight or obese individuals exhibited elevated levels of frailty.22 Similarly, Tajik et al. investigated the relationship between frailty and high BMI with the risk of heart failure, concluding that the coexistence of high BMI and frailty exacerbates the risk of developing heart failure.23 In contrast, our results showed that frail, obese patients undergoing LPM implantation exhibited a reduced risk of mortality. This observation is somewhat consistent with literature assessing all-cause mortality, although specific studies on cardiovascular complications are scarce. For example, Watanabe et al. conducted a prospective study involving 10,912 adults aged 65 and older, which revealed an inverse relationship between higher BMI and all-cause mortality.24 Several physiological mechanisms may underpin these relationships. Increased adiposity might buffer the adverse effects of frailty and procedural stress, while the larger metabolic reserves in obese patients could provide a crucial support during the perioperative phase.25 Additionally, Adipose tissue secretes various adipokines, such as leptin, which could counteract chronic low-grade inflammation commonly seen in frail individuals, contributing to better clinical outcomes.26Many studies have indicated an elevated risk of pericardial issues (pericarditis, pericardial effusion, etc.) in patients who underwent LPM implantation compared to traditional pacemakers.5,27-29 For instance, In an observational cohort study by Piccini et al., patients with LPMs experienced significantly higher pericardial complications compared to those with TVPs.30 Our study revealed that frail, obese patients exhibited a reduced risk of pericardial complications following LPM implantation. This highlights the necessity for further research to elucidate the underlying mechanisms behind this potentially protective effect. It is important to note that our data is not designed to analyze such mechanisms, making this an essential focus for future investigations. A study by Alhuarrat et al., using NIS data from 2016 to 2019, aimed to evaluate the differences in procedural complications and in-hospital outcomes between LPM and TVP implantation. Analyzing a cohort of 7,780 patients, the study identified a higher risk of VTE and an increased need for blood transfusions in the LPM group.31 Furthermore, in a retrospective study of 89 patients, Hayashi et al. noted the development of deep vein thrombosis (DVT) in 13.5% of patients post-LPM implants.32 Our study shows that frail, obese patients exhibited a reduced risk of VTE and need for blood transfusion. The LPM procedure uses a large bore sheath for device implantation that can result in prolonged immobilization, potentially increasing the risk of DVT and a need for blood transfusion in the elderly and frail population.33,34 Our results suggest that obesity might mitigate these risks, offering a valuable insight into the protective effect obesity may play within frail individuals undergoing LPM.It is essential to recognize the benefits associated with the minimally invasive nature of the LPM procedure, particularly the reduction of procedural and post-procedural complications. A notable study from the Micra post-approval registry highlighted several advantages of LPMs over TVPs. Over three years, LPMs showed a 53% reduction in major complications, mainly due to fewer lead dislodgements and less frequent system revisions. At a 36-month follow-up, the rate of system revisions was significantly lower for LPMs compared to transvenous systems (3.2% vs. 6.6%).35 Despite these advantages, our findings demonstrate that frail, obese patients face higher risks of device revision/removal. The technical challenges of implanting the LPM in obese patients, such as difficulties in accessing vascular sites and securing the device may contribute to a higher likelihood of complications. Furthermore, renal complications, which we defined as acute kidney injury, showed an elevated risk in frail, obese patients. This prevalence may stem from the higher volume of contrast used during implantation or the inclusion of patients with pre-existing renal insufficiency. Further research is warranted to identify the contributing factors and to develop strategies to enhance the safety and efficacy of LPM devices within this vulnerable population.LimitationsNotably, several limitations are inherent to the NIS database. First, the use of ICD codes for identifying diseases and procedures could lead to inaccuracies, despite the Agency for Healthcare Research and Quality's (AHRQ) stringent measures to reduce miscoding and maintain data integrity. Second, the NIS is limited to inpatient admissions and does not include outpatient data or longitudinal follow-up, which limits insights into long-term outcomes and readmission patterns. Third, the database does not distinguish among various leadless pacemaker models, such as Micra and Aveir, hindering comparative analyses of outcomes across these devices. Fourth, the NIS does not capture critical details such as medications and specific procedural data, including steps involved in the leadless pacemaker implantation process, such as operator experience, use of intraprocedural imaging, and contrast utilization. Lastly, the absence of detailed patient-level data, such as lifestyle factors, precludes a more comprehensive risk stratification and understanding of the interactions between BMI, frailty, and LPM outcomes. ConclusionOur study demonstrates that frail, obese patients showed reduced risks of mortality, pericardial complications, venous thromboembolism (VTE), and the need for blood transfusion compared to non-obese patients. However, this group exhibited elevated risks of renal complications and device removal/revision. These observations indicate a potential protective effect of higher BMI in frail individuals undergoing LPM implantation. Future research should aim to elucidate the mechanisms behind these protective effects and optimize implantation strategies. Moreover, tailored patient management that considers both BMI and frailty is essential for enhancing clinical outcomes in this population. Acknowledgements: None  References:1.           Toon LT, Roberts PR. 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Heart failure, frailty, and pre-frailty: A systematic review and meta-analysis of observational studies. Int J Cardiol. Oct 1 2020;316:161-171. doi:10.1016/j.ijcard.2020.04.04322.        Jayanama K, Theou O, Godin J, Mayo A, Cahill L, Rockwood K. Relationship of body mass index with frailty and all-cause mortality among middle-aged and older adults. BMC Med. Oct 24 2022;20(1):404. doi:10.1186/s12916-022-02596-723.        Tajik B, Voutilainen A, Sankaranarayanan R, et al. Frailty alone and interactively with obesity predicts heart failure: Kuopio Ischaemic Heart Disease Risk Factor Study. ESC Heart Failure. 2023;10(4):2354-2361. doi:https://doi.org/10.1002/ehf2.1439224.        Watanabe D, Yoshida T, Watanabe Y, Yamada Y, Miyachi M, Kimura M. Frailty modifies the association of body mass index with mortality among older adults: Kyoto-Kameoka study. Clinical Nutrition. 2024/02/01/ 2024;43(2):494-502. doi:https://doi.org/10.1016/j.clnu.2024.01.00225.        Hainer V, Aldhoon-Hainerová I. Obesity Paradox Does Exist. Diabetes Care. 2013;36(Supplement_2):S276-S281. doi:10.2337/dcS13-202326.        Brydon L. Adiposity, leptin and stress reactivity in humans. Biol Psychol. Feb 2011;86(2):114-20. doi:10.1016/j.biopsycho.2010.02.01027.        Alhuarrat MAD, Kharawala A, Renjithlal S, et al. Comparison of in-hospital outcomes and complications of leadless pacemaker and traditional transvenous pacemaker implantation. Europace. Aug 2 2023;25(9)doi:10.1093/europace/euad26928.        El-Chami MF, Bockstedt L, Longacre C, et al. Leadless vs. transvenous single-chamber ventricular pacing in the Micra CED study: 2-year follow-up. Eur Heart J. Mar 21 2022;43(12):1207-1215. doi:10.1093/eurheartj/ehab76729.        Cantillon DJ, Dukkipati SR, Ip JH, et al. Comparative study of acute and mid-term complications with leadless and transvenous cardiac pacemakers. Heart Rhythm. Jul 2018;15(7):1023-1030. doi:10.1016/j.hrthm.2018.04.02230.        Piccini JP, El-Chami M, Wherry K, et al. Contemporaneous Comparison of Outcomes Among Patients Implanted With a Leadless vs Transvenous Single-Chamber Ventricular Pacemaker. JAMA Cardiol. Oct 1 2021;6(10):1187-1195. doi:10.1001/jamacardio.2021.262131.        Alhuarrat MAD, Kharawala A, Renjithlal S, et al. Comparison of in-hospital outcomes and complications of leadless pacemaker and traditional transvenous pacemaker implantation. EP Europace. 2023;25(9)doi:10.1093/europace/euad26932.        Hayashi T, Shishido KS, Moriyama NM, Tobita KT, Murakami MM, Saito SS. Deep vein thrombosis after leadless pacemaker implantation. European Heart Journal. 2022;43(Supplement_2)doi:10.1093/eurheartj/ehac544.47933.        Okabe T, Afzal MR, Houmsse M, et al. Tine-Based Leadless Pacemaker: Strategies for Safe Implantation in Unconventional Clinical Scenarios. JACC: Clinical Electrophysiology. 2020/10/01/ 2020;6(10):1318-1331. doi:https://doi.org/10.1016/j.jacep.2020.08.02134.        Gupta S, Cho K, Papagiannis J, Tisma-Dupanovic S, Borsa J. A novel technique for extraction of a leadless pacemaker that embolized to the pulmonary artery in a young patient: A case report. HeartRhythm Case Rep. Oct 2020;6(10):724-728. doi:10.1016/j.hrcr.2020.07.00235.        El-Chami MF, Garweg C, Clementy N, et al. Leadless pacemakers at 5-year follow-up: the Micra transcatheter pacing system post-approval registry. European Heart Journal. 2024;45(14):1241-1251. doi:10.1093/eurheartj/ehae101  Figure LegendFigure 1: Diagram of patient sample selection that details inclusion and exclusion criteria from initial NIS admissions to a final weighted sample.