Abstract
Introduction:
Targeted temperature management (TTM) is recommended for unresponsive patients following return of spontaneous circulation after cardiac arrest. However, the degree to which patients with cardiac arrest have access to this therapy on a national level is unknown. Understanding hospital- and patient-level factors associated with receipt of TTM could inform interventions to improve access to this treatment among appropriate patients.
Methods:
This was a retrospective analysis using National Inpatient Sample data from 2016–2019. We used ICD10 diagnosis and procedure codes to identify adult patients with in-hospital and out-of-hospital cardiac arrest and receipt of TTM. We used logistic regression using GEE clustering on hospital-year, and controlling for medical comorbidities, to evaluate patient and hospital factors associated with receiving TTM.
Results:
We identified 478,419 patients with cardiac arrest. Of those, 4088 (0.85%) received TTM. Hospital use of TTM was driven by large, nonprofit, urban, teaching hospitals with less use at other hospital types. There was significant regional variation in TTM capabilities, with the proportion of hospitals providing TTM ranging from over 21% in the Mid-Atlantic region to less than 11% in the East and West South Central and Mountain regions. At the patient level, age > 74 (OR: 0.54, p<0.001), female gender (OR: 0.89, p>0.001) and Hispanic ethnicity (OR: 0.74, p<0.001) were all associated with decreased odds of receiving TTM. Compared to patients with private insurance, patients with Medicare (OR: 0.75, p<0.001) and Medicaid (OR: 0.89, p=0.027) were less likely to receive TTM. Part of these differences were driven by inequitable access to TTM-capable hospitals.
Conclusions:
TTM is rarely utilized after cardiac arrest. Hospital use of TTM is predominately limited to a subset of academic hospitals with substantial regional variation. Advanced age, female gender, Hispanic ethnicity, and Medicare or Medicaid insurance are all associated with a decreased likelihood of receiving TTM.
Keywords: targeted temperature management, cardiac arrest, hypothermia, disparities
Introduction
Cardiac arrest accounts for a substantial proportion of admissions to cardiac intensive care units and is a leading cause of morbidity and mortality in the United States and worldwide.(1, 2) Targeted temperature management (TTM) is the recommended treatment for cardiac arrest patients who have achieved return of spontaneous circulation (ROSC) and are unresponsive.(3) Contemporary use of TTM is supported by several randomized trials as an uniquely helpful therapy that improves patient mortality and neurologic outcomes.(4, 5, 6, 7, 8) The ideal target temperature remains controversial as targeted normothermia has shown similar outcomes to therapeutic hypothermia in large trials.(9, 10) However, there is broad agreement that some form of TTM including appropriate protocols and utilization of temperature management devices improves outcomes.(11)
However, there has been slow implementation of TTM due to its substantial cost and resource utilization.(12) As a result, the degree to which eligible patients who had a cardiac arrest have access to this therapy on a national level is unknown. There is ample reason to believe that access could vary on several contexts relevant to equity. Prior studies have shown lower access to many cardiovascular technologies in rural areas and at safety-net hospitals, for example. In terms of patient characteristics, prior studies have shown significant differences in rates of bystander cardiopulmonary resuscitation and coronary angiography for cardiac arrest, as well as neurologic outcomes and mortality depending on age, gender, race/ethnicity, and socioeconomic status.(13, 14, 15, 16) As a result, understanding patient factors that predict the receipt of TTM is important to promote its equitable use.
Understanding hospital- and patient-level factors associated with receipt of TTM could inform interventions to improve access to this treatment among appropriate patients. Further, TTM is emblematic of the types of advanced cardiac care that require changes in patterns of care delivery and thus may diffuse slowly into clinical practice. (17) Therefore, understanding patterns of hospitals that invest resources to appropriately perform and optimize TTM has important implications for optimizing post-cardiac arrest care, and for other emerging cardiovascular and critical care technologies.
In this study, we sought to understand how TTM has been used in the United States. Our objectives where to: 1) determine how often TTM is used as a treatment for cardiac arrest patients; 2) identify and analyze the characteristics of hospitals that do and do not use TTM; and 3) determine factors that predict the use of TTM from a patient perspective.
Methods:
Data Source and Study Sample
This was a retrospective analysis using 2016–2019 data from the National Inpatient Sample (NIS), which is part of the Healthcare Cost and Utilization Project (HCUP). The NIS is sponsored by the Agency for Healthcare Research and Quality and is the largest publicly available, all-payer, inpatient healthcare database in the United States. It is a 20% representative sample of all US hospitals and captures information on more than 7 million hospitalizations annually.(18) We included all hospitalizations for patients age ≥ 18 years old with a diagnosis of cardiac arrest using the International Classification of Diseases, Tenth Edition (ICD-10) codes and based on previously reported methods(19, 20, 21, 22). The NIS is a hospitalization-level data set, as patients cannot be linked over time. Therefore, every record in the data set represents a unique hospitalization rather than a unique patient. However, for ease of interpretation, and because the likelihood of multiple cardiac arrests among the same individual is reasonably low, we refer to “patients” rather than “hospitalizations” throughout the manuscript.
Patients hospitalized with out-of-hospital cardiac arrest were identified based on codes for cardiac arrest (I46.2, I46.8, I46.9). We also included patients hospitalized with non-perfusing ventricular arrhythmias (ventricular fibrillation, ventricular flutter) based on diagnosis codes I49.01, I49.02. Finally, we included patients with ventricular tachycardia (I47.2) if it was combined with a procedure code for TTM in order to capture patients with cardiac arrest due to ventricular tachycardia and to exclude patients with stable ventricular tachycardia. Patients hospitalized with in-hospital cardiac arrest were identified by a procedure code for cardiopulmonary resuscitation (CPR) (5A12012). Patients that received TTM were identified by ICD-10 procedure codes for hypothermia (6A4Z1ZZ and 6A4Z0ZZ).
Demographic data including age, sex, race/ethnicity, insurance status, rurality designation, comorbidities, hospital location and presenting rhythm (shockable vs. nonshockable, based on ICD-10 codes) were extracted from the NIS. Of the 528,400 hospitalizations from 2016–2019 with cardiac arrest, we excluded 27,848 due to age < 18 or missing age (N=9,407), missing race/ethnicity (N=15,628), missing payor status (N=557), missing rurality designation (N=2,178), or missing sex (N=78). To avoid double-counting patients, we excluded the first part of the hospitalization for patients that were transferred to another acute-care hospital (N=22,195), though the second part of their hospitalization, ie that at the accepting hospital, remained in the dataset. We also collected data on the characteristics of hospitals that performed TTM for cardiac arrest. Collected variables included hospital region, ownership, teaching status, bed size status, proportion minority, and portion Medicaid/self-pay.
Hospital-Level Analysis
To better understand the characteristics of hospitals that perform TTM for cardiac arrest, we compared hospitals that performed TTM to those that cared for cardiac arrest patients but did not. First, we identified hospitals that performed at least 1 TTM procedure per year. Given that NIS data captures a 20% sample of admissions, these hospitals would be expected to perform ≥ 5 TTM procedures per year if extrapolated to an 100% dataset. We then compared these hospitals to those that did not perform TTM (i.e. expected to perform <5 TTM procedures per year if extrapolated to 100% dataset) across hospital characteristics defined according to HCUP and including hospital region (New England, East North Central, West North Central, Middle Atlantic, South Atlantic, East South Central, West South Central, Mountain, Pacific), ownership (Governmental, nonfederal; Private, non-profit; Private, investor-own), teaching and rurality status (Rural, Urban Teaching, Urban Nonteaching), bed size status (Small, Medium, Large), proportion minority (top 20%), and proportion Medicaid/self-pay (top 20%). To identify regular users of TTM, we identified hospitals that performed 5 TTM procedures per year (i.e. ≥ 25 TTM procedures per year if extrapolated to 100% dataset) and compared them to hospitals that did not. Comorbidities were defined using the Elixhauser approach, which has been previously validated for use in administrative data.(23)
Patient-Level Predictors
At the patient level, our primary predictor variables were age (18–34, 35–54, 55–64, 65–74, >75), race/ethnicity (White, Black, Hispanic, Native American, Asian, Other), sex, insurance status (Private, Medicaid, Medicare, No Charge, Other, Self-Pay), hospital geographic location (New England, East North Central, West North Central, Middle Atlantic, South Atlantic, East South Central, West South Central, Mountain, Pacific), degree of rurality (Urban (>1,000,000 total population), Town (50,000–999,999 total population), Rural (<50,000 total population), comorbidities, and presenting rhythm (Shockable, Nonshockable).
Patient-Level Outcome
Our primary outcome was receipt of TTM after cardiac arrest.
Analysis
First, we compared patient demographics between hospitalizations with cardiac arrest that received TTM to those that did not. Second, to determine the influence of comorbidities on patient selection for TTM, we assessed the prevalence of TTM use among patients for each of the 30 comorbidities that together make up the Elixhauser comorbidity index (Appendix Table 1).(24) Third, we compared demographics at the hospital level between hospitals that performed ≥ 5 TTM procedures per year and those that performed < 5 TTM procedures per year. We then repeated the comparison for hospital that performed ≥ 25 TTM procedures per year to evaluate characteristics of hospitals that were high-utilizers of TTM.
Next, we developed a logistic regression model that describes the odds of receiving TTM depending on patient demographics and characteristics. Finally, in order to determine the extent to which access to a TTM-capable hospital explained the variation seen in patient receipt of TTM, we utilized a separate logistic regression model to describe the odds of receiving TTM depending on patient demographics and characteristics only in the subset of hospitalizations at hospitals that demonstrated the capacity to perform TTM. In both cases, we used logistic regression fit using the GEE, clustering on hospital-year to account for within hospital similarities while accounting for medical comorbidities to evaluate patient factors associated with receiving TTM. All ORs presented represent population averaged estimates.
Data were analyzed using R 4.1.2 and SAS 9.4. The study was considered non-human-subjects research by the Washington University Office of Human Research Protection due to the deidentified nature of the data, and the requirement for informed consent was waived.
Results:
Patient Characteristics
We identified 478,419 hospitalizations for patients with a diagnosis of cardiac arrest (Table 1). Cardiac arrest was increasingly more common with advancing age and the highest proportion of patients were age > 74 (35.6%). Patients were predominately male (63.2%) and were mostly from White (68.6%) and Black (18.4%) racial groups, with smaller proportions classified as Hispanic ethnicity, Asian/Pacific Islander, Native American, or other race. Two-thirds had Medicare insurance, and 55% lived in urban areas. The majority of patients presented with a shockable rhythm (71.1%). Many key comorbidities were highly prevalent, including chronic pulmonary disease, congestive heart failure, diabetes, and renal failure. Among all patients hospitalized for cardiac arrest, overall use of TTM was low at 0.85%. When limiting the analysis to TTM-capable hospitals (those that performed ≥ 5 TTM procedures per year), mean use of TTM was 3.7% (median: 2.4% (interquartile range, 1.35–4.4%)) (Figure 1)
Table 1:
Patient Characteristics
| Variable | Total (N=478419) | No TTM (N=474331) | TTM (N=4088) |
|---|---|---|---|
|
| |||
| Age | |||
| 18–34 | 15371 (3.2%) | 15078 (3.2%) | 293 (7.2%) |
| 35–54 | 69157 (14.5%) | 68240 (14.4%) | 917 (22.4%) |
| 55–64 | 98161 (20.5%) | 97091 (20.5%) | 1070 (26.2%) |
| 65–74 | 125615 (26.3%) | 124576 (26.3%) | 1039 (25.4%) |
| > 74 | 170115 (35.6%) | 169346 (35.7%) | 769 (18.8%) |
| Sex | |||
| Male | 302200 (63.2%) | 299578 (63.2%) | 2622 (64.1%) |
| Female | 176219 (36.8%) | 174753 (36.8%) | 1466 (35.9%) |
| Race/Ethnicity | |||
| White | 328110 (68.6%) | 325382 (68.6%) | 2728 (66.7%) |
| Black | 88032 (18.4%) | 87274 (18.4%) | 758 (18.5%) |
| Hispanic | 36412 (7.6%) | 36148 (7.6%) | 264 (6.5%) |
| Asian or Pacific Islander | 10900 (2.3%) | 10748 (2.3%) | 152 (3.7%) |
| Native American | 2508 (0.5%) | 2470 (0.5%) | 38 (0.9%) |
| Other | 12457 (2.6%) | 12309 (2.6%) | 148 (3.6%) |
| Payor Status | |||
| Medicare | 311169 (65.0%) | 309174 (65.2%) | 1995 (48.8%) |
| Medicaid | 52506 (11.0%) | 51752 (10.9%) | 754 (18.4%) |
| Private | 86399 (18.1%) | 85384 (18.0%) | 1015 (24.8%) |
| Self-pay/No charge | 16577 (3.5%) | 16374 (3.5%) | 203 (5.0%) |
| Other | 11768 (2.5%) | 11647 (2.5%) | 121 (3.0%) |
| Degree of Rurality | |||
| urban | 263103 (55.0%) | 260912 (55.0%) | 2191 (53.6%) |
| town | 142979 (29.9%) | 141566 (29.8%) | 1413 (34.6%) |
| rural | 72337 (15.1%) | 71853 (15.1%) | 484 (11.8%) |
| Hospital Division | |||
| New England | 21070 (4.4%) | 20867 (4.4%) | 203 (5.0%) |
| Middle Atlantic | 66002 (13.8%) | 65247 (13.8%) | 755 (18.5%) |
| East North Central | 81618 (17.1%) | 80880 (17.1%) | 738 (18.1%) |
| West North Central | 26020 (5.4%) | 25746 (5.4%) | 274 (6.7%) |
| South Atlantic | 107898 (22.6%) | 107116 (22.6%) | 782 (19.1%) |
| East South Central | 33665 (7.0%) | 33521 (7.1%) | 144 (3.5%) |
| West South Central | 53800 (11.2%) | 53491 (11.3%) | 309 (7.6%) |
| Mountain | 25863 (5.4%) | 25722 (5.4%) | 141 (3.4%) |
| Pacific | 62483 (13.1%) | 61741 (13.0%) | 742 (18.2%) |
| Rhythm | |||
| Non-shockable | 138186 (28.9%) | 136247 (28.7%) | 1939 (47.4%) |
| Shockable | 340233 (71.1%) | 338084 (71.3%) | 2149 (52.6%) |
| Weighted Elixhauser Score | |||
| Below 5 | 79342 (16.6%) | 79031 (16.7%) | 311 (7.6%) |
| 5 or above | 399077 (83.4%) | 395300 (83.3%) | 3777 (92.4%) |
| Select Comorbidities | |||
| Chronic pulmonary disease | 115251 (24.1%) | 114491 (24.1%) | 760 (18.6%) |
| Coagulation | 58138 (12.2%) | 57579 (12.1%) | 559 (13.7%) |
| Congestive Heart Failure | 266068 (55.6%) | 264274 (55.7%) | 1794 (43.9%) |
| Diabetes, complicated | 91090 (19.0%) | 90483 (19.1%) | 607 (14.8%) |
| Fluid and electrolyte disorders | 218510 (45.7%) | 215711 (45.5%) | 2799 (68.5%) |
| Hypertension, complicated | 210528 (44.0%) | 209410 (44.1%) | 1118 (27.3%) |
| Hypertension, uncomplicated | 108032 (22.6%) | 107257 (22.6%) | 775 (19.0%) |
| Liver disease | 46494 (9.7%) | 45540 (9.6%) | 954 (23.3%) |
| Other neurological disorders | 114745 (24.0%) | 111569 (23.5%) | 3176 (77.7%) |
| Peripheral vascular disorders | 67374 (14.1%) | 67156 (14.2%) | 218 (5.3%) |
| Renal failure | 129924 (27.2%) | 129232 (27.2%) | 692 (16.9%) |
| Valvular disease | 59746 (12.5%) | 59538 (12.6%) | 208 (5.1%) |
TTM = targeted temperature managment
Figure 1:
Proportion of Targeted Temperature Management (TTM) Procedures Per Hospital Year Among TTM-Capable Hospitals.
Hospital Characteristics
There were 1,918 hospital-years across the four-year study period from hospitals that performed at least 5 TTM procedures per year, and 10,902 hospital-years in the data from hospitals that did not (Table 2). Hospitals that performed ≥ 5 TTM procedures were disproportionately large, urban, teaching hospitals with private, nonprofit ownership. They were also less likely to be in the top quintile for Medicaid/Self-Pay hospitalizations compared to hospitals that did not perform TTM. There was no difference between hospitals based on whether they had a high proportion of minority patients. Patterns were similar when a cutoff of 25 TTM procedures was used to identify TTM utilizers, with large, urban teaching hospitals dominating this group (Appendix Table 2).
Table 2:
Hospital-Year Characteristics
| Variable | Performs ≥5 TTM procedures/yr (N=1918) | Performs <5 TTM procedures/yr (N=10902) | Total (N=12820) | P-value |
|---|---|---|---|---|
|
| ||||
| Ownership | ||||
| Governmental, nonfederal | 178 (9.3%) | 1634 (15.0%) | 1812 (14.1%) | <0.001 |
| Private, not-profit | 1486 (77.5%) | 7310 (67.1%) | 8796 (68.6%) | |
| Private, invest-own | 254 (13.2%) | 1958 (18.0%) | 2212 (17.3%) | |
| Teaching Status | ||||
| Rural | 60 (3.1%) | 3324 (30.5%) | 3384 (26.4%) | <0.001 |
| Urban nonteaching | 459 (23.9%) | 3318 (30.4%) | 3777 (29.5%) | |
| Urban teaching | 1399 (72.9%) | 4260 (39.1%) | 5659 (44.1%) | |
| Bedsize Status | ||||
| Small | 365 (19.0%) | 4670 (42.8%) | 5035 (39.3%) | <0.001 |
| Medium | 587 (30.6%) | 3243 (29.7%) | 3830 (29.9%) | |
| Large | 966 (50.4%) | 2989 (27.4%) | 3955 (30.9%) | |
| Minority-Serving Status | ||||
| Top quintile | 432 (22.5%) | 2421 (22.2%) | 2853 (22.3%) | 0.954 |
| Non top quintile | 1486 (77.5%) | 8481 (77.8%) | 9967 (77.7%) | |
| Medicare/Self Pay Serving Status | ||||
| Top quintile | 317 (16.5%) | 2395 (22.0%) | 2712 (21.2%) | <0.001 |
| Non top quintile | 1601 (83.5%) | 2395 (78.0%) | 10108 (78.8% | |
| Region | ||||
| New England | 96 (5.0%) | 469 (4.3%) | 565 (4.4%) | <0.001 |
| Middle Atlantic | 314 (16.4%) | 1167 (10.7%) | 1481 (11.6%) | |
| East North Central | 351 (18.3%) | 1897 (17.4%) | 2248 (17.5%) | |
| West North Central | 120 (6.3%) | 912 (8.4%) | 1032 (8.1%) | |
| South Atlantic | 378 (19.7%) | 2009 (18.4%) | 2387 (18.6%) | |
| East South Central | 85 (4.4%) | 744 (6.8%) | 829 (6.5%) | |
| West South Central | 183 (9.5%) | 1566 (14.4%) | 1749 (13.6%) | |
| Mountain | 88 (4.6%) | 718 (6.6%) | 806 (6.3%) | |
| Pacific | 303 (15.8%) | 1420 (13.0%) | 1723 (13.4%) | |
Hospitals cannot be linked from year to year in the NIS, so these analyses are performed at the hospital-year level.
TTM = targeted temperature managment
There was significant regional variation in the use of TTM. The proportion of hospitals providing TTM ranged from over 21% in the Mid-Atlantic region to less than 11% in the East South Central (10.3%), West South Central (10.5%), and Mountain regions (10.9%). (Figure 2)
Figure 2:
Percentage of Hospitals Performing at Least 5 Targeted Temperature Management Procedures by Census Division
Patient Predictors of Receiving Targeted Temperature Management
When evaluating patient demographic factors associated with the use of TTM and controlling for the influence of comorbidities, there were several noteworthy findings. When compared to the youngest age cohort, patients age > 74 were less likely to receive TTM (OR: 0.54 (0.46–0.64), p<0.001). Compared to White race, Asian (OR: 1.25 (1.04–1.49) p=0.015) and Native American (OR: 1.45 (1.10–2.08), p=0.043) patients were more likely to receive TTM, and Hispanic patients were less likely to receive TTM (OR: 0.74 (0.64–0.85), p<0.001). Compared to private insurance, patients with Medicaid (OR: 0.89 (0.80–0.99), p=0.027) and Medicare (OR: 0.75 (0.68–0.83), p<0.001) were less likely to receive TTM. Finally, females were less likely to receive TTM compared to males (OR: 0.89 (0.83–0.95), p<0.001).
When evaluating the effect of hospital region and comorbidities in the model, our findings were similar to those from the univariate analysis. There was significant geographic variation in the likelihood of receiving TTM. Patients presenting in the East South (OR: 0.37 (0.27–0.52), p<0.001), West South (OR: 0.49 (0.37–0.65), p<0.001), South Atlantic (OR: 0.64 (0.49–0.83), p<0.001), and Mountain (OR: 0.41 (0.30–0.56), p<0.001) regions had the lowest odds of receiving TTM when compared to those in New England. Finally, patients presenting with shockable rhythms were less likely to receiving TTM than those presenting with nonshockable rhythms (OR: 0.85 (0.77–0.94), P<0.001). (Table 3, Appendix Table 3)
Table 3:
Odds of Receiving Targeted Temperature Management After Cardiac Arrest
| Variable | Adjusted Odds Ratio (CI) | P-value |
|---|---|---|
|
| ||
| Age | ||
| 18–34 | Ref | Ref |
| 35–54 | 1.061 (0.921–1.223) | 0.412 |
| 55–64 | 1.009 (0.874–1.164) | 0.902 |
| 64–74 | 0.935 (0.801–1.01) | 0.396 |
| >74 | 0.539 (0.455–0.639) | <0.001 |
| Sex | ||
| Male | Ref | Ref |
| Female | 0.891 (0.832–0.954) | <0.001 |
| Race/Ethnicity | ||
| White | Ref | Ref |
| Asian | 1.245 (1.044–1.487) | 0.015 |
| Black | 0.955 (0.867–1.053) | 0.356 |
| Hispanic | 0.740 (0.641–0.854) | <0.001 |
| Native American | 1.450 (1.101–2.079) | 0.043 |
| Other | 1.047 (0.873–1.256) | 0.617 |
| Insurance | ||
| Private | Ref | Ref |
| Medicaid | 0.888 (0.799–0.987) | 0.027 |
| Medicare | 0.752 (0.682–0.829) | <0.001 |
| No Charge | 1.187 (0.685–2.055) | 0.541 |
| Other | 0.818 (0.666–1.004) | 0.055 |
| Self-Pay | 0.847 (0.717–1.000) | 0.051 |
| Rurality Category | ||
| Urban | Ref | Ref |
| Rural | 1.013 (0.888–1.155) | 0.852 |
| Town | 1.260 (1.134–1.399) | <0.001 |
| Hospital Region | ||
| New England | Ref | Ref |
| East North | 0.885 (0.691–1.134) | 0.334 |
| East South | 0.373 (0.267–0.520) | <0.001 |
| Middle Atlantic | 1.174 (0.911–1.513) | 0.215 |
| Mountain | 0.407 (0.295–0.562) | <0.001 |
| Pacific | 1.027 (0.797–1.323) | 0.837 |
| South Atlantic | 0.639 0.494–0.825) | <0.001 |
| West North | 0.955 (0.699–1.305) | 0.772 |
| West South | 0.489 (0.369–0.647) | <0.001 |
| Presenting Rhythm | ||
| Nonshockable | Ref | Ref |
| Shockable | 0.851 (0.774–0.935) | <0.001 |
Model adjusts for Elixhauser comorbidities and all listed covariates.
When limiting the analysis to only TTM-capable hospitals, patient receipt of TTM continued to vary based on age, sex, race/ethnicity, and insurance status, although the differences were attenuated in most cases. Rurality was an exception, and rural patients were less likely than their urban counterparts to receive TTM (OR: 0.75 (0.74–0.77), p<0.001). Presenting rhythm type was also an exception: among TTM-capable hospitals, patients presenting with a shockable rhythm (OR: 1.12 (1.09–1.14), p<0.001) were more likely to receive TTM when compared with patients with a nonshockable rhythm. (Table 4, Appendix Table 4)
Table 4:
Odds of Receiving Targeted Temperature Management (TTM) After Cardiac Arrest Among TTM-Capable Hospitals
| Variable | Adjusted Odds Ratio (CI) | P-value |
|---|---|---|
|
| ||
| Age | ||
| 18–34 | Ref | Ref |
| 35–54 | 0.958 (0.923–0.994) | 0.022 |
| 55–64 | 0.934 (0.900–0.969) | <0.001 |
| 64–74 | 0.907 (0.872–0.942) | <0.001 |
| >74 | 0.820 (0.789–0.853) | <0.001 |
| Sex | ||
| Male | Ref | Ref |
| Female | 0.950 (0.938–0.962) | <0.001 |
| Race/Ethnicity | ||
| White | Ref | Ref |
| Asian | 0.862 (0.827–0.898) | <0.001 |
| Black | 0.973 (0.957–989) | <0.001 |
| Hispanic | 0.661 (0.645–678) | <0.001 |
| Native American | 1.198 (1.104–1.299) | <0.001 |
| Other | 0.976 (0.940–1.01) | 0.207 |
| Insurance | ||
| Private | Ref | Ref |
| Medicaid | 0.929 (0.908–0.951) | <0.001 |
| Medicare | 0.933 (0.916–0.951) | <0.001 |
| No Charge | 0.983 (0.869–1.11) | 0.782 |
| Other | 0.850 (0.816–0.886) | <0.001 |
| Self-Pay | 0.851 (0.820–0.883) | <0.001 |
| Rurality Category | ||
| Urban | Ref | Ref |
| Rural | 0.752 (0.738–0.766) | <0.001 |
| Town | 1.026 (1.012–1.040) | <0.001 |
| Presenting Rhythm | ||
| Nonshockable | Ref | Ref |
| Shockable | 1.115 (1.094–1.137) | <0.001 |
Model adjusts for Elixhauser comorbidities and all listed covariates.
TTM = targeted temperature management
Discussion
Our study had several significant findings. First, TTM is rarely utilized as a treatment after cardiac arrest. Second, there was significant heterogeneity in hospital use of TTM that varied with both geography and hospital characteristics. Finally, patients with advanced age, female gender, Hispanic ethnicity, and Medicare and Medicaid insurance were less likely to receive TTM. Part of this variation was related to differences in access to TTM-capable hospitals.
We noted that TTM was used as a treatment after cardiac arrest in less than 1% of patients overall, and in 3.7% of patients at TTM-capable hospitals. During the years represented in this study, TTM was clearly recommended in guidelines for patients who were unresponsive after cardiac arrest. Our data was unable to discriminate responsive vs. unresponsive patients after cardiac arrest as well as the number of patients that were able to achieve ROSC, so the number of potential candidates for appropriate use of TTM is unknown. However, the very low percentage of patients that receive TTM suggests that it is likely underutilized, which is consistent with prior literature. (25, 26) Our results are similar to a prior study conducted using the NIS, which showed 1.35% use of TTM among cardiac arrest patients between 2007 and 2010. Our reported rate of TTM use among TTM-capable hospitals is slightly lower than rates reported in the Get With the Guidelines Registry, at 6% use of TTM, and the Resuscitation Outcomes Consortium registry, at 7.6% use of TTM among all patients. (27, 28, 29) Our results differ more strikingly from the CARES registry data that showed a higher proportion of TTM use for post-cardiac arrest patients. (26) However, many registries are conducted primarily at large, non-profit, urban teaching hospitals, which we found to have a disproportionately high use of TTM; our data add a contemporary, national, real-world estimate to the literature.
There was significant variation in hospital use of TTM. TTM use was predominately relegated to large, non-profit, urban teaching hospitals that were less likely to be in the top quintile for Medicaid/self pay hospitalizations. Other hospital types were significantly less likely to use TTM. When analyzing hospitals that were high utilizers of TTM (≥25 TTM procedures/year), the discrepancy was between large, non-profit, urban teaching hospitals and other hospital types was further magnified. These results are consistent with a smaller study using the NIS that showed increased TTM use for teaching hospitals. (21) There was also notable regional variation in the use of TTM, a phenomenon that has not been previously described in a national dataset. TTM was predominately used on the east and west coast, with relatively sparse use among hospitals in the south, mid-west and mountain regions. The reasons for the regional variation are not entirely clear, but higher utilizing regions likely contain a higher proportion of large, non-profit, urban teaching hospitals.
Rates of TTM use varied with age, gender, race/ethnicity, insurance type, and comorbidities, differences that were only partially attenuated when we limited our sample to hospitals that could theoretically provide this procedure. Older patients were less likely to receive TTM than their younger counterparts. Older patients have a higher prevalence of comorbidities and frailty and may be perceived to derive less benefit from TTM, though there is no evidence to support this notion. As Medicare insurance is predominately used among older adults, the decreased likelihood of Medicare recipients to receive TTM is likely due to the same reason. Female patients were less likely to receive TTM, despite adjustment for the effect of comorbidities. Several prior studies have suggested women experience worse outcomes after cardiac arrest compared to men. (30, 31) Women presenting with cardiac arrest tend to be older and less likely to have a witnessed arrest, shockable rhythm, or to receive bystander CPR. (32, 33, 34) The fact that women are less likely to receive TTM despite their demonstrated unfavorable risk profile and outcomes raises concern for a treatment disparity. Next, there was variation in the odds of receiving TTM by race/ethnicity. The most notable difference was among Hispanic patients, who were less likely to receive TTM when compared to their white counterparts. Racial and ethnic disparities in treatment and outcomes after cardiac arrest are widely reported, and our data adds to the existing literature on the subject by identifying one potential mechanism. (14, 16, 35, 36) Compared with patients with private insurance, patients with Medicaid were less likely to receive TTM. Prior data suggests patients with a high burden of social risk have lower rates of bystander CPR and higher mortality than those in other socioeconomic groups. (15, 37, 38, 39, 40, 41) The decreased rates of TTM in the cohort with Medicaid may be due to higher illness severity on presentation, but a treatment disparity is likely.
There is uncertainty regarding how TTM will be applied in the future. American Heart Association Guidelines have strongly advocated for the use of TTM after cardiac arrest for more than a decade.(3, 42, 43, 44) A recent large randomized trial comparing therapeutic hypothermia to normothermia showed similar outcomes.(6, 10) Irrespective of how the pendulum swings regarding which temperature is most appropriate for patients requiring TTM, our data suggests that despite guideline support, TTM has been rarely and likely inequitably used. These findings have implications for both targeted hypothermia and targeted normothermia as well as other emerging treatments for cardiac arrest, because TTM requires deliberate protocols, additional nursing and clinician expertise, and resource investment in temperature management devices. The significant heterogeneity seen in our study suggests clinical interventions are needed to expand access to TTM among appropriate post-cardiac arrest patients.
Our study has limitations. Administrative data lacks the granularity to distinguish responsive vs unresponsive patients after cardiac arrest as well as which patients were able to achieve ROSC after cardiac arrest, so we were unable to determine which subset of patients had a strict indication for TTM among those that suffered cardiac arrest. Additionally, data regarding goals of care are not available in the NIS, which may influence the decision to pursue TTM. Patients that underwent targeted normothermia may not have been coded with the procedure code for hypothermia, and thus we may have missed patients that received potentially appropriate TTM. Because our data was derived from the NIS, the applicability of our data to populations outside the United States is unknown.
Conclusions:
In this study, we found that TTM was rarely utilized as a therapy after cardiac arrest. There was significant variation in how TTM was used at both the hospital level and patient level. At the hospital level, TTM was predominately used by a small subset of academic hospitals, and there was substantial regional variation. At the patient level, advanced age, female gender, Hispanic ethnicity, and Medicare or Medicaid insurance were associated with a decreased likelihood of receiving TTM, and at least some of these differences were driven by inequitable access to TTM-capable hospitals. Clinical interventions are needed to expand access to this therapy among appropriate patients.
Clinical Perspective.
What is new?
Targeted temperature management (TTM) is rarely utilized as a therapy after cardiac arrest
TTM is predominately used by a small subset of academic hospitals with less use at other hospital types
There is significant regional variation in how TTM is used
Patients with advanced age, female gender, Hispanic ethnicity, and Medicare or Medicaid insurance are less likely to receive TTM
What are the Clinical Implications?
Clinical interventions are needed to expand access to TTM among appropriate patients.
Acknowledgements:
Tierney Lanter for her help with data organization.
Disclosures:
Dr. Joynt Maddox receives research support from the National Heart, Lung, and Blood Institute (R01HL143421), National Institute of Nursing Research (U01NR020555-01) and National Institute on Aging (R01AG060935, R01AG063759, and R21AG065526), and from Humana. She also serves on the Health Policy Advisory Council for the Centene Corporation (St. Louis, MO). The other authors report no conflicts.
Abbreviations:
- TTM
Targeted temperature management
- ROSC
Return of spontaneous circulation
- NIS
National Inpatient Sample
- HCUP
Healthcare Cost and Utilization Project
- ICD-10
International Classification of Diseases, Tenth Edition
- CPR
cardiopulmonary resuscitation
Appendix
Appendix Table 1:
Elixhauser Comorbidities
| Congestive Heart Failure |
| Cardiac Arrhythmias |
| Valvular Disease |
| Pulmonary Circulation Disorders |
| Peripheral Vascular Disease |
| Hypertension, Uncomplicated |
| Hypertension, Complicated |
| Paralysis |
| Other Neurologic Disorders |
| Chronic Pulmonary Disease |
| Diabetes, Uncomplicated |
| Diabetes, Complicated |
| Hypothyroidism |
| Renal Failure |
| Liver Disease |
| Peptic Ulcer Disease, Excluding Bleeding |
| AIDS/HIV |
| Lymphoma |
| Metastatic Cancer |
| Solid Tumor without Metastasis |
| Rheumatologic Arthritis/Collagen Vascular Disorders |
| Coagulation |
| Obesity |
| Weight Loss |
| Fluid and Electrolyte Disorders |
| Blood Loss Anemia |
| Deficiency Anemia |
| Alcohol Abuse |
| Drug Abuse |
| Psychoses |
| Depression |
Appendix Table 2:
Hospital Characteristics of High Targeted Temperature Management Utilizers
| Variable | Performs ≥25 TTM procedures/yr (N=108) | Performs <25 TTM procedures/yr (N=12712) | Total (N=12820) | P-value |
|---|---|---|---|---|
|
| ||||
| Ownership | ||||
| Governmental, nonfederal | 11 (10.2%) | 1801 (14.2%) | 1812 (14.1%) | 0.174 |
| Private, not-profit | 86 (79.6%) | 8710 (68.5%) | 8796 (68.6%) | |
| Private, invest-own | 11 (10.2%) | 2201 (17.3%) | 2212 (17.3%) | |
| Teaching Status | ||||
| Rural | 0 (0%) | 3384 (26.6%) | 3384 (26.4%) | <0.001 |
| Urban nonteaching | 9 (8.3%) | 3768 (29.6%) | 3777 (29.5%) | |
| Urban teaching | 99 (91.7%) | 5560 (43.7%) | 5659 (44.1%) | |
| Bedsize Status | ||||
| Small | 12 (11.1%) | 5023 (39.5%) | 5035 (39.3%) | <0.001 |
| Medium | 21 (19.4%) | 3809 (30.0%) | 3830 (29.9%) | |
| Large | 75 (69.4%) | 3880 (30.5%) | 3955 (30.9%) | |
| Minority-Serving Status | ||||
| Top quintile | 24 (22.2%) | 2829 (22.3%) | 2853 (22.3%) | 1 |
| Non top quintile | 84 (77.8%) | 9883 (77.7%) | 9967 (77.7%) | |
| Medicare/Self Pay Serving Status | ||||
| Top quintile | 18 (16.7%) | 2694 (21.2%) | 2712 (21.2%) | 0.527 |
| Non top quintile | 90 (83.3%) | 10018 (78.8%) | 10108 (78.8%) | |
| Region | ||||
| New England | 7 (6.5%) | 558 (4.4%) | 565 (4.4%) | 0.018 |
| Middle Atlantic | 25 (23.1%) | 1456 (11.5%) | 1481 (11.6%) | |
| East North Central | 19 (17.6%) | 2229 (17.5%) | 2248 (17.5%) | |
| West North Central | 11 (10.2%) | 1021 (8.0%) | 1032 (8.1%) | |
| South Atlantic | 20 (18.5%) | 2367 (18.6%) | 2387 (18.6%) | |
| East South Central | 2 (1.9%) | 827 (6.5%) | 829 (6.5%) | |
| West South Central | 5 (4.6%) | 1744 (13.7%) | 1749 (13.6%) | |
| Mountain | 1 (0.9%) | 805 (6.3%) | 806 (6.3%) | |
| Pacific | 18 (16.7%) | 1705 (13.4%) | 1723 (13.4%) | |
Appendix Table 3:
Odds of Receiving Targeted Temperature Management after Cardiac Arrest by Comorbidity
| Diagnosis | Adjusted Odds Ratio (CI) | P-value |
|---|---|---|
|
| ||
| No diagnosis | Ref | Ref |
| Congestive Heart Failure | 1.092 (1.005–1.185) | 0.037 |
| Valvular Disease | 0.737 (0.629 – 0.863) | <0.001 |
| Pulmonary Circulation Disorders | 0.894 (0.787 – 1.017) | 0.089 |
| Peripheral Vascular Disease | 0.620 (0.536 – 0.718) | <0.001 |
| Hypertension, Uncomplicated | 0.878 (0.799 – 0.966) | 0.008 |
| Hypertension, Complicated | 0.726 (0.658 – 0.801) | <0.001 |
| Paralysis | 0.445 (0.352– 0.562) | <0.001 |
| Other Neurologic Disorders | 9.214 (8.356 – 10.159) | <0.001 |
| Chronic Pulmonary Disease | 0.918 (0.844 – 0.998) | 0.045 |
| Diabetes, Uncomplicated | 0.778 (0.690 – 0.879) | <0.001 |
| Diabetes, Complicated | 0.986 (0.899 – 1.081) | 0.757 |
| Hypothyroidism | 0.702 (0.600 – 0.820) | <0.001 |
| Renal Failure | 0.774 (0.702 – 0.854) | <0.001 |
| Liver Disease | 1.510 (1.385 – 1.645) | <0.001 |
| Metastatic Cancer | 0.653 (0.508 – 0.840) | <0.001 |
| Solid Tumor without Metastasis | 0.604 (0.493 – 0.740) | <0.001 |
| Rheumatologic Arthritis/Collagen Vascular Disorders | 0.768 (0.574 – 1.026) | 0.074 |
| Coagulation | 0.785 (0.703 – 0.876) | <0.001 |
| Obesity | 0.932 (0.827 – 1.050) | 0.248 |
| Weight Loss | 0.554 (0.490 – 0.627) | <0.001 |
| Fluid and Electrolyte Disorders | 1.621 (1.486 – 1.768) | <0.001 |
| Deficiency Anemia | 0.634 (0.483 – 0.833) | 0.001 |
| Alcohol Abuse | 0.709 (0.617 – 0.814) | <0.001 |
| Drug Abuse | 1.083 (0.954 – 1.231) | 0.219 |
| Depression | 0.680 (0.582– 0.796) | <0.001 |
Appendix Table 4:
Odds of Receiving Targeted Temperature Management (TTM) After Cardiac Arrest by Comorbidity Among TTM-Capable Hospitals
| Diagnosis | Adjusted Odds Ratio (CI) | P-value |
|---|---|---|
|
| ||
| No diagnosis | Ref | Ref |
| Congestive Heart Failure | 1.07 (1.057 – 1.089) | <0.001 |
| Valvular Disease | 1.118 (1.097 – 1.138) | <0.001 |
| Pulmonary Circulation Disorders | 1.068 (1.047 – 1.089) | <0.001 |
| Peripheral Vascular Disease | 1.049 (1.031 – 1.068) | <0.001 |
| Hypertension, Uncomplicated | 0.981 (0.964 – 0.998) | 0.027 |
| Hypertension, Complicated | 0.952 (0.936 – 0.968) | <0.001 |
| Paralysis | 1.163 (1.119 – 1.207) | <0.001 |
| Other Neurologic Disorders | 1.055 (1.040 – 1.071) | <0.001 |
| Chronic Pulmonary Disease | 0.885 (0.872 – 0.898) | <0.001 |
| Diabetes, Uncomplicated | 0.910 (0.892 – 0.928) | <0.001 |
| Diabetes, Complicated | 0.952 (0.937 – 0.968) | <0.001 |
| Hypothyroidism | 0.928 (0.908 – 0.949) | <0.001 |
| Renal Failure | 0.992 (0.976 – 1.009) | 0.359 |
| Liver Disease | 1.041 (1.019 – 1.064) | <0.001 |
| Metastatic Cancer | 0.968 (0.931 – 1.006) | 0.100 |
| Solid Tumor without Metastasis | 0.960 (0.932 – 0.990) | 0.009 |
| Rheumatologic Arthritis/Collagen Vascular Disorders | 1.029 (0.987 – 1.073) | 0.176 |
| Coagulation | 1.096 (1.076 – 1.117) | <0.001 |
| Obesity | 0.969 (0.951 – 0.989) | 0.002 |
| Weight Loss | 0.929 (0.911 – 0.948) | <0.001 |
| Fluid and Electrolyte Disorders | 0.995 (0.983 – 1.008) | 0.449 |
| Deficiency Anemia | 0.946 (0.912 – 0.981) | 0.003 |
| Alcohol Abuse | 0.932 (0.905 – 0.960) | <0.001 |
| Drug Abuse | 0.996 (0.965 – 1.028) | 0.815 |
| Depression | 0.972 (0.949 – 0.996) | 0.022 |
TTM = targeted temperature management
Footnotes
Prior presentations: Presented as a poster at the American Heart Association Scientific Sessions 2022.
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