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Outcomes and Hospital Utilization in Patients With Papillary Muscle Rupture Associated With Acute Myocardial Infarction
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Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction Bhaskar Bhardwaj MD , Gurusukhmandeep Sidhu MD , Sudarshan Balla MD , Varun Kumar MD , Arun Kumar MD , Kul Aggarwal MD , Mary L. Dohrmann MD , Martin A. Alpert MD PII: DOI: Reference:
S0002-9149(20)30019-9 https://doi.org/10.1016/j.amjcard.2019.12.051 AJC 24384
To appear in:
The American Journal of Cardiology
Received date: Revised date: Accepted date:
16 September 2019 17 December 2019 23 December 2019
Please cite this article as: Bhaskar Bhardwaj MD , Gurusukhmandeep Sidhu MD , Sudarshan Balla MD , Varun Kumar MD , Arun Kumar MD , Kul Aggarwal MD , Mary L. Dohrmann MD , Martin A. Alpert MD , Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction, The American Journal of Cardiology (2020), doi: https://doi.org/10.1016/j.amjcard.2019.12.051
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Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction
Short title: Outcomes and Hospital Utilization with Papillary Muscle Rupture
Bhaskar Bhardwaj, MD (a), Gurusukhmandeep Sidhu, MD (b), Sudarshan Balla, MD (c) Varun Kumar, MD (d), Arun Kumar, MD (a), Kul Aggarwal, MD, (a), Mary L. Dohrmann, MD (a), Martin A. Alpert, MD (a)
From the University of Missouri School of Medicine, Columbia, Missouri, USA (a), the Tulane University School of Medicine, New Orleans, Louisiana USA (b), the West Virginia Univerity Health Sciences Center, Morgantown, West Virginia USA (c), and the University of South Florida Morsani College of Medicine, Tampa, Florida USA (d)
Funding Sources: None Declarations of Interest: None
Correspondence: Martin A. Alpert, MD Room CE-338, University of Missouri Health Science Center 5 Hospital Drive Columbia, MO 65212, USA Phone: 573-882-2296 Fax: 573-884-7743 Email: [email protected] ABSTRACT
1
Papillary muscles rupture (PMR) is a rare complication of acute myocardial infarction (MI) that can lead to severe hemodynamic compromise, acute heart failure, and death. This study was designed to assess demographics, outcomes, and hospital utilization trends in the management of PMR associated with acute MI. Data were derived from the National Inpatient Sample for the years 2005-2014. ICD-9 codes 410.0-410.9 were used to identify patients with acute MI. ICD-9 code 429.6 was used to identify patients with PMR. ICD-9 procedures codes 35.23, 35.24 and 35.12 were used to identify patients undergoing mitral valve replacement (MVR) or repair. Of the 3,244,799 admissions, 932 were complicated by PMR (incidence of 0.029%). The majority of patients with PMR were ≥ 65 years old (60.1%) and male (60.4%). Of those with PMR, 57.5% underwent MVR. Compared to patients without PMR, those with PMR had a significantly higher in-hospital mortality rate (5.3 vs. 36.3%, p<0.001), cost of hospitalization ($20,205 vs. $74,383, p<0.001) and length of hospital stay (4.67±02 vs. 11.2±0.80 days, p<0.001). Predictors of inhospital mortality in PMR patients were age, inferior wall acute MI, and cardiac arrest. Predictors of MVR in PMR patients were age, female gender, concomitant coronary artery bypass grafting, mechanical circulatory support, longer length of stay, and admission to a large hospital. In conclusion, patients with PMR associated with acute MI have higher risk of inhospital mortality, greater cost of hospitalization and longer length of stay than patients acute MI without PMR. Key Words: papillary muscle rupture, acute myocardial infarction, in-hospital mortality, length of stay, cost of hospitalization
2
Papillary muscle rupture (PMR) is a rare, but potentially life-threating complication of acute myocardial infarction (MI).1,-9 Patients with PMR commonly experience rapid clinical deterioration characterized by pulmonary edema and cardiogenic shock due to severe acute mitral regurgitation.1-6 This dreaded mechanical complication of acute MI is associated with an extremely unfavorable prognosis. With medical treatment alone the reported mortality rates approaches 50% at 24 hours and 80% at 1 week.1-9 Current clinical guidelines recommend surgical treatment of PMR.2,10 However, many patients with PMR fail to receive surgery due to prohibitive risk. As a result, the risk of death from cardiogenic shock or refractory heart failure remains high.2-6 The purpose of this study was to analyze contemporary trends in the management of PMR with special emphasis on clinical outcomes and predictors of in-hospital mortality. METHODS Data were derived from the National Inpatient Sample (NIS) for the years of 2005-2014. The NIS is the largest publicly-available all payer inpatient database in the United States.11 It is part of the family of datasets developed by the Healthcare Case and Utilization Project which is sponsored by the Agency for Healthcare Research and Quality.12 Prior to 2012, this database was known as the Nationwide Inpatient Sample.12 At that time it consisted of data related to discharges from 20% of stratified community hospitals in United States (general, specialty, short-term, non-federal).12 From 2012 onward the NIS has contained data from all hospitals participating in the Healthcare Case and Utilization Project.11 The change in NIS design in 2012 produced no significant change in data analysis. The sampling change resulted in national estimates that are more accurate than those in prior reports. Details relating to the redesign can be accessed via the following link: 3
https://www.hcup-us.ahrq.gov/db/nation/nis/report/NISRedesignFinalReport 040914.pdf. Each hospital discharge or death was de-identified and was filed with the NIS as a unique entry with 1 primary diagnosis and up to 24 secondary diagnoses for a particular hospitalization. In addition, each entry contains data regarding patient demographics, insurance status, comorbidities, length of stay, cost of care, and in-hospital events.9,10 This dataset has been previously-used to evaluate national trends in healthcare usage, patterns and complications of major procedures, racial and economic disparities, and cost and outcomes of hospitalization. In this study subjects were identified using International Classification of Diseases, 9th Revision (ICD-9) codes. ICD-9 codes 410.0-410.9 were used to identify patients with acute MI. In the NIS, the first listed diagnosis (principal diagnosis) is characterized by the condition primarily responsible for necessitating an admission for inpatient care. Patients with PMR were identified using ICD-9 code 429.6. We further utilized ICD-9 procedure codes 35.23 and 35.24 to identify patients undergoing mitral valve replacement (MVR). Those undergoing mitral valve repair were identified using ICD-9 procedure code 35.12. ICD-9 codes utilized in this study are listed in Supplemental Table 1. These codes have been used previously for the same dataset. 14,15 Patient and hospital-related variables were included among the baseline characteristics to assess potential confounding variables. Prior to 2004 these hospitals were identified based on the Metropolitan Statistical Areas. Beginning in 2004 these hospitals were selected based on the Core Statistical Areas. Hospitals were considered to have teaching status if they had ≥1 ACGME-approved residency programs or had a full-time equivalent intern or resident to bed ratio ≥0.25 and were a member of the Council of Teaching Hospitals. Criteria for classification of hospital type, region, and bedsize can be accessed via the following link: https://www.hcupus.ahrq.gov/db/vars/hosp_bedsize/nisnote .jsp. The severity of co-morbidities was 4
characterized using the Elixhauser co-morbidity index which employs 24 co-morbidities in a dichotomous manner to predict in-hospital morbidity.13 Co-morbidities assessed and their associated ICD-9 codes using this method are listed in Supplemental Table 2.14 This index can be used only with ICD-9 codes for the years encompassed by this study. The primary outcome in this analysis was in-hospital mortality. The secondary outcomes were length of stay and cost of hospitalization. In order to evaluate the cost of hospitalization, NIS data were merged with cost-to-charge ratios available from the Healthcare Case and Utilization Project. The cost of each inpatient stay was calculated by multiplying the total hospital charge by the cost to charge ratio. National estimates were generated from NIS data using trend weights and published Healthcare Case and Utilization Project methods. For analysis of the subpopulation in this study, we used a domain variable for the entire dataset to ensure our estimated population statistics and measures of variance were accurate. All statistical analyses were conducted using a SPSS complex sample procedure because the NIS dataset was selected using a representative, stratified and clustered sampling method and not a random sampling method. This approach has been validated with Strategical Analysis Systems (SAS, Cary, NC) and Stata 1C 11.0 (Stata Corp, College Station, TX) procedures recommended by NIS to provide similar estimates to 3 decimals places. To create a nationally representative sample of the United States population, weighted values of each patient level observation were generated. The Pearson chi square test of trend of proportions was utilized using the Cochrane Armitage test via the p-trend command in Stata for categorical variables. For continuous variables, we used a non-parametric test for trend described by Cuzick similar to the Wilcoxon Rank Sum test. Continuous variables were presented as mean values ± 1 standard error of the mean or ±1 standard deviation. Categorical variables were presented as frequency 5
(percentage). The chi square test was used to compare proportions of categorical variables. Student’s t-test (2-tailed) was used to compare mean values of continuous variables. Surveyspecific logistic regression and general linear modeling utilizing the F statistic was used to create multivariable models to evaluate factors associated with in-hospital mortality while controlling for potential confounding variables. A multivariable regression model was used to identify patient characteristics which were predictors of in-hospital mortality and MVR. A p value <0.05 was required for statistical significance. Statistical analyses were conducted using SPSS 24 software (IBM Co, Armonk, NY, USA). RESULTS During the study period (2005-2014), we selected 3,244,799 acute MI discharges or deaths, of which 932 (0.029%) were complicated by PMR. The incidence varied little from year to year during the study period. Table 1 shows demographic and other characteristics of patients with and without PMR. Most patients in both subgroups were ≥65 years of age (60.1%), white, and men (57.1%). There was no significant difference in mean age between patients with and without PMR group. The proportion of men was significantly higher than the proportion of women in both groups. There was a higher proportion of whites compared to blacks and Hispanics in the PMR compared to the group without PMR. The proportion of patients with heart failure, hypertension, and coagulopathy was significantly greater in patients with PMR than in those without PMR. The Elixhauser co-morbidity index was significantly higher in the PMR group than in the group without PMR. The proportion of patients with inferior wall MI was significantly greater in the PMR group than in the group without PMR, whereas the proportion of patients with anterior wall MI was not significantly different between the 2 groups. A greater proportion of patients with PMR underwent coronary artery bypass grafting compared to those 6
without PMR, whereas the proportion of patient receiving percutaneous coronary intervention was similar between the 2 groups. The proportion of patients requiring mechanical circulatory support (primarily intra-aortic balloon pump) was significantly greater in the PMR group than in the non-PMR group. Median household income was reported in quartiles and was similar between the 2 groups for all quartiles. Table 2 shows hospital/facility utilization in patients with and without PMR. Data were derived from discharging hospitals. For both groups, patients were hospitalized more commonly in medium to large hospitals than in small hospitals. The proportion of patients in the group with PMR was greater in large hospitals compared to group without PMR. More patients were hospitalized in private non-profit and urban teaching hospitals in the PMR group than in the nonPMR group. The cost of hospitalization was significantly higher in the PMR group than in the non- PMR group. The proportion of patients from the northeast, midwest and west regions was similar between the 2 groups. The proportion of patients hospitalized in the south region was higher in the non-PMR group than in the PMR group and also higher than in each of the other regions. The mean cost of hospitalization was significantly higher in the group with PMR than in the group without PMR. The proportion of patients transferred to a skilled nursing facility, intermediate-care facility or to home health care was significantly higher in the PMR group than in the non-PMR group. Table 3 shows multivariable predictors of in-hospital mortality. Data are reported as odds ratios with 95% confidence intervals. Predictors of in-hospital mortality were age plus 5 years, inferior wall MI, and cardiac arrest. Gender, race, median household income, percutaneous coronary intervention, coronary artery bypass grafting, mechanical circulatory support, the
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presence of cardiogenic shock, Elixhauser co-morbidity index, hospital size, hospital ownership, type of admission, and hospital region were not predictive of in-hospital mortality. Among all patients with PMR, 57.5% underwent MVR. Table 4 shows multivariable predictors of mitral valve replacement. Data are reported as odds ratios with 95% confidence intervals. Variables that predicted mitral valve replacement included age plus 5 years, female gender, single vessel disease, multi-vessel disease, coronary artery bypass grafting any mechanical circulatory support, use of the intra-aortic balloon pump, cardiogenic shock, inferior MI, anterior wall, Elixhauser co-morbidity index, and medium and large hospital size, and hospital region. Variables that did not predict mitral valve replacement were percutaneous coronary intervention, cardiac arrest, median household income, hospital ownership, and type of admission. DISCUSSION PMR, although rare, is responsible for approximately 1-5% of deaths in patients with acute MI.1-4, 7-9 Most cases occur 2-7 days after acute MI, although delayed rupture has been reported.1-8 Early reperfusion with thrombolysis or percutaneous coronary intervention has reduced the incidence of PMR from 1-5% prior to 1990 to < 0.05%.15-18 PMR may be complete (usually occurring at the base of the papillary muscle) or partial occurring at one of the tips (heads) of a papillary muscle.1-8 Prior studies suggest that rupture of the posteromedial papillary muscle occurs more commonly than rupture of the anterolateral papillary muscle.1-8,19 Our findings support this observation. This occurs because the posteromedial papillary muscle receives its blood supply solely from the posterior descending artery, whereas the anterolateral papillary muscle is supplied by both the left anterior descending and left circumflex coronary 8
arteries.2,19 PMR often occurs during the first MI and nearly half of its victims have single vessel disease.1-8,19 Infarctions are commonly associated with poor collaterals.2 In one autopsy study of patients with PMR, infarcted myocardium involved only 19% of the left ventricle.5 This study is the largest to date involving patients with PMR. Patients with PMR associated with acute MI had significantly higher in-hospital mortality, higher cost of hospitalization and longer length of stay compared to patients without PMR. PMR occurred more commonly in patients with inferior wall MI than in those with anterior wall MI and was a strong predictor of in-hospital mortality and MVR. Mechanical circulatory support (predominately intra-aortic balloon pump) was used to a significantly greater extent in patients with PMR than in those without PMR. The incidence of PMR in this study in patients with acute MI was 0.029% and varied little from year to year. In previously reported registry studies, the incidence of PMR in patients with acute MI ranged from 0.20-0.26%. 1,2,7,8 There are several potential explanations for this disparity. These include improved management of acute MI with emphasis on prompt coronary revascularization, an overall decrease in the incidence of acute MI, and incomplete or inaccurate reporting to registries. The in-hospital mortality rate in this study was 36% for patients with PMR associated with acute MI. Prior studies have reported in-hospital mortality rates ranging from 24-55% in patients with acute mitral regurgitation associated with acute MI.2,4,6,16 Bouma et al reported an in-hospital mortality rate of 25% for patients with PMR associated with acute MI. 20 In some studies, concomitant coronary revascularization with percutaneous coronary intervention or coronary artery bypass grafting (with mitral valve replacement) improved cardiovascular 9
outcomes in patients with PMR associated with acute MI.1,5, whereas other studies failed to show benefit of coronary revascularization in this setting.20,24 In this analysis, coronary artery bypass grafting was strongly associated with mitral valve replacement in patients with post-infarction PMR in contrast to some prior studies.21,28 Neither modality was associated with a statistically significant reduction in in-hospital mortality. However, this study was not randomized and we cannot exclude selection bias regarding the use of these procedures. In this study 57.5% of patients of PMR associated with acute MI underwent MVR. Thompson and colleagues reported that 46% of patients with PMR associated with acute MI underwent mitral valve replacement, whereas Tavakoli and co-workers reported that 67.7% of patients with post-infarction PMR underwent mitral valve replacement.6, 25. Mechanical circulatory support, most commonly intra-aortic balloon counter-pulsation, is commonly employed in patients with PMR associated with acute MI.27 Mitral valve repair has been performed primarily in patients with partial PMR.28-30 Mitral valve repair in such patients has been associated with good outcomes due to preservation of chordal integrity and annular structure, shorter surgical times and the lack of need for long-term anti-coagulation in most patients. Several case reports have described successful use of the MitraClip, (Abbott Vascular) for patients with PMR associated with acute MI.28, 30 There are several important study limitations. The NIS is an administrative database that de-identifies patient information, thereby preventing validation of the accuracy of ICD-9 codes. For this reason, it is difficult to precisely assess the relation of comorbidities to in-hospital events. In addition, the timing and sequence of certain clinical events (eg. cardiac arrest) during hospitalization cannot be assessed with accuracy and precision. We did not determine whether prior MI had occurred. The NIS database does not contain details of imaging, medication, 10
laboratory results, and hemodynamic data. Moreover, details of angiographic variables and surgical techniques were not available. The location of acute MI tends to be under-coded in the NIS, thus predisposing to possible bias. Data for the NIS is collected from discharging hospitals. Thus, it is not possible to ascertain the details occurring at admitting hospitals for patients who were transferred to a different facility. Post-hospitalization (longitudinal) data were not available. In conclusion, information derived from the largest database of patients with PMR associated with acute MI show that there has been a substantial decrease in the incidence of PMR compared to earlier studies over. Compared to acute MI patients without PMR, those with PMR associated with acute MI have higher in-hospital mortality, higher cost of hospitalization, and longer length of hospital stay.
Author Contributions Bhaskar Bhardwaj: Conceptualization, Visualization, Methodology, Formal analysis, Investigation, Data curation, Writing (original draft); Gurusukhmandeep Sidhu: Visualization, Methodology, Formal analysis, Writing (original draft); Sudarshan Balla: Conceptualization, Formal analysis, Writing (organized draft); Varun Kumar: Conceptualization, Writing (review and editing); Arun Kumar: Visualization, Writing (review and editing); Kul Aggarwal: Writing (original draft, review, and editing); Mary L. Dohrmann: Writing (review and editing); Martin A. Alpert: Supervision, Visualization, Formal analysis, Writing (original draft, review, and editing).
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14. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43:1130-1139. 15. GUSTO Investigators. An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction. N Engl J Med 1993; 329:673-682. 16. French JK, Hellkamp AS, Armstrong PW, Cohen E, Kleiman NS, O’Connor CM, Holmes DR, Hockman JS Granger CB, Mahaffey KW. Mechanical complications after percutaneous coronary intervention in ST-elevation myocardial infarction (from APEXAMI). Am J Cardiol 2010; 105:59-63. 17. Kinn JW, O’Neill WW, Benzuly KH, Jones DE, Grines CL. Primary angioplasty reduces risk of myocardial rupture compared to thrombolysis for acute myocardial infarction. Cathet Cardiovasc Diagn 1997; 42: 151-157. 18. Badheka AO, Patel NJ, Grover P, Singh V, Arora S, Chothani A, Mehta K, Deshmukh A, Savani GT, Patel A, Panaich SS, Shah N, Rathod A, Brown M, Mohamad T, Tamburrino FV, Kar S, Makkar R, O’Neill WW, Demarchena E, Schreiber T, Grines CL, Rihal CS, Cohen MG. Impact of annual operator and intuitional volume on percutaneous coronary intervention outcomes: A 5-year United States experience (2005-2009). Circulation 2014; 130:1392-1406. 19. Sanders RJ, Neuberger KT, Ravin A. Rupture of papillary muscles: occurrence of rupture of the posterior muscle in posterior myocardial infarction. Dis Chest 1957 ;31:316-323. 20. Bouma W, Wijdh-den Hamer IJ, Koene BM, Kuijpers M, Naolour E, Erasmus ME, van der Horst IC, Gurman JH 3rd, Gorman RC, Mariana MA. Predictors of in-hospital mortality after mitral valve surgery for post-myocardial infarction post-myocardial 14
infarction papillary muscle rupture. J Cardiothorac Surg 2014 Oct 18. doi: 10.1186/s13019-014-0171-z. 21. Kishon Y, Oh JK, Schaff HV, Mullany CJ, Tajik AJ, Gersh BJ. Mitral valve operation in postinfarction rupture of papillary muscle: immediate results and long-term follow-up of 22 patients. Mayo Clin Proc 1992; 67:1023-1030. 22. Chevalier P, Burri H, Fahrat F, Cucherat M, Jegaden O, Obadia JF, Kirkorian G, Touboui P. Perioperative outcome and long-term survival of surgery for acute post-infarction mitral regurgitation. Eur J Cardiothorac Surg 2004; 26:330-335. 23. Russo A, Suri RM, Grigioni F, Roger VA, Oh JK, Mahoney DW, Schaff HV, EnriquezSarano M. Clinical outcome after surgical correction of mitral regurgitation due to papillary muscle rupture. Circulation 2008; 118:1528-1534. 24. Schroeter T, Lehmann S, Misfeld M, Borger M, Subramanian S, Mohr FW, Bakthiary F. Clinical outcome afer mitral valve surgery due to ischemic papillary muscle rupture. Ann Thorac Surg 2013; 95:820-824. 25. Tavakoli R, Weber A, Vogt P, Brunner HP, Pretre R, Turina M. Surgical management of cacute mitral valve regurgitation due to post-infarction papillary muscle rupture. J Heart Valve Dis 2002; 11:20-25 26. Tepe NA, Edmunds LH, Jr. Operation for acute postinfarction mitral insufficiency and cardiogenic shock. J Thorac Cardiovasc Surg 1985; 89:525-530. 27. Rihal CS, Naidu SS, Givertz MM, Szeto WY, Burke JA, Kapur NK, Kern M, Garratt KN, Goldstein JA, Dimas V, Tu T. 2015 SCAI/ACC/HFSA/STS Clinical Expert Consensus Statement on the Use of Percutaneous Mechanical Circultory Support Devices in Cardiovascular Care: Endorsed by the American Heart Association, the Cardiological 15
Society of India, and Sociedad Latino Americana de Cardiologia Intervecion; Affirmation of Value by the Canadian Association of Interventional CardiologyAssociation Canadienne de Cardiologie d’intervention. J Am Coll Cardiol 2015; 65:e7e26. 28. Wolff R. Cohen G. Peterson C, Wong S, Hockman E, Lo J, Strauss BH, Cohen EA. MitraClip for papillary muscle rupture in patient with cardiogenic shock. Can J Cardiol 2014 Nov; 30(11):1461.e13-4. doi:10.1016/cjca.2014.07015. 29. Feldman T, Foster E, Glower DD, Kar S, Rinaldi MJ, Fali PS, Smalling R, Siegel R, Rose GA, Engeron E. Percutaneous repair or surgery for mitral regurgitation. N Engl J Med 2011; 364:1395-1406. 30. Estevez-Loureiro R, Arzamendi D, Freixa X, Cardenal R, Carrasco-Chinchilla F, Semador-Frutos A, Pan M, Sabbate M, Diaz J, Hernandez JM, Serra A, FernandezVazquez F; Spanish Working Group on MitraClip. Percutaneous mitral valve repair for acute mitral regurgitation after an acute myocardial infarction. J Am Coll Cardiol 2015; 66: 91-92.
Table 1
Characteristics of Patients with and without Papillary Muscle Rupture
Papillary Muscle Rupture
16
Variable
Absent (n=3,243,847)
Present (n=932)
p
60.4%
65.1%
<0.001
39.6%
34.9%
<0.001
Gender Male Female
Race
<0.001
White
75.7%
83.3%
Black
10.4%
3.1%
Hispanic
7.6%
3.7%
Mean age (years)
63.3±14.2*
66.3±11.4*
Age subgroups (years)
0.012 0.002
18-44
5.4%
2.5%
45-64
37.8%
39.8%
65-84
43.1%
51.8%
≥85
13.7%
5.9%
Heart failure
0.8%
6.0%
<0.0001
Valvular heart disease
0.2%
8.2%
<0.0001
Co-morbidities
17
Hypertension
71.5%
52.0%
<0.0001
Chronic lung Disease
21.0%
22.0%
0.724
Table 1 (continued)
Papillary Muscle Rupture Variable
Absent
Present
Coagulopathy
5.1%
29.0%
<0.0001
Diabetes mellitus (uncomplicated)
30.1%
16.5%
0.022
Diabetes mellitus (uncomplicated)
6.8%
2.6%
0.022
Chronic liver disease
1.4%
1.1%
0.656
Peripheral arterial disease
12.1%
9.0%
0.192
12.8±0.06**
5.2±0.9**
0.001
Elixhauser co-morbidity index
p
Infarct location Anterior
10.4%
7.3%
0.15
Inferior
14.3%
46.6%
<0.001
Revascularization modality
18
Percutaneous coronary intervention Coronary artery bypass grafting
45.6%
47.4%
0.600
8.5%
37.5%
<0.001
Mechanical circulatory support
<0.001
Intra-aortic balloon pump
4.8%
69.4%
Percutaneous ventricular assist device
0.3%
3.2%
5.2%
36.3%
In-hospital mortality
<0.001
Median household income (percentile)
0.450
0-25th
29.6%
27.0%
26th-50th
26.7%
23.5%
51st -75th
23.7%
27.6%
76th- 100th
20.0%
21.9%
Categorical variables are presented as percentage of weighted number of patients in each subgroups. Continuous variables are presented as mean values ± 1 standard deviation (*) or ± 1 standard error of the mean (**); n=weighted number of patients
Table 2 Baseline Hospital Facility Utilization Characteristics in Patients with and without Papillary Muscle Rupture
Papillary Muscle Rupture Variable
Absent
19
Present
p
Hospital size
0.003
Small
9.2%
6.0%
Medium
40.9%
36.9%
Large
49.9%
57.1%
Hospital ownership
0.007
Government (non-federal)
10.3%
4.8%
Private: Non-profit
74.0%
83.3%
Private: For-profit
15.7%
11.9%
Hospital type
0.130
Rural
9.2%
6.0%
Urban non-teaching
40.9%
36.9%
Urban teaching
49.9%
57.1%
Non-elective
94.1%
95.3%
0.500
Elective
5.9%
4.7%
0.500
Weekend
26.3%
21.7%
0.160
Timing of admission
20
Table 2 (continued)
Papillary Muscle Rupture Variable
Length of stay (day) Cost of hospitalization
Absent
Present
p
4.67±0.02*
11.2±0.8*
<0.0001
$20,205±133**
$74,388±4736**
Hospital region
<0.0001 <0.001
Northeast
20.4%
24.1%
Midwest
19.3%
23.2%
South
41.5%
27.9%
West
18.8%
24.9%
Discharge disposition
<0.001
Routine
61.1%
12.6%
Transfer to short-term care facility
9.3%
12.1%
Transfer to a skilled nursing facility,
12.6%
27.3%
intermediate care facility or home health care Categorical values are presented as percentage of weighted number of patients in each subgroup. Continuous variables are presented as mean values ± 1 standard error of the mean (*) ±1 standard deviation (**). 21
Table 3 Multivariable Analysis: Predictors of In-hospital Mortality
Variable Age +5 years
95% Confidence Interval
p
1.177
1.050-1.319
0.005
0.700
0.360-1.380
0.30
Odds Ratio
Gender Male (referent) Female Race White (referent)
0.28
Black
0.407
0.106-1.569
Hispanic
1.050
0.204-5.490
Anterior myocardial infarction
3.192
0.632-16.132
0.16
Inferior myocardial infarction
2.583
1.224-5.448
0.013
Percutaneous coronary intervention
0.95
22
Single vessel
1.618
0.437-5.991
0.713
0.084-6.047
Coronary artery bypass grafting
0.507
0.231-1.115
0.09
Cardiogenic shock
0.712
0.315-1.609
0.414
coronary artery disease
Multi-vessel coronary artery disease
Table 3 (continued) Variable
Odds ratio
95% Confidence Interval
p
Intra-aortic balloon pump
2.084
0.952-4.564
0.07
Cardiac arrest
9.029
3.589-22.713
<0.0001
Elixhauser co-morbidity index
1.011
0.987-1.036
0.38
Median household income
0.810
First quartile (lowest, referent)
23
0.680
0.310-1.492
0.883
0.374-2.082
0.815
0.316-2.101
Second quartile
Third quartile
Fourth quartile (highest) Hospital size
0.550
Small (referent) Medium
2.262
0.521-9.825
Large
1.879
0.440-8.035
Hospital type
0.540
Rural (referent) Urban: non-teaching
1.917
0.533-60.817
Urban: teaching
1.325
0.407-4.307
Hospital ownership
0.67
Government (non-federal, referent) 0.711
0.202-2.504
1.078
0.235-4.945
Private (non-profit) Private (for profit)
24
Table 3 (continued) Variable
Odds ratio
95% Confidence Interval
p
Type of admission Non-elective (referent) Elective
0.711
0.202-2.504
0.45
Weekend
1.078
0.235-4.945
0.21
Hospital region
0.65
Northwest (referent) Midwest
1.038
0.395-2.723
South
0.990
0.403-2.433
West
0.535
0.205-1.395
Table 4 Multivariable Analysis: Predictors of Mitral Valve Replacement Variable
95% Confidence Interval
Odds Ratio
25
p
Age plus 5 years
1.040
1.017-1.063
Gender
0.001 <0.001
Male (referent) Female
1.672
1.504-1.858
Race
0.024
White (referent) Black
0.835
0.684-1.020
Hispanic
1.082
0.881-1.329
Anterior myocardial infarction
0.347
0.271-0.446
<0.001
Inferior myocardial infarction
1.862
1.626-2.131
<0.005
0.622
0.454-0.853
0.003
0.534
0.322-0.885
0.015
Percutaneous coronary intervention
Single vessel coronary artery disease
Multi-vessel coronary artery
26
disease
Coronary artery bypass grafting
Cardiogenic shock
57.180
46.420-70.433
<0.001
3.110
2.697-3.586
<0.001
Table 4 (continued) Variable
Odds Ratio
95% Confidence Interval
p
1.862
1.126-2.131
<0.001
2.594
1.645-4.091
<0.001
Cardiac arrest
1.075
0.862-1.341
0.52
Elixhauser co-morbidity index
1.018
1.014-1.022
<0.01
Intra-aortic balloon pump
Percutaneous ventricular assist device
Median household income
0.66
First quartile (referent) 1.014
0.879-1.171
27
Second quartile
0.980
0.838-1.146
1.083
0.922-1.271
Third quartile
Fourth quartile (highest)
Hospital size
<0.001
Small (referent) Medium
1.338
1.012-1.769
Large
1.722
1.340-2.214
Hospital type
0.02
Rural (referent) Urban: non-teaching
1.060
0.874-1.331
Urban: teaching
1.602
0.235-4.945
Hospital ownership
0.88
Government (non-federal, referent)
Private (non-profit)
0.202-2504 1.711
Private (for profit)
0.235-4.945 1.078
28
Table 4 (continued) Variable
Odds ratio
95% Confidence Interval
p
Type of admission Non-elective (referent) Elective
1.167
0.973-1.398
0.095
Weekend
0.938
0.822-1.070
0.34
Hospital region
0.001
Northwest (referent) Midwest
0.693
0.562-0.894
South
0.676
0.550-0.831
West
0.718
0.575-0.897
29
Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction Bhaskar Bhardwaj MD , Gurusukhmandeep Sidhu MD , Sudarshan Balla MD , Varun Kumar MD , Arun Kumar MD , Kul Aggarwal MD , Mary L. Dohrmann MD , Martin A. Alpert MD PII: DOI: Reference:
S0002-9149(20)30019-9 https://doi.org/10.1016/j.amjcard.2019.12.051 AJC 24384
To appear in:
The American Journal of Cardiology
Received date: Revised date: Accepted date:
16 September 2019 17 December 2019 23 December 2019
Please cite this article as: Bhaskar Bhardwaj MD , Gurusukhmandeep Sidhu MD , Sudarshan Balla MD , Varun Kumar MD , Arun Kumar MD , Kul Aggarwal MD , Mary L. Dohrmann MD , Martin A. Alpert MD , Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction, The American Journal of Cardiology (2020), doi: https://doi.org/10.1016/j.amjcard.2019.12.051
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Outcomes and Hospital Utilization in Patients with Papillary Muscle Rupture Associated with Acute Myocardial Infarction
Short title: Outcomes and Hospital Utilization with Papillary Muscle Rupture
Bhaskar Bhardwaj, MD (a), Gurusukhmandeep Sidhu, MD (b), Sudarshan Balla, MD (c) Varun Kumar, MD (d), Arun Kumar, MD (a), Kul Aggarwal, MD, (a), Mary L. Dohrmann, MD (a), Martin A. Alpert, MD (a)
From the University of Missouri School of Medicine, Columbia, Missouri, USA (a), the Tulane University School of Medicine, New Orleans, Louisiana USA (b), the West Virginia Univerity Health Sciences Center, Morgantown, West Virginia USA (c), and the University of South Florida Morsani College of Medicine, Tampa, Florida USA (d)
Funding Sources: None Declarations of Interest: None
Correspondence: Martin A. Alpert, MD Room CE-338, University of Missouri Health Science Center 5 Hospital Drive Columbia, MO 65212, USA Phone: 573-882-2296 Fax: 573-884-7743 Email: [email protected] ABSTRACT
1
Papillary muscles rupture (PMR) is a rare complication of acute myocardial infarction (MI) that can lead to severe hemodynamic compromise, acute heart failure, and death. This study was designed to assess demographics, outcomes, and hospital utilization trends in the management of PMR associated with acute MI. Data were derived from the National Inpatient Sample for the years 2005-2014. ICD-9 codes 410.0-410.9 were used to identify patients with acute MI. ICD-9 code 429.6 was used to identify patients with PMR. ICD-9 procedures codes 35.23, 35.24 and 35.12 were used to identify patients undergoing mitral valve replacement (MVR) or repair. Of the 3,244,799 admissions, 932 were complicated by PMR (incidence of 0.029%). The majority of patients with PMR were ≥ 65 years old (60.1%) and male (60.4%). Of those with PMR, 57.5% underwent MVR. Compared to patients without PMR, those with PMR had a significantly higher in-hospital mortality rate (5.3 vs. 36.3%, p<0.001), cost of hospitalization ($20,205 vs. $74,383, p<0.001) and length of hospital stay (4.67±02 vs. 11.2±0.80 days, p<0.001). Predictors of inhospital mortality in PMR patients were age, inferior wall acute MI, and cardiac arrest. Predictors of MVR in PMR patients were age, female gender, concomitant coronary artery bypass grafting, mechanical circulatory support, longer length of stay, and admission to a large hospital. In conclusion, patients with PMR associated with acute MI have higher risk of inhospital mortality, greater cost of hospitalization and longer length of stay than patients acute MI without PMR. Key Words: papillary muscle rupture, acute myocardial infarction, in-hospital mortality, length of stay, cost of hospitalization
2
Papillary muscle rupture (PMR) is a rare, but potentially life-threating complication of acute myocardial infarction (MI).1,-9 Patients with PMR commonly experience rapid clinical deterioration characterized by pulmonary edema and cardiogenic shock due to severe acute mitral regurgitation.1-6 This dreaded mechanical complication of acute MI is associated with an extremely unfavorable prognosis. With medical treatment alone the reported mortality rates approaches 50% at 24 hours and 80% at 1 week.1-9 Current clinical guidelines recommend surgical treatment of PMR.2,10 However, many patients with PMR fail to receive surgery due to prohibitive risk. As a result, the risk of death from cardiogenic shock or refractory heart failure remains high.2-6 The purpose of this study was to analyze contemporary trends in the management of PMR with special emphasis on clinical outcomes and predictors of in-hospital mortality. METHODS Data were derived from the National Inpatient Sample (NIS) for the years of 2005-2014. The NIS is the largest publicly-available all payer inpatient database in the United States.11 It is part of the family of datasets developed by the Healthcare Case and Utilization Project which is sponsored by the Agency for Healthcare Research and Quality.12 Prior to 2012, this database was known as the Nationwide Inpatient Sample.12 At that time it consisted of data related to discharges from 20% of stratified community hospitals in United States (general, specialty, short-term, non-federal).12 From 2012 onward the NIS has contained data from all hospitals participating in the Healthcare Case and Utilization Project.11 The change in NIS design in 2012 produced no significant change in data analysis. The sampling change resulted in national estimates that are more accurate than those in prior reports. Details relating to the redesign can be accessed via the following link: 3
https://www.hcup-us.ahrq.gov/db/nation/nis/report/NISRedesignFinalReport 040914.pdf. Each hospital discharge or death was de-identified and was filed with the NIS as a unique entry with 1 primary diagnosis and up to 24 secondary diagnoses for a particular hospitalization. In addition, each entry contains data regarding patient demographics, insurance status, comorbidities, length of stay, cost of care, and in-hospital events.9,10 This dataset has been previously-used to evaluate national trends in healthcare usage, patterns and complications of major procedures, racial and economic disparities, and cost and outcomes of hospitalization. In this study subjects were identified using International Classification of Diseases, 9th Revision (ICD-9) codes. ICD-9 codes 410.0-410.9 were used to identify patients with acute MI. In the NIS, the first listed diagnosis (principal diagnosis) is characterized by the condition primarily responsible for necessitating an admission for inpatient care. Patients with PMR were identified using ICD-9 code 429.6. We further utilized ICD-9 procedure codes 35.23 and 35.24 to identify patients undergoing mitral valve replacement (MVR). Those undergoing mitral valve repair were identified using ICD-9 procedure code 35.12. ICD-9 codes utilized in this study are listed in Supplemental Table 1. These codes have been used previously for the same dataset. 14,15 Patient and hospital-related variables were included among the baseline characteristics to assess potential confounding variables. Prior to 2004 these hospitals were identified based on the Metropolitan Statistical Areas. Beginning in 2004 these hospitals were selected based on the Core Statistical Areas. Hospitals were considered to have teaching status if they had ≥1 ACGME-approved residency programs or had a full-time equivalent intern or resident to bed ratio ≥0.25 and were a member of the Council of Teaching Hospitals. Criteria for classification of hospital type, region, and bedsize can be accessed via the following link: https://www.hcupus.ahrq.gov/db/vars/hosp_bedsize/nisnote .jsp. The severity of co-morbidities was 4
characterized using the Elixhauser co-morbidity index which employs 24 co-morbidities in a dichotomous manner to predict in-hospital morbidity.13 Co-morbidities assessed and their associated ICD-9 codes using this method are listed in Supplemental Table 2.14 This index can be used only with ICD-9 codes for the years encompassed by this study. The primary outcome in this analysis was in-hospital mortality. The secondary outcomes were length of stay and cost of hospitalization. In order to evaluate the cost of hospitalization, NIS data were merged with cost-to-charge ratios available from the Healthcare Case and Utilization Project. The cost of each inpatient stay was calculated by multiplying the total hospital charge by the cost to charge ratio. National estimates were generated from NIS data using trend weights and published Healthcare Case and Utilization Project methods. For analysis of the subpopulation in this study, we used a domain variable for the entire dataset to ensure our estimated population statistics and measures of variance were accurate. All statistical analyses were conducted using a SPSS complex sample procedure because the NIS dataset was selected using a representative, stratified and clustered sampling method and not a random sampling method. This approach has been validated with Strategical Analysis Systems (SAS, Cary, NC) and Stata 1C 11.0 (Stata Corp, College Station, TX) procedures recommended by NIS to provide similar estimates to 3 decimals places. To create a nationally representative sample of the United States population, weighted values of each patient level observation were generated. The Pearson chi square test of trend of proportions was utilized using the Cochrane Armitage test via the p-trend command in Stata for categorical variables. For continuous variables, we used a non-parametric test for trend described by Cuzick similar to the Wilcoxon Rank Sum test. Continuous variables were presented as mean values ± 1 standard error of the mean or ±1 standard deviation. Categorical variables were presented as frequency 5
(percentage). The chi square test was used to compare proportions of categorical variables. Student’s t-test (2-tailed) was used to compare mean values of continuous variables. Surveyspecific logistic regression and general linear modeling utilizing the F statistic was used to create multivariable models to evaluate factors associated with in-hospital mortality while controlling for potential confounding variables. A multivariable regression model was used to identify patient characteristics which were predictors of in-hospital mortality and MVR. A p value <0.05 was required for statistical significance. Statistical analyses were conducted using SPSS 24 software (IBM Co, Armonk, NY, USA). RESULTS During the study period (2005-2014), we selected 3,244,799 acute MI discharges or deaths, of which 932 (0.029%) were complicated by PMR. The incidence varied little from year to year during the study period. Table 1 shows demographic and other characteristics of patients with and without PMR. Most patients in both subgroups were ≥65 years of age (60.1%), white, and men (57.1%). There was no significant difference in mean age between patients with and without PMR group. The proportion of men was significantly higher than the proportion of women in both groups. There was a higher proportion of whites compared to blacks and Hispanics in the PMR compared to the group without PMR. The proportion of patients with heart failure, hypertension, and coagulopathy was significantly greater in patients with PMR than in those without PMR. The Elixhauser co-morbidity index was significantly higher in the PMR group than in the group without PMR. The proportion of patients with inferior wall MI was significantly greater in the PMR group than in the group without PMR, whereas the proportion of patients with anterior wall MI was not significantly different between the 2 groups. A greater proportion of patients with PMR underwent coronary artery bypass grafting compared to those 6
without PMR, whereas the proportion of patient receiving percutaneous coronary intervention was similar between the 2 groups. The proportion of patients requiring mechanical circulatory support (primarily intra-aortic balloon pump) was significantly greater in the PMR group than in the non-PMR group. Median household income was reported in quartiles and was similar between the 2 groups for all quartiles. Table 2 shows hospital/facility utilization in patients with and without PMR. Data were derived from discharging hospitals. For both groups, patients were hospitalized more commonly in medium to large hospitals than in small hospitals. The proportion of patients in the group with PMR was greater in large hospitals compared to group without PMR. More patients were hospitalized in private non-profit and urban teaching hospitals in the PMR group than in the nonPMR group. The cost of hospitalization was significantly higher in the PMR group than in the non- PMR group. The proportion of patients from the northeast, midwest and west regions was similar between the 2 groups. The proportion of patients hospitalized in the south region was higher in the non-PMR group than in the PMR group and also higher than in each of the other regions. The mean cost of hospitalization was significantly higher in the group with PMR than in the group without PMR. The proportion of patients transferred to a skilled nursing facility, intermediate-care facility or to home health care was significantly higher in the PMR group than in the non-PMR group. Table 3 shows multivariable predictors of in-hospital mortality. Data are reported as odds ratios with 95% confidence intervals. Predictors of in-hospital mortality were age plus 5 years, inferior wall MI, and cardiac arrest. Gender, race, median household income, percutaneous coronary intervention, coronary artery bypass grafting, mechanical circulatory support, the
7
presence of cardiogenic shock, Elixhauser co-morbidity index, hospital size, hospital ownership, type of admission, and hospital region were not predictive of in-hospital mortality. Among all patients with PMR, 57.5% underwent MVR. Table 4 shows multivariable predictors of mitral valve replacement. Data are reported as odds ratios with 95% confidence intervals. Variables that predicted mitral valve replacement included age plus 5 years, female gender, single vessel disease, multi-vessel disease, coronary artery bypass grafting any mechanical circulatory support, use of the intra-aortic balloon pump, cardiogenic shock, inferior MI, anterior wall, Elixhauser co-morbidity index, and medium and large hospital size, and hospital region. Variables that did not predict mitral valve replacement were percutaneous coronary intervention, cardiac arrest, median household income, hospital ownership, and type of admission. DISCUSSION PMR, although rare, is responsible for approximately 1-5% of deaths in patients with acute MI.1-4, 7-9 Most cases occur 2-7 days after acute MI, although delayed rupture has been reported.1-8 Early reperfusion with thrombolysis or percutaneous coronary intervention has reduced the incidence of PMR from 1-5% prior to 1990 to < 0.05%.15-18 PMR may be complete (usually occurring at the base of the papillary muscle) or partial occurring at one of the tips (heads) of a papillary muscle.1-8 Prior studies suggest that rupture of the posteromedial papillary muscle occurs more commonly than rupture of the anterolateral papillary muscle.1-8,19 Our findings support this observation. This occurs because the posteromedial papillary muscle receives its blood supply solely from the posterior descending artery, whereas the anterolateral papillary muscle is supplied by both the left anterior descending and left circumflex coronary 8
arteries.2,19 PMR often occurs during the first MI and nearly half of its victims have single vessel disease.1-8,19 Infarctions are commonly associated with poor collaterals.2 In one autopsy study of patients with PMR, infarcted myocardium involved only 19% of the left ventricle.5 This study is the largest to date involving patients with PMR. Patients with PMR associated with acute MI had significantly higher in-hospital mortality, higher cost of hospitalization and longer length of stay compared to patients without PMR. PMR occurred more commonly in patients with inferior wall MI than in those with anterior wall MI and was a strong predictor of in-hospital mortality and MVR. Mechanical circulatory support (predominately intra-aortic balloon pump) was used to a significantly greater extent in patients with PMR than in those without PMR. The incidence of PMR in this study in patients with acute MI was 0.029% and varied little from year to year. In previously reported registry studies, the incidence of PMR in patients with acute MI ranged from 0.20-0.26%. 1,2,7,8 There are several potential explanations for this disparity. These include improved management of acute MI with emphasis on prompt coronary revascularization, an overall decrease in the incidence of acute MI, and incomplete or inaccurate reporting to registries. The in-hospital mortality rate in this study was 36% for patients with PMR associated with acute MI. Prior studies have reported in-hospital mortality rates ranging from 24-55% in patients with acute mitral regurgitation associated with acute MI.2,4,6,16 Bouma et al reported an in-hospital mortality rate of 25% for patients with PMR associated with acute MI. 20 In some studies, concomitant coronary revascularization with percutaneous coronary intervention or coronary artery bypass grafting (with mitral valve replacement) improved cardiovascular 9
outcomes in patients with PMR associated with acute MI.1,5, whereas other studies failed to show benefit of coronary revascularization in this setting.20,24 In this analysis, coronary artery bypass grafting was strongly associated with mitral valve replacement in patients with post-infarction PMR in contrast to some prior studies.21,28 Neither modality was associated with a statistically significant reduction in in-hospital mortality. However, this study was not randomized and we cannot exclude selection bias regarding the use of these procedures. In this study 57.5% of patients of PMR associated with acute MI underwent MVR. Thompson and colleagues reported that 46% of patients with PMR associated with acute MI underwent mitral valve replacement, whereas Tavakoli and co-workers reported that 67.7% of patients with post-infarction PMR underwent mitral valve replacement.6, 25. Mechanical circulatory support, most commonly intra-aortic balloon counter-pulsation, is commonly employed in patients with PMR associated with acute MI.27 Mitral valve repair has been performed primarily in patients with partial PMR.28-30 Mitral valve repair in such patients has been associated with good outcomes due to preservation of chordal integrity and annular structure, shorter surgical times and the lack of need for long-term anti-coagulation in most patients. Several case reports have described successful use of the MitraClip, (Abbott Vascular) for patients with PMR associated with acute MI.28, 30 There are several important study limitations. The NIS is an administrative database that de-identifies patient information, thereby preventing validation of the accuracy of ICD-9 codes. For this reason, it is difficult to precisely assess the relation of comorbidities to in-hospital events. In addition, the timing and sequence of certain clinical events (eg. cardiac arrest) during hospitalization cannot be assessed with accuracy and precision. We did not determine whether prior MI had occurred. The NIS database does not contain details of imaging, medication, 10
laboratory results, and hemodynamic data. Moreover, details of angiographic variables and surgical techniques were not available. The location of acute MI tends to be under-coded in the NIS, thus predisposing to possible bias. Data for the NIS is collected from discharging hospitals. Thus, it is not possible to ascertain the details occurring at admitting hospitals for patients who were transferred to a different facility. Post-hospitalization (longitudinal) data were not available. In conclusion, information derived from the largest database of patients with PMR associated with acute MI show that there has been a substantial decrease in the incidence of PMR compared to earlier studies over. Compared to acute MI patients without PMR, those with PMR associated with acute MI have higher in-hospital mortality, higher cost of hospitalization, and longer length of hospital stay.
Author Contributions Bhaskar Bhardwaj: Conceptualization, Visualization, Methodology, Formal analysis, Investigation, Data curation, Writing (original draft); Gurusukhmandeep Sidhu: Visualization, Methodology, Formal analysis, Writing (original draft); Sudarshan Balla: Conceptualization, Formal analysis, Writing (organized draft); Varun Kumar: Conceptualization, Writing (review and editing); Arun Kumar: Visualization, Writing (review and editing); Kul Aggarwal: Writing (original draft, review, and editing); Mary L. Dohrmann: Writing (review and editing); Martin A. Alpert: Supervision, Visualization, Formal analysis, Writing (original draft, review, and editing).
11
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13
14. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43:1130-1139. 15. GUSTO Investigators. An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction. N Engl J Med 1993; 329:673-682. 16. French JK, Hellkamp AS, Armstrong PW, Cohen E, Kleiman NS, O’Connor CM, Holmes DR, Hockman JS Granger CB, Mahaffey KW. Mechanical complications after percutaneous coronary intervention in ST-elevation myocardial infarction (from APEXAMI). Am J Cardiol 2010; 105:59-63. 17. Kinn JW, O’Neill WW, Benzuly KH, Jones DE, Grines CL. Primary angioplasty reduces risk of myocardial rupture compared to thrombolysis for acute myocardial infarction. Cathet Cardiovasc Diagn 1997; 42: 151-157. 18. Badheka AO, Patel NJ, Grover P, Singh V, Arora S, Chothani A, Mehta K, Deshmukh A, Savani GT, Patel A, Panaich SS, Shah N, Rathod A, Brown M, Mohamad T, Tamburrino FV, Kar S, Makkar R, O’Neill WW, Demarchena E, Schreiber T, Grines CL, Rihal CS, Cohen MG. Impact of annual operator and intuitional volume on percutaneous coronary intervention outcomes: A 5-year United States experience (2005-2009). Circulation 2014; 130:1392-1406. 19. Sanders RJ, Neuberger KT, Ravin A. Rupture of papillary muscles: occurrence of rupture of the posterior muscle in posterior myocardial infarction. Dis Chest 1957 ;31:316-323. 20. Bouma W, Wijdh-den Hamer IJ, Koene BM, Kuijpers M, Naolour E, Erasmus ME, van der Horst IC, Gurman JH 3rd, Gorman RC, Mariana MA. Predictors of in-hospital mortality after mitral valve surgery for post-myocardial infarction post-myocardial 14
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Table 1
Characteristics of Patients with and without Papillary Muscle Rupture
Papillary Muscle Rupture
16
Variable
Absent (n=3,243,847)
Present (n=932)
p
60.4%
65.1%
<0.001
39.6%
34.9%
<0.001
Gender Male Female
Race
<0.001
White
75.7%
83.3%
Black
10.4%
3.1%
Hispanic
7.6%
3.7%
Mean age (years)
63.3±14.2*
66.3±11.4*
Age subgroups (years)
0.012 0.002
18-44
5.4%
2.5%
45-64
37.8%
39.8%
65-84
43.1%
51.8%
≥85
13.7%
5.9%
Heart failure
0.8%
6.0%
<0.0001
Valvular heart disease
0.2%
8.2%
<0.0001
Co-morbidities
17
Hypertension
71.5%
52.0%
<0.0001
Chronic lung Disease
21.0%
22.0%
0.724
Table 1 (continued)
Papillary Muscle Rupture Variable
Absent
Present
Coagulopathy
5.1%
29.0%
<0.0001
Diabetes mellitus (uncomplicated)
30.1%
16.5%
0.022
Diabetes mellitus (uncomplicated)
6.8%
2.6%
0.022
Chronic liver disease
1.4%
1.1%
0.656
Peripheral arterial disease
12.1%
9.0%
0.192
12.8±0.06**
5.2±0.9**
0.001
Elixhauser co-morbidity index
p
Infarct location Anterior
10.4%
7.3%
0.15
Inferior
14.3%
46.6%
<0.001
Revascularization modality
18
Percutaneous coronary intervention Coronary artery bypass grafting
45.6%
47.4%
0.600
8.5%
37.5%
<0.001
Mechanical circulatory support
<0.001
Intra-aortic balloon pump
4.8%
69.4%
Percutaneous ventricular assist device
0.3%
3.2%
5.2%
36.3%
In-hospital mortality
<0.001
Median household income (percentile)
0.450
0-25th
29.6%
27.0%
26th-50th
26.7%
23.5%
51st -75th
23.7%
27.6%
76th- 100th
20.0%
21.9%
Categorical variables are presented as percentage of weighted number of patients in each subgroups. Continuous variables are presented as mean values ± 1 standard deviation (*) or ± 1 standard error of the mean (**); n=weighted number of patients
Table 2 Baseline Hospital Facility Utilization Characteristics in Patients with and without Papillary Muscle Rupture
Papillary Muscle Rupture Variable
Absent
19
Present
p
Hospital size
0.003
Small
9.2%
6.0%
Medium
40.9%
36.9%
Large
49.9%
57.1%
Hospital ownership
0.007
Government (non-federal)
10.3%
4.8%
Private: Non-profit
74.0%
83.3%
Private: For-profit
15.7%
11.9%
Hospital type
0.130
Rural
9.2%
6.0%
Urban non-teaching
40.9%
36.9%
Urban teaching
49.9%
57.1%
Non-elective
94.1%
95.3%
0.500
Elective
5.9%
4.7%
0.500
Weekend
26.3%
21.7%
0.160
Timing of admission
20
Table 2 (continued)
Papillary Muscle Rupture Variable
Length of stay (day) Cost of hospitalization
Absent
Present
p
4.67±0.02*
11.2±0.8*
<0.0001
$20,205±133**
$74,388±4736**
Hospital region
<0.0001 <0.001
Northeast
20.4%
24.1%
Midwest
19.3%
23.2%
South
41.5%
27.9%
West
18.8%
24.9%
Discharge disposition
<0.001
Routine
61.1%
12.6%
Transfer to short-term care facility
9.3%
12.1%
Transfer to a skilled nursing facility,
12.6%
27.3%
intermediate care facility or home health care Categorical values are presented as percentage of weighted number of patients in each subgroup. Continuous variables are presented as mean values ± 1 standard error of the mean (*) ±1 standard deviation (**). 21
Table 3 Multivariable Analysis: Predictors of In-hospital Mortality
Variable Age +5 years
95% Confidence Interval
p
1.177
1.050-1.319
0.005
0.700
0.360-1.380
0.30
Odds Ratio
Gender Male (referent) Female Race White (referent)
0.28
Black
0.407
0.106-1.569
Hispanic
1.050
0.204-5.490
Anterior myocardial infarction
3.192
0.632-16.132
0.16
Inferior myocardial infarction
2.583
1.224-5.448
0.013
Percutaneous coronary intervention
0.95
22
Single vessel
1.618
0.437-5.991
0.713
0.084-6.047
Coronary artery bypass grafting
0.507
0.231-1.115
0.09
Cardiogenic shock
0.712
0.315-1.609
0.414
coronary artery disease
Multi-vessel coronary artery disease
Table 3 (continued) Variable
Odds ratio
95% Confidence Interval
p
Intra-aortic balloon pump
2.084
0.952-4.564
0.07
Cardiac arrest
9.029
3.589-22.713
<0.0001
Elixhauser co-morbidity index
1.011
0.987-1.036
0.38
Median household income
0.810
First quartile (lowest, referent)
23
0.680
0.310-1.492
0.883
0.374-2.082
0.815
0.316-2.101
Second quartile
Third quartile
Fourth quartile (highest) Hospital size
0.550
Small (referent) Medium
2.262
0.521-9.825
Large
1.879
0.440-8.035
Hospital type
0.540
Rural (referent) Urban: non-teaching
1.917
0.533-60.817
Urban: teaching
1.325
0.407-4.307
Hospital ownership
0.67
Government (non-federal, referent) 0.711
0.202-2.504
1.078
0.235-4.945
Private (non-profit) Private (for profit)
24
Table 3 (continued) Variable
Odds ratio
95% Confidence Interval
p
Type of admission Non-elective (referent) Elective
0.711
0.202-2.504
0.45
Weekend
1.078
0.235-4.945
0.21
Hospital region
0.65
Northwest (referent) Midwest
1.038
0.395-2.723
South
0.990
0.403-2.433
West
0.535
0.205-1.395
Table 4 Multivariable Analysis: Predictors of Mitral Valve Replacement Variable
95% Confidence Interval
Odds Ratio
25
p
Age plus 5 years
1.040
1.017-1.063
Gender
0.001 <0.001
Male (referent) Female
1.672
1.504-1.858
Race
0.024
White (referent) Black
0.835
0.684-1.020
Hispanic
1.082
0.881-1.329
Anterior myocardial infarction
0.347
0.271-0.446
<0.001
Inferior myocardial infarction
1.862
1.626-2.131
<0.005
0.622
0.454-0.853
0.003
0.534
0.322-0.885
0.015
Percutaneous coronary intervention
Single vessel coronary artery disease
Multi-vessel coronary artery
26
disease
Coronary artery bypass grafting
Cardiogenic shock
57.180
46.420-70.433
<0.001
3.110
2.697-3.586
<0.001
Table 4 (continued) Variable
Odds Ratio
95% Confidence Interval
p
1.862
1.126-2.131
<0.001
2.594
1.645-4.091
<0.001
Cardiac arrest
1.075
0.862-1.341
0.52
Elixhauser co-morbidity index
1.018
1.014-1.022
<0.01
Intra-aortic balloon pump
Percutaneous ventricular assist device
Median household income
0.66
First quartile (referent) 1.014
0.879-1.171
27
Second quartile
0.980
0.838-1.146
1.083
0.922-1.271
Third quartile
Fourth quartile (highest)
Hospital size
<0.001
Small (referent) Medium
1.338
1.012-1.769
Large
1.722
1.340-2.214
Hospital type
0.02
Rural (referent) Urban: non-teaching
1.060
0.874-1.331
Urban: teaching
1.602
0.235-4.945
Hospital ownership
0.88
Government (non-federal, referent)
Private (non-profit)
0.202-2504 1.711
Private (for profit)
0.235-4.945 1.078
28
Table 4 (continued) Variable
Odds ratio
95% Confidence Interval
p
Type of admission Non-elective (referent) Elective
1.167
0.973-1.398
0.095
Weekend
0.938
0.822-1.070
0.34
Hospital region
0.001
Northwest (referent) Midwest
0.693
0.562-0.894
South
0.676
0.550-0.831
West
0.718
0.575-0.897
29