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Infection reduces survival in cardiovascular implantable electronic device (CIED) recipients. However, the clinical predictors of short- and long-term mortality in patients with CIED infection are not well understood. We retrospectively reviewed all patients with CIED infection who were admitted to Mayo Clinic from January 1991 to December 2008. Survival data were obtained from the medical records and the United Sates Social Security Index. The purported risk factors for short-term (30-day) and long-term (>30-day) mortality were analyzed using univariate and multivariate models. Overall, 415 cases of CIED infection were identified during the study period. The mean follow-up duration for the 243 patients who were alive at the last follow-up visit was 6.9 years. In a multivariate model, heart failure (odds ratio 9.31, 95% confidence interval 2.08 to 41.67), corticosteroid therapy (odds ratio 4.04, 95% confidence interval 1.40 to 11.60), and presentation with CIED-related infective endocarditis (odds ratio 5.60, 95% confidence interval 2.25 to 13.92) were associated with increased short-term mortality. The factors associated with long-term mortality in the multivariate model included patient age (hazard ratio 1.20, 95% confidence interval 1.06 to 1.36), heart failure (hazard ratio 2.01, 95% confidence interval 1.42 to 2.86), metastatic malignancy (hazard ratio 5.99, 95% confidence interval 1.67 to 21.53), corticosteroid therapy (hazard ratio 1.97, 95% confidence interval 1.22 to 3.18), renal failure (hazard ratio 1.94, 95% confidence interval 1.37 to 2.74), and CIED-related infective endocarditis (hazard ratio 1.68, 95% confidence interval 1.17 to 2.41). In conclusion, these data suggest that the development of CIED-related infective endocarditis and the presence of co-morbid conditions are associated with increased short- and long-term mortality in patients with CIED infection.
Infection is a serious complication of cardiovascular implantable electronic device (CIED) implantation, requiring prompt, complete device removal and systemic antibiotic therapy.
However, our understanding of the factors associated with poor outcomes in patients with infected devices remains limited. In the present investigation, we identified the clinical predictors associated with increased short- and long-term mortality among patients with infection involving CIEDs.
We retrospectively reviewed all patients with CIED infection who were admitted to Mayo Clinic (Rochester, Minnesota) from January 1, 1991 to December 31, 2008. The cases of CIED infection were identified from the Mayo Clinic Heart Rhythm Device Database, the surgical index, and the computerized central diagnostic index.
Cases of CIED infection were further classified as pocket infection or endovascular infection (bloodstream infection or CIED-related infective endocarditis [CIED-IE]). The diagnosis of CIED-IE was determined from echocardiographic findings of an oscillating intracardiac mass on a heart valve or device lead, visualization of a cardiac abscess, or new dehiscence of a prosthetic valve.
The follow-up duration was calculated from the date of hospital admission to the date of death or the last follow-up visit. In addition to extracting the follow-up data from the available medical records, updated vital status and death dates (if available) were obtained from the United States Social Security Index on December 1, 2010. Because the Social Security Index data can have a lag time of ≤6 months, patients who were still alive according to this Index were assumed to be alive through June 1, 2010.
Short-term mortality was defined as death within 30 days of admission to the hospital. The associations of clinical features with short-term mortality were evaluated using Wilcoxon rank sum, chi-square, and Fisher’s exact tests. These associations were further evaluated using univariate and multivariate logistic regression models and summarized with odds ratios and 95% confidence intervals. A multivariate model was developed using a stepwise selection process with the p value for a feature to enter or leave the model set at 0.05. Model discrimination (i.e., how well the features in the model separated patients who died within 30 days of admission from the patients still alive at 30 days after admission) was summarized using the area under a receiver operating characteristic curve (AUC). The AUC can range from 0.5 to 1.0, with higher values indicating improved predictive ability. Model calibration (i.e., how well the predicted probabilities estimated by the model agreed with the observed short-term mortality) was summarized using the Hosmer and Lemeshow goodness-of-fit test. A statistically significant p value from this test would reject the null hypothesis that the features in the model fit the data well.
Overall survival was estimated using the Kaplan-Meier method. The follow-up duration was calculated from the date of admission to the date or death or the last follow-up visit. Associations of the features with the interval to death were evaluated using univariate and multivariate Cox proportional hazards regression models and summarized with hazard ratios and 95% confidence intervals. A multivariate model was developed using stepwise selection with the p value for a feature to enter or leave the model set at 0.05. Model discrimination (i.e., how well the features in the model separated patients who died from those who were censored at the last follow-up visit) was summarized using a concordance index (c index). The c index corresponds to the proportion of all usable pairs of patients in whom the observed and predicted survival times were concordant. Similar to the AUC, the c index can range from 0.5 to 1.0, with higher values indicating improved predictive ability.
Statistical analyses were performed using the SAS software package (SAS Institute, Cary, North Carolina). All statistical tests were 2-sided, and p <0.05 was considered statistically significant.
Overall, 415 patients with CIED infection were identified from January 1, 1991 to December 31, 2008. The clinical patient characteristics are summarized in Table 1. Of the 415 patients, 1 patient who was alive at hospital discharge was excluded from the analysis of short-term mortality because the 30-day follow-up data were not available. Of the remaining 414 patients, 23 (5.6%) died within 30 days after admission and 391 (94.4%) were alive at 30 days after admission. The univariate associations of the candidate predictors with short- and long-term mortality are summarized in Table 1.
Table 1Univariate associations of clinical features with short-term (30-day) and long-term (>30-day) mortality associated with cardiovascular implantable electronic device (CIED) infection
Patients who had CIEDs implanted for a longer duration and those who presented with a more acute onset of symptoms had greater short-term mortality. Several co-morbid conditions, including chronic obstructive pulmonary disease, malignancy, chronic corticosteroid therapy, and a lower body mass index, were also associated with greater short-term mortality. Patients with greater short-term mortality were more likely to present with leukocytosis, worsening renal function, positive blood culture findings, Staphylococcus aureus infection, and echocardiographic findings indicative of CIED-IE (Table 1).
The results of a multivariate model developed using the candidate predictors from the univariate analysis are summarized in Table 2. Heart failure, chronic corticosteroid therapy, and CIED-IE were all significantly associated with increased short-term mortality in this multivariate model. Patients with heart failure, for instance, were >9 times more likely to die within 30 days of admission than were patients without heart failure. Similarly, patients receiving chronic corticosteroid therapy had a 4-fold and those with CIED-IE a 5.6-fold increase in short-term mortality. The AUC for these features in this model was 0.83 (95% confidence interval 0.75 to 0.91). The p value from the Hosmer and Lemeshow goodness-of-fit test was 0.76, indicating that the features in the model fit the data well and that the model was well calibrated.
Table 2Multivariate model to predict short-term (30-day) mortality among patients with cardiovascular implantable electronic device (CIED) infection
The mean predicted probability of short-term mortality obtained from the model was 0.056 (median 0.039. interquartile range 0.004 to 0.039, range 0.004 to 0.476). The mean predicted probability for patients who died within 30 days of admission was 0.181 (median 0.184, interquartile range 0.039 to 0.184, range 0.004 to 0.476), significantly greater than the mean predicted probability (0.048, median 0.039, interquartile range 0.004 to 0.039, range 0.004 to 0.476; p <0.001) for patients who were still alive at 30 days after admission. For a given patient, the predicted probability of short-term mortality could be calculated using the coefficients from the model, assigning 1 if the patient had heart failure and 0 otherwise, 1 if the patient were taking corticosteroids and 0 otherwise, and 1 if evidence of CIED-IE were present and 0 otherwise, as follows:
The various combinations of heart failure, corticosteroid therapy, and CIED-IE observed in the present study and the predicted probabilities of short-term mortality are summarized in Table 3.
Table 3Predicted probability of short-term (30-day) mortality according to presence or absence of heart failure, corticosteroid use, and cardiovascular implantable electronic device-related infective endocarditis (CIED-IE) for 414 patients
At the last available follow-up visit, 172 patients had died at a mean duration of 3 years after admission (median 2 years, range 1 day to 15.2 years). The mean follow-up duration for the 243 patients still alive at the last available follow-up visit was 7 years (median 5.7 years, range 11 days to 19.3 years). Only 2 patients had <1 year of follow-up. The estimated overall survival rate at 1, 5, and 10 years after admission was 85% (95% confidence interval 81 to 88, number still at risk 350), 64% (95% confidence interval 60 to 69, number still at risk 174), and 51% (95% confidence interval 45 to 57, number still at risk 78), respectively (Figure 1).
The univariate associations of the candidate predictors of long-term mortality are summarized in Table 1. Increasing age of the device recipients and certain co-morbidities, including coronary artery disease, heart failure, atrial fibrillation, diabetes mellitus, chronic renal disease, hemodialysis dependence, chronic obstructive pulmonary disease, peripheral vascular disease, chronic skin conditions, implanted central venous catheter, malignancy, and chronic corticosteroid therapy, were associated with significantly greater rates of long-term mortality. Moreover, patients who initially presented with device infection to an outside facility and those who had CIED-IE had greater long-term mortality rates. Laboratory test findings, such as leukocytosis, anemia, elevated serum creatinine, positive blood cultures, and infection with S. aureus, were also associated with greater rates of long-term mortality (Table 1).
The multivariate model developed using the subset of candidate predictors that were significant on univariate analysis is summarized in Table 4. Patient age, de novo device implantation, system revision or an upgrade procedure (compared to generator or lead replacement alone), heart failure, active or metastatic malignancy, chronic corticosteroid therapy, renal failure, and CIED-IE were all significantly associated with greater long-term mortality in the multivariate model. Each 10-year increase in age was associated with a 20% increased risk of death. Patients with heart failure had a twofold and those with metastatic malignancy a sixfold greater likelihood of death in the long term compared to patients who did not. Chronic corticosteroid therapy and acute kidney injury at the initial presentation were also associated with a twofold increase in long-term mortality. Patients with CIED-IE had a 1.7-fold increased risk of long-term mortality. The c index for the features in the model was 0.75.
Table 4Multivariate model to predict long-term mortality in patients with cardiovascular implantable electronic device (CIED) infection
Our investigation is one of the largest to analyze the risk factors associated with short- and long-term mortality in patients with CIED infection. Moreover, using data from our multivariate models, we devised a simple and an easy-to-use prediction tool for short-term mortality (Table 3) that includes readily available clinical information, such as the presence or absence of heart failure, history of corticosteroid therapy, and echocardiographic evidence of CIED-IE. We believe this model will provide useful prognostic information to patients and providers when making critical management decisions such as the urgency of device removal in cases of CIED-IE and patient counseling regarding the expected survival after infected device replacement.
In our multivariate analysis, heart failure, chronic corticosteroid therapy, and CIED-IE were independent predictors of both short- and long-term mortality. Although some of these associations have been previously reported (albeit in a smaller cohort of patients),
evaluated the clinical predictors associated with increased 6-month mortality in 210 patients with CIED infection. In a multivariate analysis, systemic embolization, moderate or severe tricuspid valve regurgitation, abnormal right ventricular function, and abnormal renal function were associated with increased 6-month mortality. However, they did not differentiate between predictors of short- and long-term mortality, which, as we have demonstrated, might be different and should be analyzed separately. Moreover, additional risk factors that were significant in our study, including metastatic malignancy and chronic corticosteroid therapy, were not evaluated in their smaller cohort.
Increasing age and multiple device revisions were associated with greater long-term mortality in our patient population. Similarly, renal failure was associated with a twofold increase in long-term mortality. These finding are consistent with earlier observations that impaired renal function is not only associated with increased risk of infection in CIED recipients,
Because our study cohort consisted only of patients with CIED infection, it is unclear whether the observed increase in long-term mortality associated with co-morbid conditions is related to device infection status or not. It could be argued that older age, malignancy, renal failure and heart failure reduce long-term survival, regardless of the presence CIED infection. This issue was explored in a recent publication that included >200,000 Medicare beneficiaries with CIED procedures and compared the admission and long-term morality in patients with and without CIED infection.
In the present investigation, CIED infection was associated with a significant, device-dependent, increase in admission mortality and long-term mortality, even after adjustment for age, gender, race, and 28 co-morbidities included in the Elixhauser Co-morbidity Index.
made similar observations in another study that included 2,476 patients who received initial implantable cardioverter-defibrillator/cardiac resynchronization therapy from 2000 to 2009. After controlling for confounders, patients with CIED infection had a 2.4-fold greater risk of mortality than patients without CIED infection.
Overall, our study findings support earlier observations
and have identified novel risk factors associated with increased short- and long-term mortality in patients with CIED infection. Future investigators should explore these clinical predictors in prospective, multicenter studies.
Our investigation has certain limitations inherent to retrospective study designs, including biases, such as recall bias and reviewer bias. To minimize these biases, objective criteria and standardized and reproducible definitions
were used to categorize the CIED infection cases. No patient was excluded because of age, gender, ethnicity, or illness severity. However, our institution is a tertiary care center with expertise in device removal, and referral bias could have affected our clinical data. The patients in our cohort, for example, could have had more co-morbid conditions than the general population. Thus, selection biases might limit the generalization of our findings to other populations.
We thank Joanne E. Spencer, RN, and Nancy Acker, RN, for their important contributions in data collection.
Dr. Friedman has received honoraria from or has been a consultant to Medtronic (Minneapolis, Minnesota), Guidant (Indianapolis, Indiana), and Astra Zeneca (London, United Kingdom); has participated in research sponsored by Medtronic, Astra Zeneca via Beth Israel, Guidant, St. Jude (Little Canada, Minnesota), and Bard; has intellectual property rights with Bard EP (Murray Hill, New Jersey), Hewlett Packard (Palo Alto, California), Medical Positioning, Inc. (Kansas City, Missouri) (all <$10,000). Dr. Hayes has received honoraria from Medtronic, Boston Scientific (Natick, Massachusetts), St. Jude Medical, Sorin Medical (Via Benigno Crespi, Italy), and Biotronik (Berlin, Germany); is on the advisory board for St. Jude Medical and Medtronic; and is on the steering committee for Medtronic and St. Jude Medical (all <$10,000). Dr. Baddour receives royalty payments for authorship from, UpToDate, Inc. (Waltham, Massachusetts) (<$20,000) and editor-in-chief payments from the Massachusetts Medical Society (Waltham, Massachusetts) (Journal Watch Infectious Diseases; <$20,000). Dr. Sohail has received funding from TyRx Inc. (Monmouth Junction, New Jersey) for previous research unrelated to the present study (Bloom et al. PACE 2011;34:133–142), administered according to a sponsored research agreement that prospectively defined the scope of the research effort and corresponding budget.
Impact of timing of device removal on mortality in patients with cardiovascular implantable electronic device infections.