Methods
The Nationwide Inpatient Sample (NIS) was used to derive patient-relevant information between January 2003 and December 2014. The NIS is the largest publicly available all-payer administrative claims-based database and contains information about patient discharges from approximately 1000 non-federal hospitals in 45 states. It contains clinical and resource utilisation information on 5–8 million discharges annually, with safeguards to protect the privacy of individual patients, physicians and hospitals. These data are stratified to represent approximately 20% of US inpatient hospitalisations across different hospital and geographic regions (random sample). National estimates of the entire US hospitalised population were calculated using the Agency for Healthcare Research and Quality sampling and weighting method. The institutional review board approved the study and waived informed consent requirements because the data are derived from a nationwide deidentified database.
We used (International Classification of Diseases-Ninth Revision-Clinical Modification procedure code 35.23) to select patients aged 40 years and older who underwent bioprosthetic MVR during the study period. Patients who underwent redo valve surgery (codes 35.95), those with mitral stenosis (codes 394.0) or infective endocarditis (codes 421, 42.10, 42.11, 42.19, 03642, 09884, 11281, 1154) or had codes for mitral valve repair or mechanical MVR during the same admission were excluded (figure 1). The outcomes of patients who underwent isolated valve replacement were then studied and compared with those of patients submitted for valve replacement combined with other cardiac surgery.
Study flow chart.
The trends of bioprosthetic MVR for MV during the 12-year study period were assessed using weighted numbers (national estimates). Baseline patients’ comorbidities and procedural characteristics were described for both the isolated and combined MVR groups. Trends of in-hospital mortality during the study period for both groups were described. Trend weights accounting for changes in the NIS sampling design are only available for data between 1998 and 2011. For 2012 and 2014, trend weights were not available, and the standard survey weights were used. To estimate the cost of hospitalisation, the NIS data were merged with cost-to-charge ratios available from the Healthcare Cost and Utilization Project. We estimated the cost of each inpatient stay by multiplying the total hospital charge with cost-to-charge ratios. Postoperative morbidities, length of stay (LOS), disposition patterns and cost of care were also evaluated. Patient-relevant descriptive statistics are presented as frequencies with percentages for categorical variables and as means with SD for continuous variables. Baseline characteristics were compared between the groups using a Pearson χ2 test for categorical variables and an independent-samples t-test for continuous variables. Trends over time were examined using a Mann-Kendall test for trend (a non-parametric test to determine the presence and direction of a trend over time). All statistical analyses were performed using SPSS V.24 (IBM).
We also sought to identify independent predictors of in-hospital mortality in our study cohort. Hence, we entered 22 clinical, procedural and hospital characteristics into univariate and multivariate logistic regression models to assess their possible predictive value of in-hospital mortality after valve surgery (online supplementary e-table 1,2).