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ScienceDirect Journal of Electrocardiology 48 (2015) 426 – 429 www.jecgonline.com
Non-critical care telemetry and in-hospital cardiac arrest outcomes☆ Rami Mohammad, MD, a,⁎, 1 Sachil Shah, MD, a, 2 Elie Donath, MD, a Nicholas Hartmann, MD, a Ann Rasmussen, RN, b Shaun Isaac, MD, b Steven Borzak, MD a, c a
University of Miami Miller School of Medicine, Palm Beach Regional Campus, Atlantis, FL b JFK Medical Center, Atlantis, FL c Charles E. Schmidt School of Medicine, Florida Atlantic University, Boca Raton, FL
Abstract
Background: Telemetry is increasingly used to monitor hospitalized patients with lower intensities of care, but its effect on in-hospital cardiac arrest (IHCA) outcomes in non-critical care patients is unknown. Hypothesis: Telemetry utilization in non-critical care patients does not affect IHCA outcomes. Methods: A retrospective cohort analysis of all patients in non-critical care beds that experienced a cardiac arrest in a university-affiliated teaching hospital during calendar years 2011 and 2012 was performed. Data were collected as part of AHA Get With the Guidelines protocol. The independent variable and exposure studied were whether patients were on telemetry or not. Telemetry was monitored from a central location. The primary endpoint was return of spontaneous circulation (ROSC) and the secondary end point was survival to discharge. Results: Of 123 IHCA patients, the mean age was 75 ± 15 and 74 (61%) were male. 80 (65%) patients were on telemetry. Baseline demographics were similar except for age; patients on telemetry were younger with mean age of 70.3 vs. 76.8 in the non-telemetry group (p = 0.024). 72 patients (60%) achieved ROSC and 46 (37%) achieved survival to discharge. By univariate analysis, there was no difference between patients that had been on telemetry vs. no telemetry in ROSC (OR = 1.13, p = 0.76) or survival to discharge (OR = 1.18, p = 0.67). Similar findings were obtained with multivariate analysis for ROSC (0.91, p = 0.85) and survival to discharge (OR = 0.92, p = 0.87). Conclusions: The use of cardiac telemetry in non-critical care beds, when monitored remotely in a central location, is not associated with improved IHCA outcomes. © 2015 Elsevier Inc. All rights reserved.
Keywords:
General clinical cardiology; Monitoring
Introduction Cardiac monitoring was first introduced for critically ill patients, but today it is increasingly used to monitor patients, often remotely via telemetry, in the general inpatient setting. The AHA and ACC/ECCC have both published guidelines listing diagnostic indications for when cardiac monitoring is appropriate and indicated [1,2], but these guidelines are generally ignored [3,4], and do not reflect the current trends of increased telemetry monitoring in non-critical patients [5]. The impact of telemetry in non-critical patients is controversial. In two separate studies, Estrada et al. demonstrated that the utilization of telemetry in non-critical care settings rarely ☆
Conflict of interest and financial disclosures by all authors: None. ⁎ Corresponding author. 2830-3 Woodsview Dr, Beavercreek, Ohio, 45431. E-mail address: [email protected] 1 Current Affiliation: Hospitalist in Springfield, Ohio. 2 Current Affiliation: Georgetown University, Washington, D.C.
http://dx.doi.org/10.1016/j.jelectrocard.2015.02.003 0022-0736/© 2015 Elsevier Inc. All rights reserved.
leads to a change in clinical decision-making [6], and recommended a revision of current classification to match its limited utility [7]. Despite telemetry not being an intervention, its use could lead to unnecessary testing, higher costs of care [3], and potentially adverse effects. We studied the relationship of telemetry use in non-critical care patients to determine whether its utilization affects in-hospital cardiac arrest (IHCA) outcomes.
Methods We retrospectively identified all patients N 18 years of age that were admitted to non-critical care beds that experienced a cardiac arrest at our institution, a 500-bed teaching affiliate of the University of Miami Miller School of Medicine (John F. Kennedy Medical Center, Atlantis, Florida) from January 1, 2011, through December 31, 2012. The data were prospectively collected as part of our hospital’s participation in the American
R. Mohammad et al. / Journal of Electrocardiology 48 (2015) 426–429
Heart Association’s Get With The Guidelines® (GWTG)Resuscitation quality improvement program. Cardiac arrest was defined as either pulselessness, or a pulse with inadequate perfusion requiring chest compressions and/or defibrillation of ventricular fibrillation or pulseless ventricular tachycardia, and in which the event elicited a hospital-wide emergency response (“code blue”). Resuscitation efforts commenced immediately upon recognition of a cardiac arrest event by the healthcare staff, and advanced cardiac life support (ACLS) measures were instituted upon arrival of the medical emergency team. The emergency team consisted of an emergency room physician, respiratory therapist, general nursing staff and a nurse trained in critical care. All physicians and nursing staff were required to maintain current ACLS CPR certification. All cardiac arrest events occurring in non-critical care beds were included in the study. Events occurring in the emergency room (ER), interventional and post interventional specialized units (holding areas for patients undergoing or just undergone procedures) were excluded. Additional exclusion criteria included: patients less than 18 years of age, and patients with do not resuscitate (DNR) advance directives. Patients were excluded if return of spontaneous circulation (ROSC) occurred without chest compressions or defibrillation, or if the event occurred after brain death had been established. A retrospective collection of clinical data was performed in the cohort, which included patient demographics and the pre-existing conditions listed in the GWTG® registry. The location of the patient in the 24 h prior to the event was recorded. Pre-arrest and arrest variables were documented and included; vital signs four hours prior to code, whether the event was witnessed, cause of code, whether a rapid response was called, time of arrest, type of rhythm, type of ventilation, timing of compressions, and time at which the code ended. Outcome data included ROSC and survival to hospital discharge. ROSC was defined as the presence of a sustained pulse for N 20 min with no further need for chest compression, and in the absence of a pacemaker or cardiopulmonary bypass/extracorporeal CPR. Survival to discharge was defined as being discharged alive out of inpatient status (either to home, to hospice or transfer to another facility). Only ECG rhythms were monitored via telemetry. It was generally available without shortage or triage, and was provided on the order of the admitting physician. All floors are capable of telemetry monitoring, which is monitored centrally in a single location by trained, full-time telemetry technicians. The monitoring system is a GE Apex Pro FH system, with 5 technicians monitoring up to 280 patients in a central location. Each patient is monitored with a 5-lead ECG that is set up by the nursing staff. Alarms are standard, include rhythm-based algorithms, and by default include rate alarms for heart rate less than 50 or greater than 150. In the event of a third-tier alarm (e.g., ventricular fibrillation/ tachycardia or asystole) the technician calls the nursing station using a special red phone which is immediately answered by any available staff member on the nursing unit, who then makes an immediate bedside assessment of the patient. Response times from a third tier alarm to bedside assessment were not measured for this study but are typically within 30–90 s.
427
Table 1 Baseline characteristics of study participants.
Males (%) Mean Age (SD) Comorbid condition CHF this admission CHF prior admission Diabetes Mellitus Hypotension Malignancy Pneumonia Liver Disease Electrolyte abnormalities History of MI AKI Septicemia Respiratory Insufficiency
No telemetry (n = 43)
Telemetry (n = 80)
p-value
26 (60.5%) 76.8 (1.5)
48 (60%) 70.3 (2.6)
0.96 0.02
3 (7.0%) 5 (11.6%) 11 (25.6%) 10 (23.3%) 5 (11.6%) 4 (9.3%) 2 (4.7%) 2 (4.7%) 3 (7.0%) 17 (39.5%) 19 (44.2%) 12 (27.9%)
17 (21.3%) 16 (20%) 20 (25%) 13 (16.3%) 8 (10%) 18 (22.5%) 15 (18.8%) 8 (10%) 6 (7.5%) 38 (47.5%) 29 (36.3%) 34 (42.5%)
0.04 0.24 0.94 0.34 0.78 0.07 0.03 0.30 0.91 0.40 0.39 0.11
The primary endpoint of our analysis was ROSC, and the secondary endpoint was survival to discharge. The independent variable was whether patients were on telemetry prior to the resuscitation event. Analysis was performed using univariate and multivariate logistic regression for the primary and secondary endpoints. Variables considered included age, gender and the pre-existing conditions listed in Table 1. The University of Miami Institutional Review Board approved the study protocol: ID number 20120235. Results Of the total 509 cardiac arrests recorded between January 1, 2011, and December 31 2012, 240 occurred outside the critical care areas with 123 patients (51%) fulfilling the inclusion criteria. The remaining 117 (49%) arrests occurred in the emergency room (ER), interventional and post interventional specialized units and were excluded from the study. Mean age of all 123 patients was 75 years ± 15; and 74 (60%) patients were male. 80 (65%) patients were on telemetry at the time of cardiac arrest. Baseline population characteristics, by telemetry monitoring, are shown in Table 1. Telemetry patients were younger (70.3 compared to 76.8 years) and more likely to have CHF (21.3% compared to 7.0%) and liver disease (18.8% compared to 4.7%). The outcomes data and analysis are summarized in Table 2. Of 123 patients, 80 of them were on telemetry and 43 were not on telemetry at the time of cardiac arrest. Data for the ROSC outcome were available for 120 patients, of these 47 of 77 (61%) patients on telemetry achieved ROSC compared to 25 of 43 (58%) patients who were not on telemetry. This resulted in an unadjusted OR of 1.13 (95% CI 0.53–2.41, p = 0.76) and an adjusted OR of 0.91 (95% CI 0.35–2.31, p = 0.85). Data for the survival to discharge outcome were available on all 123 patients, of these 31 of 80 (39%) patients on telemetry survived to discharge compared to 15 of 43 (35%) patients who were not on telemetry. This resulted in an unadjusted OR of 1.18 (95% CI 0.55–2.46, p = 0.67) and an adjusted OR of 0.92 (95% CI 0.37–2.33, p = 0.87).
428
R. Mohammad et al. / Journal of Electrocardiology 48 (2015) 426–429
Table 2 Outcomes and univariate and multivariate analysis. Telemetry Logistic regression analysis of patients on telemetry vs Return of spontaneous circulation 47/77 (61%) Survival to discharge 31/80 (39%) Subgroup analysis: by cardiac rhythm Asystole/PEA Return of spontaneous circulation 39/62 (63%) Survival to discharge 23/62 (37%) VF/VT Return of spontaneous circulation 6/12 (50%) Survival to discharge 4/12 (33%)
No Telemetry
Odds Ratio
no telemetry (n = 123) 25/43 (58%) 1.13 15/43 (35%) 1.18
95% Confidence Interval
P-value
Adjusted OR
95% Confidence Interval
P-value
0.53–2.41 0.55–2.56
0.76 0.67
0.91 0.92
0.35–2.31 0.37–2.33
0.85 0.87
17/29 (59%) 10/29 (35%)
1.20 1.12
0.49–2.95 0.45–2.82
0.70 0.81
1.02 0.78
0.33–3.13 0.24–2.64
0.98 0.70
8/12 (67%) 5/12 (42%)
0.50 0.70
0.10–2.60 0.13–3.68
0.41 0.67
– –
– –
– –
Additional subgroup analyses were performed. The type of cardiac rhythm (at time of cardiac arrest) was available for 115 patients; 91 (79%) patients were found to be in PEA/ asystole compared to 24 (21%) patients found to be initially in VF/VT. Notably, the likelihood of ROSC or survival to discharge (depending on whether patients were on telemetry or not) was not clearly different based on the type of cardiac rhythm noted at time of cardiac arrest. Discussion Our study found that there was no difference in IHCA outcomes in non-critical care patients whether they were monitored with telemetry or not. There were no significant differences in ROSC and survival to discharge between the 2 groups. Our findings contradict the majority of current evidence reported on telemetry use and its impact on cardiac arrests in non-critical patients [6–11]. Physicians face a reality that a large number of hospital arrests do occur in the unmonitored setting. Nonetheless, the majority of telemetry monitored non-critical patients do not meet current AHA indications and/or do not have any cardiac history [4]. When surveyed, 50% of ordering physicians reported that they believed telemetry would be able “to detect clinical deterioration early” [4]. Although clinical deterioration does precede a cardiac arrest [12], there is no evidence that telemetry by itself is able to detect this deterioration. When “monitoring” has been reported to detect this deterioration and/or even improve arrest outcomes [12], it has been in reference to complex monitoring efforts such the creation of dedicated units, better nurse-to-patient ratios, or the utilization of other physiological monitors such as pulse oximetery, and not cardiac telemetry alone. In addition, studies that have specifically reported on the benefits of telemetry have included monitored arrests from the ICU setting [10,11,13–15]. Thus the assumed benefits of telemetry may have been by association rather than a true effect, and may be explained by better nurse-to-patient ratios [16], better ACLS response times [10,14,15] and a high likelihood of witnessing an arrest event [17,18], all of which are more likely in the ICU setting. In addition, the utility of information obtained from telemetry during an arrest event has been questioned. An important caveat has been identified in a study by Bhalala et al., which found that in 35% of cardiac arrests, the first
documented rhythms detected by the response team were discordant from the initial arrhythmia detected by the telemetry monitor [19]. It may be that unless the emergency response team has access to real-time telemetry data (such as in-room monitors as opposed to data collected from remote centers), it would be of little utility to the resuscitative effort. Given these limitations, it is not surprising that in the long run the overall yield (and hence efficacy) of telemetry contributing to arrest survival was found to be extremely low. Schull et al. found the overall yield of monitor-signaled survival rate to be only 0.02% in his study of 8932 telemetry ward patients [20]. Only one study specifically considered the impact of telemetry on IHCA survival in non-critical patients [8]. Cleverley at al. studied 668 non-critical care in-hospital cardiac arrests, and compared the events that occurred in the telemetry-monitored setting to the unmonitored ones. Contrary to our study, Cleverley found that the telemetrymonitored patients had an increased chance of survival of an initial cardiac arrest, and that telemetry-monitoring was found to be an independent predictor of survival to hospital discharge. However, in Cleverley’s study, telemetry monitoring was only available in 4.5% of the non-critical care beds, which necessarily forced triage decisions on telemetry, a process likely to have introduced selection bias. Cleverley’s study did not report on differences in patient demographics. Historically when telemetry availability is limited, patients with cardiac conditions and other unique demographics may be more likely to be assigned telemetry monitoring. One study found that when coronary care beds for suspected MI patients were markedly restricted due to a temporary nursing shortage, clinicians made triage decisions and managed to select the highest risk patients for the scarce resource without a specific policy or protocol [21]. Our study had several important limitations. Our sample size was relatively small. The majority of rhythms detected at the time of arrest were PEA/Asystole consistent with current IHCA rhythm trends [22]. These rhythms are less amenable to successful resuscitation and potentially could have diminished a possible benefit of telemetry on outcomes. We did not collect information on whether arrest events were witnessed or not. Witnessing an arrest has been reported to improve arrest outcomes [17,18]. This may be due to an earlier initiation of resuscitation efforts when the symptoms and signs of an arrest are recognized. Unwitnessed events make it difficult if not impossible to ascertain the true timing
R. Mohammad et al. / Journal of Electrocardiology 48 (2015) 426–429
of an arrest. Arrests were defined in our study when non-perfusion was clinically detected by the code team and not by a telemetry rhythm. In a dedicated monitoring unit, the time interval between a telemetry event and when an arrest is clinically detected is probably negligible. In the non-critical care setting, especially when telemetry is being monitored in a central location, the time interval between those two events is likely to be substantial. This is likely to affect arrest outcomes. Unfortunately we were not able to determine these intervals from our study data set but the subject could be the basis of future research. In addition we did not collect information on ACLS response times; they were likely similar since all codes were conducted by the same medical emergency team in a single multi-story hospital building. The appropriateness of telemetry utilization in our center was not assessed. We aimed to study the association of the prevailing practice of telemetry utilization with IHCA outcomes, and not that of telemetry in a triaged or enriched population. We believe our study results are more relevant to current practice. Telemetry patients were younger and more likely to have heart failure and liver disease. It is possible that their outcome may be worse than older patients without these conditions, and that telemetry improved their outcome so that it nearly equaled that of the unmonitored patients. We think that this scenario is unlikely because a multivariate analysis accounted for some basic differences in groups. Nevertheless, despite our use of multivariate analysis, there are likely still important unmeasured differences between monitored and unmonitored patients which we could not account for and which could have influenced the occurrence of a cardiac arrest event and its outcome. In addition, although our ROSC rates were comparable to reported literature, our survival to discharge rates were higher than previously reported [18], which could be explained by our population that did not include the critically ill, whose IHCA survival rates predominate the current literature. In summary, we found that telemetry on the general inpatient floor, when monitored remotely in a central location, was not associated with better outcomes of cardiac arrest events. Further study is warranted to define the role of telemetry monitoring, better select patients, and define outcomes which might be suitable to measure. Acknowledgments We would like to thank the American Heart Association’s Get With The Guidelines® (GWTG)-Resuscitation quality improvement initiative.
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American Association of Critical-Care Nurses. Circulation 2004;110(17):2721–46. Recommended guidelines for in-hospital cardiac monitoring of adults for detection of arrhythmia. Emergency Cardiac Care Committee members. J Am Coll Cardiol 1991;18(6):1431–3. Henriques-Forsythe MN, Ivonye CC, Jamched U, Kamuguisha LK, Olejeme KA, Onwuanyi AE. Is telemetry overused? Is it as helpful as thought? Cleve Clin J Med 2009;76(6):368–72. Najafi N, Auerbach A. Use and outcomes of telemetry monitoring on a medicine service. Arch Intern Med 2012;172(17):1349–50. Chen EH, Hollander JE. When do patients need admission to a telemetry bed? J Emerg Med 2007;33(1):53–60. Estrada CA, Rosman HS, Prasad NK, Battilana G, Alexander M, Held AC, et al. Role of telemetry monitoring in the non-intensive care unit. Am J Cardiol 1995;76(12):960–5. Estrada CA, Rosman HS, Prasad NK, Battilana G, Alexander M, Held AC, et al. Evaluation of guidelines for the use of telemetry in the nonintensive-care setting. J Gen Intern Med 2000;15(1):51–5. Cleverley K, Mousavi N, Stronger L, Ann-Bordun K, Hall L, Tam JW, et al. The impact of telemetry on survival of in-hospital cardiac arrests in non-critical care patients. Resuscitation 2013;84(7):878–82. Ferguson RP, Phelan T, Haddad T, Hinduja A, Dubin NH. Survival after in-hospital cardiopulmonary resuscitation. South Med J 2008;101(10):1007–11. Sandroni C, Ferro G, Santangelo S, Tortora F, Mistura L, Cavallaro F, et al. In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response. Resuscitation 2004;62(3):291–7. Suraseranivongse S, Chawaruechai T, Saengsung P, Komoltri C. Outcome of cardiopulmonary resuscitation in a 2300-bed hospital in a developing country. Resuscitation 2006;71(2):188–93. Naeem N, Montenegro H. Beyond the intensive care unit: a review of interventions aimed at anticipating and preventing in-hospital cardiopulmonary arrest. Resuscitation 2005;67(1):13–23. Brady WJ, Gurka KK, Mehring B, Peberdy MA, O'Connor RE. Inhospital cardiac arrest: impact of monitoring and witnessed event on patient survival and neurologic status at hospital discharge. Resuscitation 2011;82(7):845–52. Herlitz J, Bang A, Aune S, Ekstrom L, Lundstrom G, Holmberg S. Characteristics and outcome among patients suffering in-hospital cardiac arrest in monitored and non-monitored areas. Resuscitation 2001;48(2):125–35. Herlitz J, Aune S, Bang A, Fredriksson M, Thoren AB, Ekstrom L, et al. Very high survival among patients defibrillated at an early stage after inhospital ventricular fibrillation on wards with and without monitoring facilities. Resuscitation 2005;66(2):159–66. Chen LM, Nallamothu BK, Spertus JA, Li Y, Chan PS. Association between a hospital's rate of cardiac arrest incidence and cardiac arrest survival. JAMA Intern Med 2013;173(13):1186–95. Dumot JA, Burval DJ, Sprung J, Waters JH, Mraovic B, Karafa MT, et al. Outcome of adult cardiopulmonary resuscitations at a tertiary referral center including results of "limited" resuscitations. Arch Intern Med 2001;161(14):1751–8. Saklayen M, Liss H, Markert R. In-hospital cardiopulmonary resuscitation. Survival in 1 hospital and literature review. Medicine 1995;74(4):163–75. Bhalala US, Bonafide CP, Coletti CM, Rathmanner PE, Nadkarni VM, Berg RA, et al. Correlations between first documented cardiac rhythms and preceding telemetry in patients with code blue events. J Hosp Med 2013;8(4):225–8. Schull MJ, Redelmeier DA. Continuous electrocardiographic monitoring and cardiac arrest outcomes in 8,932 telemetry ward patients. Acad Emerg Med 2000;7(6):647–52. Singer DE, Carr PL, Mulley AG, Thibault GE. Rationing intensive care—physician responses to a resource shortage. N Engl J Med 1983;309(19):1155–60. Girotra S, Nallamothu BK, Spertus JA, Li Y, Krumholz HM, Chan PS. Trends in survival after in-hospital cardiac arrest. N Engl J Med 2012;367(20):1912–20.
ScienceDirect Journal of Electrocardiology 48 (2015) 426 – 429 www.jecgonline.com
Non-critical care telemetry and in-hospital cardiac arrest outcomes☆ Rami Mohammad, MD, a,⁎, 1 Sachil Shah, MD, a, 2 Elie Donath, MD, a Nicholas Hartmann, MD, a Ann Rasmussen, RN, b Shaun Isaac, MD, b Steven Borzak, MD a, c a
University of Miami Miller School of Medicine, Palm Beach Regional Campus, Atlantis, FL b JFK Medical Center, Atlantis, FL c Charles E. Schmidt School of Medicine, Florida Atlantic University, Boca Raton, FL
Abstract
Background: Telemetry is increasingly used to monitor hospitalized patients with lower intensities of care, but its effect on in-hospital cardiac arrest (IHCA) outcomes in non-critical care patients is unknown. Hypothesis: Telemetry utilization in non-critical care patients does not affect IHCA outcomes. Methods: A retrospective cohort analysis of all patients in non-critical care beds that experienced a cardiac arrest in a university-affiliated teaching hospital during calendar years 2011 and 2012 was performed. Data were collected as part of AHA Get With the Guidelines protocol. The independent variable and exposure studied were whether patients were on telemetry or not. Telemetry was monitored from a central location. The primary endpoint was return of spontaneous circulation (ROSC) and the secondary end point was survival to discharge. Results: Of 123 IHCA patients, the mean age was 75 ± 15 and 74 (61%) were male. 80 (65%) patients were on telemetry. Baseline demographics were similar except for age; patients on telemetry were younger with mean age of 70.3 vs. 76.8 in the non-telemetry group (p = 0.024). 72 patients (60%) achieved ROSC and 46 (37%) achieved survival to discharge. By univariate analysis, there was no difference between patients that had been on telemetry vs. no telemetry in ROSC (OR = 1.13, p = 0.76) or survival to discharge (OR = 1.18, p = 0.67). Similar findings were obtained with multivariate analysis for ROSC (0.91, p = 0.85) and survival to discharge (OR = 0.92, p = 0.87). Conclusions: The use of cardiac telemetry in non-critical care beds, when monitored remotely in a central location, is not associated with improved IHCA outcomes. © 2015 Elsevier Inc. All rights reserved.
Keywords:
General clinical cardiology; Monitoring
Introduction Cardiac monitoring was first introduced for critically ill patients, but today it is increasingly used to monitor patients, often remotely via telemetry, in the general inpatient setting. The AHA and ACC/ECCC have both published guidelines listing diagnostic indications for when cardiac monitoring is appropriate and indicated [1,2], but these guidelines are generally ignored [3,4], and do not reflect the current trends of increased telemetry monitoring in non-critical patients [5]. The impact of telemetry in non-critical patients is controversial. In two separate studies, Estrada et al. demonstrated that the utilization of telemetry in non-critical care settings rarely ☆
Conflict of interest and financial disclosures by all authors: None. ⁎ Corresponding author. 2830-3 Woodsview Dr, Beavercreek, Ohio, 45431. E-mail address: [email protected] 1 Current Affiliation: Hospitalist in Springfield, Ohio. 2 Current Affiliation: Georgetown University, Washington, D.C.
http://dx.doi.org/10.1016/j.jelectrocard.2015.02.003 0022-0736/© 2015 Elsevier Inc. All rights reserved.
leads to a change in clinical decision-making [6], and recommended a revision of current classification to match its limited utility [7]. Despite telemetry not being an intervention, its use could lead to unnecessary testing, higher costs of care [3], and potentially adverse effects. We studied the relationship of telemetry use in non-critical care patients to determine whether its utilization affects in-hospital cardiac arrest (IHCA) outcomes.
Methods We retrospectively identified all patients N 18 years of age that were admitted to non-critical care beds that experienced a cardiac arrest at our institution, a 500-bed teaching affiliate of the University of Miami Miller School of Medicine (John F. Kennedy Medical Center, Atlantis, Florida) from January 1, 2011, through December 31, 2012. The data were prospectively collected as part of our hospital’s participation in the American
R. Mohammad et al. / Journal of Electrocardiology 48 (2015) 426–429
Heart Association’s Get With The Guidelines® (GWTG)Resuscitation quality improvement program. Cardiac arrest was defined as either pulselessness, or a pulse with inadequate perfusion requiring chest compressions and/or defibrillation of ventricular fibrillation or pulseless ventricular tachycardia, and in which the event elicited a hospital-wide emergency response (“code blue”). Resuscitation efforts commenced immediately upon recognition of a cardiac arrest event by the healthcare staff, and advanced cardiac life support (ACLS) measures were instituted upon arrival of the medical emergency team. The emergency team consisted of an emergency room physician, respiratory therapist, general nursing staff and a nurse trained in critical care. All physicians and nursing staff were required to maintain current ACLS CPR certification. All cardiac arrest events occurring in non-critical care beds were included in the study. Events occurring in the emergency room (ER), interventional and post interventional specialized units (holding areas for patients undergoing or just undergone procedures) were excluded. Additional exclusion criteria included: patients less than 18 years of age, and patients with do not resuscitate (DNR) advance directives. Patients were excluded if return of spontaneous circulation (ROSC) occurred without chest compressions or defibrillation, or if the event occurred after brain death had been established. A retrospective collection of clinical data was performed in the cohort, which included patient demographics and the pre-existing conditions listed in the GWTG® registry. The location of the patient in the 24 h prior to the event was recorded. Pre-arrest and arrest variables were documented and included; vital signs four hours prior to code, whether the event was witnessed, cause of code, whether a rapid response was called, time of arrest, type of rhythm, type of ventilation, timing of compressions, and time at which the code ended. Outcome data included ROSC and survival to hospital discharge. ROSC was defined as the presence of a sustained pulse for N 20 min with no further need for chest compression, and in the absence of a pacemaker or cardiopulmonary bypass/extracorporeal CPR. Survival to discharge was defined as being discharged alive out of inpatient status (either to home, to hospice or transfer to another facility). Only ECG rhythms were monitored via telemetry. It was generally available without shortage or triage, and was provided on the order of the admitting physician. All floors are capable of telemetry monitoring, which is monitored centrally in a single location by trained, full-time telemetry technicians. The monitoring system is a GE Apex Pro FH system, with 5 technicians monitoring up to 280 patients in a central location. Each patient is monitored with a 5-lead ECG that is set up by the nursing staff. Alarms are standard, include rhythm-based algorithms, and by default include rate alarms for heart rate less than 50 or greater than 150. In the event of a third-tier alarm (e.g., ventricular fibrillation/ tachycardia or asystole) the technician calls the nursing station using a special red phone which is immediately answered by any available staff member on the nursing unit, who then makes an immediate bedside assessment of the patient. Response times from a third tier alarm to bedside assessment were not measured for this study but are typically within 30–90 s.
427
Table 1 Baseline characteristics of study participants.
Males (%) Mean Age (SD) Comorbid condition CHF this admission CHF prior admission Diabetes Mellitus Hypotension Malignancy Pneumonia Liver Disease Electrolyte abnormalities History of MI AKI Septicemia Respiratory Insufficiency
No telemetry (n = 43)
Telemetry (n = 80)
p-value
26 (60.5%) 76.8 (1.5)
48 (60%) 70.3 (2.6)
0.96 0.02
3 (7.0%) 5 (11.6%) 11 (25.6%) 10 (23.3%) 5 (11.6%) 4 (9.3%) 2 (4.7%) 2 (4.7%) 3 (7.0%) 17 (39.5%) 19 (44.2%) 12 (27.9%)
17 (21.3%) 16 (20%) 20 (25%) 13 (16.3%) 8 (10%) 18 (22.5%) 15 (18.8%) 8 (10%) 6 (7.5%) 38 (47.5%) 29 (36.3%) 34 (42.5%)
0.04 0.24 0.94 0.34 0.78 0.07 0.03 0.30 0.91 0.40 0.39 0.11
The primary endpoint of our analysis was ROSC, and the secondary endpoint was survival to discharge. The independent variable was whether patients were on telemetry prior to the resuscitation event. Analysis was performed using univariate and multivariate logistic regression for the primary and secondary endpoints. Variables considered included age, gender and the pre-existing conditions listed in Table 1. The University of Miami Institutional Review Board approved the study protocol: ID number 20120235. Results Of the total 509 cardiac arrests recorded between January 1, 2011, and December 31 2012, 240 occurred outside the critical care areas with 123 patients (51%) fulfilling the inclusion criteria. The remaining 117 (49%) arrests occurred in the emergency room (ER), interventional and post interventional specialized units and were excluded from the study. Mean age of all 123 patients was 75 years ± 15; and 74 (60%) patients were male. 80 (65%) patients were on telemetry at the time of cardiac arrest. Baseline population characteristics, by telemetry monitoring, are shown in Table 1. Telemetry patients were younger (70.3 compared to 76.8 years) and more likely to have CHF (21.3% compared to 7.0%) and liver disease (18.8% compared to 4.7%). The outcomes data and analysis are summarized in Table 2. Of 123 patients, 80 of them were on telemetry and 43 were not on telemetry at the time of cardiac arrest. Data for the ROSC outcome were available for 120 patients, of these 47 of 77 (61%) patients on telemetry achieved ROSC compared to 25 of 43 (58%) patients who were not on telemetry. This resulted in an unadjusted OR of 1.13 (95% CI 0.53–2.41, p = 0.76) and an adjusted OR of 0.91 (95% CI 0.35–2.31, p = 0.85). Data for the survival to discharge outcome were available on all 123 patients, of these 31 of 80 (39%) patients on telemetry survived to discharge compared to 15 of 43 (35%) patients who were not on telemetry. This resulted in an unadjusted OR of 1.18 (95% CI 0.55–2.46, p = 0.67) and an adjusted OR of 0.92 (95% CI 0.37–2.33, p = 0.87).
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Table 2 Outcomes and univariate and multivariate analysis. Telemetry Logistic regression analysis of patients on telemetry vs Return of spontaneous circulation 47/77 (61%) Survival to discharge 31/80 (39%) Subgroup analysis: by cardiac rhythm Asystole/PEA Return of spontaneous circulation 39/62 (63%) Survival to discharge 23/62 (37%) VF/VT Return of spontaneous circulation 6/12 (50%) Survival to discharge 4/12 (33%)
No Telemetry
Odds Ratio
no telemetry (n = 123) 25/43 (58%) 1.13 15/43 (35%) 1.18
95% Confidence Interval
P-value
Adjusted OR
95% Confidence Interval
P-value
0.53–2.41 0.55–2.56
0.76 0.67
0.91 0.92
0.35–2.31 0.37–2.33
0.85 0.87
17/29 (59%) 10/29 (35%)
1.20 1.12
0.49–2.95 0.45–2.82
0.70 0.81
1.02 0.78
0.33–3.13 0.24–2.64
0.98 0.70
8/12 (67%) 5/12 (42%)
0.50 0.70
0.10–2.60 0.13–3.68
0.41 0.67
– –
– –
– –
Additional subgroup analyses were performed. The type of cardiac rhythm (at time of cardiac arrest) was available for 115 patients; 91 (79%) patients were found to be in PEA/ asystole compared to 24 (21%) patients found to be initially in VF/VT. Notably, the likelihood of ROSC or survival to discharge (depending on whether patients were on telemetry or not) was not clearly different based on the type of cardiac rhythm noted at time of cardiac arrest. Discussion Our study found that there was no difference in IHCA outcomes in non-critical care patients whether they were monitored with telemetry or not. There were no significant differences in ROSC and survival to discharge between the 2 groups. Our findings contradict the majority of current evidence reported on telemetry use and its impact on cardiac arrests in non-critical patients [6–11]. Physicians face a reality that a large number of hospital arrests do occur in the unmonitored setting. Nonetheless, the majority of telemetry monitored non-critical patients do not meet current AHA indications and/or do not have any cardiac history [4]. When surveyed, 50% of ordering physicians reported that they believed telemetry would be able “to detect clinical deterioration early” [4]. Although clinical deterioration does precede a cardiac arrest [12], there is no evidence that telemetry by itself is able to detect this deterioration. When “monitoring” has been reported to detect this deterioration and/or even improve arrest outcomes [12], it has been in reference to complex monitoring efforts such the creation of dedicated units, better nurse-to-patient ratios, or the utilization of other physiological monitors such as pulse oximetery, and not cardiac telemetry alone. In addition, studies that have specifically reported on the benefits of telemetry have included monitored arrests from the ICU setting [10,11,13–15]. Thus the assumed benefits of telemetry may have been by association rather than a true effect, and may be explained by better nurse-to-patient ratios [16], better ACLS response times [10,14,15] and a high likelihood of witnessing an arrest event [17,18], all of which are more likely in the ICU setting. In addition, the utility of information obtained from telemetry during an arrest event has been questioned. An important caveat has been identified in a study by Bhalala et al., which found that in 35% of cardiac arrests, the first
documented rhythms detected by the response team were discordant from the initial arrhythmia detected by the telemetry monitor [19]. It may be that unless the emergency response team has access to real-time telemetry data (such as in-room monitors as opposed to data collected from remote centers), it would be of little utility to the resuscitative effort. Given these limitations, it is not surprising that in the long run the overall yield (and hence efficacy) of telemetry contributing to arrest survival was found to be extremely low. Schull et al. found the overall yield of monitor-signaled survival rate to be only 0.02% in his study of 8932 telemetry ward patients [20]. Only one study specifically considered the impact of telemetry on IHCA survival in non-critical patients [8]. Cleverley at al. studied 668 non-critical care in-hospital cardiac arrests, and compared the events that occurred in the telemetry-monitored setting to the unmonitored ones. Contrary to our study, Cleverley found that the telemetrymonitored patients had an increased chance of survival of an initial cardiac arrest, and that telemetry-monitoring was found to be an independent predictor of survival to hospital discharge. However, in Cleverley’s study, telemetry monitoring was only available in 4.5% of the non-critical care beds, which necessarily forced triage decisions on telemetry, a process likely to have introduced selection bias. Cleverley’s study did not report on differences in patient demographics. Historically when telemetry availability is limited, patients with cardiac conditions and other unique demographics may be more likely to be assigned telemetry monitoring. One study found that when coronary care beds for suspected MI patients were markedly restricted due to a temporary nursing shortage, clinicians made triage decisions and managed to select the highest risk patients for the scarce resource without a specific policy or protocol [21]. Our study had several important limitations. Our sample size was relatively small. The majority of rhythms detected at the time of arrest were PEA/Asystole consistent with current IHCA rhythm trends [22]. These rhythms are less amenable to successful resuscitation and potentially could have diminished a possible benefit of telemetry on outcomes. We did not collect information on whether arrest events were witnessed or not. Witnessing an arrest has been reported to improve arrest outcomes [17,18]. This may be due to an earlier initiation of resuscitation efforts when the symptoms and signs of an arrest are recognized. Unwitnessed events make it difficult if not impossible to ascertain the true timing
R. Mohammad et al. / Journal of Electrocardiology 48 (2015) 426–429
of an arrest. Arrests were defined in our study when non-perfusion was clinically detected by the code team and not by a telemetry rhythm. In a dedicated monitoring unit, the time interval between a telemetry event and when an arrest is clinically detected is probably negligible. In the non-critical care setting, especially when telemetry is being monitored in a central location, the time interval between those two events is likely to be substantial. This is likely to affect arrest outcomes. Unfortunately we were not able to determine these intervals from our study data set but the subject could be the basis of future research. In addition we did not collect information on ACLS response times; they were likely similar since all codes were conducted by the same medical emergency team in a single multi-story hospital building. The appropriateness of telemetry utilization in our center was not assessed. We aimed to study the association of the prevailing practice of telemetry utilization with IHCA outcomes, and not that of telemetry in a triaged or enriched population. We believe our study results are more relevant to current practice. Telemetry patients were younger and more likely to have heart failure and liver disease. It is possible that their outcome may be worse than older patients without these conditions, and that telemetry improved their outcome so that it nearly equaled that of the unmonitored patients. We think that this scenario is unlikely because a multivariate analysis accounted for some basic differences in groups. Nevertheless, despite our use of multivariate analysis, there are likely still important unmeasured differences between monitored and unmonitored patients which we could not account for and which could have influenced the occurrence of a cardiac arrest event and its outcome. In addition, although our ROSC rates were comparable to reported literature, our survival to discharge rates were higher than previously reported [18], which could be explained by our population that did not include the critically ill, whose IHCA survival rates predominate the current literature. In summary, we found that telemetry on the general inpatient floor, when monitored remotely in a central location, was not associated with better outcomes of cardiac arrest events. Further study is warranted to define the role of telemetry monitoring, better select patients, and define outcomes which might be suitable to measure. Acknowledgments We would like to thank the American Heart Association’s Get With The Guidelines® (GWTG)-Resuscitation quality improvement initiative.
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