We hypothesized that the availability of a transcatheter aortic valve implantation (TAVI) program in hospitals impacts the overall management of patients with aortic valve disease and hence may also improve postprocedural outcomes of conventional surgical aortic valve replacement (SAVR). The aim of the present study was to compare the inhospital outcomes of SAVR in centers with versus without availability of a TAVI program in an unrestricted large nationwide patient population >50 years of age. SAVRs performed on patients aged >50 years were identified from the Nationwide Inpatient Sample (NIS) for the years 2011 and 2012 using the International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes. SAVR cases were divided into 2 categories: those performed at hospitals with a TAVI program (SAVR-TAVI) and those without (SAVR-non-TAVI). A total of 9,674 SAVR procedures were identified: 4,526 (46.79%) in the SAVR-TAVI group and 5,148 (53.21%) in SAVR-non-TAVI group. The mean age of the study population was 70.2 ± 0.1 years with majority (53%) of the patients aged >70 years. The mean Charlson’s co-morbidity score for patients in SAVR-TAVI group was greater (greater percentage of patients were aged >80 years, had hypertension, congestive heart failure, renal failure, and peripheral arterial disease) than that of patients in SAVR-non-TAVI group (1.6 vs 1.4, p <0.001). The propensity score matching analysis showed a statistically significant lower inhospital mortality (1.25% vs 1.72%, p = 0.001) and complications rate (35.6% vs 37.3%, p = 0.004) in SAVR-TAVI group compared to SAVR-non-TAVI group. The mean length of hospital stay was similar in the 2 groups the cost of hospitalization was higher in the SAVR-TAVI group ($43,894 ± 483 vs $41,032 ± 473, p <0.0001). Having a TAVI program was a significant predictor of reduced mortality and complications rate after SAVR in multivariate analysis. In conclusion, this largest direct comparative analysis demonstrates that SAVRs performed in centers with a TAVI program are associated with significantly lower mortality and complications rates compared to those performed in centers without a TAVI program.
Transcatheter aortic valve implantation (TAVI) has emerged as a novel therapeutic option for aortic valvular stenosis in high-risk surgical patients with severe co-morbidities. The technology has rapidly gained worldwide acceptance, and since the Food and Drug Administration approval in November 2011, it has disseminated to a large number of hospitals throughout the United States. TAVI requires a complex decision-making and active involvement of an integrated “Heart Team” comprising physicians with diverse expertise. We hypothesized that the availability of a TAVI program in hospitals impacts the overall management of patients with aortic valve disease and hence may also improve postprocedural outcomes of conventional surgical aortic valve replacement (SAVR). The aim of the present study was to compare the inhospital outcomes of SAVR in centers with versus without availability of a TAVI program in an unrestricted large nationwide patient population aged >50 years.
Methods
Data were obtained from the Nationwide Inpatient Sample (NIS) for the years 2011 and 2012. NIS is a part of a family of databases developed for the Healthcare Cost and Utilization Project (HCUP) and is sponsored by the Agency for Healthcare Research and Quality (AHRQ). Data from the NIS have previously been used to identify, track, and analyze national trends in health care usage, patterns of major procedures, access, disparity of care, trends in hospitalizations, charges, quality, and outcomes. We analyzed data from NIS 2011 and 2012 using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes for implantation of a bioprosthetic (code 35.21) or mechanical (code 35.22) aortic valve. Only patients aged >50 years with isolated SAVR were included and those who underwent concomitant coronary artery bypass graft surgery, other valvular, or pericardial procedures were excluded from the analysis. The remaining SAVR cases were divided into 2 categories: those performed at hospitals with a TAVI program (SAVR-TAVI) and those without (SAVR-non-TAVI). The TAVI-capable hospitals were identified with ICD-9-CM procedure codes of 35.05 and 35.06.
We defined severity of co-morbid conditions using Deyo modification of Charlson Comorbidity Index (CCI). This index contains 17 co-morbid conditions with differential weights. The score ranges from 0 to 33, with greater scores corresponding to greater burden of co-morbid diseases. The primary outcome was all-cause inhospital mortality. Procedural complications were identified by Patient Safety Indicators (PSIs), version 4.4, March 2012, which have been established by the AHRQ to monitor preventable adverse events during hospitalization. These indicators are based on ICD-9-CM codes and Medicare severity Diagnosis Related Groups, and each PSI has specific inclusion and exclusion criteria. This method of identifying patients undergoing procedures, co-morbid conditions, and associated complications has previously been used in several studies.
Other outcomes studied were the length of hospital stay and cost of hospitalization. To estimate cost of hospitalization, the NIS data were merged with cost-to-charge ratios available from the HCUP. We estimated the cost of each inpatient stay by multiplying the total hospital charge with cost-to-charge ratios. Cost for each year was calculated in terms of the 2012 cost, after adjusting for inflation according to the latest consumer price index (CPI) data released by US government on January 16, 2013.
Stata IC 11.0 (Stata-Corp, College Station, Texas) and SAS 9.3 (SAS Institute Inc, Cary, North Carolina) was used for analyses. Differences between categorical variables were tested using the chi-square test and differences between continuous variables were tested using the Student t test, continuous variables with Gaussian distributions, and Kruskal–Wallis rank sum tests for continuous variables with non-Gaussian distributions.
Multivariate logistic regression was generated to identify the independent multivariate predictors of inhospital mortality. All interactions were thoroughly tested. Collinearity was assessed using variance inflation factor.
We used propensity-scoring method to establish matched cohorts to control for imbalances of patients’ and hospitals’ characteristics between the 2 groups that may have influenced the primary outcome. A propensity score was assigned to each hospitalization. This was based on multivariate logistic regression model that examined the impact of 12 variables (patient demographics, co-morbidities, and hospital characteristics) on the likelihood of treatment assignment. Patients with similar propensity score in the 2 treatment groups were matched using a 1 to 1 scheme without replacement using greedy algorithm.
Results
A total of 9,674 SAVR procedures (which translates to an estimated 47,410 procedures performed in 1,110 hospitals) were identified of which 4,526 (46.79%) were performed in hospitals with availability of TAVI (SAVR-TAVI group) and 5,148 (53.21%) in non-TAVI hospitals (SAVR-non-TAVI group). Table 1 demonstrates the baseline characteristics of the study population. The mean age of the study population was 70.2 ± 0.1 years with majority (53%) of the patients aged >70 years; 58% were men and 78% were whites. There were significant differences between the baseline characteristics of patients in the 2 groups ( Table 1 ). A greater percentage of patients in SAVR-TAVI group were aged >80 years, had hypertension, congestive heart failure, renal failure, and peripheral arterial disease, whereas more patients in SAVR-non-TAVI group had diabetes and obesity. The mean Charlson co-morbidity score for patients in SAVR-TAVI group was greater than that of patients in SAVR-non-TAVI group (1.6 vs 1.4, p <0.001). The outcomes of both groups are reported in Table 2 .
| SAVR in TAVI vs Non-TAVI hospital of 2011 – 2012 | A) Unmatched Group | B) Matched Group (1-1 Matching) for age, gender, Charlson’s score, bedsize, location and teaching status, type of admission, weekend | ||||||
|---|---|---|---|---|---|---|---|---|
| Demographic Variables | Non-TAVI capable hospital | TAVI capable hospital | Overall | P-value | Non-TAVI capable hospital | TAVI capable hospital | Overall | P-value |
| Total No. of Observations (unweighted) | 5148 (53.21%) | 4526 (46.79%) | 9674 | 2678 (50%) | 2678 (50%) | 5356 | ||
| Total No. of Observations (weighted) | 25234.7 (53.23%) | 22176.2 (46.77%) | 47,410.9 | 13149.2 (50.25%) | 13017.06 (49.75%) | 26,166.3 | ||
| Total No. of Hospitals | 823 (74.14%) | 287 (25.86%) | 1110 | 541 (73.81%) | 192 (26.19%) | 733 | ||
| No. of SAVR procedures per hospital (Hospital Volume) for 2011 | ||||||||
| Mean +/- SE | 30.7 (0.4) | 113.3 (1.8) | 63.4 (0.1) | <0.001 | 35.0 (0.5) | 111.0 (1.0) | 75.3 (1.3) | <0.001 |
| Median (Q1, Q3) | 25 (14, 47) | 89 (49, 142) | 38 (19, 76) | 32 (19, 54) | 89 (49, 142) | 54 (26, 89) | ||
| Age Mean +/- SE(Years) | 69.80 (0.1) | 70.67 (0.2) | 70.20 (0.1) | <0.001 | 70.2 (0.2) | 70.4(0.2) | 70.3(0.1) | 0.786 |
| 50-59 | 18.1% | 17.9% | 18.0% | <0.001 | 17% | 16.9% | 17.0% | 0.973 |
| 60-69 | 30.6% | 28.0% | 29.4% | 30.5% | 30.8% | 30.7% | ||
| 70-79 | 31.8% | 29.8% | 30.8% | 31.4% | 31.2% | 31.3% | ||
| >80 | 19.6% | 24.3% | 21.8% | 21.1% | 21.2% | 21.1% | ||
| Sex | ||||||||
| Male | 57.1% | 59.5% | 58.2% | <0.001 | 58.9% | 59.0% | 59.0% | 0.897 |
| Female | 42.9% | 40.5% | 41.8% | 41.1% | 41.0% | 41.0% | ||
| Race | ||||||||
| Whites | 78.6% | 76.5% | 77.6% | <0.001 | 76.6% | 77.1% | 76.9% | <0.001 |
| Blacks | 4.4% | 4.7% | 4.5% | 5.2% | 4.9% | 5.0% | ||
| Hispanics | 5.6% | 4.9% | 5.3% | 5.3% | 4.6% | 5.0% | ||
| Others | 4.0% | 5.7% | 4.8% | 3.9% | 5.2% | 4.6% | ||
| Missing | 7.5% | 8.2% | 7.9% | 8.9% | 8.2% | 8.6% | ||
| Comorbidities | ||||||||
| Charlson Comorbidities Score (%) | ||||||||
| Mean +/- SE | 1.4 (0.02) | 1.6 (0.02) | 1.5 (0.01) | <0.001 | 1.40 (0.03) | 1.46 (0.03) | 1.43 (0.02) | 0.524 |
| 0 | 30.4% | 25.6% | 28.1% | 29.8% | 29.8% | 29.8% | 0.602 | |
| 1 | 32.8% | 33.8% | 33.3% | 33.2% | 32.7% | 33.0% | ||
| More than or equal to 2 | 36.8% | 40.6% | 38.6% | 37.0% | 37.5% | 37.2% | ||
| Obesity | 20.6% | 17.1% | 19.0% | <0.001 | 20.5% | 16.6% | 18.5% | <0.001 |
| Hypertension | 73.4% | 75.2% | 74.2% | <0.001 | 73.0% | 74.3% | 73.7% | 0.019 |
| Diabetes Mellitus | 29.2% | 26.7% | 28.0% | <0.001 | 29.1% | 25.5% | 27.3% | <0.001 |
| Congestive Heart Failure | 25.4% | 33.5% | 29.2% | <0.001 | 24.2% | 31.9% | 28.0% | <0.001 |
| History of Chronic Pulmonary Disease | 20.7% | 20.9% | 20.8% | 0.599 | 20.3% | 20.6% | 20.4% | 0.501 |
| Peripheral Vascular Disease | 17.4% | 22.6% | 19.8% | <0.001 | 18.3% | 21.6% | 20.0% | <0.001 |
| Fluid-electrolyte abnormalities & Renal Failure | 33.9% | 41.8% | 37.6% | <0.001 | 36.7% | 38.3% | 37.5% | 0.011 |
| Neurological disorder or paralysis | 6.1% | 6.2% | 6.1% | 0.810 | 6.5% | 5.9% | 6.2% | 0.036 |
| Anemia or coagulopathy | 40.4% | 42.2% | 41.2% | 0.000 | 40.4% | 42.2% | 41.3% | 0.003 |
| Depression, Psychosis, or Substance Abuse | 9.6% | 9.5% | 9.6% | 0.639 | 9.5% | 9.6% | 9.6% | 0.767 |
| Median Household Income Category for patient’s Zip code | ||||||||
| 1. 0-25th percentile | 21.7% | 20.1% | 21.0% | <0.001 | 20.0% | 19.0% | 19.5% | <0.001 |
| 2. 26-50th percentile | 25.6% | 20.9% | 23.4% | 24.0% | 20.5% | 22.3% | ||
| 3. 51-75th percentile | 27.7% | 25.3% | 26.6% | 30.2% | 25.2% | 27.7% | ||
| 4. 76-100th percentile | 23.0% | 32.1% | 27.3% | 24.0% | 34.1% | 29.0% | ||
| missing | 1.9% | 1.6% | 1.8% | 1.9% | 1.2% | 1.6% | ||
| Primary Payer | ||||||||
| Medicare | 65.2% | 65.1% | 65.2% | 0.867 | 66.5% | 64.5% | 65.5% | <0.001 |
| Medicaid | 2.9% | 2.8% | 2.9% | 3.0% | 2.8% | 2.9% | ||
| Private including HMOs & PPOs | 28.2% | 28.4% | 28.3% | 26.7% | 29.6% | 28.1% | ||
| Other/Self-pay/No charge | 3.6% | 3.6% | 3.6% | 3.7% | 3.0% | 3.4% | ||
| missing | 0.2% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1% | ||
| Hospital Characteristics | ||||||||
| Bed size of Hospital depending on Location & Teaching Status | ||||||||
| Small | 10.4% | 2.0% | 6.5% | <0.001 | 3.4% | 3.5% | 3.4% | 0.275 |
| Medium | 22.2% | 15.1% | 18.9% | 24.8% | 25.7% | 25.2% | ||
| Large | 67.1% | 82.9% | 74.5% | 71.8% | 70.9% | 71.3% | ||
| Hospital Location & Teaching Status | ||||||||
| Rural | 4.0% | 0.7% | 2.5% | <0.001 | 1.2% | 1.2% | 1.2% | 0.694 |
| Urban Non-teaching | 41.0% | 9.7% | 26.3% | 16.0% | 16.3% | 16.1% | ||
| Urban Teaching | 54.7% | 89.6% | 71.0% | 82.9% | 82.5% | 82.7% | ||
| Hospital Region | ||||||||
| Northeast | 18.0% | 27.4% | 22.4% | <0.001 | 23.4% | 30.5% | 26.9% | <0.001 |
| Midwest | 18.3% | 20.6% | 19.4% | 18.1% | 18.1% | 18.1% | ||
| South | 25.2% | 30.8% | 27.8% | 25.5% | 28.1% | 26.8% | ||
| West | 22.5% | 14.8% | 18.9% | 20.2% | 17.6% | 18.9% | ||
| Type of Admission | ||||||||
| Non-elective | 16.3% | 17.2% | 16.7% | 0.001 | 14.8% | 14.5% | 14.6% | 0.501 |
| Elective | 83.5% | 82.6% | 83.1% | 85.2% | 85.5% | 85.4% | ||
| Admission Day | ||||||||
| Weekdays | 97.1% | 96.0% | 96.6% | <0.001 | 97.1% | 97.0% | 97.0% | 0.821 |
| Weekends | 2.9% | 4.0% | 3.4% | 2.9% | 3.0% | 3.0% | ||
| Disposition | ||||||||
| Home | 39.2% | 37.9% | 38.6% | <0.001 | 39.4% | 36.9% | 38.2% | <0.001 |
| Home Health Care | 34.9% | 38.3% | 36.5% | 35.3% | 41.2% | 38.2% | ||
| Transfer to Short-term Hospital/other facilities | 23.9% | 22.3% | 23.1% | 23.3% | 20.7% | 22.0% | ||
| Length of hospital-stay – Median (Quartile 1 , 3), days | 6 (5, 8) | 7 (5, 9) | 6 (5, 9) | 6 (5, 8) | 6 (5, 9) | 6 (5, 9) | ||
| Cost ($) ∗ (Mean, SE) | 41831.72 (339) | 44840.75 (433) | 43198.76 (270) | <0.001 | 41032.3 (473) | 43894.14 (483) | 42388.19 (339) | <0.001 |
| SAVR in TAVI vs Non-TAVI center | A) Unmatched dataset | B) Matched Group (1-1 Matching) for age, gender, Charlson’s score, bedsize, location and teaching status, type of admission, weekend | ||||||
|---|---|---|---|---|---|---|---|---|
| Complications (%) | Non-TAVI capable | TAVI capable | Overall | P-value | Non-TAVI capable | TAVI capable | Overall | P-value |
| Death | 1.8% | 1.5% | 1.7% | 0.036 | 1.7% | 1.3% | 1.5% | 0.002 |
| Any Complications | 36.0% | 36.5% | 36.2% | 0.204 | 37.3% | 35.6% | 36.5% | 0.004 |
| Any Complications + Death | 36.5% | 36.9% | 36.7% | 0.375 | 38.0% | 35.9% | 36.9% | 0.001 |
| Stroke | 0.9% | 1.2% | 1.0% | 0.007 | 1.0% | 0.9% | 1.0% | 0.803 |
| Transmural MI | 0.9% | 1.0% | 0.9% | 0.151 | 0.9% | 1.1% | 1.0% | 0.139 |
| Deep sternal wound infection | 0.6% | 0.6% | 0.6% | 0.610 | 0.6% | 0.5% | 0.6% | 0.186 |
| Hemorrhage requiring transfusion | 19.8% | 18.4% | 19.1% | 0.000 | 22.2% | 18.0% | 20.1% | <0.001 |
| Cardiac Complications | 12.8% | 13.2% | 13.0% | 0.203 | 12.5% | 13.3% | 12.9% | 0.048 |
| Iatrogenic Cardiovascular Complications | 11.9% | 12.5% | 12.2% | 0.085 | 11.8% | 12.7% | 12.3% | 0.021 |
| Pericardial complications | 0.9% | 0.6% | 0.8% | 0.000 | 0.8% | 0.5% | 0.6% | 0.007 |
| Respiratory failure | 5.6% | 6.2% | 5.9% | 0.011 | 5.2% | 5.6% | 5.4% | 0.099 |
| Sepsis | 1.9% | 2.9% | 2.4% | <0.001 | 2.1% | 2.6% | 2.4% | 0.004 |
| Acute Kidney Injury requiring Dialysis | 1.2% | 0.9% | 1.0% | 0.003 | 1.1% | 0.8% | 1.0% | 0.018 |
| PE and DVT | 0.7% | 1.0% | 0.8% | <0.001 | 0.5% | 1.2% | 0.9% | <0.001 |
| Anesthetics Complications | 0.0% | 0.0% | 0.0% | NA | 0.0% | 0.0% | 0.0% | NA |
| Cardiac Device/Prosthetics/ Graft Complications | 0.5% | 0.5% | 0.5% | 0.752 | 0.3% | 0.6% | 0.5% | 0.000 |
Propensity score–matched analysis is reported in Table 1 . In this analysis, multiple patient- and hospital-level variables that could have affected the results were adjusted for. These variables included age, gender, Charlson co-morbidity score, bed size of the hospital, location of the hospital and teaching status, type, and timing of admissions (emergent vs nonemergent and weekend vs nonweekend). The propensity score–matching analysis showed a statistically significant lower inhospital mortality (1.25% vs 1.72%, p = 0.001) and complications rates (35.6% vs 37.3%, p = 0.004) in SAVR-TAVI group compared to SAVR-non-TAVI group ( Figure 1 ). The mean length of hospital stay was similar in the 2 groups; however, the cost of hospitalization was greater in the SAVR-TAVI group. In the multivariate analysis of propensity score–matched population, having a TAVI program in the hospital was a significant predictor of reduced mortality (odds ratio [OR] 0.71, 95% confidence interval [CI] 0.6 to 0.8, p = 0.0015), whereas a greater burden of co-morbidities (CCI >2) was an independent predictor of higher inhospital mortality (OR 5.2, 95% CI 3.6 to 7.5, p <0.0001) ( Table 3 ). Similarly, having a TAVI program was an independent predictor of reduced complication rates in patients undergoing SAVR ( Table 3 ). In another multivariate model that included hospital SAVR volume, presence of a TAVI program was a significant predictor of reduced mortality and complication rates ( Supplementary Table 1 ).
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