Outcomes for Hemodialysis Patients Given Cardiopulmonary Resuscitation for Cardiac Arrest at Outpatient Dialysis Clinics : Journal of the American Society of Nephrology

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Clinical Epidemiology

Outcomes for Hemodialysis Patients Given Cardiopulmonary Resuscitation for Cardiac Arrest at Outpatient Dialysis Clinics

Pun, Patrick H.1,2; Dupre, Matthew E.1,3; Starks, Monique A.1; Tyson, Clark1; Vellano, Kimberly4; Svetkey, Laura P.2; Hansen, Steen5; Frizzelle, Brian G.6; McNally, Bryan4; Jollis, James G.1; Al-Khatib, Sana M.1; Granger, Christopher B.1;  the CARES Surveillance Group

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Journal of the American Society of Nephrology 30(3):p 461-470, March 2019. | DOI: 10.1681/ASN.2018090911
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Patients on hemodialysis are one of the highest risk groups for out-of-hospital cardiac arrest (OHCA). Among patients on hemodialysis, OHCA occurs 20 times more frequently compared with the general population and accounts for >25% of all deaths.1,2 OHCA occurs frequently within outpatient dialysis facilities (up to 23% of all hemodialysis patient OHCA),3–5 and previous reports suggest that only 56% of patients with dialysis clinic OHCA survive to hospital admission, 24% survive to hospital discharge, and only 8% are alive after 1 year.6,7

OHCA survival in the general population is dramatically improved by early cardiopulmonary resuscitation (CPR) and defibrillation by bystanders before the arrival of emergency medical services (EMS).8,9 Given the high risk of cardiac arrest in patients on dialysis, basic life support training for outpatient dialysis staff and availability of automated external defibrillators (AEDs) in dialysis clinics have been recommended by dialysis practice guidelines10; however, the frequencies of dialysis staff–initiated CPR and AED utilization during in-center cardiac arrest and the association with survival have not been examined.

The objective of this study was to describe the characteristics of OHCA and resuscitation efforts in a representative and diverse sample of hemodialysis clinics. Because the southeastern United States has among the highest density of patients with ESRD and outpatient hemodialysis clinics in the country,2 we examined resuscitation efforts of dialysis staff and cardiac arrest outcomes in this region in order to examine predictors of staff CPR and AED utilization, and further examine whether staff CPR and AED utilization are associated with improved outcomes.


Data Source

The Cardiac Arrest Registry to Enhance Survival (CARES) is a voluntary, prospective clinical registry of patients with OHCA in the United States coordinated by Emory University. The registry has previously been described in detail.11,12 Patients with a confirmed OHCA (defined as apneic and unresponsive) for whom resuscitation is attempted, even those with termination of resuscitation before hospital arrival, are included in the registry. Patients with a do-not-resuscitate order who either do not receive resuscitation or whose resuscitation efforts are terminated are excluded from the CARES registry by protocol. When a cardiac arrest occurs, data are collected from 911 dispatch centers, EMS agencies, and receiving hospitals and are reviewed and confirmed for accuracy and completeness by a dedicated analyst during the data-review process. For this study, we examined data collected from participating sites within 71 participating counties in North Carolina (catchment area population approximately 8 million of approximately 10 million total state population) and nine metropolitan counties surrounding Atlanta, Georgia (catchment area population approximately 5.2 million of approximately 10 million total state population) between January 1, 2010 and December 31, 2016 (see Supplemental Table 1 for complete listing of participating counties). Standardized international Utstein definitions for defining clinical variables and outcomes from cardiac arrest were used to ensure uniformity.13 The location of each cardiac arrest was cataloged in the database with the exact street address, as well as a categorization of the location type (i.e., private residence, business, health care facility, nursing home). ArcGIS 10.5 software (ESRI, Redlands, CA) was used to geocode each OHCA location to specific latitude and longitude coordinates. Nonphysical locations such as post-office boxes and locations without street addresses were not geocoded and were excluded from the analysis. Overall, a 95% address-level geocoding rate was achieved.

To identify locations of outpatient dialysis clinics, we used the Centers for Medicare and Medicaid Services–maintained Dialysis Facility Compare master data file for complete data on dialysis facilities.14 The Dialysis Facility Compare tool is a web-based publicly available dataset containing detailed information on dialysis facilities including exact street address, dates each facility began operations, and, for those facilities no longer providing services, the date of facility closing. Additional information on clinic ownership (profit or nonprofit status, chain organization), total number of dialysis stations, and a quality of patient care “star” rating is provided for each dialysis clinic. The star rating is between one and five stars (with three stars representing the national average and five stars representing a much higher than average quality score compared with other facilities), and star ratings are on the basis of a composite score incorporating a variety of quality measures including standardized mortality ratios; standardized hospitalization ratios; use of dialysis catheters; and laboratory data on dialysis adequacy, anemia management, and bone and mineral disease. Dialysis facilities that were hospital based, did not provide in-center hemodialysis services, did not operate within the study period, or did not have addresses that indicated a freestanding location (i.e., addresses with suite numbers) were excluded. We crossreferenced the dialysis facility location list with comprehensive data on hemodialysis facilities maintained by ESRD Network 6, which oversees all dialysis facilities in the southeastern United States, to confirm accuracy of address information and to ensure that no facilities were missed. There was 100% concordance between the two lists. We used ArcGIS software to similarly geocode the location of street address of each facility. The Duke University Medical Center Institutional Review Board approved the study and a waiver for the requirement of informed consent was granted because the analysis included only deidentified patient data.

Selection of Study Cohort

Our primary objective was to examine the resuscitation efforts of dialysis staff before the arrival of EMS; therefore, we excluded patients who had cardiac arrest after arrival of EMS services on the scene. Patients <18 years old were also excluded. To identify dialysis clinic events, we used geocoded location information and proximity-matching software (ArcGIS) to identify all events occurring within 200 m of dialysis facility locations. Matching events were then reviewed by hand to exclude nonmatching street addresses, events classified as occurring at private residences, and nursing homes. We validated a sample of 25 identified events (6% of total) by contacting individual dialysis clinics to verify the occurrence of an in-center cardiac arrest event on the identified date; all but one event was successfully verified by available dialysis clinic records.

Predictors and Outcome Measures

The main predictors examined for this study were resuscitative efforts through CPR, and application of an AED either by medical professional bystanders within dialysis clinic locations (dialysis staff) or by 911-activated EMS paramedics and first responders. The main outcome measures were patient survival to hospital discharge and survival with favorable neurologic outcome, which was defined as cerebral performance category 1 or 2, with 1 representing full recovery or mild disability, and 2, moderate disability but independent in activities of daily living.15

Statistical Analyses

Categoric data were examined using simple proportions, and means (SD) were calculated for continuous data. Statistical significance was assessed using the Fisher exact test or chi-squared test for categoric data and the Wilcoxon–Mann–Whitney test for continuous data. Multiple logistic regression models were used to examine the association between baseline covariates and dialysis staff intervention, as well as the association between dialysis staff intervention and patient outcomes. Models were adjusted for demographic variables (age, sex, and race), cardiac arrest characteristics (witnessed versus unwitnessed arrest, presenting cardiac arrest rhythm), dialysis clinic characteristics (profit versus nonprofit, number of dialysis stations, Medicare quality star indicator), and geographic region (North Carolina counties versus metropolitan Atlanta counties). Models also accounted for clustering at the dialysis clinic level to obtain adjusted odds ratios (aORs) with robust 95% confidence intervals (95% CIs). Additional sensitivity analyses accounted for county-level clustering to obtain robust SEMs and minimize possible confounding related to differences in patient characteristics, EMS structure, and overall care related to geographic factors. A P value of <0.05 was considered statistically significant; all statistical tests were two-sided. All analyses were performed using Stata version 15.0 (StataCorp).


A total of 31,869 OHCA events were identified within the North Carolina CARES study counties, and 15,539 OHCA events within the metropolitan Atlanta counties. Figure 1 illustrates the study flow chart with exclusions. The final cohort included 398 OHCA dialysis clinic events occurring within 158 unique dialysis clinics; in 324 of the events (81%), dialysis staff initiated CPR before the arrival of 911 responders. A heat map depicting the geographic location and density of dialysis clinic events included in this study is shown in Figure 2.

Figure 1.:
Study flowsheet. ATL, Atlanta; NC, North Carolina.
Figure 2.:
Location and density of out-of-hospital dialysis clinic cardiac arrests, CARES registry 2010–2016. North Carolina (left hand panel); Metropolitan Atlanta area (right hand panel).

Table 1 compares the characteristics of cardiac arrests according to who initiated CPR. There were no differences in age, sex, or race between the two groups. A greater proportion of cardiac arrests were witnessed (88% versus 73%) among dialysis staff–initiated CPR cardiac arrests. Sixty-six percent of all dialysis clinic arrests presented with a nonshockable first monitored rhythm. Less than half (48%) survived to hospital admission; of these, 54% survived to hospital discharge (26% overall), and, among discharged patients, 82% had favorable neurologic status on discharge (22% overall).

Table 1. - Patient characteristics of dialysis unit cardiac arrests according to bystander dialysis staff–initiated CPR
Characteristic EMS/First Responder–Initiated CPR, n=74 Bystander Dialysis Staff–Initiated CPR, n=324 P Value
 Mean age (SD) 62.0 (13.7) 64.6 (12.0) 0.07
 Women 42 of 74 (57) 151 of 324 (46) 0.12
 Race 0.17
  White 16 of 74 (22) 103 of 324 (32)
  Black 48 of 74 (65) 184 of 324 (57)
  Other 5 of 74 (7) 10 of 324 (3)
  Missing data 5 of 74 (7) 27 of 324 (8)
Cardiac arrest characteristics
 Witnessed arrest 54 of 74 (73) 286 of 324 (88) 0.00
 First monitored rhythm 0.06 a
  Nonshockable rhythm 56 of 74 (76) 208 of 324 (64)
   Asystole 17 of 74 (23) 50 of 324 (15)
   Idioventricular/PEA 23 of 74 (31) 82 of 324 (25)
   Unknown nonshockable rhythm 16 of 74 (22) 76 of 324 (23)
   Missing data 0 of 74 (0) 1 of 324 (0)
  Shockable rhythm total, % 18 of 74 (24) 116 of 324 (36)
   Ventricular fibrillation 10 of 74 (14) 43 of 324 (13)
   Ventricular tachycardia 1 of 74 (1) 3 of 324 (1)
   Unknown shockable rhythm 7 of 74 (9) 69 of 324 (22)
CPR characteristics
 AED first applied by <0.001
  EMS/first responder 61 of 74 (82) 127 of 324 (39)
  Dialysis staff bystander 13 of 74 (18) 195 of 324 (60)
  Missing data 0 of 74 (0) 2 of 324 (1)
 AED shock delivered 34 of 74 (46) 151 of 324 (47) 0.45
  Missing data 0 of 74 (0) 1 of 324 (0)
 First AED shock delivered by <0.001
  EMS/first responder 27 of 74 (36) 58 of 324 (18)
  HD staff 5 of 74 (7) 83 of 324 (26)
  Missing data 2 of 74 (3) 10 of 324 (3)
Dialysis clinic characteristics
 Dialysis ownership 0.30
  Nonprofit nonchain 2 of 74 (3) 7 of 324 (2)
  Nonprofit chain 2 of 74 (3) 28 of 324 (9)
  Profit nonchain 3 of 74 (4) 18 of 324 (6)
  Profit chain 67 of 74 (91) 271 of 324 (83)
 Clinic Medicare star rating 0.29
  1–2 8 of 74 (11) 55 of 324 (17)
  3 35 of 74 (47) 164 of 324 (51)
  4–5 28 of 74 (38) 100 of 324 (31)
  Missing data 3 of 74 (4) 5 of 324 (2)
 Clinic location
  Atlanta metropolitan area 42 of 74 (57) 106 of 324 (33) <0.001
  NC 32 of 74 (43) 218 of 324 (67)
  Mean number of dialysis chairs (SD) 22.0 (7.0) 27.3 (10.9) <0.001
Cardiac arrest outcomes
 Return of spontaneous circulation 28 of 74 (38) 166 of 324 (51) 0.04
  Missing data 0 of 74 (0) 0 of 324 (0)
 Survival to hospital admission 29 of 74 (39) 164 of 324 (51) 0.06
  Missing data 0 of 74 (0) 5 of 325 (2)
 Overall survival to hospital discharge 10 of 74 (14) 94 of 324 (29) <0.01
  Missing data 0 of 74 (0) 5 of 324 (2)
 Cerebral performance category score >2 at discharge 8 of 74 (11) 78 of 324 (24) 0.01
  Missing data 0 of 74 (0) 3 of 324 (1)
Data are provided as n (%), except where indicated otherwise. PEA, pulseless electrical activity; HD, hemodialysis; NC, North Carolina.
aP value is for comparison of shockable versus nonshockable rhythms.

Dialysis staff initiated application of an AED before the arrival of 911 responders in 52% of events (n=208). Table 2 compares the patient characteristics between OHCAs with first AED application by dialysis staff versus 911 responder. There was a greater proportion of shockable first monitored rhythms among patients for whom dialysis staff were the first to apply the AED (41% versus 25%), and, accordingly, a greater proportion of OHCAs with AED shocks delivered (54% versus 38%) among the dialysis staff–initiated AED application group compared with 911 responder initial AED application.

Table 2. - Patient characteristics of dialysis unit cardiac arrests according to whether AED use was initiated by dialysis staff
Characteristic EMS/First Responder First Application of AED, n=188 Bystander Dialysis Staff–Initiated Application of AED, n=208 P Value
 Mean age (SD) 64.2 (12.8) 64.2 (11.6) 0.86
 Women 100 of 188 (53) 93 of 208 (44) 0.09
 Race 0.25
  White 60 of 188 (32) 59 of 208 (28)
  Black 102 of 188 (54) 128 of 208 (62)
  Other 7 of 188 (4) 8 of 208 (4)
  Missing data 19 of 188 (10) 13 of 208 (6)
Cardiac arrest characteristics
 Witnessed arrest 159 of 188 (85) 181 of 208 (87) 0.58
 First monitored rhythm 0.001 a
  Shockable rhythm 47 of 188 (25) 85 of 208 (41)
   Ventricular fibrillation 31 of 188 (16) 21 of 208 (10)
   Ventricular tachycardia 3 of 188 (2) 1 of 208 (0)
   Unknown shockable rhythm 13 of 188 (7) 63 of 208 (31)
  Nonshockable rhythm 140 of 188 (75) 123 of 208 (69)
   Asystole 40 of 188 (21) 26 of 208 (12)
   Idioventricular/PEA 57 of 188 (30) 48 of 208 (23)
   Unknown nonshockable rhythm 43 of 188 (23) 49 of 208 (23)
   Missing 1 of 188 (1) 0 of 208 (0)
CPR characteristics
 CPR first initiated by <0.001
  EMS/first responder 61 of 188 (32) 13 of 208 (6)
  Dialysis staff bystander 127 of 188 (68) 195 of 208 (94)
 AED shock delivered 72 of 188 (38) 112 of 208 (54) 0.002
  Missing data 1 of 188 (1) 0 of 208 (0)
 First AED shock delivered by <0.001
  EMS/first responder 64 of 188 (34) 20 of 208 (10)
  HD staff 0 of 188 (0) 88 of 208 (43)
  Missing data 8 of 188 (4) 4 of 208 (2)
Dialysis clinic characteristics
 Dialysis ownership 0.43
  Nonprofit nonchain 4 of 188 (2) 5 of 208 (2)
  Nonprofit chain 15 of 188 (8) 15 of 208 (8)
  Profit nonchain 13 of 188 (7) 7 of 208 (3)
  Profit chain 156 of 188 (83) 181 of 208 (87)
 Clinic Medicare star rating <0.01
  1–2 30 of 188 (16) 32 of 208 (15)
  3 79 of 188 (42) 119 of 208 (57)
  4–5 73 of 188 (39) 55 of 208 (26)
  Missing data 6 of 188 (3) 2 of 208 (1)
 Clinic location
  Atlanta metropolitan area 88 of 188 (47) 58 of 208 (28) <0.001
  NC 100 of 188 (53) 150 of 208 (72)
  Mean number of dialysis chairs (SD) 23.4 (8.4) 29.1 (11.5) <0.001
Post–cardiac arrest outcomes
 Return of spontaneous circulation 85 of 188 (45) 108 of 208 (52) 0.18
  Missing data 0 of 188 (0) 0 of 208 (0)
 Survival to hospital admission 89 of 188 (47) 103 of 208 (50) 0.57
  Missing data 1 of 188 (1) 4 of 208 (2)
 Overall Survival to hospital discharge 42 of 188 (22) 62 of 208 (30) 0.08
  Missing data 1 of 188 (1) 4 of 208 (2)
 Cerebral performance category score >2 at discharge 36 of 188 (19) 50 of 208 (24) 0.23
  Missing data 1 of 188 (1) 2 of 208 (1)
Data are provided as n (%), except where indicated otherwise. PEA, pulseless electrical activity; HD, hemodialysis; NC, North Carolina.
aP value is for comparison of shockable versus nonshockable rhythms.

Tables 3 and 4 examine factors associated with dialysis staff–initiated CPR and AED application, respectively. Men were more likely to receive CPR from dialysis staff (aOR, 1.80; 95% CI, 1.00 to 3.23; P=0.05) compared with women. Age and race were not associated with dialysis staff–initiated CPR. Witnessed OHCAs were three times more likely to receive CPR from dialysis staff compared with unwitnessed events (aOR, 3.33; 95% CI, 1.59 to 6.98). Dialysis staff were more likely to provide CPR in larger dialysis clinics with a greater number of dialysis stations (aOR, 1.04; 95% CI, 1.01 to 1.08, per increase in one dialysis station). There was no significant association between dialysis staff CPR, the profit or chain status of dialysis clinics, and the Medicare star quality indicator score. Similar to predictors of dialysis staff–initiated CPR, staff-initiated AED application was also more likely within larger dialysis clinics; otherwise there were no significant patient or cardiac arrest characteristics associated with dialysis staff AED use (Table 4). Further adjusting SEMs for geographic clustering of events at the county level did not significantly modify the 95% CIs for any of the examined predictors in Tables 3 and 4.

Table 3. - Multivariable predictors for provision of dialysis staff–initiated CPR
Variable Odds Ratio (95% CI) P Value
Patient characteristics
 Age (per year increase) 1.01 (0.99 to 1.03) 0.38
  Female 1.00
  Male 1.8 (1.00 to 3.23) 0.05
  White 1.00
  Black 0.64 (0.33 to 1.23) 0.18
  Other 0.30 (0.07 to 1.19) 0.09
  Unknown 0.90 (0.32 to 2.53) 0.85
Cardiac arrest characteristics
 Witnessing of cardiac arrest
  Unwitnessed 1.00
  Witnessed 3.33 (1.59 to 6.98) 0.001
 Presenting cardiac arrest rhythm
  Nonshockable 1.00
  Shockable 1.91 (1.00 to 3.65) 0.05
Dialysis clinic characteristics
 Facility type
  Profit-, chain-based clinic 0.81 (0.32 to 2.07) 0.66
  Other (nonprofit, nonchain) 1.00
  Number of dialysis chairs (per increase in one chair) 1.04 (1.01 to 1.08) 0.01
 Medicare star quality indicator
  4–5 1.00
  3 1.28 (0.67 to 2.44) 0.45
  1–2 2.46 (0.81 to 7.50) 0.11

Table 4. - Multivariable predictors for dialysis staff–initiated AED use
Variable Odds Ratio (95% CI) P Value
Patient characteristics
 Age (per year increase) 1.00 (0.98 to 1.02) 0.87
  Female 1.00
  Male 1.39 (0.89 to 2.16) 0.15
  White 1.00
  Black 1.20 (0.74 to 1.94) 0.46
  Other 1.18 (0.33 to 4.19) 0.79
  Unknown 0.67 (0.25 to 1.79) 0.43
Cardiac arrest characteristics
 Witnessing of cardiac arrest
  Unwitnessed 1.00
  Witnessed 1.24 (0.68 to 2.27) 0.48
 Presenting cardiac arrest rhythm
  Nonshockable 1.00
  Shockable 2.32 (1.47 to 3.65) <0.001
Dialysis clinic characteristics
 Facility type
  Profit-, chain-based clinic 1.78 (0.96 to 3.32) 0.07
  Other (nonprofit, nonchain) 1.00
  Number of dialysis chairs (per increase in one chair) 1.05 (1.02 to 1.08) 0.001
 Medicare star quality indicator
  4–5 1.00
  3 1.74 (1.03 to 2.95) 0.04
  1–2 1.73 (0.83 to 3.60) 0.14

Table 5 summarizes the association between dialysis staff resuscitation efforts and patient outcomes. Compared with 911 responder–initiated CPR, dialysis staff–initiated CPR was associated with a three-fold higher rate of survival to hospital discharge (aOR, 2.87; 95% CI, 1.18 to 6.97) and favorable neurologic status upon discharge (aOR, 3.15; 95% CI, 1.11 to 8.92). Further SEM adjustment for geographic clustering of events at the county level did not significantly modify the 95% CIs for any of the outcomes. There was no significant association between dialysis staff–initiated AED application and patient outcomes.

Table 5. - Effect of dialysis staff bystander-initiated CPR on cardiac arrest outcome
Variable Dialysis Staff–Initiated CPR, OR (95% CI) P Value Dialysis Staff–Initiated AED application, OR (95% CI) P Value Dialysis Staff–Initiated AED Application; Shockable First Rhythm Only (n=132), OR (95% CI) P Value
Return of spontaneous circulation 1.37 (0.71 to 2.64) 0.35 1.24 (0.75 to 2.03) 0.40 1.12 (0.44 to 2.87) 0.80
Survival to admission 1.56 (0.83 to 2.93) 0.17 0.91 (0.59 to 1.41) 0.68 1.73 (0.73 to 4.11) 0.21
Survival to discharge 2.87 (1.18 to 6.97) 0.02 1.32 (0.81 to 2.13) 0.26 2.09 (0.83 to 5.25) 0.12
Favorable neurologic status 3.15 (1.11 to 8.92) 0.03 1.07 (0.60 to 1.90) 0.81 2.08 (0.68 to 6.38) 0.20
OR, odds ratio.

Because rapid AED application by dialysis staff would be expected to have the greatest benefit among patients with an initial shockable rhythm, we performed an analysis among 132 patients within this subgroup. There was no significant association with dialysis staff AED application compared with 911 responder AED application in fully adjusted models, as shown in Table 5. However, because of the sample size and possible overfitting, we performed sensitivity analyses without adjustment for geographic region and clinic-level clustering. In this analysis, there was a significant positive association between dialysis staff–initiated AED application and survival to hospital admission (aOR, 2.30; 95% CI, 1.10 to 4.81) and a nonsignificant trend toward improved survival to hospital discharge (aOR, 2.21; 95% CI, 0.96 to 5.05; P=0.06) and favorable neurologic status on discharge (aOR, 2.39; 95% CI, 0.99 to 5.80; P=0.06).


To our knowledge, our study offers the largest in-depth examination of resuscitation practices by dialysis staff after OHCA within outpatient dialysis clinics. Although the bystander CPR rate (81%) in dialysis clinics was higher than those typically observed in OHCAs occurring in homes (approximately 40%) and public areas (approximately 60%),16 it is surprisingly low given the highly monitored environment of the dialysis clinic with the availability of basic life support-trained staff serving a high-risk population. The low utilization of AEDs by dialysis staff (53%) is also troubling and consistent with a previous study reporting a 53% staff AED application rate (18 of 34 cases) in Seattle-area dialysis clinics.6 The combination of bystander CPR and bystander AED placement has been shown to result in the best outcomes compared with later initiation of these care elements, and there is no reason why such care should not also be consistently applied in dialysis centers.8

Universal barriers to performing bystander CPR include challenges in recognizing a cardiac arrest, uncertainty about how to perform CPR, and fear of performing CPR incorrectly.17 In the dialysis clinic setting, cardiac arrests could potentially be more difficult to recognize because patients often sleep during treatment making a collapse less recognizable. Although continuous cardiac telemetry monitoring is typically not available in outpatient dialysis clinics, a pulseless condition during dialysis treatment should cause an abrupt change in vascular access pressure and result in a machine alarm; however, vascular access machine alarms are relatively frequent during treatment and staff desensitization to alarms could easily occur, causing a delay in cardiac arrest recognition. A delay in cardiac arrest recognition could have also been explained by arrests occurring in less monitored settings within the dialysis clinic (i.e., patient waiting rooms or bathrooms). The exact location and timing of cardiac arrests in relation to the dialysis treatment were not available, but previous studies have indicated that the majority of OHCAs within dialysis facilities occur during treatment (70%); only 20% occur after treatment, and 10% occur before treatment.6 In our study, 85% of OHCAs were categorized as “witnessed” arrest. In the CARES registry, OHCAs were categorized as “witnessed” when EMS reports indicated that cardiac arrests were recognized immediately, and “unwitnessed” when there was a delay of a few minutes or more in recognition. The finding of three-fold increased likelihood of dialysis staff CPR in witnessed cardiac arrests suggests that improved monitoring and recognition of cardiac arrest could improve staff participation in CPR outcomes.

Even if delays in cardiac arrest recognition occur, the lack of staff participation in CPR after recognition and activation of EMS is more difficult to explain. Insufficient frequency of CPR training and experience with cardiac arrests in dialysis clinics could have led to decreased confidence (self-efficacy) in performing CPR and a subsequent unwillingness to attempt CPR. The observed increase in staff-initiated CPR within larger dialysis clinics could reflect a greater familiarity with the management of cardiac arrest due to a higher frequency of events. Other factors which may lead to decreased CPR self-efficacy could have been related to uncertainty about how to adapt traditional CPR procedures and techniques to the unique environment of the dialysis clinic (for example, providing chest compressions for patients in a dialysis chair, how to safely apply an AED to patients who are still connected to dialysis machines) as well as how and when to perform CPR in the context of other hemodialysis procedural concerns (such as managing the significant blood volume remaining in the extracorporeal circuit during a cardiac arrest).

A final consideration is that decreased staff motivation to provide CPR could explain suboptimal participation rates, perhaps due to negative perceptions about the benefit of CPR in chronically ill patients on hemodialysis. The growing number of recent studies highlighting overall poor outcomes of cardiac arrest in patients on dialysis18–20 and the appropriately increased scrutiny within dialysis clinics on confirming the lack of do-not-resuscitate orders before initiating CPR21,22 and uncertainty about patient resuscitation wishes could have led to delays in appropriate CPR due to staff liability concerns. The decreased likelihood of staff CPR among female patients could have been due to a concern for harming patients with CPR (e.g., causing rib fractures with chest compressions) because female patients on dialysis could be perceived to be frailer, or due to concerns about partially undressing female patients for AED application in the dialysis environment. Similar gender disparities in bystander CPR have also been observed in the general population.23 Overall, further study on the challenges and dilemmas that dialysis clinic personnel face in managing cardiac arrest is needed to guide the development of programs and interventions to improve CPR use in dialysis clinics.

Our study found a lower rate of initial shockable ventricular arrhythmias (33%) compared with a previous smaller study of hemodialysis clinic OHCA (67%),6 which is notable because shockable arrhythmias are associated with improved outcomes and can be reversed with defibrillation.24 Our findings are consistent with recent prospective studies utilizing implantable loop monitors, which have also noted a marked predominance of nonventricular arrhythmias among patients on dialysis.20,25,26 Although less than ten patients total in these prospective monitoring studies experienced sudden cardiac arrests, only one was due to ventricular arrhythmia, with the remainder related to bradyarrhythmias or asystole; among all clinically significant arrhythmias captured, bradycardia, asystole, and supraventricular tachycardia were observed far more frequently than ventricular tachycardias. The high rate of nonventricular arrhythmias may also explain decreased efficacies of implantable cardioverter defibrillators27 and automated external defibrillators7 that have been noted previously. However, we also found that shockable rhythm was substantially more likely when dialysis staff initiated CPR (34% compared with 24% for 911 responder–initiated CPR) and when dialysis staff were the first to apply the AED (41% compared with 25% for 911 responder AED application). These findings are not surprising, because early CPR and AED application will be more likely to occur at a time when the arrhythmia has not deteriorated to asystole, and further highlights the importance of rapid bystander CPR as key to improving the chances of successful defibrillation and OHCA survival overall.

Several limitations of our study should be noted. Because participation in the CARES registry by county EMS services was voluntary, this could have resulted in selection bias; however, CARES-reporting North Carolina counties composed nearly 90% of the total state population, arguing for the generalizability of the study cohort as very nearly representative of the entire state. Although our cohort examined a broad population area in the southeastern United States with a high prevalence of ESRD, our results may not be generalizable to other United States regions. Potential misclassification of dialysis clinic events and the potential for inclusion of subjects who were not patients on dialysis could have occurred, even though we minimized this by multiple levels of location validation as well as validation of a subset of events with dialysis clinic records. Sample size limitations also limited our ability to detect and explore associations within subgroups. Although we uncovered strong associations between dialysis staff CPR and important outcomes, including survival to hospital discharge and neurologic status on discharge, we did not have access to longer term survival data. Another important limitation of our study is that we could not obtain further details on the circumstances underlying the lack of dialysis staff CPR and AED use, because this information was not collected in CARES/EMS records, and was not available to us from clinic records. Further prospective studies are needed to better understand potential barriers to CPR performance in dialysis clinics. Finally, although we accounted for basic demographic differences, we did not have information on other factors that may have influenced survival outcomes such as patient comorbidities, CPR quality, and how much time elapsed between cardiac arrest recognition and administration of CPR and defibrillation; thus, as with all observational studies, the relationship between dialysis staff CPR and outcomes can only be viewed as associative rather than causal.

In conclusion, when dialysis staff provided CPR, it was strongly associated with improved survival outcomes. However, CPR was not provided by staff in nearly 20% of all in-clinic OHCA events, and AEDs were not applied by dialysis staff in almost 50% of events. These findings suggest important opportunities to improve care and survival in this high-risk population. Further investigation is needed to understand potential facilitators and barriers to preforming CPR in the unique setting of the outpatient dialysis clinic.



Published online ahead of print. Publication date available at www.jasn.org.

See related editorial, “Cardiopulmonary Resuscitation in Outpatient Dialysis Clinics: Perception of Futility?,” on pages .

We thank Divya Bajpai for assistance with creating the visual abstract.

This work was supported by the National Institutes of Health under grant award 1R03DK113324 awarded to P.H.P.

P.H.P. designed the study; M.E.D., M.A.S., S.H., B.G.F., S.M.A.-K., B.M., and L.P.S. provided input on study design; C.T., K.V., C.B.G., and J.G.J. assisted with data acquisition; M.E.D., B.G.F., and P.H.P. analyzed the data; P.H.P., M.E.D., C.T., K.V., L.P.S., S.H., S.M.A.-K., and C.B.G. drafted and revised the manuscript; all authors approved the final version of the manuscript.

Supplemental Material

This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2018090911/-/DCSupplemental.

Supplemental Table 1. List of participating counties contributing OHCA data to CARES in North Carolina and Metropolitan Atlanta included in this study.


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cardiovascular; hemodialysis; cardiovascular events; Life-threatening; dialysis complications

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