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Perioperative Strokes and Early Outcomes in Mitral Valve Surgery: A Nationwide Analysis
Journal of Cardiothoracic and Vascular Anesthesia ] (]]]]) ]]]–]]]
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Journal of Cardiothoracic and Vascular Anesthesia ] (]]]]) ]]]–]]]
Original Article
Perioperative Strokes and Early Outcomes in Mitral Valve Surgery: A Nationwide Analysis Reshmi Udesh, MBBSn, Amol Mehta, BS†, Thomas G. Gleason, MDn, Lawrence Wechsler, MDn, Parthasarathy D. Thirumala, MD, MSn,†,1 n
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA † University of Pittsburgh School of Medicine, Pittsburgh, PA
Objective: To demonstrate the role of perioperative stroke as an independent risk factor for in-hospital morbidity and mortality after mitral valve surgery and review the trends in the early outcomes of mitral valve surgery over the past decade. Design: Using data from the National Inpatient Sample database for analysis, all patients who underwent isolated mitral valve procedures were identified using International Classification of Diseases-Ninth Revision codes. Univariate and multivariate analyses of risk factors of in-hospital mortality and morbidity were performed. Setting: Multi-institutional. Participants: The study comprised patients who underwent mitral valve procedures from 1999 to 2011. Interventions: Mitral valve repair or replacement. Measurements and Main Results: Data on 21,821 patients showed an in-hospital mortality of 5.5% and morbidity of 63.30% (po 0.05). Perioperative strokes were experienced by 3.89% of the cohort after isolated mitral valve surgery (po 0.05). Independent predictors of adverse outcomes were age, female sex, emergency surgery, arrhythmias, hypertension, renal failure, coagulopathy, neurologic disorders, weight loss, anemia, postoperative cardiac arrest, and myocardial infarction. Perioperative strokes were found to be the strongest risk factor for postoperative mortality (odds ratio 2.34, 95% confidence interval 1.83-2.98) and morbidity (odds ratio 4.53, 95% confidence interval 3.34-6.15). Conclusion: Age, female sex, emergency surgery, arrhythmias, hypertension, renal failure, coagulopathy, neurologic disorders, weight loss, fluid and electrolyte imbalance, anemia, postoperative cardiac arrest, and myocardial infarction were found to be significant predictors of morbidity and mortality after mitral valve surgery, with perioperative strokes posing the strongest risk. The trends in the last 10 years indicated a decrease in mortality and an increase in morbidity. Preoperative risk stratification and intraoperative identification for impending strokes appear warranted. & 2016 Elsevier Inc. All rights reserved.
Key Words: mitral valve surgery; perioperative stroke; in-hospital mortality; morbidity
MITRAL VALVE (MV) repair or replacement for MV disease, with nearly15,000 procedures reported annually in the United States, is one of the most common valve surgeries 1
Address reprint requests to Parthasarathy D. Thirumala, MD, MS, Center for Clinical Neurophysiology, Department of Neurological Surgery, UPMC Presbyterian-Suite B-400, 200 Lothrop Street, Pittsburgh, PA 15213. E-mail address: [email protected] (P.D. Thirumala).
performed.1 A surgical mortality of 1.2% and 4.8% has been reported for MV repair and replacement, respectively.1,2 Perioperative strokes, defined as neurologic deficits developing within 30 days of the procedure, affects postoperative mortality rates and quality of life after MV surgery (MVS).3-7 Previous studies have shown that adverse neurologic outcomes have significant economic implications, with patients requiring a longer stay in intensive care units and hospitals.3,7
http://dx.doi.org/10.1053/j.jvca.2016.12.006 1053-0770/& 2016 Elsevier Inc. All rights reserved.
Please cite this article as: Udesh R, et al. (2017), http://dx.doi.org/10.1053/j.jvca.2016.12.006
R. Udesh et al. / Journal of Cardiothoracic and Vascular Anesthesia ] (]]]]) ]]]–]]]
2
Examining the factors that influence early morbidity and mortality rates may give insight on improved practices, better patient selection, and efficient management of patient comorbidities.8,9 Therefore, identification of individuals at risk may lead to a significant reduction in cost and the improvement of resource allocation without compromising quality of care.3,8,9 Several institutional studies have reported the following risk factors to be associated with adverse postoperative outcomes after MVS: age, female sex, urgent or emergency admissions, diabetes mellitus (DM), hypertension, coronary artery disease (CAD), congestive cardiac failure (CHF), atrial fibrillation, bacterial endocarditis, previous history of symptomatic carotid stenosis (strokes or transient ischemic attacks), renal failure, hepatic disease, periprocedural myocardial infarction (MI), and arrhythmias.10-12 A large-scale nationwide analysis on the impact of perioperative strokes on MVS outcomes has not been done before. The primary aim of this study was to demonstrate the role of perioperative strokes as an independent risk factor for inhospital mortality after MVS. The secondary aim was to study the role of stroke as an independent risk factor for postoperative morbidity, defined as a length of stay greater than 14 days and/or discharge to a location other than home. Finally, the authors also examined the trends over time in the postoperative outcomes after MVS. Understanding the implications of strokes on early postoperative outcomes warrants further investigation into strategies such as preoperative risk stratification,13,14 intraoperative neurophysiologic monitoring,15-17 and care pathways to better manage hospital stay and resource allocation.8,9 Methods Patient Population The study population was selected from the National Inpatient Sample (NIS) for the years 1999 to 2011 using International Classification of Diseases, Ninth RevisionClinical Modification (ICD-9-CM) diagnosis and procedure codes. Patients who underwent isolated MV repair (ICD-9CM-35.12) or MV replacement (ICD-9-CM-35.23 and 35.24) were selected for the study. Patients younger than 18 years were excluded from the selection. Baseline characteristics available for analysis included age, sex, race, admission status, transfer status, Elixhauser comorbidities and other additional comorbid conditions such as CAD, atrial fibrillation, bacterial endocarditis, postoperative cardiac arrest, and MI. The NIS database provides 29 Elixhauser comorbidities based on standard ICD-9 codes.18 In addition to these, a complete list of ICD-9 codes that were used to define other variables is provided in Supplementary Table 1. Risk stratification was performed using the Van Walraven (VWR) score, which is a summary score for the Elixhauser Comorbidity Index developed by modeling in-hospital mortality with inpatient admission data.19 The summary score is a weighted combination of the 29 Elixhauser comorbidities, in which a larger comorbidity weight indicates a stronger association between a comorbidity
and in-hospital mortality. The primary outcome studied was in-hospital mortality. The secondary outcome, postoperative morbidity, was characterized as a long length of stay (greater than 14 days) or discharge to a place other than home.20 Statistical Analysis All statistical analyses were performed using the SPSS software, Version 23 (IBM Corp, Armonk, NY); Stata Student Edition, Version 14.0 SE (StataCorp, College Station, TX); and SAS Student Edition, Version 9.3 TS1M2 Rev.15w25 (SAS Institute, Cary, NC). The Elixhauser Comorbidity Index was created using the comorbidity software, available at the Health Care Utilization Project (HCUP) website.21 In order to create the comorbidity index, the authors matched the appropriate version of the software with the year and the discharge quarters of the study data set. To ensure that the data sample was weighted adequately, after extraction, the hospital weights file provided by HCUP were merged with the study’s extracted data. This ensured that all hospitals were accounted for at least once. To ensure standard errors were accurate, the survey command in Stata was used for all analyses, grouping by the hospital identification and stratifying using the strata provided by HCUP. All data are presented as mean 7 standard deviation or percentages. Univariate comparisons between groups were performed using unpaired t-tests for continuous variables and an adjusted Wald test for categorical variables. For the multivariate regression, only statistically significant variables were chosen, with a p value of o 0.05. Six models of multivariate regression for mortality and 5 for morbidity were run. Each subsequent model was adjusted for different variables. Variables that the authors believed might compromise their model (small group size, missing variables altering the population) were excluded sequentially, and the odds ratio (OR) for perioperative stroke across all models was compared. One model with the most patients to report was selected, and all other models are included in Supplementary Tables 2 and 3. Results Baseline Characteristics Data on 21,821 patients who underwent isolated MV repair or replacement from 1999 to 2011 were obtained. Baseline characteristics of the study population are given in Table 1. The overall in-hospital mortality and morbidity rates were 5.5% (1,645) and 63.30% (18,877), respectively. Perioperative strokes occurred in 3.89% (1,160) of the total cohort (p o0.05). The average age of the patient population was 62.47 714.03 years. The majority of patients who underwent MVS were found to be younger than 65 years (52.86%, p o0.05) and female (60.17%, p o 0.05). A statistically significant increase was observed in the incidence of inhospital mortality and morbidity across age groups (Table 1). The incidences of in-hospital mortality and morbidity in patients who developed perioperative stroke were 15.94% and
Please cite this article as: Udesh R, et al. (2017), http://dx.doi.org/10.1053/j.jvca.2016.12.006
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3
Table 1 Baseline Characteristics and In-Hospital Mortality and Morbidity Data of the Patient Population In-Hospital Mortality % (n) Variables
% of Patients
Age (mean 7 SD) o65 65-74 75-84 485 Sex Female Male Race/ethnicity White African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Elective Risk category Average van Walraven score Low risk (VWR o5) Moderate risk (VWR 5-14) High risk (VWR 414) Comorbidities Congestive heart failure Multivalvular disease Pulmonary hypertension Peripheral vascular disease Paralysis Other neurologic disorders Chronic pulmonary disease Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Renal failure Liver disease Peptic ulcer disease with bleeding AIDS Lymphoma Metastatic cancer Solid tumor without metastasis Collagen vascular disorders Coagulopathy Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Nutritional anemias Alcohol abuse Drug abuse Psychoses Depression Hypertension Metabolic syndrome Cardiac arrhythmias: preoperative Delirium Coronary atherosclerosis Angina
62.4 7 14 52.8% 27.0% 20.5% 1.2%
Yes
3.4% 5.9% 9.3% 15.55%
No
(538) (478) (571) (58)
7.8% 5.3% 4.5% 5.3%
(1,107) (1,167) (1,074) (1,587)
Postoperative Morbidity % (n)
p Value
Yes
0.000 0.080 0.000 0.000
53.9% 67.9% 79.9% 92.8%
(8,481) (5,436) (4,874) (338)
No
74.2% 61.8% 59.2% 63.1%
(10,396) (13,414) (14,003) (18,539)
p Value
0.000 0.000 0.000 0.000
60.1% 39.8%
5.8% (1,049) 5.0% (596)
0.002 0.002
66.2% (11,850) 59.3% (7,027)
0.000 0.000
74.8% 10.2% 7.8% 3.0% 0.4% 3.4%
5.7% 5.8% 5.2% 4.5% 5.3% 4.9%
0.712 0.712 0.712 0.712 0.712 0.712
65.2% 70.2% 61.5% 56.6% 60.8% 60.4%
0.000 0.000 0.000 0.000 0.000 0.000
18.6% 19.2% 62.0%
10.2% 7.6% 3.4%
0.000 0.000 0.000
82.8% (4,076) 72.2% (3,689) 55.4% (9,054)
3.4% (732) 10.3% (913) 9.5% (774) 4.0% (871) 17.9% (139) 5.1% (1,506)
0.000 0.000 0.000
57.1% (11,938) 77.1% (6,220) 92.2% (719)
78.4% (6,939) 58.4% (12,657) 62.7% (18,158)
0.000 0.000 0.000
11.6% 10.1% 8.0% 8.4% 8.0% 8.2% 5.6% 4.9% 7.3% 4.0% 13.9% 12.8% 1.9% 10.6% 6.1% 2.5% 5.6% 4.7% 9.3% 3.7% 13.7% 9.5% 6.0% 3.7% 4.4% 2.0% 4.3% 2.7% 4.4% 9.0% 4.7% 0.9% 6.1% 4.8%
0.000 0.000 0.021 0.000 0.069 0.003 0.727 0.080 0.077 0.000 0.000 0.000 0.003 0.313 0.744 0.264 0.9232 0.3025 0.0000 0.000 0.000 0.000 0.657 0.000 0.251 0.000 0.225 0.000 0.000 0.570 0.000 0.000 0.027 0.634
83.9% 82.4% 79.1% 74.2% 89.0% 76.8% 68.7% 68.9% 81.6% 66.5% 83.5% 78.5% 54.2% 71.8% 76.2% 75.1% 61.3% 67.5% 74.9% 62.1% 93.7% 76.7% 76.5% 67.3% 68.6% 74.8% 76.5% 64.2% 64.3% 40.6% 65.7% 75.4% 68.8% 65.8%
62.4% 62.8% 63.1% 62.9% 63.1% 63.0% 62.2% 62.7% 63.1% 63.1% 61.5% 63.2% 63.5% 63.4% 63.4% 63.4% 63.5% 63.3% 61.3% 63.5% 62.3% 59.9% 63.3% 62.8% 63.4% 63.3% 63.2% 63.4% 62.9% 63.5% 58.6% 63.4% 62.2% 63.4%
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.043 0.263 0.000 0.104 0.341 0.024 0.000 0.278 0.000 0.000 0.000 0.000 0.018 0.000 0.000 0.589 0.038 0.047 0.000 0.008 0.000 0.475
2.75 7 0.12 70.3% 27.0% 2.6% 4.6% 3.5% 2.2% 4.5% 1.3% 2.9% 19.4% 12.2% 1.9% 8.9% 8.9% 1.4% 0.5% 0.1% 0.4% 0.1% 2.1% 2.5% 15.8% 6.3% 3.5% 20.9% 1.3% 14.3% 1.6% 1.6% 1.5% 4.6% 41.8% 0.0% 68.1% 0.3% 19.7% 0.8%
(984) (138) (95) (32) (6) (40)
(163) (105) (54) (116) (31) (73) (326) (181) (42) (108) (372) (54) (3) (4) (9) (1) (34) (36) (447) (70) (146) (598) (24) (159) (21) (10) (20) (38) (557) (2) (965) (1) (357) (12)
5.2% 5.3% 5.4% 5.3% 5.4% 5.4% 5.4% 5.6% 5.4% 5.6% 4.6% 5.4% 5.5% 5.5% 5.5% 5.5% 5.5% 5.5% 4.7% 5.6% 5.2% 4.4% 5.5% 5.8% 5.5% 5.5% 5.5% 5.6% 6.2% 5.5% 7.1% 5.5% 5.3% 5.5%
(1,482) (1,540) (1,591) (1,529) (1,614) (1,572) (1,319) (1,464) (1,603) (1,537) (1,273) (1,591) (1,642) (1,641) (1,636) (1,644) (1,611) (1,609) (1,198) (1,575) (1,499) (1,047) (1,621) (1,486) (1,624) (1,635) (1,625) (1,607) (1,088) (1,643) (680) (1,644) (1,288) (1,633)
Please cite this article as: Udesh R, et al. (2017), http://dx.doi.org/10.1053/j.jvca.2016.12.006
(11,194) (1,663) (1,119) (396) (65) (487)
(1,175) (870) (540) (1,012) (353) (686) (3,981) (2,521) (466) (1,772) (2,229) (333) (86) (25) (111) (29) (377) (512) (3,551) (1,171) (989) (4,792) (300) (2,867) (336) (369) (354) (885) (7,978) (9) (13,309) (75) (4,035) (163)
0.000 0.000 0.000
(17,693) (17,998) (18,328) (17,856) (18,515) (18,182) (14,887) (16,437) (18,402) (17,096) (16,639) (18,535) (18,782) (18,843) (18,757) (18,839) (18,491) (18,356) (15,317) (17,697) (17,879) (14,076) (18,568) (16,001) (18,352) (18,499) (18,514) (17,983) (10,890) (18,868) (5,568) (18,802) (14,842) (18,714)
R. Udesh et al. / Journal of Cardiothoracic and Vascular Anesthesia ] (]]]]) ]]]–]]]
4 Table 1 (continued )
In-Hospital Mortality % (n) Variables Perioperative complications Perioperative stroke Postoperative cardiac arrest and MI Gastrointestinal complications Bacterial endocarditis
% of Patients
3.8% 15.5% 0.01% 7.7%
Yes
15.9% (183) 8.1% (373) 0% 10.1% (235)
No
5.1% 5.1% 5.5% 5.1%
Postoperative Morbidity % (n)
p Value
(1,462) (1,272) (1,645) (1,410)
0.000 0.000 0.046 0.000
Yes
92.1% 69.3% 51.2% 90.5%
(1,069) (3,212) (2) (2,081)
No
62.3% 62.4% 63.5% 61.2%
(17,808) (15,665) (18,875) (16796)
p Value
0.000 0.000 0.633 0.000
Abbreviations: MI, myocardial infarction; VWR, Von Walraven score.
92.15%, respectively (po 0.05). As previously mentioned, the risk stratification was performed using the VWR score, with a mean score of 2.75 70.12 for the total cohort. A low-risk score (VWR o 5) was observed in 70.3% of the cohort, with a statistically significant increase in adverse outcomes for patients with higher scores (p o 0.05).
Univariate Predictors of In-hospital Mortality and Morbidity The univariate predictors (baseline, surgical, and postoperative) of in-hospital mortality and morbidity included age, female sex, emergency surgery, CHF, multivalvular disease, hypertension, arrhythmias, CAD, bacterial endocarditis, pulmonary hypertension, peripheral vascular disease (PVD), previous neurologic disorders, hypothyroidism, renal failure, liver disease, peptic ulcer disease, coagulopathy, weight loss, fluid and electrolyte disorders, nutritional anemia, alcohol abuse, drug abuse, postoperative cardiac arrest and MI, and delirium (Tables 1 and 2). Other factors with significant univariate association to postoperative morbidity included chronic pulmonary disorders, DM, collagen vascular disorders, chronic blood loss anemia, psychoses, and metabolic syndrome (Tables 1 and 2).
Independent Predictors of In-hospital Mortality and Morbidity by Multivariate Analysis Results from the multivariate logistic regression analysis are listed in Table 3. Several models of the analysis were performed to ensure that all patients of the study cohort were examined (Supplementary Tables 2 and 3). The authors report 2 models that had the most significant findings in Table 3. Perioperative strokes were found to be the strongest risk factor for postoperative mortality (OR 2.34, 95% CI 1.83-2.98) and morbidity (OR 4.53, 95% CI 3.34-6.15). Other variables that were found to be independent predictors of in-hospital morbidity and mortality were age, female sex, emergency surgery, arrhythmias, hypertension, PVD, hypothyroidism, renal failure, coagulopathy, neurologic disorders, weight loss, fluid and electrolyte imbalance, anemia, substance abuse, postoperative cardiac arrest, and MI. Ethnicity/race of the patient was not found to be a significant predictor of outcome.
Trend Analysis (1999 to 2011) A trend analysis over the 10 years was performed for the average age of the patients, average VWR score, and incidences of perioperative strokes and in-hospital mortality and morbidity (Supplementary Table 4). There was a 2-fold and 4-fold increase in the proportion of patients categorized as moderate risk and high risk, respectively. Postoperative mortality rates were decreased across all age groups and risk groups. Morbidity rates over the years were increased across all risk groups and among higher age groups. Perioperative stroke rates remained more or less the same at 4% to 5% from 1999 to 2011 (Fig 1; Supplementary Figs 1-7). Discussion Results of this study showed advanced age, female sex, and emergency admissions to be significant predictors of inhospital mortality and morbidity. The risk with increasing age has been attributed to the incidence of age-related comorbidities such hypertension, DM, renal failure, MI, cerebrovascular disease, CHF, and atrial fibrillation.10,22,23 Studies also have shown that women have a 3-fold higher risk of developing strokes3 with a higher mortality risk in both the emergency and elective settings.10 With advancements in cardiac surgical care, including anesthetic and surgical techniques, the mortality rates have decreased after MVS over the last 2 decades.11 However, ischemic stroke after cardiac surgery still remains a devastating complication with a consequent longer postoperative hospital stay, intensive care for respiratory support, prolonged immobilization, and increased referral to care centers.7 The postoperative morbidity rate has shown a steady increase in all risk groups. Inadequate preoperative risk stratification or screening for comorbidities and poor patient selection may have contributed to this increase despite advancements in the technical aspects of the procedure. Multivariate analysis showed perioperative strokes to be the strongest predictor of in-hospital morbidity and mortality, which makes it imperative for clinicians to recognize the events leading up to them. Prior studies suggested preoperative conditions such as symptomatic or asymptomatic carotid stenosis, atrial fibrillation, bacterial endocarditis, CHF, CAD, and PVD as causative factors for strokes.3,24 Intraoperatively,
Please cite this article as: Udesh R, et al. (2017), http://dx.doi.org/10.1053/j.jvca.2016.12.006
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5
Table 2 Perioperative Variables Analyzed as Univariate Predictors of In-Hospital Mortality and Morbidity In-Hospital Mortality
Variables Age o65 65-74 75-84 Z85 Sex Female Race/ethnicity White African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Elective Risk category Average Van Walraven score Low risk (VWRo5) Moderate risk (VWR 5-14) High risk (VWR414) Comorbidities Congestive heart failure Multivalvular disease Pulmonary hypertension Peripheral vascular disease Paralysis Other neurologic disorders Chronic pulmonary disease Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Renal failure Liver disease Peptic ulcer disease with bleeding AIDS Lymphoma Metastatic cancer Solid tumor without metastasis Collagen vascular disorders Coagulopathy Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Nutritional anemia Drug abuse Psychoses Depression Hypertension Cardiac arrhythmias- preoperative Delirium Coronary atherosclerosis Angina Bacterial endocarditis Perioperative complications Perioperative stroke Postoperative cardiac arrest and MI
Postoperative Morbidity
% of Patients
Unadjusted OR (95% CI)
95% CI
p Value
Unadjusted OR (95%CI)
52.8% 27.0% 20.5% 1.2%
0.41 1.11 2.19 3.23
0.37-0.46 0.99-1.24 1.96-2.44 2.40-4.35
0.000 0.075 0.000 0.000
0.40 1.31 2.73 7.55
0.38-0.43 1.23-1.39 2.52-2.97 5.06-11.27
0.000 0.000 0.000 0.000
60.1%
1.18
1.06-1.31
0.002
1.34
1.27-1.42
0.000
74.8% 10.2% 7.8% 3.0% 0.4% 3.49%
NA 0.72 0.31 NA NA NA
NA 0.61-0.85 0.27-0.36 NA NA NA
NA 0.000 0.000 NA NA NA
NA 1.26 0.85 0.70 0.83 0.81
NA 1.11-1.42 0.73-0.99 0.55-0.88 0.54-1.27 0.68-0.98
NA 0.000 0.035 0.003 0.385 0.027
18.6% 19.2% 62.0%
NA 0.72 0.31
NA 0.61-0.85 0.27-0.36
NA 0.000 0.000
NA 0.54 0.26
NA 0.45-0.65 0.26-0.02
NA 0.000 0.000
2.7570.12 70.3% 27.0% 2.6%
0.32 2.53 4.00
0.28-0.35 2.25-2.85 3.29-4.87
0.000 0.000 0.000
0.37 2.40 7.03
0.33-0.40 2.18-2.64 5.45-9.07
0.000 0.000 0.000
4.6% 3.5% 2.2% 4.5% 1.3% 2.9% 19.4% 12.2% 1.9% 8.9% 8.9% 1.4% 0.5% 0.1% 0.4% 0.1% 2.1% 2.5% 15.8% 6.3% 3.5% 20.9% 1.3% 14.3% 1.6% 1.5% 4.6% 41.8% 68.1% 0.3% 19.7% 0.8% 7.7%
5.11 3.83 3.63 1.63 1.50 1.59 1.02 0.87 1.37 0.70 3.29 2.57 0.34 2.12 1.13 0.45 1.02 0.85 2.06 0.65 2.90 2.70 1.10 0.63 0.35 0.78 0.48 0.69 0.65 0.16 1.14 0.87 2.10
4.0-6.51 2.90-5.08 2.38-5.54 1.33-1.99 1.04-2.18 1.24-2.04 0.90-1.16 0.75-1.02 1.01-1.87 0.57-0.87 2.91-3.71 1.91-3.46 0.11-1.07 0.74-6.10 0.57-2.23 0.06-3.34 0.72-1.43 0.60-1.19 1.83-2.33 0.50-0.83 2.40-3.51 2.02-2.55 0.73-1.66 0.53-0.75 0.19-0.66 0.49-1.22 0.33-0.68 0.62-0.77 0.58-0.72 0.02-1.16 1.02-1.29 0.49-1.57 1.79-2.46
0.000 0.000 0.000 0.000 0.031 0.000 0.725 0.095 0.045 0.001 0.000 0.000 0.065 0.164 0.732 0.438 0.923 0.339 0.000 0.001 0.000 0.000 0.644 0.000 0.001 0.277 0.000 0.000 0.000 0.070 0.021 0.652 0.000
41.62 9.38 20.18 1.70 4.77 1.94 1.33 1.32 2.60 1.16 3.18 2.12 0.68 1.41 1.85 1.74 0.91 1.20 1.89 0.94 9.08 2.20 1.89 1.22 1.73 1.89 1.03 1.06 1.35 1.77 1.34 1.11 6.03
20.65-83.9 2.75-14.04 8.25-49.33 1.48-1.95 3.49-6.51 1.63-2.31 1.24-1.43 1.21-1.43 2.03-3.31 1.05-1.28 2.82-3.58 1.68-2.67 0.48-0.97 0.69-2.87 1.28-2.68 0.85-3.57 0.75-1.11 1.02-1.41 1.72-2.08 0.84-1.05 7.14-11.54 1.95-2.47 1.45-2.47 1.11-1.34 1.41-2.12 1.47-2.42 0.91-1.17 1.00-1.12 1.27-1.44 1.12-2.78 1.24-1.45 0.83-1.49 5.15-7.07
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.031 0.344 0.001 0.130 0.344 0.026 0.000 0.273 0.000 0.000 0.000 0.000 0.000 0.000 0.592 0.045 0.000 0.014 0.000 0.489 0.000
3.9% 15.5%
3.53 1.65
2.98-4.19 1.45-1.87
0.000 0.000
7.08 1.36
5.68-8.83 1.25-1.48
0.000 0.000
Abbreviations: CI, confidence interval; NA, not available; OR, odds ratio.
95% CI
p Value
R. Udesh et al. / Journal of Cardiothoracic and Vascular Anesthesia ] (]]]]) ]]]–]]]
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Table 3 Multivariate Analysis of In-Hospital Morbidity and Mortality
Variables Age Sex Female Race/ethnicity African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Admission source Emergency department Another hospital Court/other law enforcement Von Walraven score Low risk (VWR o5) Intermediate risk (VWR 5-14) High risk (VWR 414) Postoperative risk factors Perioperative stroke Postoperative cardiac arrest and MI Preoperative risk factors Cardiac arrhythmias Delirium Coronary atherosclerosis Angina Bacterial endocarditis
In-Hospital Mortality
Postoperative Morbidity
Model 3 (n ¼ 22,213) OR (95% CI)
Model 2 (n ¼ 22,250) OR (95% CI)
1.05* (1.04-1.06) 1.25† (1.07-1.46)
1.04* (1.04-1.05) 1.51* (1.37-1.65)
1.12 0.87 1.41 0.42 1.04
(0.85-1.47) (0.64-1.20) (0.73-2.73) (0.49-3.65) (0.70-1.55)
1.44* 0.97 0.81 1.53 0.94
(1.21-1.71) (0.79-1.19) (0.54-1.21) (0.73-3.20) (0.72-1.23)
1.77* (1.28-2.44) 1.73* (1.39-2.16)
1.99* (1.54-2.58) 1.41‡ (1.04-1.90)
1.77* (1.33-2.37) 1.35‡ (1.04-1.75) 2.33* (1.51-3.62)
1.89* (1.47-2.43) 2.09* (1.61-2.72) 1.71‡ (1.08-2.69)
NA 2.26* (1.89-2.70)
NA 2.16* (1.90-2.45)
4.74* (3.32-6.78)
5.74* (3.71-5.90)
2.14* (1.56-2.86) 1.56* (1.29-1.89)
4.77* (3.20-7.14) 1.39* (1.20-1.60)
0.42* (0.36-0.51)
1.18* 2.27‡ 1.09 0.82 4.75*
0.97 (0.81-1.16) 1.59* (1.22-2.08)
(1.07-1.29) (1.03-4.99) (0.98-1.23) (0.53-1.26) (3.62-6.24)
Abbreviation: CI, confidence interval; MI, myocardial infarction; NA, not available; OR, odds ratio; VWR, Von Walraven score. n p o 0.001. † p o 0.01. ‡ p o 0.05.
thromboembolic events and cerebral hypoperfusion25,26 and in the immediate postoperative period low cardiac output and arrhythmias also have been reported to be culpable in previously published literature.4,27 In addition to being independent predictors of adverse outcomes, variables such as DM, hypertension, and PVD indirectly contribute to the risk by precipitating ischemic strokes due to atheromatous embolization, impaired autoregulation of cerebral blood flow, aggressive blood pressure reduction, and particulate embolization from valve vegetations in infective endocarditis.28 Cerebrovascular disease and carotid stenosis appeared to be significant contributors to intraoperative cerebral ischemia after cardiac surgery.29,30 The role of preoperative carotid Doppler screening and carotid intervention before cardiac surgery needs to be explored further. Transcranial Doppler monitoring is used to detect embolic debris in the cerebral circulation; its use in
Fig 1. Trends in perioperative stroke, mortality, and morbidity after MVS (1999 to 2011).
aortic valve procedures has been studied, but its applications in other valve procedures require further examination.31 A handful of studies have reported the role of subclinical infarcts after MVS; postoperative magnetic resonance imaging screening to detect these silent ischemic lesions is being studied.32,33 The current literature showed that pulmonary artery hypertension significantly affected outcomes after MVS, with surgical mortality rates of 5% to 15%.34 Preoperative left ventricular ejection fraction, multivalvular disease,6,35 and atrial fibrillation36 were reported to further increase the risk of perioperative thrombogenicity.10 Pharmacologic treatment to improve preoperative cardiac function appears to be decisive in decreasing the risk of complications after surgery. The rationale for preoperative anemia as an independent predictor of mortality and morbidity has been attributed by Keeling et al37 to increased postsurgery transfusion requirements, as with any cardiac procedure. Despite several studies reporting the preoperative and intraoperative risk factors that contribute to adverse outcomes, there is a gap in the current literature on the ways to identify and manage these modifiable risk factors. Several risk scores and indices that predict short-term mortality rates after isolated valve surgery have been published.12,38-40 Risk stratification of patients before MVS guides the physician in better patient selection for surgical treatment.10 Furthermore, comprehensive clinical studies are required to investigate whether preventive strategies such as neuroprotective medication and the use of intraoperative neurophysiologic monitoring may improve outcomes, but these have not been evaluated in cardiac surgery.15-17 This study was a large-scale, nationwide analysis reporting early hospital outcomes after MVS in 21,821 patients. This comprehensive evaluation of risk-adjusted outcome data on a large cohort provided valuable insight into areas for quality improvement. However, this study was not without limitations. Because the NIS database is an administrative discharge-level database, data on strokes and death were measured only if they occurred during hospitalization, with the possibility that some cases of strokes may not have been recognized or reported. Any data on strokes or death that occurred after discharge but within the 30-day postoperative mark were unavailable. There
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could be considerable variability in the preoperative and postoperative care of patients among the different institutions, and this may have affected the outcome rates. The incidence of adverse events was recorded in the database, but vital information on the timing of strokes and postoperative neurologic evaluation and management was not available. Temporal changes in outcomes and risk factors also may have been related to changes in how the data were recognized and entered and assessed in the database. These factors could have affected the mortality and morbidity rates in this study.
Conclusion Patients who are older, who are women, who present for emergency admission for surgery, and who experience perioperative stroke are at higher risk of mortality and morbidity after MVS. Comprehensive clinical studies examining the factors contributing to strokes are necessary to improve overall safety and efficacy of MV procedures. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1053/j.jvca.2016.12.006.
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12 Hannan EL, Wu C, Bennett EV, et al. Risk index for predicting in-hospital mortality for cardiac valve surgery. Ann Thorac Surg 2007;83:921–9. 13 Anderson RP. First publications from the Society of Thoracic Surgeons National Database. Ann Thorac Surg 1994;57:6–7. 14 Nashef SA, Roques F, Michel P, et al. European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999;16: 9–13. 15 Nwachuku EL, Balzer JR, Yabes JG, et al. Diagnostic value of somatosensory evoked potential changes during carotid endarterectomy: A systematic review and meta-analysis. JAMA Neurol 2015 73–80. 16 Thiagarajan K, Cheng HL, Huang JE, et al. Is two really better than one? Examining the superiority of dual modality neurophysiological monitoring during carotid endarterectomy: A meta-analysis. World Neurosurg 2015;84:1941–9. 17 Thirumala PD, Udesh R, Muralidharan A, et al. Diagnostic value of somatosensory-evoked potential monitoring during cerebral aneurysm clipping: A systematic review. World Neurosurg 2016;89:672–80. 18 Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care 1998;36:8–27. 19 van Walraven C, Austin PC, Jennings A, et al. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 2009;47:626–33. 20 Society of Thoracic Surgeons: Adult cardiac surgery data collection: STS Adult Cardiac Surgery Database, v2.81. Available at: 〈http://www.sts.org/ sts-national-database/database-managers/adult-cardiac-surgery-database/ data-collection〉. Accessed on December 9, 2016. 21 Health Care Utilization Project: Elixhauser Comorbidity software, version 3.7. Available at: 〈http://hcup-us.ahrq.gov/toolssoftware/comorbidity/ comorbidity.jsp〉. Accessed December 9, 2016. 22 Mehta RH, Eagle KA, Coombs LP, et al. Influence of age on outcomes in patients undergoing mitral valve replacement. Ann Thorac Surg 2002;74: 1459–67. 23 Edwards FH, Peterson ED, Coombs LP, et al. Prediction of operative mortality after valve replacement surgery. J Am Coll Cardiol 2001;37: 885–92. 24 Shaw TG, Mortel KF, Meyer JS, et al. Cerebral blood flow changes in benign aging and cerebrovascular disease. Neurology 1984;34:855–62. 25 Barbut D, Caplan LR. Brain complications of cardiac surgery. Curr Probl Cardiol 1997;22:449–80. 26 Caplan LR, Hennerici M. Impaired clearance of emboli (washout) is an important link between hypoperfusion, embolism, and ischemic stroke. Arch Neurol 1998;55:1475–82. 27 Heras M, Chesebro JH, Fuster V, et al. High risk of thromboemboli early after bioprosthetic cardiac valve replacement. J Am Coll Cardiol 1995;25: 1111–9. 28 Bentsen N, Larsen B, Lassen NA. Chronically impaired autoregulation of cerebral blood flow in long-term diabetics. Stroke 1975;6:497–502. 29 Bucerius J, Gummert JF, Borger MA, et al. Stroke after cardiac surgery: A risk factor analysis of 16,184 consecutive adult patients. Ann Thorac Surg 2003;75:472–8. 30 Hogue Jr CW, De Wet CJ, Schechtman KB, et al. The importance of prior stroke for the adjusted risk of neurologic injury after cardiac surgery for women and men. Anesthesiology 2003;98:823–9. 31 Spaziano M, Francese DP, Leon MB, et al. Imaging and functional testing to assess clinical and subclinical neurological events after transcatheter or surgical aortic valve replacement: A comprehensive review. J Am Coll Cardiol 2014;64:1950–63. 32 Knipp SC, Matatko N, Schlamann M, et al. Small ischemic brain lesions after cardiac valve replacement detected by diffusion-weighted magnetic resonance imaging: Relation to neurocognitive function. Eur J Cardiothorac Surg 2005;28:88–96. 33 Floyd TF, Shah PN, Price CC, et al. Clinically silent cerebral ischemic events after cardiac surgery: Their incidence, regional vascular occurrence, and procedural dependence. Ann Thorac Surg 2006;81:2160–6. 34 Song X, Zhang C, Chen X, et al. An excellent result of surgical treatment in patients with severe pulmonary arterial hypertension following mitral valve disease. J Cardiothorac Surg 2015;10:70.
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35 Harker LA, Slichter SJ. Studies of platelet and fibrinogen kinetics in patients with prosthetic heart valves. N Engl J Med 1970;283:1302–5. 36 Bando K, Kobayashi J, Hirata M, et al. Early and late stroke after mitral valve replacement with a mechanical prosthesis: Risk factor analysis of a 24-year experience. J Thorac Cardiovasc Surg 2003;126: 358–64. 37 Keeling WB, Binongo J, Sarin EL, et al. Predicted risk of mortality, transfusion, and postoperative outcomes in isolated primary valve operations. Ann Thorac Surg 2016;101:620–4.
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Contents lists available at ScienceDirect
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Original Article
Perioperative Strokes and Early Outcomes in Mitral Valve Surgery: A Nationwide Analysis Reshmi Udesh, MBBSn, Amol Mehta, BS†, Thomas G. Gleason, MDn, Lawrence Wechsler, MDn, Parthasarathy D. Thirumala, MD, MSn,†,1 n
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA † University of Pittsburgh School of Medicine, Pittsburgh, PA
Objective: To demonstrate the role of perioperative stroke as an independent risk factor for in-hospital morbidity and mortality after mitral valve surgery and review the trends in the early outcomes of mitral valve surgery over the past decade. Design: Using data from the National Inpatient Sample database for analysis, all patients who underwent isolated mitral valve procedures were identified using International Classification of Diseases-Ninth Revision codes. Univariate and multivariate analyses of risk factors of in-hospital mortality and morbidity were performed. Setting: Multi-institutional. Participants: The study comprised patients who underwent mitral valve procedures from 1999 to 2011. Interventions: Mitral valve repair or replacement. Measurements and Main Results: Data on 21,821 patients showed an in-hospital mortality of 5.5% and morbidity of 63.30% (po 0.05). Perioperative strokes were experienced by 3.89% of the cohort after isolated mitral valve surgery (po 0.05). Independent predictors of adverse outcomes were age, female sex, emergency surgery, arrhythmias, hypertension, renal failure, coagulopathy, neurologic disorders, weight loss, anemia, postoperative cardiac arrest, and myocardial infarction. Perioperative strokes were found to be the strongest risk factor for postoperative mortality (odds ratio 2.34, 95% confidence interval 1.83-2.98) and morbidity (odds ratio 4.53, 95% confidence interval 3.34-6.15). Conclusion: Age, female sex, emergency surgery, arrhythmias, hypertension, renal failure, coagulopathy, neurologic disorders, weight loss, fluid and electrolyte imbalance, anemia, postoperative cardiac arrest, and myocardial infarction were found to be significant predictors of morbidity and mortality after mitral valve surgery, with perioperative strokes posing the strongest risk. The trends in the last 10 years indicated a decrease in mortality and an increase in morbidity. Preoperative risk stratification and intraoperative identification for impending strokes appear warranted. & 2016 Elsevier Inc. All rights reserved.
Key Words: mitral valve surgery; perioperative stroke; in-hospital mortality; morbidity
MITRAL VALVE (MV) repair or replacement for MV disease, with nearly15,000 procedures reported annually in the United States, is one of the most common valve surgeries 1
Address reprint requests to Parthasarathy D. Thirumala, MD, MS, Center for Clinical Neurophysiology, Department of Neurological Surgery, UPMC Presbyterian-Suite B-400, 200 Lothrop Street, Pittsburgh, PA 15213. E-mail address: [email protected] (P.D. Thirumala).
performed.1 A surgical mortality of 1.2% and 4.8% has been reported for MV repair and replacement, respectively.1,2 Perioperative strokes, defined as neurologic deficits developing within 30 days of the procedure, affects postoperative mortality rates and quality of life after MV surgery (MVS).3-7 Previous studies have shown that adverse neurologic outcomes have significant economic implications, with patients requiring a longer stay in intensive care units and hospitals.3,7
http://dx.doi.org/10.1053/j.jvca.2016.12.006 1053-0770/& 2016 Elsevier Inc. All rights reserved.
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2
Examining the factors that influence early morbidity and mortality rates may give insight on improved practices, better patient selection, and efficient management of patient comorbidities.8,9 Therefore, identification of individuals at risk may lead to a significant reduction in cost and the improvement of resource allocation without compromising quality of care.3,8,9 Several institutional studies have reported the following risk factors to be associated with adverse postoperative outcomes after MVS: age, female sex, urgent or emergency admissions, diabetes mellitus (DM), hypertension, coronary artery disease (CAD), congestive cardiac failure (CHF), atrial fibrillation, bacterial endocarditis, previous history of symptomatic carotid stenosis (strokes or transient ischemic attacks), renal failure, hepatic disease, periprocedural myocardial infarction (MI), and arrhythmias.10-12 A large-scale nationwide analysis on the impact of perioperative strokes on MVS outcomes has not been done before. The primary aim of this study was to demonstrate the role of perioperative strokes as an independent risk factor for inhospital mortality after MVS. The secondary aim was to study the role of stroke as an independent risk factor for postoperative morbidity, defined as a length of stay greater than 14 days and/or discharge to a location other than home. Finally, the authors also examined the trends over time in the postoperative outcomes after MVS. Understanding the implications of strokes on early postoperative outcomes warrants further investigation into strategies such as preoperative risk stratification,13,14 intraoperative neurophysiologic monitoring,15-17 and care pathways to better manage hospital stay and resource allocation.8,9 Methods Patient Population The study population was selected from the National Inpatient Sample (NIS) for the years 1999 to 2011 using International Classification of Diseases, Ninth RevisionClinical Modification (ICD-9-CM) diagnosis and procedure codes. Patients who underwent isolated MV repair (ICD-9CM-35.12) or MV replacement (ICD-9-CM-35.23 and 35.24) were selected for the study. Patients younger than 18 years were excluded from the selection. Baseline characteristics available for analysis included age, sex, race, admission status, transfer status, Elixhauser comorbidities and other additional comorbid conditions such as CAD, atrial fibrillation, bacterial endocarditis, postoperative cardiac arrest, and MI. The NIS database provides 29 Elixhauser comorbidities based on standard ICD-9 codes.18 In addition to these, a complete list of ICD-9 codes that were used to define other variables is provided in Supplementary Table 1. Risk stratification was performed using the Van Walraven (VWR) score, which is a summary score for the Elixhauser Comorbidity Index developed by modeling in-hospital mortality with inpatient admission data.19 The summary score is a weighted combination of the 29 Elixhauser comorbidities, in which a larger comorbidity weight indicates a stronger association between a comorbidity
and in-hospital mortality. The primary outcome studied was in-hospital mortality. The secondary outcome, postoperative morbidity, was characterized as a long length of stay (greater than 14 days) or discharge to a place other than home.20 Statistical Analysis All statistical analyses were performed using the SPSS software, Version 23 (IBM Corp, Armonk, NY); Stata Student Edition, Version 14.0 SE (StataCorp, College Station, TX); and SAS Student Edition, Version 9.3 TS1M2 Rev.15w25 (SAS Institute, Cary, NC). The Elixhauser Comorbidity Index was created using the comorbidity software, available at the Health Care Utilization Project (HCUP) website.21 In order to create the comorbidity index, the authors matched the appropriate version of the software with the year and the discharge quarters of the study data set. To ensure that the data sample was weighted adequately, after extraction, the hospital weights file provided by HCUP were merged with the study’s extracted data. This ensured that all hospitals were accounted for at least once. To ensure standard errors were accurate, the survey command in Stata was used for all analyses, grouping by the hospital identification and stratifying using the strata provided by HCUP. All data are presented as mean 7 standard deviation or percentages. Univariate comparisons between groups were performed using unpaired t-tests for continuous variables and an adjusted Wald test for categorical variables. For the multivariate regression, only statistically significant variables were chosen, with a p value of o 0.05. Six models of multivariate regression for mortality and 5 for morbidity were run. Each subsequent model was adjusted for different variables. Variables that the authors believed might compromise their model (small group size, missing variables altering the population) were excluded sequentially, and the odds ratio (OR) for perioperative stroke across all models was compared. One model with the most patients to report was selected, and all other models are included in Supplementary Tables 2 and 3. Results Baseline Characteristics Data on 21,821 patients who underwent isolated MV repair or replacement from 1999 to 2011 were obtained. Baseline characteristics of the study population are given in Table 1. The overall in-hospital mortality and morbidity rates were 5.5% (1,645) and 63.30% (18,877), respectively. Perioperative strokes occurred in 3.89% (1,160) of the total cohort (p o0.05). The average age of the patient population was 62.47 714.03 years. The majority of patients who underwent MVS were found to be younger than 65 years (52.86%, p o0.05) and female (60.17%, p o 0.05). A statistically significant increase was observed in the incidence of inhospital mortality and morbidity across age groups (Table 1). The incidences of in-hospital mortality and morbidity in patients who developed perioperative stroke were 15.94% and
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Table 1 Baseline Characteristics and In-Hospital Mortality and Morbidity Data of the Patient Population In-Hospital Mortality % (n) Variables
% of Patients
Age (mean 7 SD) o65 65-74 75-84 485 Sex Female Male Race/ethnicity White African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Elective Risk category Average van Walraven score Low risk (VWR o5) Moderate risk (VWR 5-14) High risk (VWR 414) Comorbidities Congestive heart failure Multivalvular disease Pulmonary hypertension Peripheral vascular disease Paralysis Other neurologic disorders Chronic pulmonary disease Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Renal failure Liver disease Peptic ulcer disease with bleeding AIDS Lymphoma Metastatic cancer Solid tumor without metastasis Collagen vascular disorders Coagulopathy Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Nutritional anemias Alcohol abuse Drug abuse Psychoses Depression Hypertension Metabolic syndrome Cardiac arrhythmias: preoperative Delirium Coronary atherosclerosis Angina
62.4 7 14 52.8% 27.0% 20.5% 1.2%
Yes
3.4% 5.9% 9.3% 15.55%
No
(538) (478) (571) (58)
7.8% 5.3% 4.5% 5.3%
(1,107) (1,167) (1,074) (1,587)
Postoperative Morbidity % (n)
p Value
Yes
0.000 0.080 0.000 0.000
53.9% 67.9% 79.9% 92.8%
(8,481) (5,436) (4,874) (338)
No
74.2% 61.8% 59.2% 63.1%
(10,396) (13,414) (14,003) (18,539)
p Value
0.000 0.000 0.000 0.000
60.1% 39.8%
5.8% (1,049) 5.0% (596)
0.002 0.002
66.2% (11,850) 59.3% (7,027)
0.000 0.000
74.8% 10.2% 7.8% 3.0% 0.4% 3.4%
5.7% 5.8% 5.2% 4.5% 5.3% 4.9%
0.712 0.712 0.712 0.712 0.712 0.712
65.2% 70.2% 61.5% 56.6% 60.8% 60.4%
0.000 0.000 0.000 0.000 0.000 0.000
18.6% 19.2% 62.0%
10.2% 7.6% 3.4%
0.000 0.000 0.000
82.8% (4,076) 72.2% (3,689) 55.4% (9,054)
3.4% (732) 10.3% (913) 9.5% (774) 4.0% (871) 17.9% (139) 5.1% (1,506)
0.000 0.000 0.000
57.1% (11,938) 77.1% (6,220) 92.2% (719)
78.4% (6,939) 58.4% (12,657) 62.7% (18,158)
0.000 0.000 0.000
11.6% 10.1% 8.0% 8.4% 8.0% 8.2% 5.6% 4.9% 7.3% 4.0% 13.9% 12.8% 1.9% 10.6% 6.1% 2.5% 5.6% 4.7% 9.3% 3.7% 13.7% 9.5% 6.0% 3.7% 4.4% 2.0% 4.3% 2.7% 4.4% 9.0% 4.7% 0.9% 6.1% 4.8%
0.000 0.000 0.021 0.000 0.069 0.003 0.727 0.080 0.077 0.000 0.000 0.000 0.003 0.313 0.744 0.264 0.9232 0.3025 0.0000 0.000 0.000 0.000 0.657 0.000 0.251 0.000 0.225 0.000 0.000 0.570 0.000 0.000 0.027 0.634
83.9% 82.4% 79.1% 74.2% 89.0% 76.8% 68.7% 68.9% 81.6% 66.5% 83.5% 78.5% 54.2% 71.8% 76.2% 75.1% 61.3% 67.5% 74.9% 62.1% 93.7% 76.7% 76.5% 67.3% 68.6% 74.8% 76.5% 64.2% 64.3% 40.6% 65.7% 75.4% 68.8% 65.8%
62.4% 62.8% 63.1% 62.9% 63.1% 63.0% 62.2% 62.7% 63.1% 63.1% 61.5% 63.2% 63.5% 63.4% 63.4% 63.4% 63.5% 63.3% 61.3% 63.5% 62.3% 59.9% 63.3% 62.8% 63.4% 63.3% 63.2% 63.4% 62.9% 63.5% 58.6% 63.4% 62.2% 63.4%
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.043 0.263 0.000 0.104 0.341 0.024 0.000 0.278 0.000 0.000 0.000 0.000 0.018 0.000 0.000 0.589 0.038 0.047 0.000 0.008 0.000 0.475
2.75 7 0.12 70.3% 27.0% 2.6% 4.6% 3.5% 2.2% 4.5% 1.3% 2.9% 19.4% 12.2% 1.9% 8.9% 8.9% 1.4% 0.5% 0.1% 0.4% 0.1% 2.1% 2.5% 15.8% 6.3% 3.5% 20.9% 1.3% 14.3% 1.6% 1.6% 1.5% 4.6% 41.8% 0.0% 68.1% 0.3% 19.7% 0.8%
(984) (138) (95) (32) (6) (40)
(163) (105) (54) (116) (31) (73) (326) (181) (42) (108) (372) (54) (3) (4) (9) (1) (34) (36) (447) (70) (146) (598) (24) (159) (21) (10) (20) (38) (557) (2) (965) (1) (357) (12)
5.2% 5.3% 5.4% 5.3% 5.4% 5.4% 5.4% 5.6% 5.4% 5.6% 4.6% 5.4% 5.5% 5.5% 5.5% 5.5% 5.5% 5.5% 4.7% 5.6% 5.2% 4.4% 5.5% 5.8% 5.5% 5.5% 5.5% 5.6% 6.2% 5.5% 7.1% 5.5% 5.3% 5.5%
(1,482) (1,540) (1,591) (1,529) (1,614) (1,572) (1,319) (1,464) (1,603) (1,537) (1,273) (1,591) (1,642) (1,641) (1,636) (1,644) (1,611) (1,609) (1,198) (1,575) (1,499) (1,047) (1,621) (1,486) (1,624) (1,635) (1,625) (1,607) (1,088) (1,643) (680) (1,644) (1,288) (1,633)
Please cite this article as: Udesh R, et al. (2017), http://dx.doi.org/10.1053/j.jvca.2016.12.006
(11,194) (1,663) (1,119) (396) (65) (487)
(1,175) (870) (540) (1,012) (353) (686) (3,981) (2,521) (466) (1,772) (2,229) (333) (86) (25) (111) (29) (377) (512) (3,551) (1,171) (989) (4,792) (300) (2,867) (336) (369) (354) (885) (7,978) (9) (13,309) (75) (4,035) (163)
0.000 0.000 0.000
(17,693) (17,998) (18,328) (17,856) (18,515) (18,182) (14,887) (16,437) (18,402) (17,096) (16,639) (18,535) (18,782) (18,843) (18,757) (18,839) (18,491) (18,356) (15,317) (17,697) (17,879) (14,076) (18,568) (16,001) (18,352) (18,499) (18,514) (17,983) (10,890) (18,868) (5,568) (18,802) (14,842) (18,714)
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4 Table 1 (continued )
In-Hospital Mortality % (n) Variables Perioperative complications Perioperative stroke Postoperative cardiac arrest and MI Gastrointestinal complications Bacterial endocarditis
% of Patients
3.8% 15.5% 0.01% 7.7%
Yes
15.9% (183) 8.1% (373) 0% 10.1% (235)
No
5.1% 5.1% 5.5% 5.1%
Postoperative Morbidity % (n)
p Value
(1,462) (1,272) (1,645) (1,410)
0.000 0.000 0.046 0.000
Yes
92.1% 69.3% 51.2% 90.5%
(1,069) (3,212) (2) (2,081)
No
62.3% 62.4% 63.5% 61.2%
(17,808) (15,665) (18,875) (16796)
p Value
0.000 0.000 0.633 0.000
Abbreviations: MI, myocardial infarction; VWR, Von Walraven score.
92.15%, respectively (po 0.05). As previously mentioned, the risk stratification was performed using the VWR score, with a mean score of 2.75 70.12 for the total cohort. A low-risk score (VWR o 5) was observed in 70.3% of the cohort, with a statistically significant increase in adverse outcomes for patients with higher scores (p o 0.05).
Univariate Predictors of In-hospital Mortality and Morbidity The univariate predictors (baseline, surgical, and postoperative) of in-hospital mortality and morbidity included age, female sex, emergency surgery, CHF, multivalvular disease, hypertension, arrhythmias, CAD, bacterial endocarditis, pulmonary hypertension, peripheral vascular disease (PVD), previous neurologic disorders, hypothyroidism, renal failure, liver disease, peptic ulcer disease, coagulopathy, weight loss, fluid and electrolyte disorders, nutritional anemia, alcohol abuse, drug abuse, postoperative cardiac arrest and MI, and delirium (Tables 1 and 2). Other factors with significant univariate association to postoperative morbidity included chronic pulmonary disorders, DM, collagen vascular disorders, chronic blood loss anemia, psychoses, and metabolic syndrome (Tables 1 and 2).
Independent Predictors of In-hospital Mortality and Morbidity by Multivariate Analysis Results from the multivariate logistic regression analysis are listed in Table 3. Several models of the analysis were performed to ensure that all patients of the study cohort were examined (Supplementary Tables 2 and 3). The authors report 2 models that had the most significant findings in Table 3. Perioperative strokes were found to be the strongest risk factor for postoperative mortality (OR 2.34, 95% CI 1.83-2.98) and morbidity (OR 4.53, 95% CI 3.34-6.15). Other variables that were found to be independent predictors of in-hospital morbidity and mortality were age, female sex, emergency surgery, arrhythmias, hypertension, PVD, hypothyroidism, renal failure, coagulopathy, neurologic disorders, weight loss, fluid and electrolyte imbalance, anemia, substance abuse, postoperative cardiac arrest, and MI. Ethnicity/race of the patient was not found to be a significant predictor of outcome.
Trend Analysis (1999 to 2011) A trend analysis over the 10 years was performed for the average age of the patients, average VWR score, and incidences of perioperative strokes and in-hospital mortality and morbidity (Supplementary Table 4). There was a 2-fold and 4-fold increase in the proportion of patients categorized as moderate risk and high risk, respectively. Postoperative mortality rates were decreased across all age groups and risk groups. Morbidity rates over the years were increased across all risk groups and among higher age groups. Perioperative stroke rates remained more or less the same at 4% to 5% from 1999 to 2011 (Fig 1; Supplementary Figs 1-7). Discussion Results of this study showed advanced age, female sex, and emergency admissions to be significant predictors of inhospital mortality and morbidity. The risk with increasing age has been attributed to the incidence of age-related comorbidities such hypertension, DM, renal failure, MI, cerebrovascular disease, CHF, and atrial fibrillation.10,22,23 Studies also have shown that women have a 3-fold higher risk of developing strokes3 with a higher mortality risk in both the emergency and elective settings.10 With advancements in cardiac surgical care, including anesthetic and surgical techniques, the mortality rates have decreased after MVS over the last 2 decades.11 However, ischemic stroke after cardiac surgery still remains a devastating complication with a consequent longer postoperative hospital stay, intensive care for respiratory support, prolonged immobilization, and increased referral to care centers.7 The postoperative morbidity rate has shown a steady increase in all risk groups. Inadequate preoperative risk stratification or screening for comorbidities and poor patient selection may have contributed to this increase despite advancements in the technical aspects of the procedure. Multivariate analysis showed perioperative strokes to be the strongest predictor of in-hospital morbidity and mortality, which makes it imperative for clinicians to recognize the events leading up to them. Prior studies suggested preoperative conditions such as symptomatic or asymptomatic carotid stenosis, atrial fibrillation, bacterial endocarditis, CHF, CAD, and PVD as causative factors for strokes.3,24 Intraoperatively,
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Table 2 Perioperative Variables Analyzed as Univariate Predictors of In-Hospital Mortality and Morbidity In-Hospital Mortality
Variables Age o65 65-74 75-84 Z85 Sex Female Race/ethnicity White African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Elective Risk category Average Van Walraven score Low risk (VWRo5) Moderate risk (VWR 5-14) High risk (VWR414) Comorbidities Congestive heart failure Multivalvular disease Pulmonary hypertension Peripheral vascular disease Paralysis Other neurologic disorders Chronic pulmonary disease Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Renal failure Liver disease Peptic ulcer disease with bleeding AIDS Lymphoma Metastatic cancer Solid tumor without metastasis Collagen vascular disorders Coagulopathy Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Nutritional anemia Drug abuse Psychoses Depression Hypertension Cardiac arrhythmias- preoperative Delirium Coronary atherosclerosis Angina Bacterial endocarditis Perioperative complications Perioperative stroke Postoperative cardiac arrest and MI
Postoperative Morbidity
% of Patients
Unadjusted OR (95% CI)
95% CI
p Value
Unadjusted OR (95%CI)
52.8% 27.0% 20.5% 1.2%
0.41 1.11 2.19 3.23
0.37-0.46 0.99-1.24 1.96-2.44 2.40-4.35
0.000 0.075 0.000 0.000
0.40 1.31 2.73 7.55
0.38-0.43 1.23-1.39 2.52-2.97 5.06-11.27
0.000 0.000 0.000 0.000
60.1%
1.18
1.06-1.31
0.002
1.34
1.27-1.42
0.000
74.8% 10.2% 7.8% 3.0% 0.4% 3.49%
NA 0.72 0.31 NA NA NA
NA 0.61-0.85 0.27-0.36 NA NA NA
NA 0.000 0.000 NA NA NA
NA 1.26 0.85 0.70 0.83 0.81
NA 1.11-1.42 0.73-0.99 0.55-0.88 0.54-1.27 0.68-0.98
NA 0.000 0.035 0.003 0.385 0.027
18.6% 19.2% 62.0%
NA 0.72 0.31
NA 0.61-0.85 0.27-0.36
NA 0.000 0.000
NA 0.54 0.26
NA 0.45-0.65 0.26-0.02
NA 0.000 0.000
2.7570.12 70.3% 27.0% 2.6%
0.32 2.53 4.00
0.28-0.35 2.25-2.85 3.29-4.87
0.000 0.000 0.000
0.37 2.40 7.03
0.33-0.40 2.18-2.64 5.45-9.07
0.000 0.000 0.000
4.6% 3.5% 2.2% 4.5% 1.3% 2.9% 19.4% 12.2% 1.9% 8.9% 8.9% 1.4% 0.5% 0.1% 0.4% 0.1% 2.1% 2.5% 15.8% 6.3% 3.5% 20.9% 1.3% 14.3% 1.6% 1.5% 4.6% 41.8% 68.1% 0.3% 19.7% 0.8% 7.7%
5.11 3.83 3.63 1.63 1.50 1.59 1.02 0.87 1.37 0.70 3.29 2.57 0.34 2.12 1.13 0.45 1.02 0.85 2.06 0.65 2.90 2.70 1.10 0.63 0.35 0.78 0.48 0.69 0.65 0.16 1.14 0.87 2.10
4.0-6.51 2.90-5.08 2.38-5.54 1.33-1.99 1.04-2.18 1.24-2.04 0.90-1.16 0.75-1.02 1.01-1.87 0.57-0.87 2.91-3.71 1.91-3.46 0.11-1.07 0.74-6.10 0.57-2.23 0.06-3.34 0.72-1.43 0.60-1.19 1.83-2.33 0.50-0.83 2.40-3.51 2.02-2.55 0.73-1.66 0.53-0.75 0.19-0.66 0.49-1.22 0.33-0.68 0.62-0.77 0.58-0.72 0.02-1.16 1.02-1.29 0.49-1.57 1.79-2.46
0.000 0.000 0.000 0.000 0.031 0.000 0.725 0.095 0.045 0.001 0.000 0.000 0.065 0.164 0.732 0.438 0.923 0.339 0.000 0.001 0.000 0.000 0.644 0.000 0.001 0.277 0.000 0.000 0.000 0.070 0.021 0.652 0.000
41.62 9.38 20.18 1.70 4.77 1.94 1.33 1.32 2.60 1.16 3.18 2.12 0.68 1.41 1.85 1.74 0.91 1.20 1.89 0.94 9.08 2.20 1.89 1.22 1.73 1.89 1.03 1.06 1.35 1.77 1.34 1.11 6.03
20.65-83.9 2.75-14.04 8.25-49.33 1.48-1.95 3.49-6.51 1.63-2.31 1.24-1.43 1.21-1.43 2.03-3.31 1.05-1.28 2.82-3.58 1.68-2.67 0.48-0.97 0.69-2.87 1.28-2.68 0.85-3.57 0.75-1.11 1.02-1.41 1.72-2.08 0.84-1.05 7.14-11.54 1.95-2.47 1.45-2.47 1.11-1.34 1.41-2.12 1.47-2.42 0.91-1.17 1.00-1.12 1.27-1.44 1.12-2.78 1.24-1.45 0.83-1.49 5.15-7.07
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.031 0.344 0.001 0.130 0.344 0.026 0.000 0.273 0.000 0.000 0.000 0.000 0.000 0.000 0.592 0.045 0.000 0.014 0.000 0.489 0.000
3.9% 15.5%
3.53 1.65
2.98-4.19 1.45-1.87
0.000 0.000
7.08 1.36
5.68-8.83 1.25-1.48
0.000 0.000
Abbreviations: CI, confidence interval; NA, not available; OR, odds ratio.
95% CI
p Value
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Table 3 Multivariate Analysis of In-Hospital Morbidity and Mortality
Variables Age Sex Female Race/ethnicity African American Hispanic Asian Native American Other/missing Admission status Emergency Urgent Admission source Emergency department Another hospital Court/other law enforcement Von Walraven score Low risk (VWR o5) Intermediate risk (VWR 5-14) High risk (VWR 414) Postoperative risk factors Perioperative stroke Postoperative cardiac arrest and MI Preoperative risk factors Cardiac arrhythmias Delirium Coronary atherosclerosis Angina Bacterial endocarditis
In-Hospital Mortality
Postoperative Morbidity
Model 3 (n ¼ 22,213) OR (95% CI)
Model 2 (n ¼ 22,250) OR (95% CI)
1.05* (1.04-1.06) 1.25† (1.07-1.46)
1.04* (1.04-1.05) 1.51* (1.37-1.65)
1.12 0.87 1.41 0.42 1.04
(0.85-1.47) (0.64-1.20) (0.73-2.73) (0.49-3.65) (0.70-1.55)
1.44* 0.97 0.81 1.53 0.94
(1.21-1.71) (0.79-1.19) (0.54-1.21) (0.73-3.20) (0.72-1.23)
1.77* (1.28-2.44) 1.73* (1.39-2.16)
1.99* (1.54-2.58) 1.41‡ (1.04-1.90)
1.77* (1.33-2.37) 1.35‡ (1.04-1.75) 2.33* (1.51-3.62)
1.89* (1.47-2.43) 2.09* (1.61-2.72) 1.71‡ (1.08-2.69)
NA 2.26* (1.89-2.70)
NA 2.16* (1.90-2.45)
4.74* (3.32-6.78)
5.74* (3.71-5.90)
2.14* (1.56-2.86) 1.56* (1.29-1.89)
4.77* (3.20-7.14) 1.39* (1.20-1.60)
0.42* (0.36-0.51)
1.18* 2.27‡ 1.09 0.82 4.75*
0.97 (0.81-1.16) 1.59* (1.22-2.08)
(1.07-1.29) (1.03-4.99) (0.98-1.23) (0.53-1.26) (3.62-6.24)
Abbreviation: CI, confidence interval; MI, myocardial infarction; NA, not available; OR, odds ratio; VWR, Von Walraven score. n p o 0.001. † p o 0.01. ‡ p o 0.05.
thromboembolic events and cerebral hypoperfusion25,26 and in the immediate postoperative period low cardiac output and arrhythmias also have been reported to be culpable in previously published literature.4,27 In addition to being independent predictors of adverse outcomes, variables such as DM, hypertension, and PVD indirectly contribute to the risk by precipitating ischemic strokes due to atheromatous embolization, impaired autoregulation of cerebral blood flow, aggressive blood pressure reduction, and particulate embolization from valve vegetations in infective endocarditis.28 Cerebrovascular disease and carotid stenosis appeared to be significant contributors to intraoperative cerebral ischemia after cardiac surgery.29,30 The role of preoperative carotid Doppler screening and carotid intervention before cardiac surgery needs to be explored further. Transcranial Doppler monitoring is used to detect embolic debris in the cerebral circulation; its use in
Fig 1. Trends in perioperative stroke, mortality, and morbidity after MVS (1999 to 2011).
aortic valve procedures has been studied, but its applications in other valve procedures require further examination.31 A handful of studies have reported the role of subclinical infarcts after MVS; postoperative magnetic resonance imaging screening to detect these silent ischemic lesions is being studied.32,33 The current literature showed that pulmonary artery hypertension significantly affected outcomes after MVS, with surgical mortality rates of 5% to 15%.34 Preoperative left ventricular ejection fraction, multivalvular disease,6,35 and atrial fibrillation36 were reported to further increase the risk of perioperative thrombogenicity.10 Pharmacologic treatment to improve preoperative cardiac function appears to be decisive in decreasing the risk of complications after surgery. The rationale for preoperative anemia as an independent predictor of mortality and morbidity has been attributed by Keeling et al37 to increased postsurgery transfusion requirements, as with any cardiac procedure. Despite several studies reporting the preoperative and intraoperative risk factors that contribute to adverse outcomes, there is a gap in the current literature on the ways to identify and manage these modifiable risk factors. Several risk scores and indices that predict short-term mortality rates after isolated valve surgery have been published.12,38-40 Risk stratification of patients before MVS guides the physician in better patient selection for surgical treatment.10 Furthermore, comprehensive clinical studies are required to investigate whether preventive strategies such as neuroprotective medication and the use of intraoperative neurophysiologic monitoring may improve outcomes, but these have not been evaluated in cardiac surgery.15-17 This study was a large-scale, nationwide analysis reporting early hospital outcomes after MVS in 21,821 patients. This comprehensive evaluation of risk-adjusted outcome data on a large cohort provided valuable insight into areas for quality improvement. However, this study was not without limitations. Because the NIS database is an administrative discharge-level database, data on strokes and death were measured only if they occurred during hospitalization, with the possibility that some cases of strokes may not have been recognized or reported. Any data on strokes or death that occurred after discharge but within the 30-day postoperative mark were unavailable. There
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could be considerable variability in the preoperative and postoperative care of patients among the different institutions, and this may have affected the outcome rates. The incidence of adverse events was recorded in the database, but vital information on the timing of strokes and postoperative neurologic evaluation and management was not available. Temporal changes in outcomes and risk factors also may have been related to changes in how the data were recognized and entered and assessed in the database. These factors could have affected the mortality and morbidity rates in this study.
Conclusion Patients who are older, who are women, who present for emergency admission for surgery, and who experience perioperative stroke are at higher risk of mortality and morbidity after MVS. Comprehensive clinical studies examining the factors contributing to strokes are necessary to improve overall safety and efficacy of MV procedures. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1053/j.jvca.2016.12.006.
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