Abstract
Background
Leadless pacing has recently emerged as a promising therapy. The impact of frailty on the prognosis of these patients is currently unknown.
Objective
The purpose of this study was to assess the association between frailty and clinical outcomes in patients undergoing leadless pacemaker implantation.
Methods
We included adult patients who underwent leadless pacemaker implantation using the National Inpatient Sample from 2017 to 2019. Frailty was evaluated using the Hospital Frailty Risk Score and stratified into low, intermediate, and high risk. Primary outcomes were in-hospital mortality and any complication (vascular, pericardial, pneumothorax, infectious, or device related), and secondary outcomes were the length of hospital stay and total charges.
Results
A total of 16,825 patients were included in the final analysis, with 62% at intermediate or high risk of frailty. There was a higher risk of in-hospital mortality in patients at high (adjusted risk ratio [aRR] 6.37, 95% confidence interval [CI] 3.31–12.26) or intermediate (aRR 5.15, 95% CI 3.04–8.72) risk of frailty compared with those at low risk. Similarly, those at high or intermediate risk of frailty had higher total expenses and stayed in the hospital longer. Patients with a high (aRR 1.14, 95% CI 0.71–1.81) or intermediate (aRR 1.19, 95% CI 0.94–1.51) risk of frailty had a similar risk of any complication as patients with a low risk.
Conclusion
Frailty was common in patients undergoing leadless pacemaker implantation. Higher levels of frailty were a strong predictor of in-hospital mortality, length of hospital stay, and hospital charges, except for any complication.
Keywords: Frailty, Leadless pacemaker, Mortality, Complications
Key Findings.
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Frailty was an independent predictor of in-hospital mortality in patients undergoing leadless pacemaker implantation.
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Frailty was not associated with any complication (vascular, pericardial, pneumothorax, infectious, or device related).
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Frailty was associated with higher total charges and longer hospital stay.
Introduction
Leadless pacemaker implantation has emerged as a promising alternative to transvenous pacemaker therapy for patients with bradyarrhythmias.1 Currently, the use of leadless pacing is indicated in those patients who only require ventricular pacing, have active or previous infection and those who have problems with venous access such as venous occlusion and end-stage renal disease on dialysis.2 Although the efficacy and safety of leadless pacemakers have been demonstrated in previous studies, there are limited data on the prognostic factors in this population.3 Identifying these factors related to poor outcomes can help clinicians to develop individualized care plans and optimize pre- and postoperative management to reduce the risk of mortality and complications.4
Frailty is a clinical syndrome characterized by decreased physiological reserve and increased vulnerability to stressors.5 Frailty assessment has gained recognition as an important predictor of adverse outcomes in the elderly population, and its utility in predicting outcomes in various clinical settings has been demonstrated.6 It has been shown to be associated with increased mortality, hospitalization, and other adverse events in patients undergoing cardiac interventions, including cardiac surgery and transcatheter aortic valve replacement.7 However, the role of frailty in predicting outcomes in patients undergoing leadless pacemaker implantation has not been well characterized. Therefore, this study aims to evaluate the association between frailty and clinical outcomes in patients undergoing leadless pacemaker implantation using nationwide real-world data.
Methods
Study design
We performed a retrospective cohort study using the National Inpatient Sample (NIS) database between 2017 and 2019. The NIS database is a publicly available, all-payer database of hospitalizations in the United States.8 It represents a 20% stratified random sample of hospital discharges and includes more than seven million hospital admissions from over 97% of hospitals participating in the Healthcare Cost and Utilization Project.8
Study population
We identified our study population using the International Classification of Diseases–Tenth Revision (ICD-10) diagnostic and procedural codes. We sampled the NIS database to identify patients ≥18 years of age that underwent Micra (Medtronic, Minneapolis, MN) leadless pacemaker implantation, as specified by the ICD-10 procedure code 02HK3NZ.
Frailty
Frailty was defined using the Hospital Frailty Risk Score (HFRS), which was developed by Gilbert et al. and has been previously validated in studies using the NIS database.9,10 The HFRS uses an ICD-10 coding algorithm from routinely collected administrative health data to calculate a weighted score ranging from 0.1 to 7.1, based on 109 clinical conditions. Frailty was presented as continuous data and was also stratified into the low risk of frailty (HFRS <5), intermediate risk of frailty (HFRS 5–15), and high risk of frailty (HFRS >15), as previously described.
Outcomes
The primary outcome was in-hospital mortality and any complication (vascular, pericardial, pneumothorax, infectious, or device retrieval/replacement). The ICD-10 codes for all complications are shown in Supplemental Table 1. The secondary outcomes were the length of hospital stay and hospital charges.
Covariates
Patient demographic data, including age (in years), sex (male or female), race (White, Black, Hispanic, or other), household income quartile ($1–$45,999, $46,000–$58,999, $59,000–78,999, or $79,000 or more), and primary expected payer (Medicare, Medicaid, private, or other), were obtained from the NIS database. Hospital characteristics, such as bed size volume (small, medium, or large), region (Northeast, South, Midwest, and West), location (rural, urban nonteaching, or urban teaching), and control/ownership of hospital (government nonfederal, private nonprofit, or private investor owned), were also collected. Comorbidities, such as hypertension, diabetes, acute myocardial infarction, congestive heart failure, cerebrovascular disease, cancer, dementia, peripheral vascular disease, rheumatoid disease, chronic obstructive pulmonary disease, liver disease, and renal disease, were evaluated using the Charlson Comorbidity Index.
Statistical analysis
All analyses were weighted using discharge weights as recommended by the Agency for Healthcare Research and Quality. Categorical variables are presented as frequencies and percentages and numerical variables as mean ± SD or median (interquartile ranges [IQR]), as appropriate. The chi-square test was used for categorical variables and the unpaired t test or Mann-Whitney U test was used for continuous variables, as appropriate. In addition, the Cochran-Armitage test was performed to assess the variation in the percentage of primary outcomes throughout the study period, dividing each year into 4 quarters. To assess the association between frailty (low, intermediate, and high risk) and binary outcomes (in-hospital mortality and any complication), we performed univariable and multivariable Poisson regression models with a robust variance estimator to estimate crude risk ratio (cRR) and adjusted risk ratio (aRR) with their corresponding 95% confidence intervals (CIs).11 Linear regression models were used for continuous outcomes (length of hospital stay and hospital charges). The following predefined covariates were selected to be included in the adjusted models: age, sex, race, bed size of the hospital, location of the hospital, primary expected payer, and Charlson Comorbidity Index. We also used restricted cubic splines to model the nonlinear relationship between HFRS and primary outcomes, using 4 knots at prespecified points (percentiles 5, 35, 65, and 95). We did not impute missing values (Supplemental Table 2). As a sensitivity analysis, we performed the evaluation of primary and secondary outcomes using the Elixhauser Comorbidity Index instead of the Charlson Comorbidity Index. All statistical analyses were conducted using STATA v17 (StataCorp, College Station, TX). A 2-tailed P value <.05 was considered statistically significant.
Results
Patient and hospital characteristics
We included a total of 16,825 weighted admissions who underwent leadless pacemaker implantation (Figure 1). Table 1 shows the baseline characteristics of the study population. The mean age was 75.5 ± 12.5 years, about half of the patients were male (55.1%), and the majority were White (76.8%). The most frequent comorbidities were hypertension (79.8%), congestive heart failure (55.1%), renal disease (41.2%), and diabetes (38.7%) (Supplemental Table 3). In relation to hospital characteristics, the majority of patients were in a large hospital bed size (66.5%), urban teaching type (82.7%), and private nonprofit (79%). The 3 most common primary diagnoses leading to hospitalization were the following: sick sinus syndrome (18.2%), complete atrioventricular block (14.1%), and infection and inflammatory reaction due to other cardiac and vascular devices, implants, and grafts (6.6%) (Supplemental Table 4). In general, patients who died during hospitalization were younger, had higher total charges, and had higher Charlson Comorbidity Index scores than those who survived. Patients who experienced at least 1 complication during hospitalization were also younger, had more elective admissions, and had also higher total charges compared with those without complications (Table 1). The median Charlson Comorbidity Index score was 3 (IQR 1–5), with the majority (73%) having a score of at least 2 points. The median length of hospital stay was 6 days (IQR 3–11 days), being longer for those who presented any complication and with no difference between those who died and those who did not. The median HFRS was 6.3 (IQR 3.3–10) for all patients, being significantly higher in patients who died during hospitalization (P < .001) and similar in those who presented any complication (P = .330) compared with those who did not die or present complications, respectively (Table 1). The 3 most common defining diagnoses of frailty were chronic kidney disease (40.9%); disorders of fluid, electrolyte, and acid-base balance (36.6%); and personal history of unspecified disease of the respiratory system (30.3%) (Supplemental Table 5). The majority of patients showed an intermediate risk of frailty (55%), followed by low risk (38.1%) and high risk (6.9%) of frailty. There was no missing data for any of the outcomes.
Figure 1.
Flow diagram for the selection of study participants.
Table 1.
Characteristics of included patients
| Characteristic | Total | In-hospital mortality |
Any complication |
||||
|---|---|---|---|---|---|---|---|
| Yes | No | P value | Yes | No | P value | ||
| Weighted number of patients | 16,825 | 875 | 15,950 | 1585 | 15,240 | ||
| Age, y | 75.5 ± 12.5 | 71.7 ± 12.2 | 75.7 ± 12.5 | <.001 | 73.5 ± 12.8 | 75.7 ± 12.5 | .004 |
| Sex | .590 | .110 | |||||
| Male | 9270 (55.1) | 465 (53.1) | 8805 (55.2) | 805 (50.8) | 8465 (55.5) | ||
| Female | 7555 (44.9) | 410 (46.9) | 7145 (44.8) | 780 (49.2) | 6775 (44.5) | ||
| Race | .004 | .003 | |||||
| White | 12,590 (76.8) | 555 (65.3) | 12,035 (77.4) | 1060 (68.4) | 11,530 (77.6) | ||
| Black | 1600 (9.8) | 125 (14.7) | 1475 (9.5) | 215 (13.9) | 1385 (9.3) | ||
| Hispanic | 1205 (7.3) | 90 (10.6) | 1115 (7.2) | 145 (9.4) | 1060 (7.1) | ||
| Other | 1005 (6.1) | 80 (9.4) | 925 (5.9) | 130 (8.4) | 875 (5.9) | ||
| Household income | .370 | .800 | |||||
| $1–$45,999 | 4255 (25.7) | 250 (28.9) | 4005 (25.5) | 375 (24.1) | 3880 (25.8) | ||
| $46,000–$58,999 | 4330 (26.1) | 225 (26.0) | 4105 (26.1) | 425 (27.3) | 3905 (26.0) | ||
| $59,000–$78,999 | 4135 (24.9) | 170 (19.7) | 3965 (25.2) | 370 (23.8) | 3765 (25.0) | ||
| $79,000 or more | 3865 (23.3) | 220 (25.4) | 3645 (23.2) | 385 (24.8) | 3480 (23.2) | ||
| Type of admission | .130 | <.001 | |||||
| Nonelective | 14,205 (84.5) | 770 (88.5) | 13,435 (84.3) | 1160 (73.4) | 13,045 (85.7) | ||
| Elective | 2605 (15.5) | 100 (11.5) | 2505 (15.7) | 420 (26.6) | 2185 (14.3) | ||
| Length of hospital stay, d | 6 (3–11) | 6 (2–17) | 6 (3–11) | .390 | 9 (4–19) | 6 (3–10) | <.001 |
| Primary expected payer | <.001 | .054 | |||||
| Medicare | 13,895 (82.6) | 625 (71.4) | 13,270 (83.2) | 1230 (77.6) | 12,665 (83.1) | ||
| Medicaid | 875 (5.2) | 55 (6.3) | 820 (5.1) | 100 (6.3) | 775 (5.1) | ||
| Private | 1610 (9.6) | 140 (16.0) | 1470 (9.2) | 185 (11.7) | 1425 (9.4) | ||
| Other | 445 (2.6) | 55 (6.3) | 390 (2.4) | 70 (4.4) | 375 (2.5) | ||
| Total charges, $ | 133,345 (90,212–231,076) | 188,215 (95,487–291,846) | 131,933 (90,163–226,000) | .002 | 189,827 (112,641–360,475) | 129,519 (88,735–219,771) | <.001 |
| Bed size of hospital | .700 | .130 | |||||
| Small | 1615 (9.6) | 75 (8.6) | 1540 (9.7) | 135 (8.5) | 1480 (9.7) | ||
| Medium | 4015 (23.9) | 230 (26.3) | 3785 (23.7) | 315 (19.9) | 3700 (24.3) | ||
| Large | 11,195 (66.5) | 570 (65.1) | 10,625 (66.6) | 1135 (71.6) | 10,060 (66.0) | ||
| Location of hospital | .170 | .120 | |||||
| Rural | 445 (2.6) | 35 (4.0) | 410 (2.6) | 15 (0.9) | 430 (2.8) | ||
| Urban nonteaching | 2465 (14.7) | 160 (18.3) | 2305 (14.5) | 220 (13.9) | 2245 (14.7) | ||
| Urban teaching | 13,915 (82.7) | 680 (77.7) | 13,235 (83.0) | 1350 (85.2) | 12,565 (82.4) | ||
| Region of hospital | .041 | .310 | |||||
| Northeast | 3290 (19.6) | 155 (17.7) | 3135 (19.7) | 330 (20.8) | 2960 (19.4) | ||
| Midwest | 3520 (20.9) | 125 (14.3) | 3395 (21.3) | 335 (21.1) | 3185 (20.9) | ||
| South | 6430 (38.2) | 350 (40.0) | 6080 (38.1) | 535 (33.8) | 5895 (38.7) | ||
| West | 3585 (21.3) | 245 (28.0) | 3340 (20.9) | 385 (24.3) | 3200 (21.0) | ||
| Control/ownership of hospital | .390 | .890 | |||||
| Government, nonfederal | 1885 (11.2) | 125 (14.3) | 1760 (11.0) | 185 (11.7) | 1700 (11.2) | ||
| Private, nonprofit | 13,285 (79.0) | 660 (75.4) | 12,625 (79.2) | 1235 (77.9) | 12,050 (79.1) | ||
| Private, investor owned | 1655 (9.8) | 90 (10.3) | 1565 (9.8) | 165 (10.4) | 1490 (9.8) | ||
| Charlson Comorbidity Index | 3 (1–5) | 4 (2–6) | 3 (1–5) | <.001 | 3 (2–5) | 3 (1–5) | .230 |
| Charlson Comorbidity Index | .001 | .180 | |||||
| 0 | 1815 (10.8) | 50 (5.7) | 1765 (11.1) | 135 (8.5) | 1680 (11.0) | ||
| 1 | 2725 (16.2) | 80 (9.1) | 2645 (16.6) | 225 (14.2) | 2500 (16.4) | ||
| ≥2 | 12,285 (73.0) | 745 (85.1) | 11,540 (72.4) | 1225 (77.3) | 11,060 (72.6) | ||
| Elixhauser Comorbidity Index | 6 (4–7) | 6 (5–8) | 6 (4–7) | <.001 | 6 (5–7) | 6 (4–7) | <.001 |
| Hospital Frailty Risk Score | 6.3 (3.3–10) | 9.3 (7–12.6) | 6.1 (3.2–9.7) | <.001 | 6.7 (3.5–10.2) | 6.3 (3.2–9.9) | .330 |
Values are n, mean ± SD, n (%), or median (interquartile range). Numbers do not add up to the total due to missing values.
Primary outcomes
The proportion of patients who died during hospitalization was 5.2% (Table 2). Patients at high and intermediate risk of frailty had significantly higher in-hospital mortality rates compared with those at low risk (8.6% vs 7.4% vs 1.4%, P <.001) (Table 2 and Figure 2). Patients at high or intermediate risk of frailty had a higher risk of in-hospital mortality than those at low risk in either the crude (for high risk: cRR 6.11, 95% CI 3.28–11.38; for intermediate risk: cRR 5.27, 95% CI 3.24–5.58) or adjusted (for high risk: aRR 6.37, 95% CI 3.31–12.26; for intermediate risk: aRR 5.15, 95% CI 3.04–8.72) models (Table 3 and Figure 3). Hospital location, primary expected payer, and Charlson Comorbidity Index were the only independent predictors of in-hospital mortality (Table 3 and Figure 3).
Table 2.
Primary and secondary outcomes according to frailty status
| Characteristic | Total | Low risk of frailty | Intermediate risk of frailty | High risk of frailty | p Value |
|---|---|---|---|---|---|
| Weighted number of patients | 16,825 | 6410 | 9250 | 1165 | |
| In-hospital mortality | 875 (5.2) | 90 (1.4) | 685 (7.4) | 100 (8.6) | <.001 |
| Any complication | 1585 (9.4) | 560 (8.7) | 925 (10.0) | 100 (8.6) | .44 |
| Vascular complication | 390 (2.3) | 145 (2.3) | 230 (2.5) | 15 (1.3) | .51 |
| Pericardial complication | 595 (3.5) | 205 (3.2) | 365 (3.9) | 25 (2.1) | .26 |
| Pneumothorax | 215 (1.3) | 55 (0.9) | 140 (1.5) | 20 (1.7) | .23 |
| Infectious complication | 75 (0.4) | 10 (0.2) | 50 (0.5) | 15 (1.3) | .038 |
| Device complication | 515 (3.1) | 185 (2.9) | 285 (3.1) | 45 (3.9) | .73 |
| Total charges, $ | 133,345 (90,212–231,076) | 111,899 (80,065–173,368) | 149,102 (99,688–262,116) | 193,598 (114,856–299,470) | <.001 |
| Length of hospital stay, d | 6 (3–11) | 4 (2–6) | 7 (4–14) | 12 (7–20) | <.001 |
Values are n, n (%), or median (interquartile range).
Figure 2.
Clinical outcomes among nationwide admissions with leadless pacemaker implantation according to frailty status.
Table 3.
Univariable and multivariable analyses between frailty and primary outcomes
| Characteristic | In-hospital mortality |
Any complication |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Crude model |
Adjusted model∗ |
Crude model |
Adjusted model∗ |
|||||||||
| cRR | 95% CI | P Value | aRR | 95% CI | P Value | cRR | 95% CI | P Value | aRR | 95% CI | P Value | |
| Frailty | ||||||||||||
| Low risk | Ref. | Ref. | Ref. | Ref. | ||||||||
| Intermediate risk | 5.27 | 3.24–8.58 | <.001 | 5.15 | 3.04–8.72 | <.001 | 1.14 | 0.91–1.43 | .236 | 1.19 | 0.94–1.51 | .157 |
| High risk | 6.11 | 3.28–11.38 | <.001 | 6.37 | 3.31–12.26 | <.001 | 0.98 | 0.62–1.55 | .939 | 1.14 | 0.71–1.81 | .587 |
| Age | 0.98 | 0.97–0.99 | <.001 | 0.98 | 0.97–0.99 | .005 | 0.99 | 0.98–0.99 | .002 | 0.99 | 0.98–1.00 | .208 |
| Sex | ||||||||||||
| Female | Ref. | Ref. | Ref. | Ref. | ||||||||
| Male | 0.92 | 0.69–1.23 | .594 | 0.87 | 0.65–1.17 | .355 | 0.84 | 0.68–1.04 | .105 | 0.82 | 0.66–1.01 | .068 |
| Race | ||||||||||||
| Other | Ref. | Ref. | Ref. | Ref. | ||||||||
| White | 0.55 | 0.33–0.92 | .022 | 0.63 | 0.37–1.06 | .082 | 0.65 | 0.44–0.95 | .027 | 0.68 | 0.46–0.99 | .050 |
| Black | 0.98 | 0.54–1.79 | .951 | 0.92 | 0.49–1.69 | .782 | 1.04 | 0.65–1.63 | .869 | 1.01 | 0.64–1.59 | .957 |
| Hispanic | 0.94 | 0.49–1.79 | .847 | 0.93 | 0.49–1.75 | .816 | 0.93 | 0.57–1.53 | .775 | 0.90 | 0.55–1.47 | .684 |
| Type of admission | ||||||||||||
| Elective | Ref. | Ref. | Ref. | Ref. | ||||||||
| Nonelective | 1.41 | 0.89–2.23 | .138 | 0.90 | 0.58–1.41 | .653 | 0.51 | 0.40–0.67 | <.001 | 0.49 | 0.38–0.62 | <.001 |
| Bed size of hospital | ||||||||||||
| Small | Ref. | Ref. | Ref. | Ref. | ||||||||
| Medium | 1.23 | 0.69–2.18 | .469 | 1.27 | 0.71–2.28 | .416 | 0.94 | 0.61–1.44 | .774 | 0.93 | 0.60–1.45 | .760 |
| Large | 1.09 | 0.65–1.85 | .732 | 0.98 | 0.57–1.69 | .938 | 1.21 | 0.83–1.78 | .322 | 1.16 | 0.79–1.71 | .444 |
| Location of hospital | ||||||||||||
| Rural | Ref. | Ref. | Ref. | Ref. | ||||||||
| Urban nonteaching | 0.82 | 0.37–1.81 | .632 | 0.73 | 0.35–1.51 | .391 | 2.65 | 0.84–8.34 | .096 | 2.43 | 0.77–7.70 | .132 |
| Urban teaching | 0.62 | 0.29–1.29 | .201 | 0.48 | 0.24–0.95 | .036 | 2.88 | 0.94–8.81 | .064 | 2.53 | 0.82–7.80 | .106 |
| Primary expected payer | ||||||||||||
| Other | Ref. | Ref. | Ref. | Ref. | ||||||||
| Medicare | 0.36 | 0.20–0.65 | .001 | 0.42 | 0.24–0.74 | .003 | 0.56 | 0.34–0.92 | .023 | 0.56 | 0.33–0.94 | .027 |
| Medicaid | 0.51 | 0.23–1.13 | .096 | 0.40 | 0.18–0.91 | .029 | 0.73 | 0.38–1.37 | .323 | 0.58 | 0.31–1.11 | .099 |
| Private | 0.70 | 0.36–1.36 | .294 | 0.73 | 0.39–1.41 | .360 | 0.73 | 0.41–1.29 | .279 | 0.71 | 0.40–1.24 | .231 |
| Charlson Comorbidity Index | 1.14 | 1.09–1.20 | <.001 | 1.09 | 1.02–1.16 | .007 | 1.04 | 0.99–1.08 | .105 | 1.03 | 0.98–1.08 | .239 |
aRR = adjusted risk ratio; cRR = crude risk ratio; CI = confidence interval.
Adjusted for frailty, age, sex, race, type of admission, bed size of hospital, location of hospital, primary expected payer, and Charlson Comorbidity Index.
Figure 3.
Forest plot showing the adjusted risk ratios (aRRs) with their 95% confidence intervals (CIs) for associations between predictors and primary outcomes.
Any complication during hospitalization was present in 9.4% of patients (Table 2). Among these, the most common complications were pericardial (3.5%) and device related (3.1%). Patients at high, intermediate, or low risk had a similar proportion of any complication during hospitalization (8.6% vs 10% vs 8.7%, P = .440) (Table 2 and Figure 2). Overall, the proportions of most types of complications (vascular, pericardial, pneumothorax, infectious, and device related) were similar between frailty groups, except for infectious complications, which were higher in patients with high and intermediate risk of frailty compared with low risk (1.3% vs 0.5% vs 0.2%, P = .038) (Table 2 and Figure 2). Patients at high or intermediate risk of frailty had a similar risk of any complication compared with those at low risk in either the crude (for high risk: cRR 0.98, 95% CI 0.62–1.55; for intermediate risk: cRR 1.14, 95% CI 0.91–1.43) or adjusted (for high risk: aRR 1.14, 95% CI 0.71–1.81; for intermediate risk: aRR 1.19, 95% CI 0.94–1.51) model (Table 3 and Figure 3). Only type of admission and primary expected payer were independently associated with any complication (Table 3 and Figure 3).
Figure 4 shows the restricted cubic splines modeling for primary outcomes according to HFRS values. The restricted cubic spline plot demonstrated a nonlinear relationship between HFRS and in-hospital mortality/any complication, with an increasing risk at HFRS >5 points only for in-hospital mortality. The temporal trend of in-hospital mortality and any complications for each quarter of the years from 2017 to 2019 is shown in Figure 5. There was a significant reduction in the percentage of in-hospital mortality (P for trend = .001) and any complication (P for trend = .003) over the years.
Figure 4.
Restricted cubic spline model showing the adjusted risk ratio (aRR) for (A) in-hospital mortality and (B) any complication with Hospital Frailty Risk Score in patients undergoing leadless pacemaker implantation. The gray regions represent the 95% confidence interval (CI).
Figure 5.
Time trends of (A) in-hospital mortality and (B) any complication in patients undergoing leadless pacemaker implantation according to frailty status from 2017 to 2019.
Secondary outcomes
In comparison with patients with low risk of frailty, individuals with high and intermediate risk incurred greater total charges ($193,598 vs $149,102 vs $111,899, P <.001) and longer hospital stays (12 days vs 7 days vs 4 days, P <.001) (Table 2). Those at high or intermediate risk of frailty had higher total expenses and stayed in the hospital longer than those at low risk, according to both crude and adjusted models (Supplemental Table 6).
Sensitivity analyses
Overall, the adjusted results on the association between frailty and primary and secondary outcomes were similar when using the Elixhauser Comorbidity Index compared with the Charlson Comorbidity Index (Supplemental Tables 7 and 8).
Discussion
The major findings of our study were as follows: (1) intermediate or high risk of frailty was a common condition among patients undergoing leadless pacing; (2) frailty was a strong independent predictor of in-hospital mortality, length of hospital stay, and hospital charges; and (3) there was no significant difference in the risk of any complication between frailty groups overall or by type, except for infectious complication.
In patients undergoing leadless pacemaker implantation, certain prognostic factors have been identified, which may aid in predicting the risk of adverse events.12,13 A previous study highlighted patient-related characteristics, such as sex and race, as well as comorbidities like heart failure, coronary artery disease, and peripheral artery disease; cardiogenic shock; and device infection, all of which were associated with an increased risk of all-cause mortality during hospitalization.14 Our study focused on investigating the role of frailty as a predictor of in-hospital mortality following the procedure, independently of other well-established prognostic factors. The results demonstrated that frailty indeed emerged as a significant and robust predictor of in-hospital mortality. Interestingly, we observed a nonlinear relationship, wherein the risk of death showed a noteworthy increase when the frailty score, as measured by the HFRS, exceeded 5 points. The underlying pathophysiological mechanisms linking frailty to adverse events are well documented and include systemic inflammation, oxidative stress, and altered neurohormonal regulation.15 These factors contribute to an augmented risk of adverse outcomes in frail individuals. Additionally, frailty is associated with a higher incidence of both cardiac and noncardiac comorbidities, further amplifying the overall risk of mortality.7 On the other hand, there is a greater risk of a fatal outcome when the implantation occurs during a nonelective hospitalization, a significant finding in our study where the vast majority of hospitalizations were nonelective, indicating a higher-risk group. Moreover, the implantation of a pacemaker itself may trigger stress responses in frail patients, thereby leading to prolonged hospital stays, increased morbidity, and ultimately poorer outcomes.
In general, leadless pacemaker implantation has been considered to be a safer procedure than transvenous pacing, in terms of lower risk of pacemaker extrusion, pocket infection or hematoma, lead dislodgement, and pneumothorax.16 However, 2 previous studies based on national registries in the United States have found that the leadless pacemaker group had a higher rate of certain complications such as vascular complications, bleeding, venous thromboembolism, thrombus formation in the device, and pericardial complications.14,17 Likewise, both studies reported higher in-hospital all-cause mortality in the leadless pacemaker group, probably attributed to the higher proportion of comorbidities in these patients. It is important to acknowledge that the use of large introducer sheaths in leadless pacemaker implantation may increase the risk of vascular and pericardial complications. Several factors associated with postprocedural complications have been reported, including demographics, comorbidities, and device-related factors.14,18 Vincent and colleagues14 identified sex and race as potential predictors, with female and non-White patients having a higher risk of developing complications, such as bleeding/hematoma, postoperative infection, pericardial complication, or device-related issues. Additionally, comorbidities like heart failure and malignancy, as well as device infection and subsequent sepsis, have been linked to increased rates of complications. In our study, we did not find a significant association with the pooled complication rate based on the adjusted analysis; however, we did observe a higher proportion of infectious complications in patients with a higher risk of frailty compared with those with a low risk. Frailty has been associated with impaired immune function, including decreased T and B cell function, and increased proinflammatory cytokine production.19,20 Although infectious complications were relatively rare in our study (0.4%), they can lead to increased morbidity and mortality. Consequently, careful patient selection and close monitoring are crucial to minimizing the risk of complications following leadless pacemaker implantation.
Our study significantly contributes to the existing literature on frailty and its impact on cardiovascular disease, particularly in the context of patients undergoing pacemaker implantation. Previous investigations have highlighted frailty as a predictor of adverse outcomes in various cardiovascular interventions, such as cardiac surgery, transcatheter aortic valve replacement, and percutaneous coronary intervention.7 Furthermore, an extensive study investigated the influence of frailty on cardiac implantable electronic device procedures, including permanent pacemaker implantation, in the United States from 2004 to 2014. The study revealed that frailty is associated with higher complication rates, increased costs, prolonged hospitalization, and elevated mortality, independent of the patient’s age or the type of cardiac implantable electronic device.10 However, to the best of our knowledge, our study represents the first attempt to explore the association between frailty and outcomes specifically in patients undergoing leadless pacemaker implantation. Identifying frailty in these patients holds great potential for enhancing clinical care by allowing clinicians to develop personalized and tailored care plans that address the specific needs of each individual. For instance, frail patients may necessitate more frequent monitoring or additional support from caregivers to optimize their outcomes.5 Recognizing frailty status can also facilitate shared decision making, empowering patients and their families to make informed choices about the risks and benefits of the procedure based on their unique frailty profile.
Frailty often coexists with multiple comorbidities, functional impairment, and cognitive decline, underscoring the importance of preoperative evaluation and optimization of these factors to enhance outcomes in frail patients undergoing leadless pacemaker implantation.21 Implementing interventions such as medication adjustments, exercise programs, or nutritional support, as supported by previous studies, can aid in improving the overall results in this vulnerable patient population.15 It is essential for healthcare providers to include a frailty assessment as a routine part of the evaluation process for these patients. Numerous tools are available to assess frailty across various clinical settings, particularly for patients with cardiovascular diseases. Utilizing these tools can streamline the assessment process and aid clinicians in accurately identifying frailty in their patients.22 By considering the identified frailty status, clinicians can craft comprehensive care plans that are tailored to address the specific needs and vulnerabilities of each patient. Such an approach may ultimately lead to improved outcomes and a higher quality of care for patients undergoing leadless pacemaker implantation who are at increased risk due to frailty.
Our study has some limitations. First, because it uses information from administrative health data, there is a risk of measurement error in the coding of diagnoses and procedures, especially for comorbidities and complications during hospitalization. Second, medium- and long-term outcomes could not be assessed, as the cohort only recorded information during hospitalization. Third, information on medications, echocardiographic findings, laboratory results, and pacemaker settings was not available. Fourth, the majority of admissions were not elective, and therefore complications (vascular, pericardial, pneumothorax, and infectious) could have occurred before another procedure (eg, cardiac surgery) or after Micra insertion. Finally, the definition of frailty was not based on tools used in routine clinical practice, such as the Fried frailty phenotype or Clinical Frailty Scale.23 Instead, we used a validated instrument to assess frailty using administrative data according to a broad number of defining diagnoses of frailty.24
Conclusion
Our study showed that higher levels of frailty were a strong predictor of in-hospital mortality in patients undergoing leadless pacemaker implantation. In addition, it was associated with the length of hospital stay and total charges. In contrast, it was not significantly associated with any complications. Identifying patients who are at high risk of frailty can help clinicians better tailor care and interventions to improve outcomes. Further studies are needed to validate these results and explore possible interventions to improve outcomes in frail patients undergoing this procedure.
Acknowledgments
Funding Sources
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Disclosures
The authors have no conflicts to disclose.
Authorship
All authors attest they meet the current ICMJE criteria for authorship.
Ethics Statement
This is a retrospective cohort study using the National Inpatient Sample database, which is publicly available and de-identified; therefore, institutional review board approval and patient consent were not required.
Footnotes
Supplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.hroo.2023.12.007.
Appendix. Supplementary data
References
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