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Trends in Utilization of Mechanical Circulatory Support in Patients Hospitalized After Out-of-Hospital Cardiac Arrest
Objective: This study sought to examine the trends and predictors of mechanical circulatory support (MCS) use in patients hospitalized after out-of-hospital cardiac arrest (OHCA).
Background: There is a paucity of data regarding MCS use in patients hospitalized after OHCA.
Methods: We conducted an observational analysis of MCS use in 985,861 patients hospitalized after OHCA between January 2008 and December 2014 in the Nationwide Inpatient Sample database. On multivariable analysis, we also assessed factors associated with MCS use and survival to discharge.
Results: Among the 985,861 patients, 52,842 (5.4%) had MCS utilized. Intra-aortic balloon pump (IABP) was the most commonly used MCS after OHCA with frequency of 48,307 (4.9%), followed by extracorporeal membrane oxygenation (ECMO) 3,746 (0.4%), and percutaneous ventricular assist devices (PVAD) 3,352 (0.3%). From 2008 to 2014, there was a relative increase in the utilization of MCS by 13.6% from 5% in 2008 to 5.7% in 2014 (P trend < 0.001). The use of IABP demonstrated a decline by 4.3% from 2008 to 2014 (4.9% to 4.7%, P trend <0.001), whereas PVAD use increased by 1700% (0.04% to 0.7%, P trend <0.001), and ECMO use increased by 392% (0.1% to 0.7%, P trend <0.001). Younger, male patients with myocardial infarction, higher co-morbid conditions, VT/VF as initial rhythm, and presentation to a large urban hospital were more likely to receive percutaneous MCS implantation. Survival to discharge was significantly higher in patients who were selected to receive MCS (56.9% vs. 43.1%, OR: 1.16, 95% CI: (1.11-1.21), p-value <0.001).
Conclusions: There is a steady increase in the use of MCS in OHCA, especially PVAD and ECMO, despite lack of randomized clinical trial data supporting their outcomes. Our study shows a possible suggestion of better outcomes in this highly selected group of patients. More definitive randomized studies are needed to assess accurately the optimal role of MCS in this patient population.
Key Words: Out of hospital cardiac arrest, mechanical circulatory support, intra-aortic balloon pump, extra corporeal bypass with membrane oxygenator, percutaneous ventricular assist device
OHCA: Out of hospital cardiac Arrest
MCS: Mechanical circulatory support
PVAD: Percutaneous ventricular assist device
ECMO: Extra corporeal bypass with membrane oxygenator
PVAD: Percutaneous ventricular assist device
CS: Cardiogenic shock
VT: Ventricular tachycardia
Vfib: Ventricular fibrillation
PEA: Pulseless electric activity
In the Unites States, each year approximately 360,000 people experience emergency medical service (EMS) assessed out of hospital cardiac arrest (OHCA) 1.There are several factors that play a role in determining survival after the OHCA i.e., age, initial rhythm, resuscitation delay, delays in intubation and defibrillation etc2-4. Mortality within 24 hours of return of spontaneous circulation (ROSC) typically is attributed to refractory shock leading to recurrent cardiac arrest or multi-organ system failure5,6 (5-8). Cardiogenic shock (CS) requiring vasopressor support is seen in up to 50% of the survivors of OHCA7 (9). Higher mean arterial pressures after OHCA are associated with better survival8 (10). Historically, mechanical circulatory support (MCS) was limited to intra-aortic balloon pumps (IABP) and ECMO9-11 (11-13). Since IABP were not associated with improvement in mortality, their use compared with newer devices has been challenged (14, 15). Other MCS devices, such as Impella (Abiomed Inc., Danvers, Massachusetts), Tandem Heart (Cardiac Assist, Inc., Pittsburgh, Pennsylvania), and ECMO, which possess an ability to provide greater hemodynamic support than IABP, have a potential to improve clinical outcomes13-15 (16-18). As defined by the 2015 Society for Cardiovascular Angiography and Interventions/American College of Cardiology/Heart Failure Society of America/Society of Thoracic Surgeons Clinical Expert Consensus on the use of percutaneous MCS in cardiovascular care, the primary objective of MCS is to reduce myocardial oxygen demand and left ventricular stroke work while providing adequate coronary perfusion16(19). There is a paucity of data with regards to the trends of MCS use after OHCA in the United States. We studied the national inpatient sample (NIS) database to examine the current trends of MCS in patients with OHCA.
Data were obtained from 2008-2014 NIS databases. NIS database is a part of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality. Details regarding NIS have been previously described17(20). In brief, NIS is the largest publicly available inpatient health care database in the United States and contains the data of 20% stratified sample of US community hospitals. Discharge weights are provided for each patient discharge record and can be used to obtain national estimates. Data in NIS are drawn from all the states participating in HCUP, which make up to 97% of the US population. NIS has the data from approximately 8 million hospitalizations. Each hospitalization is de-identified and maintained in the NIS as a unique entry with 1 primary discharge diagnosis and less than 24 secondary diagnoses during that hospitalization. Each entry also carries information on patient’s demographics, insurance status, comorbidities, primary and secondary procedures, hospital charges and in-hospital outcomes.
Patients with OHCA were identified by the International classification of Diseases, Ninth edition, Clinical Modification (ICD-9 CM) code 427.5 if present in any diagnostic fields. This code has shown a positive predictive value upto 82 %18%( 21-23). We included all patients 18 years or older and excluded all observations with ICD-9-CM code 99.60 (cardiopulmonary resuscitation) or 99.63 (closed chest cardiac massage) to avoid inclusion of in-hospital cardiac arrest (IHCA). Some prior studies on NIS database have utilized a similar approach to identify IHCA19,20 and OHCA21,22(28-30). We further excluded patients with do not resuscitate (DNR) orders (N = 149, 024), or records with missing data on age, sex, survival and/or discharge disposition, or type of admission as pregnancy or trauma related (N = 12, 294). Our final study sample included 985, 861 patients with OHCA (Figure 1). Patients with ventricular tachycardia (VT) or ventricular fibrillation (VF) were identified by ICD-9-CM code 427.1 or 427.41 (n=249, 422, 25.3%). Patient records without either of these codes were considered to have pulseless electric activity (PEA) or asystole as the cardiac arrest rhythm (n=736, 439, 74.7%). The ICD-9-CM codes 37.61, 37.68, and 37.65 were used to identify patients who underwent IABP, PVAD (included both Impella and Tandem Heart), and ECMO placement, respectively.
Outcomes of Interest:
The primary measure of interest was the use of MCS in patients with OHCA. We studied temporal trends in MCS use from 2008 to 2014 and the factors associated with their use. We also compared trends of survival to hospital discharge in patients with and without MCS use. These outcomes were assessed in subgroups of the patients with VT/VF and PEA/asystole as a cause of OHCA.
Definition of Variables
We used NIS variables to identify patient age, sex, and race. We divided race into white, black, Hispanic, and others (Asian or Pacific Islander, Native American, and others). We defined the severity of comorbid conditions by using the Deyo modification of the Charlson Comorbidity Index. This index contains 17 comorbid conditions with differential weights. The score ranges from 0 to 33, with higher scores corresponding to greater burden of comorbid diseases. Facilities were considered to be teaching hospitals if they had an American Medical Association–approved residency program, were a member of the Council of Teaching Hospitals, or had a full-time equivalent interns and resident to patient ratio of 0.25 or higher. Hospital location (rural/urban) and bed size were also recorded. The bed size cutoff points divided into small, medium, and large have been done so that approximately one-third of the hospitals in a given region, location, and teaching status combination would fall within each bed size category. A list of ICD-9-CM and Clinical Classifications Software codes provided by the Agency for Healthcare Research and Quality used to identify comorbidities is provided in Supplementary Table 1.
We calculated the weighted estimates by applying stratum weights to the discharges according to the stratum from which the discharge was drawn. Pearson χ2 test was utilized for examining the baseline characteristics for categorical variables (expressed in percentages); the unpaired, 2- tailed t test for normally distributed continuous variables reported as mean [ SD or SE]; and the Wilcoxon signed rank test if continuous variables were not normally distributed in the study population. We used the Cochrane- Armitage test to evaluate the trend of MCS (IABP, PVAD, and ECMO) and survival to discharge during the study period. A P-value of less than 0.05 was considered significant. We generated a multivariable logistic regression model to calculate the odds ratio (OR) of MCS according to year. Multivariate model was adjusted for age, sex, race, myocardial infarction, initial cardiac arrest rhythm (VT/VF Vs. PEA/asystole), Deyo modification of Charlson comorbidity index, primary expected payer, median household income, and hospital level variables including hospital bed size, location, teaching status, and region. We created similar models to identify independent predictors of survival to hospital discharge.
Data were complete for all covariates except race (10.1% missing), median household income (2.5% missing), hospital bed size (0.6% missing), hospital location (0.7% missing), and primary expected payer (0.2% % missing). We performed multiple imputations for missing values using the fully conditional specification method (an iterative Markov Chain Monte Carlo algorithm) in STATA statistical software, version 11.0 (Stata Corp). Results with and without multiple imputations were not significantly different; therefore, only the former are presented.
This study was deemed exempt from approval by the University of Miami Miller school of Medicine Institutional Review board as HCUP is a publicly variable database that contains de-identified patient data and no patient consent was required.
Baseline characteristics and use of MCS:
Of the 985,861 patients with OHCA, 52,842 (5.4%) underwent utilization of MCS. Mean age (SD) of the study population was 66.1 (16) years, 44.1 % were females, and 67.9% were white. Differences in the baseline characteristics of the patient population with and without MCS are summarized in the Table 1.
Younger, white males, with history of coronary artery disease, heart failure, higher scores on Deyo modification of the Charlson co-morbidity index as well as VT/VF as a cause of OHCA were more likely to receive MCS as shown in Table-2. On the other hand, patients with history of chronic obstructive pulmonary disease and chronic kidney disease were less likely to receive MCS. Finally, we found higher utilization of MCS in patients with higher socio-economic class admitted to large, urban teaching hospital located in the Midwest and Northeast region of the United States.
Temporal Trends of the Mechanical Circulatory Support Utilization:
From 2008 to 2014 the utilization of MCS has relatively increased by 13.6% from 5% in 2008 to 5.7% in 2014 (P trend < 0.001). IABP was the most commonly used MCS after OHCA with a frequency of 48,307 (4.9%), followed by ECMO 3,746 (0.4%), and PVAD 3,352 (0.3%). Trend of IABP use decreased by 4.3% from 2008 to 2014 (4.9% to 4.7%, P trend <0.001), whereas PVAD utilization increased by 1700% (0.04% to 0.7%, P trend <0.001), and ECMO by 392% (0.1% to 0.7%, P trend <0.001). Trends in the use of IABP decreased in both VT/VF (11.5% to 10.0%, P trend <0.001) and PEA/asystole (2.8% to 2.7%, P trend <0.001), whereas the trends for utilization of PVAD and ECMO increased in both VT/VF (PVAD = 0.1% to 1.5%, P trend <0.001; ECMO = 0.2% to 1.2%, P trend <0.001), and PEA/asystole (PVAD = 0.02% to 0.42%, P trend <0.001; ECMO = 0.11% to 0.5%, P trend <0.001) ( Figure 2, Supplementary Table-2).
After adjustment, OR for overall MCS use was statistically significant in 2014 compared with 2008 (OR: 1.11, 95% CI: 1.02 to 1.21, p=0.01). Adjusted odds of IABP use was not significantly different in 2014 compared with 2008 (OR: 0.94, 95% CI: 0.86 to 1.03, p=0.16). However, adjusted OR of PVAD (OR: 13.39, 95% CI: 7.07 to 25.34, p<0.001) and ECMO (OR: 3.68, 95% CI: 2.52 to 5.39, p<0.001) were significantly higher in 2014 compared with 2008. Based on initial rhythm, adjusted OR for MCS use in VT/VF was not significantly different (OR: 1.01, 95% CI: 0.90 to 1.13, p 0.878), whereas OR for MCS use in PEA/asystole was higher in 2014 compared with 2008 (OR 1.26, 95% CI: 1.11 to 1.42, p< 0.001).
Independent Patient and Hospital Factors Associated with Mechanical Circulatory support use
After adjustment, on multivariable analysis, patients were more likely to get MCS if they were younger, male sex, White, other race ((Asian or Pacific Islander, Native American, and others), higher Deyo modification of the Charlson Comorbidity Index, VT/VF being the cause of OHCA, and had acute myocardial infarction. Patients who were selected to receive MCS were more likely to have private insurance, higher socio-economic status and were more likely to present to a large teaching hospital of the Midwest region in the United States. (Table 2)
Trends of survival to hospital discharge in patients with Mechanical Circulatory Support:
Survival to discharge in our study population was 43.9%. Overall survival to discharge was significantly higher in patients who were selected to receive MCS (56.9% vs. 43.1%, p-value<0.001). Survival to discharge increased significantly in patients who were not selected to receive MCS (39.6% to 46.7%, P trend<0.001) over the study period. Trend for survival to discharge decreased in patients who were selected to receive MCS (59.3% to 55.7%, p-value<0.001) during the study period. This was predominantly driven by decreased survival in patients on IABP. Survival to discharge had significantly increased in patients who were selected to receive PVAD (33.3% to 44.5%, p<0.001) or ECMO (31.6% to 44.8%, p<0.001). (Figure-3, Supplementary table 3)
Independent Patient and Hospital Factors associated with Survival to discharge:
After adjustment, on multivariate analysis, patients more likely to survive were younger, white, had myocardial infarction, VT/VF OHCA, and had lower Deyo modification of the Charlson Comorbidity Index. Survivors were more likely to be from a higher socio-economic class, more likely to have a private insurance, including health maintenance organization, and presented to large teaching hospital (Table 3). After adjustment, survival to hospital discharge also increased significantly over the study period from 2008 to 2014. Survival to discharge was also higher in patients who were selected to receive MCS use.
In this nationwide cohort study, we examined temporal trends as well as patient and hospital characteristics of percutaneous MCS utilization following OHCA. Our analysis yielded several findings that merit further consideration. First, the use of MCS has increased significantly in patients with OHCA in the United States during the study period (2008-2014). IABP was the most commonly utilized MCS. However, use of IABP decreased significantly during the study period and utilization of PVAD and ECMO increased exponentially. Second, younger, male patients with myocardial infarction, higher co-morbid conditions, VT/VF as initial rhythm, and presentation to large urban hospitals were more likely to receive percutaneous MCS implantation. Third, survival to hospital discharge was higher in patients who were selected to receive MCS. However, the survival to discharge with MCS use might be influenced by selection bias.
OHCA remains a major public health concern. Considerable progress has been made in the care of OHCA patients; however survival still remains very low. This has promoted a drive towards exploring mechanical circulatory support possibly to improve hemodynamic derangements and promote myocardial protection. In the 2016 statement from interventional council of American College of Cardiology, an algorithm for the use of MCS among patients with cardiac arrest has been proposed23 (31). Stretch et al and Khera et al studied overall trends of short-term MCS use in all patients > 18 years from the NIS database24,25 (32,33). In another study, Khera and colleagues examined temporal trends of percutaneous MCS use in the setting of percutaneous coronary intervention using the same database until 201226 (34). Because of the inclusion of a heterogeneous patient population, these studies do not provide direct insight into MCS use in the patients with OHCA. Furthermore, in these studies patients who received ECMO were either not included25,26(33, 34) or excluded24 (32) from the analysis. In our study, we have examined temporal trends, patient and hospital characteristics, and in-hospital outcomes of 8620 percutaneous MCS devices in the setting of OHCA, and we have also included trend of ECMO utilization from 2008 to 2014 in this selected group of patient population compared with the prior studies done through NIS24-26 (32-34).
Early MCS can provide hemodynamic support as well as myocardial protection, which can in turn aid in resolution of shock and can also limit further secondary cerebral injury from hypoperfusion. Out of the various MCS devices, ECMO has been traditionally utilized for post cardiac arrest cardiopulmonary and hemodynamic support27,28 (35,36). The 2015 American Heart Association Guidelines for cardiac arrest have utilized the term extracorporeal cardiopulmonary resuscitation (ECPR) to describe the initiation of veno-arterial ECMO emergently in patients with cardiac arrest with a goal of providing a quick support until the restoration of spontaneous circulation for a reversible cause of cardiac arrest29 . Utilization of ECMO for IHCA has been promising30 but for OHCA data has been scarce and conflicting2-4,31.32 (2-4,). In the CHEER (mechanical CPR, Hypothermia, ECMO and Early Reperfusion) trial, patients with both IHCA and OHCA had better outcomes with ECMO support33 (44). In a study done by Maekawa et al on 162 patients with witnessed cardiac arrest, survival at 3 months was better with ECPR (29.2% vs. 8.3%, p=0.018) compared with conventional CPR32 (43). Furthermore, Sakamoto et al34 (45) also demonstrated improved neurologic outcomes at 1 and 6 months follow up in patients who received a treatment bundle including ECPR in OHCA patients with VT/VF compared with non-ECPR. In our analysis, we found that utilization of ECMO increased significantly from 194 (0.14%) in 2008 to 1043 (0.69%) in 2014. Survival was also increased from 31.6% in 2008 to 44.8% in 2014.
In our study, we found an overall 4.9% decrease in IABP use from 2008 to 2014 in all patients with OHCA. We also found almost 19-fold increase in overall use of PVAD for all OHCA and 22-fold increase in OHCA with PEA/asystole as initial rhythm. Efficacy Study of LV Assist Device to Treat Patients With Cardiogenic Shock [ISAR-SHOCK] trial randomized 26 patients with cardiogenic shock between Impella 2.5 and IABP (46). In this trial, 85% of patients in Impella arm and 69% of IABP arm had cardiac arrest prior to randomizations. Overall 30-day survival in this trial was 46%. In the “IMPella versus IABP Reduces mortality in STEMI patients treated with primary PCI in Severe cardiogenic SHOCK” (IMPRESS in Severe Shock) trial which was a randomized, prospective, open-label, multi-center trial, 48 patients with cardiogenic shock were assigned to Impella CP (n=24) or IABP (n=24). In this trial, 92% of the overall population had cardiac arrest and overall 30-day mortality was 50% in IABP arm and 46% in Impella CP arm35 (47). Manzo-Silberman et al studied 78 post cardiac arrest patients from a single center registry and concluded that Impella LP 2.5 is feasible in this patient population36 (48). Overall 28 days mortality in this study was 77% in Impella LP 2.5 group and 70.5% in the IABP group. There are several factors that could explain difference in mortality between different studies, including but not limited to different inclusion criteria, heterogeneous clinical variables in different studies, and most important ; timing of the device placement. There are some external factors like delays in CPR, quality of CPR, bystander CPR and time of arrival of emergency medical personnel could impact on survival2-4 (2-4). Our data represents outcomes from real-world clinical practice in the United States. In our study, overall survival to discharge was 58.5% in OHCA patients who were selected to receive IABP, and 44.2% in patients who were selected for other PVAD. The data on the use of Tandem Heart in cardiac arrest is scarce. Currently, experience of utilizing Tandem Heart in cardiac arrest is limited mostly to cardiogenic shock or PCI14,25,38 (17, 33, 49).
Our study indicates the overall upward trend in the utilization of MCS in OHCA from 2008 to 2014 despite lack of randomized clinical trial data supporting their outcomes. IABP use initially increased from 4.9% in 2008 to 5.4% in 2011, followed by significant decrease, which was compensated by rise in PVAD and ECMO utilizations. Sandhu et alhad studied the temporal trends of MCS in cardiogenic shock patients undergoing PCI through the Cath-PCI registry and found that use of the IABP decreased during their study from 2009 to 201339 (50). Similarly, Agarwal et alstudied temporal trends of MCS in cardiogenic shock in STEMI patients from the NIS and found that there was a steady increase in the use of IABP from 2003 to 2009, followed by decline during the period 2010 to 201240 (51). The decline in the use of IABP compared with newer percutaneous MCS can be explained in part due to better perceived hemodynamic support from newer devices compared with IABP13-15,
Our results should be interpreted in the lights of following limitations. We relied on the ICD-9 codes for our data collection and case identification. This method has been utilized in prior studies with a very good positive predictive value, but the coding practices vary with each hospital. We were able to collect the data only from the patients who were successfully transported to the hospitals after the OHCA. The data about patients who died in the field, during transport, or in the ER could not be captured. We tried to exclude all patients with IHCA in our analysis, but it is possible that some IHCA were included due to coding errors. In addition, some of the OHCA patients could have received CPR in the hospital as continuation of the resuscitative efforts and might have been erroneously excluded from the study cohort. The NIS database has some limitations, as it does not collect clinical data such as labs, angiographic data, or other clinical variables. Data on presentations such as witnessed vs. unwitnessed arrest, or the duration of CPR, and time to insertion of MCS after arrival are not available. As Impella and tandem heart shares same ICD 9 code, it is not possible to distinguish among these two devices. The higher survival is likely from selection bias since sicker patients with more comorbidities were not selected for MCS support. Younger, male patients with myocardial infarction, VT/VF as initial rhythm, and presentation to large urban hospital are more likely to receive percutaneous MCS implantation. All of these characteristics have been shown to be associated with better survival. Being an observational study, a causal relation between MCS use and survival cannot be implied. NIS sampling design and the above limitations are unlikely to affect the primary purpose of the study which is to report trends of MCS use in OHCA patients. The main strength of this study is the representation of real-world clinical practice of MCS use in OHCA in the United States with a large sample size.
Among patients with OHCA, the use MCS has been steadily increasing in the study period of 2008-2014 despite lack of randomized clinical trial data supporting their outcomes. Use of ECMO and PVAD has shown an exponential increase, whereas the use of IABP has been steadily declining over the study period. Younger, male patients with myocardial infarction, higher co-morbid conditions, VT/VF as initial rhythm, and presentation to large urban hospitals are more likely to receive percutaneous MCS implantation. The survival to discharge was higher among the patients with MCS use, though we cannot determine if that is due to selection bias and residual confounding or whether there may be a true benefit. Adequately powered randomized studies are needed to assess the impact of MCS use among patients with OHCA.
Patel NJ: None
Patel N: None
Bhardwaj B: None
Golwala H: None
Arora S: None
Patel S: None
Patel N: None
Hernandez GA: None
Badheka Apurva: None
Alfonso CE: None
Cohen MG: None
Bhatt DL –
Dr. Deepak L. Bhatt discloses the following relationships – Advisory Board: Cardax, Elsevier Practice Update Cardiology, Medscape Cardiology, Regado Biosciences; Board of Directors: Boston VA Research Institute, Society of Cardiovascular Patient Care; Chair: American Heart Association Quality Oversight Committee; Data Monitoring Committees: Cleveland Clinic, Duke Clinical Research Institute, Harvard Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine, Population Health Research Institute; Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees), Harvard Clinical Research Institute (clinical trial steering committee), HMP Communications (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), Population Health Research Institute (clinical trial steering committee), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees); Other: Clinical Cardiology (Deputy Editor), NCDR-ACTION Registry Steering Committee (Chair), VA CART Research and Publications Committee (Chair); Research Funding: Amarin, Amgen, AstraZeneca, Bristol-Myers Squibb, Chiesi, Eisai, Ethicon, Forest Laboratories, Ironwood, Ischemix, Lilly, Medtronic, Pfizer, Roche, Sanofi Aventis, The Medicines Company; Royalties: Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); Site Co-Investigator: Biotronik, Boston Scientific, St. Jude Medical (now Abbott); Trustee: American College of Cardiology; Unfunded Research: FlowCo, Merck, PLx Pharma, Takeda.
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Table 1: Baseline characteristics among patients with OHCA with and without mechanical circulatory support.
|Variable||Without MCS||With MCS||Overall||P value|
|Age, mean ± SD * , y||66.2 +/- 16.1||63.4 +/- 13.6||66.10 +/- 16.00||<0.001|
|Myocardial Infarction, %||21.2||71.8||24.0||<0.001|
|VT † /VF ‡||23.5||56.2||25.3||<0.001|
|Coronary angiogram or PCI x, %||15.7||82.6||19.3||<0.001|
|Deyo modification of Charlson Comorbidity Index||<0.001|
|Primary expected payer, %||<0.001|
|Median household income category for patient’s zip code, %||<0.001|
|Hospital Characteristics, %|
|Hospital bed size, %||<0.001|
|Urban location, %||90.2||95.4||90.5|
|Teaching hospital, %||50.5||61.1||51.1|
|Hospital Region, %||<0.001|
|Weekends admission, %||24.5||23.5||24.4|
|Family history of CAD§||1.6||4.4||1.8||<0.001|
|Previous myocardial infarction||6.8||8.3||6.9||<0.001|
|Previous CABG ll||6.5||3.9||6.3||<0.001|
|Previous cardiac arrest||0.5||0.6||0.5||0.394|
|Congestive heart failure||33.7||46.9||34.4||<0.001|
|Chronic pulmonary disease||25.0||17.7||24.6||<0.001|
|Pulmonary circulation disorders||6.3||2.6||6.1||<0.001|
|Peripheral vascular disease||11.2||11.8||11.2||0.06|
|Chronic renal failure||25.8||17.9||25.4||<0.001|
|Fluid and electrolyte disorder||53.2||52.8||53.2||0.44|
|Hematological or oncological malignancy||7.4||2.0||7.1||<0.001|
Abbreviations: * SD: Standard deviation; †VT: ventricular tachycardia; ‡VF: ventricular fibrillation; x PCI: percutaneous coronary intervention; §CAD: coronary artery disease; ll CABG: coronary arteries bypass grafting
Table 2. Multivariable predictors of MCS/IABP/PVAD/ECMO use
|Assist device( MCS)||P-value||IABP||P-value||PVAD||P-value||ECMO||P-value|
|Odds ratio (95% CI)||Odds ratio (95% CI)||Odds ratio (95% CI)||Odds ratio (95% CI)|
|2009||1.05 (0.96-1.15)||0.246||1.05 (0.96-1.15)||0.313||2.89 (1.39-5.82)||0.004||1.10 (0.68-1.77)||0.701|
|2010||0.99 (0.91-1.09)||0.932||0.98 (0.89-1.07)||0.612||3.79 (1.89-7.57)||<0.001||1.62 (1.04-2.52)||0.033|
|2011||1.23 (1.12-1.34)||<0.001||1.17 (1.07-1.28)||0.001||6.27 (3.23-12.15)||<0.001||2.42 (1.61-3.64)||<0.001|
|2012||1.21 (1.11-1.32)||<0.001||1.11 (1.01-1.21)||0.026||8.92 (4.66-17.07)||<0.001||3.35 (2.26-4.96)||<0.001|
|2013||1.10 (1.00-1.20)||0.040||0.98 (0.89-1.07)||0.640||10.04 (5.27-19.15)||<0.001||3.17 (2.15-4.69)||<0.001|
|2014||1.11 (1.02-1.21)||0.013||0.94 (0.86-1.03)||0.156||13.39 (7.07-25.34)||<0.001||3.68 (2.52-5.39)||<0.001|
|Age (10 year increment)||0.91 (0.89-0.92)||<0.001||0.95 (0.93-0.97)||<0.001||0.85 (0.80-0.92)||<0.001||0.63 (0.59-0.66)||<0.001|
|Female||0.79 (0.75-0.83)||<0.001||0.79 (0.75-0.83)||<0.001||0.67 (0.56-0.81)||<0.001||0.88 (0.75-1.04)||0.130|
|Black||0.60 (0.55-0.65)||<0.001||0.58 (0.54-0.63)||<0.001||0.68 (0.52-0.89)||0.005||0.70 (0.56-0.89)||0.003|
|Hispanic||1.04 (0.95-1.13)||0.396||1.06 (0.97-1.16)||0.182||0.97 (0.71-1.33)||0.866||0.90 (0.67-1.22)||0.507|
|Others||1.21 (1.11-1.31)||<0.001||1.18 (1.08-1.29)||<0.001||1.22 (0.91-1.64)||0.190||1.22 (0.93-1.60)||0.512|
|Myocardial Infarction||7.20 (6.83-7.58)||<0.001||7.94 (7.51-8.40)||<0.001||5.15 (4.24-6.25)||<0.001||1.51 (1.26-1.82)||<0.001|
|VT*/VFIB†||2.35 (2.25-2.46)||<0.001||2.40 (2.29-2.52)||<0.001||2.21 (1.85-2.63)||<0.001||1.58 (1.33-1.86)||<0.001|
|Deyo modification of Charlson Comorbidity Index|
|1||1.44 (1.30-1.60)||<0.001||1.51 (1.35-1.69)||<0.001||1.79 (1.13-2.86)||0.014||1.57 (1.22-2.00)||<.001|
|>=2||1.27 (1.15-1.40)||<0.001||1.33 (1.19-1.48)||<0.001||2.38 (1.52-3.73)||0.001||1.37 (1.08-1.75)||0.010|
|Primary expected payer, %|
|Medicaid||0.97 (0.88-1.06)||0.452||1.00 (0.91-1.10)||0.968||0.91 (0.66-1.26)||0.582||0.96 (0.73-1.27)||0.788|
|Private Insurance||1.36 (1.28-1.45)||<0.001||1.35 (1.27-1.44)||<0.001||1.43 (1.15-1.77)||0.001||1.80 (1.46-2.22)||<0.001|
|Uninsured||1.09 (1.00-1.19)||0.057||1.17 (1.07-1.29)||<0.001||0.95 (0.69-1.32)||0.773||0.73 (0.52-1.02)||0.061|
|Median household income category for patient’s zip code, %|
|26-50th percentile||1.06 (1.00-1.13)||0.056||1.06 (0.99-1.13)||0.076||0.81(0.65-1.02);||0.077||1.17 (0.93-1.47)||0.182|
|51-75th percentile||1.08 (1.01-1.15)||0.019||1.07 (1.00-1.15)||0.043||0.86 (0.68-1.09)||0.211||1.35 (1.07-1.69)||0.010|
|76-100th percentile||1.17 (1.10-1.26)||<0.001||1.16 (1.08-1.24)||<0.001||1.02 (0.80-1.30)||0.858||1.42 (1.12-1.80)||0.004|
|Hospital bed size, %|
|Medium||1.35 (1.23-1.48||<0.001||1.29 (1.17-1.42)||<0.001||1.90 (1.27-2.84)||0.002||3.89 (1.94-7.79)||<0.001|
|Large||1.95 (1.79-2.12)||<0.001||1.76 (1.61-1.92||<0.001||2.99 (2.05-4.34)||13.41 (6.93-25.97)||<0.001|
|Urban location, %||1.54 (1.38-1.72)||<0.001||1.51 (1.35-1.70)||<0.001||1.71 (1.02-2.85)||0.040||1.31 (0.51-3.36)||0.576|
|Teaching hospital, %||1.49 (1.42-1.57)||<0.001||1.37 (1.30-1.44)||<0.001||2.00 (1.64-2.43)||<0.001||11.38 (8.02-16.15)||<0.001|
|Hospital Region, %|
|Midwest||1.11 (1.04-1.20)||0.003||1.19 (1.10-1.28)||<0.001||1.19 (0.91-1.54)||0.201||0.40 (0.31-0.52)||<0.001|
|South||0.94 (0.88-1.01)||0.081||0.98 (0.91-1.05)||0.539||1.00 (0.79-1.28)||0.997||0.61 (0.51-0.74)||<0.001|
|West||0.93 (0.86-1.00)||0.045||0.97 (0.90-1.05)||0.442||1.09 (0.83-1.43)||0.523||0.46 (0.36-0.59)||<0.001|
Abbreviations: VF†: ventricular fibrillation VT*: ventricular tachycardia,
Table 3. Multivariate predictors of survival to discharge
|Odds ratio (95% CI)||Odds ratio (95% CI)||Odds ratio (95% CI)|
|Assist device||1.16 ( 1.11-1.21)||<0.001||0.96 (0.90-1.03)||0.248||1.34 (1.26-1.44)||<0.001|
|2009||0.93 (0.90-0.97)||<0.001||0.89 (0.83-0.97)||0.004||0.95 (0.91-0.99)||0.025|
|2010||1.02 (0.98-1.06)||0.322||1.00 (0.93-1.08)||0.910||1.02 (0.98-1.07)||0.288|
|2011||1.23 (1.18-1.27)||<0.001||1.21 (1.12-1.31)||<0.001||1.23 (1.18-1.29)||<0.001|
|2012||1.26 (1.21-1.31)||<0.001||1.22 (1.13-1.32)||<0.001||1.27 (1.21-1.33)||<0.001|
|2013||1.32 (1.27-1.37)||<0.001||1.25 (1.16-1.34)||<0.001||1.34 (1.29-1.40)||<0.001|
|2014||1.31 (1.26-1.36)||<0.001||1.30 (1.21-1.40)||<0.001||1.31 (1.26-1.37)||<0.001|
|Age (10 year increment)||0.87 (0.86-0.88)||<0.001||0.87 (0.86-0.89)||<0.001||0.87 (0.86-0.88)||<0.001|
|Female||1.01 (0.99-1.03)||0.223||0.93 (0.89-0.97)||<0.001||1.04 (1.02-1.07)||<0.001|
|Black||0.89 (0.86-0.91)||<0.001||0.78 (0.74-0.83)||<0.001||0.93 (0.90-0.96)||<0.001|
|Hispanic||0.89 (0.86-0.92)||<0.001||0.76 (0.70-0.82)||<0.001||0.93 (0.90-0.97)||0.001|
|Others||0.85 (0.82-0.89)||<0.001||0.74 (0.68-0.80)||<0.001||0.90 (0.86-0.94)||<0.001|
|Myocardial Infarction||1.37 (1.34-1.40)||<0.001||1.63 (1.56-1.70)||<0.001||1.22 (1.19-1.26)||<0.001|
|Deyo modification of Charlson Comorbidity Index|
|1||1.01 (0.98-1.05)||0.460||1.13 (1.05-1.21)||0.001||0.96 (0.93-1.00)||0.046|
|>=2||0.86 (0.83-0.88)||<0.001||0.87 (0.82-0.93)||<0.001||0.86 (0.83-0.88)||<0.001|
|Primary expected payer, %|
|Medicaid||0.88 (0.84-0.91||<0.001||1.05 (0.97-1.13)||0.236||0.82 (0.79-0.86)||<0.001|
|Private Insurance||1.14 (1.11-1.17)||<0.001||1.44 ( 1.36-1.52)||<0.001||1.03 (1.00-1.07)||0.047|
|Uninsured||0.69 (0.66-0.72)||<0.001||0.92 (0.85-0.99)||0.034||0.61 (0.58-0.64||<0.001|
|Median household income category for patient’s zip code, %|
|26-50th percentile||1.06 (1.03-1.09)||<0.001||1.06 (1.00-1.12)||0.039||1.06 (1.02-1.09)||<0.001|
|51-75th percentile||1.08 (1.05-1.11)||<0.001||1.09 (1.03-1.15)||0.003||1.08 (1.04-1.11)||<0.001|
|76-100th percentile||1.14 (1.11-1.28)||<0.001||1.19 (1.11-1.26)||<0.001||1.12 (1.09-1.17)||<0.001|
|Hospital bed size, %|
|Medium||1.03 (0.99-1.06)||0.124||0.99 (0.92-1.07)||0.876||1.04 (0.99-1.08)||0.087|
|Large||1.08 (1.05-1.12)||<0.001||1.00 (0.93-1.07)||0.992||1.11 (1.07-1.15)||<0.001|
|Urban location, %||1.19 (1.15-1.24)||<0.001||0.95 (0.87-1.03)||0.228||1.26 (1.21-1.32)||<0.001|
|Teaching hospital, %||1.03 (1.01-1.05)||0.010||1.01 (0.97-1.05)||0.661||1.04 (1.01-1.06)||0.005|
|Hospital Region, %|
|Midwest||1.03 (1.00-1.07)||0.062||0.99 (0.93-1.06)||0.873||1.05 (1.01-1.09)||0.021|
|South||0.99 (0.96-1.02)||0.472||0.97 (0.91-1.02)||0.251||1.00 (0.97-1.03)||0.885|
|West||0.97 (0.94-1.01)||0.111||1.00 (0.94-1.07)||0.977||0.96 (0.93-1.00)||0.054|
Abbreviations: VF†: ventricular fibrillation VT*: ventricular tachycardia;
Figure 1: Study design and patient selection
All patients ≥18 years of age with OHCA
Patients who had DNR orders
N= 998, 155
All observations with missing information on age, sex and mortality or type of admission is pregnancy, newborn or trauma related or disposition is missing were excluded (n=12, 294)
N= 985, 861
Abbreviations: OHCA: out of hospital cardiac arrest; DNR: do not resuscitate
Figure 2: Temporal trends of the use of MCS. Legend:2a. Trends of the use of MCS over the study duration; 2b. Trends of the use of MCS in patients with VT/Vfib as initial rhythm 2c. Trends of the use of MCS in patients with PEA/Asystole as initial rhythm.
Abbreviations: MCS: Mechanical circulatory support; VT: Ventricular tachycardia; Vfib: Ventricular fibrillation; PEA: Pulseless electric activity
Figure 3: Survival trends with MCS use. Legend: 3a: Trends of survival in all patients with MCS use; 3b: Trends of survival in patients with VT/Vfib as initial rhythm; 3c: Trends of survival among patients with PEA/Asystole as initial rhythm
Abbreviations: MCS: Mechanical circulatory support; VT: Ventricular tachycardia; Vfib: Ventricular fibrillation
Supplementary Table 1. ICD 9 codes
|ICD 9 diagnosis and procedure codes|
|Out of hospital cardiac arrest||427.5|
|Ventricular fibrillation||427.41, 427.42|
|Coronary angiogram||88.54, 88.55, 88.56, 88.57, 00.24, 00.59,37.22, 37.23|
|PCI||36.06, 36.07,00.40, 00.41, 00.42, 00.43, 00.44, 00.45, 00.46, 00.47, 00.48,00.66|
|Acute myocardial infarction||410.1x – 410.9x|
|Coronary artery disease||414.00 – 414.07|
|Family history of coronary artery disease||V17.3|
|Prior myocardial infarction||412|
|Prior percutaneous coronary intervention||V45.82|
|Prior coronary artery bypass grafting||V45.81|
|Prior cardiac arrest||V12.53|
|Family history of sudden cardiac death||V17.41|
|Carotid artery disease||433.10|
Supplementary Table 2. Trends for MCS* use in †OHCA patients from 2008 to 2014
|PVAD ll (N)||3352||56||200||243||451||611||708||1089|
|ECMO # (N)||3746||194||286||300||508||715||722||1043|
|PEA ** /Asystole, %||736635||105125||109250||108678||104242||95607||103320||110429|
Abbreviations: *MCS: mechanical circulatory support; †OHCA: Out of Hospital Cardiac Arrest; ‡IABP: intra-aortic balloon pump; ll PVAD: percutaneous ventricular assist device; # ECMO: extra corporeal membrane oxygenation; ** PEA: pulseless electric activity
Supplementary Table 3: Trends of Survival to discharge in OHCA patients from 2008 to 2014
Abbreviations: MCS: mechanical circulatory support; IABP: intraaortic balloon pump; PVAD: percutaneous ventricular assist device; ECMO: extra corporeal membrane oxygenation; VT: ventricular tachycardia; VF: ventricular fibrillation; PEA: pulseless electric activity