Ventricular septal myomectomy (VSM) is the primary modality for left ventricular outflow tract gradient reduction in patients with obstructive hypertrophic cardiomyopathy with refractory symptoms. Comprehensive postprocedural data for VSM from a large multicenter registry are sparse. The primary objective of this study was to evaluate postprocedural mortality, complications, length of stay (LOS), and cost of hospitalization after VSM and to further appraise the multivariate predictors of these outcomes. The Healthcare Cost and Utilization Project’s Nationwide Inpatient Sample was queried from 1998 through 2010 using International Classification of Diseases, Ninth Revision, procedure codes 37.33 for VSM and 425.1 for hypertrophic cardiomyopathy. The severity of co-morbidities was defined using the Charlson co-morbidity index. Hierarchical mixed-effects models were generated to identify independent multivariate predictors of in-hospital mortality, procedural complications, LOS, and cost of hospitalization. The overall mortality was 5.9%. Almost 9% (8.7%) of patients had postprocedural complete heart block requiring pacemakers. Increasing Charlson co-morbidity index was associated with a higher rate of complications and mortality (odds ratio 2.41, 95% confidence interval 1.17 to 4.98, p = 0.02). The mean cost of hospitalization was $41,715 ± $1,611, while the average LOS was 8.89 ± 0.35 days. Occurrence of any postoperative complication was associated with increased cost of hospitalization (+$33,870, p <0.001) and LOS (+6.08 days, p <0.001). In conclusion, the postoperative mortality rate for VSM was 5.9%; cardiac complications were most common, specifically complete heart block. Age and increasing severity of co-morbidities were predictive of poorer outcomes, while a higher burden of postoperative complications was associated with a higher cost of hospitalization and LOS.
Highlights
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Higher postoperative mortality was found after VSM than reported in recent studies.
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Age was predictive of higher postoperative mortality and complications.
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Higher burden of co-morbidities predicted higher postoperative mortality and complications.
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More postoperative complications were associated with longer LOS.
Obstructive hypertrophic cardiomyopathy (HC) is a common genetic disease with variable expressivity, characterized by varying degrees of left ventricular outflow tract obstruction. Ventricular septal myectomy (VSM), with >50 years of experience, has been the mainstay for ameliorating outflow tract gradients in patients with medically refractory symptoms. Published reports have demonstrated the efficacy of VSM in terms of improvement in hemodynamics and functional status as well as reductions in syncope and sudden cardiac death after surgery. Furthermore, VSM offers the added advantage of correcting mitral valve apparatus abnormalities associated with HC. However, VSM is a complex procedure with a steep learning curve that is best performed in the hands of experienced operators at advanced tertiary centers with high volumes. Most of the available data are limited to experienced surgical centers with skilled high-volume operators. Real-world data for postprocedural outcomes for VSM from multiple centers across the nation are sparse. Concern regarding postprocedural outcomes has become increasingly germane given an increase in the number of referred patients as well the availability of a less invasive alternative (alcohol septal ablation). The main objectives of our study were (1) to evaluate the postsurgical outcomes of VSM in terms of mortality as well as complications, (2) to further elucidate potential predictors of post-operative outcomes, and (3) to study resource utilization in terms of length of stay (LOS) and cost of hospitalization associated with this surgical procedure by analyzing the largest publicly available inpatient care database.
Methods
The study cohort was derived from the Nationwide Inpatient Sample (NIS) database from 1998 through 2010, a subset of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality. The NIS is the largest publicly available all-payer inpatient care database in the United States, including data on approximately 7 million to 8 million discharges per year, and is a stratified sample designed to approximate a 20% sample of US community (nonfederal, short-term, general, and specialty) hospitals. National estimates are produced using sampling weights provided by the sponsor. Details regarding the NIS data have been previously published. Overall, the NIS contains about 8 million inpatients annually. Annual data quality assessments of the NIS are performed, which guarantee the internal validity of the database. Furthermore, comparisons against the following data sources strengthen the external validity of the NIS: the American Hospital Association Annual Survey Database, the National Hospital Discharge Survey from the National Center for Health Statistics, and Medicare Provider Analysis and Review inpatient data from the Centers for Medicare and Medicaid Services.
We queried the NIS database from 1998 through 2010 using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes 37.33 for VSM and 425.1 for HC. NIS variables were used to identify patients’ demographic characteristics, including age, gender, and race ( Table 1 ). We defined the severity of co-morbid conditions using the Deyo modification of the Charlson co-morbidity index (CCI) ( Supplementary Table 1 ). This index contains 17 co-morbid conditions with differential weights. The score ranges from 0 to 33, with higher scores corresponding to a greater burden of co-morbid diseases. Preventable procedural complications were identified by patient safety indicators (PSIs), 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. Individual PSI technical specifications were used to identify and define preventable procedural complications, namely, postprocedural respiratory failure, postprocedural renal and metabolic derangement, postprocedural pulmonary embolism or deep vein thrombosis, procedural infectious complications (including postprocedure sepsis and central venous catheter–related bloodstream infection), pressure ulcers, and accidental puncture or laceration. Other procedure-related complications, including hemorrhage requiring blood transfusion, iatrogenic cardiac complications, implantation of a permanent pacemaker implying high-grade heart block, pericardial complications, conversion to open-heart surgery, other iatrogenic cardiac complications (including coronary dissections and chamber perforations), procedural stroke or transient ischemic attack, and vascular complications, were identified using ICD-9-CM codes in any secondary diagnosis field ( Supplementary Table 2 ). To prevent classification of a preexisting condition (e.g., stroke or heart block) as a complication, cases with the ICD-9-CM code for a complication listed as the principal diagnosis were excluded. Vascular complications were defined as the PSI code for accidental puncture or the ICD-9-CM codes for injury to blood vessels, creation of an arteriovenous fistula, injury to retroperitoneum, vascular complications requiring surgery, and other vascular complications not elsewhere classified. “Any complications” was defined as the occurrence of ≥1 procedural complications. This method has been used in earlier studies.
| Age (year) (Mean ± SE) | 56.9 ± 0.6 |
| Male | 40.0% |
| White | 52.8% |
| Non-white | 11.7 |
| Charlson/deyo comorbidity index ∗ (Mean ± SE) | 0.87 ± 0.04 |
| Obesity (Body Mass Index ≥30) | 11% |
| Hypertension | 45.1% |
| Diabetes mellitus | 12.2% |
| Heart failure | 0.6% |
| Chronic pulmonary disease | 15.8% |
| Peripheral vascular disease | 4.4% |
| Renal failure | 3.2% |
| Neurological disorder/paralysis | 2.1% |
| Anemia/coagulopathy | 22.3% |
| Hematological or oncological malignancy | 0.8% |
| Weight loss/cachexia | 1.8% |
| Collagen vascular disease | 1.5% |
| Depression/substance abuse | 9.2% |
| Median household income category for patient’s zip code † | |
| 0–25 th percentile | 20.5% |
| 26–50 th percentile | 23% |
| 51–75 th percentile | 19.1% |
| 76–100 th percentile | 15.5% |
| Primary Payer | |
| Medicare/Medicaid | 46.3% |
| Private including HMO | 48.6% |
| Self pay/no charge/other | 5% |
| Missing | 0.2% |
| Hospital bed size | |
| Small | 5.7% |
| Medium | 11.4% |
| Large | 82.3% |
| Missing | 0.6% |
| Hospital Location | |
| Urban | 97.3% |
| Missing | 0.6% |
| Hospital Region | |
| Northeast | 16.5% |
| Mid West/North Central | 34.3% |
| South | 30.4% |
| West | 18.8% |
| Missing | |
| Hospital Teaching status | |
| Teaching | 77.7% |
| Missing | 0.6% |
| Admission types | |
| Emergent/Urgent | 19.7% |
| Missing | 8.3% |
| Admission day | |
| Weekend | 4.7% |
| Length of stay (Days) (Means ± SE) | 8.89 ± 0.35 |
| Cost ($) (Means ± SE) | 41,715 ± 1,611 |
| Missing | 1.3% |
| Peri-procedural complications | 30.0% |
| Disposition | |
| Home | 79.7% |
| Facility | 14.4% |
| Missing | 5.9% |
| Death | 5.9% |
∗ Charlson/Deyo comorbidity index was calculated as per – Deyo RA, Cherkin DC, Coil MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J ClinEpidemiol.1992;45:613–619.
† This represents a quartile classification of the estimated median household income of residents in the patient’s ZIP Code. These values are derived from ZIP Code-demographic data obtained from Claritas. The quartiles are identified by values of 1 to 4, indicating the poorest to wealthiest populations. Because these estimates are updated annually, the value ranges vary by year.zip is missing in 21.9% of population. [CR] .
Annual hospital volume was determined on a year-to-year basis using the unique hospital identification number to calculate the total number of procedures performed by a particular institution in a given year. Similarly, operator volume was computed using operator identification number, although not all hospitals report operator identification numbers. Furthermore, operator identification numbers were not reported in the NIS for 2010. Hospital volume was incorporated as a continuous variable in the multivariate model in increments of 3 units per year. Operator volume was similarly incorporated in 1-unit increments. Multivariate models were created incorporating hospital and operator volume, with a term to adjust for the interaction effect between hospital and operator volume. Hospital identity was incorporated as a random effect in the model to account for the effect of hospital clustering (meaning that patients treated at the same hospital may experience similar outcomes as a result of other processes of care).
The total duration of hospital stay in days was estimated for all patients, after excluding those who died in the hospital, using the information on LOS provided in the NIS data set. The NIS data set includes all patients admitted under observational or inpatient status into participating hospitals. The NIS contains data on total charges for each hospital in the databases, which represents the amounts hospitals billed for services. To calculate estimated cost of hospitalizations, the NIS data were merged with cost-to-charge ratios available from the Healthcare Cost and Utilization Project. Using the merged data elements from the cost-to-charge ratio files and the total charges reported in the NIS database, we converted the hospital total charge data to cost estimates by simply multiplying total charges by the appropriate cost-to-charge ratio. These costs are in essence standardized and can be measured across hospitals and are used in the remainder of this report. Adjusted cost for each year was calculated in terms of the 2010 cost, after adjusting for inflation according to the latest Consumer Price Index data released by the US government on January 16, 2013.
Stata IC version 11.0 (StataCorp LP, College Station, Texas) and SAS version 9.0 (SAS Institute Inc., Cary, North Carolina) were used for analyses. Weighted values of patient-level observations were generated to produce a nationally representative estimate of the entire US population of hospitalized patients. Differences between categorical variables were tested using chi-square tests, and differences between continuous variables were tested using Student’s t tests. A p value <0.05 was considered significant. The NIS data set is inherently hierarchical; that is, the data have group (i.e., hospital)–specific attributes, and within each group (i.e., hospital) there are patients who contribute patient-specific attributes to the data. Hierarchical models take into consideration the effect of nesting (e.g., patient-level effects nested within hospital-level effects). Hence, hierarchical modeling is superior to simple regression modeling for the available data set. Two-level hierarchical models (with patient-level factors nested within hospital-level factors) were created with the unique hospital identification number incorporated as a random effect within the model. Hierarchical mixed-effects logistic regression models were used for categorical dependent variables such as in-hospital mortality and procedural complications, and hierarchical mixed-effects linear regression models were used for continuous dependent variables such as cost of hospitalization and LOS. Variables with >10% missing data were not included in the multivariate models. In all multivariate models, we included hospital-level variables, such as hospital region (Northeast, South, Midwest, and West [the referent]) and teaching versus nonteaching status, and patient-level variables, such as age, gender, Deyo modification of CCI, occurrence of procedural complications, admission over the weekend, and primary payer (with Medicare or Medicaid as the referent), in addition to hospital and operator procedure volume. All interactions were thoroughly tested. Multicollinearity, defined as a perfect linear relation or a very high correlation between ≥2 predictor (independent) variables, was assessed using variance inflation factors, with values >20 suggestive of multicollinearity.
Results
Table 1 shows baseline characteristics of the study population. A total of 665 VSM procedures were available for analysis from 1998 to 2010. The mean age of the study cohort was 56.9 ± 0.6 years. Men constituted 40% of the cohort, with 52.8% being white. The mean CCI score for the cohort was calculated as 0.87 ± 0.04, with hypertension being the most common co-morbidity, present in 45.1% of the patients, while diabetes was present in 12.2% of those who underwent VSM. Most procedures were done at large (82.3%), urban (97.3%), or teaching (77.7%) hospitals. Almost 80% (79.7%) of patients were discharged home after the procedure, while 14.4% were discharged to facilities.
The overall postprocedural mortality ( Table 2 ) was 5.9%, while the rate of postprocedural complications was 30.2% ( Table 2 ). Cardiac complications were most common (15.9%), including iatrogenic cardiac complications (10.5%) and complete heart block requiring pacemaker insertion (8.7%). Vascular complications, including access-site complications, occurred in 9.6% of patients, of whom 5.4% required transfusion. Respiratory complications occurred in 3.9% of patients, while 1.4% of patients had renal or metabolic complications.
| Variable | ICD CODE | Percentage |
|---|---|---|
| Death | 5.9% | |
| Any procedural complications | 30.2% | |
| Death + Any procedural complications | 31.4% | |
| Vascular complications | 9.6% | |
| Postop-hemorrhage requiring transfusion ∗ | 998.11, 998.12, 99.0, V58.2 | 5.4% |
| Vascular complications including |
| 4.4% |
| Cardiac complications | 15.9% | |
| Iatrogenic cardiac complications | 997.1 | 10.5% |
| CHB requiring pacemaker Insertion | 37.80–83 | 8.7% |
| Pericardial complications | 423.0-Hemopericardium | 0.2% |
| 423.3-Cardiac temponad3 | ||
| 37.0-Pericardiocentesis | ||
| Respiratory complications (Post-op respiratory failure) | 512.1 | 3.9% |
| Neurological Complications | ||
| Postop-Stroke/TIA | 997.0, 997.00, 997.01, 997.02, 435.9,438.0, 4381.0, 4381.1, 4381.2, 4381.9, 4382.0, 4382.1, 4382.2, 4383.0, 4383.1, 4383.2, 4384.0, 4384.1, 4384.2, 4385.0, 4385.1, 4385.2, 4385.3, 4388.1, 4388.2, 4388.9, 438.9 | 2.6% |
| Renal and metabolic complications | 1.4% | |
| Postoperative DVT/PE | PSI † | 1.5% |
| Postop infectious complications ‡ | PSI † | 3.3% |
| Pressure ulcer rate | PSI † | 0.6% |
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