Antecedents to cardiac arrests in a teaching hospital intensive care unit

Use of point-of-care ultrasound during cardiac arrest in the intensive care unit: A cross-sectional survey

BACKGROUND

There is growing interest in the use of point-of-care ultrasound during cardiac arrest, but few studies document its use in the intensive care unit.

OBJECTIVE

We hypothesised this may reflect a low prevalence of use of point-of-care ultrasound during cardiac arrest or negative attitudes towards its use. We aimed to determine the self-reported prevalence, attitudes towards, and barriers to use of point-of-care ultrasound during cardiac arrest in the intensive care unit.

METHODS

We conducted a web-based survey over 3 months (08/08/2022-06/11/2022), of intensive care unit consultants and registrars in Victoria, Australia. Descriptive and mixed-methods analyses of Likert-type and free-text answers were performed.

RESULTS

The response rate was 91/398 (22.8%), split evenly between consultants and registrars. There was a broad range of clinical and ultrasound experience. Only 22.4% (22/91) of respondents reported using point-of-care ultrasound 75-100% of the time during their management of cardiac arrest. Respondents rated the value they place in point-of-care ultrasound during cardiac arrest 3 (interquartile range: 3-4) and that of a "skilled operator" 4 ((interquartile range; 4-5) on a 5-point scale. Free-text analysis suggested exclusion of "tamponade" (40/80 [50%] comments) as the most valuable use-case and "skill" as a personal barrier (20/73 [27.4%] comments). Personal and departmental barriers were not rated highly, although registrars perceived "lack of a structured training program" as a barrier. Respondents were equivocal in the value they gave point-of-care ultrasound during cardiac arrest but saw greater value when conducted by a skilled operator.

CONCLUSIONS

Point-of-care ultrasound was reported to be infrequently used in cardiac arrest, mostly due to self-perceived skill and lack of a structured training program.

Incidence, characteristics and predictors of mortality following cardiac arrest in ICUs of a German university hospital: A retrospective cohort study

BACKGROUND

Cardiac arrest in intensive care is a rarely studied type of in-hospital cardiac arrest.

OBJECTIVE

This study examines the incidence, characteristics, risk factors for mortality as well as long-term prognosis following cardiac arrest in intensive care.

DESIGN

Retrospective cohort study.

SETTING

Five noncardiac surgical ICUs (41 surgical and 37 medical beds) at a German university hospital between 2016 and 2019.

PATIENTS

Adults experiencing cardiac arrest defined as the need for chest compressions and/or defibrillation occurring for the first time on the ICU.

MAIN OUTCOME MEASURES

Primary endpoint: occurrence of cardiac-arrest in the ICU. Secondary endpoints: diagnostic and therapeutic measures; risk factors and marginal probabilities of no-return of spontaneous circulation; rates of return of spontaneous circulation, hospital discharge, 1-year-survival and 1-year-neurological outcome.

RESULTS

A total of 114 cardiac arrests were observed out of 14 264 ICU admissions; incidence 0.8%; 95% confidence interval (CI) 0.7 to 1.0; 45.6% received at least one additional diagnostic test, such as blood gas analysis (36%), echocardiography (19.3%) or chest x-ray (9.9%) with a resulting change in therapy in 52%, (more frequently in those with a return of spontaneous circulation vs none, P = 0.023). Risk factors for no-return of spontaneous circulation were cardiac comorbidities (OR 5.4; 95% CI, 1.4 to 20.7) and continuous renal replacement therapy (OR 5.9; 95% CI, 1.7 to 20.8). Bicarbonate levels greater than 21 mmol l-1 were associated with a higher mortality risk in combination either with cardiac comorbidities (bicarbonate <21 mmol l-1: 13%; 21 to 26 mmol l-1: 45%; >26 mmol l-1: 42%) or with a SOFA at least 2 (bicarbonate <21 mmol l-1: 8%; 21 to 26 mmol l-1: 40%; >26 mmol l-1: 37%). In-hospital mortality was 78.1% (n=89); 1-year-survival-rate was 10.5% (95% CI, 5.5 to 17.7) and survival with a good neurological outcome was 6.1% (95% CI, 2.5 to 12.2).

CONCLUSION

Cardiac arrest in ICU is a rare complication with a high mortality and low rate of good neurological outcome. The development of a structured approach to resuscitation should include all available resources of an ICU and adequately consider the complete diagnostic and therapeutic spectra as our results indicate that these are still underused. The development of prediction models of death should take into account cardiac and hepatic comorbidities, continuous renal replacement therapy, SOFA at least 2 before cardiac arrest and bicarbonate level. Further research should concentrate on identifying early predictors and on the prevention of cardiac arrest in ICU.

Post-cardiac arrest syndrome in adult hospitalized patients

Adult In-hospital Cardiac Arrest (IHCA) is defined as the loss of circulation of an in-patient. Following high-quality cardiopulmonary resuscitation (CPR), if the return of spontaneous circulation (ROSC) is achieved, the post-cardiac arrest syndrome develops (PCAS). This review is intended to discuss the current diagnosis and treatment of PCAS. To approach this topic, a bibliography search was conducted through direct digital access to the scientific literature published in English and Spanish between 2014 and 2020, in MedLine, SciELO, Embase and Cochrane. This search resulted in 248 articles from which original articles, systematic reviews, meta-analyses and clinical practice guidelines were selected for a total of 56 documents. The etiologies may be divided into 56% of in-hospital cardiac, and 44% of non-cardiac arrests. The incidence of this physiological collapse is up to 1.6 cases/1,000 patients admitted, and its frequency is higher in the intensive care units (ICU), with an overall survival rate of 13% at one year. The primary components of PCAS are brain injury, myocardial dysfunction and the persistence of the precipitating pathology. The mainstays for managing PCAS are the prevention of cardiac arrest, ventilation support, control of peri-cardiac arrest arrythmias, and interventions to optimize neurologic recovery. A knowledgeable healthcare staff in PCAS results in improved patient survival and future quality of life. Finally, there is clear need to do further research in the Latin American Population.

Epidemiology of intensive care unit cardiac arrest: Characteristics, comorbidities, and post-cardiac arrest organ failure - A prospective observational study

BACKGROUND

Critically ill patients in intensive care units can frequently suffer from cardiac arrest (ICU-CA), the incidence of ICU-CA is associated with high mortality. Most studies on ICU-CA focused on risk factors and intra-arrest determinants. However, there is a lack of data on organ failure after ICU-CA and its clinical implications for outcome. This study aimed to investigate ICU-CA incidence, outcome and the occurrence of organ failure after ICU-CA.

METHODS

We conducted a prospective observational study over a 1-year at 12 intensive care units of a tertiary care university hospital. We included all consecutive adult patients suffering cardiac arrest (CA) during the ICU stay. Incidence, clinical and neurological outcome, as well as organ failure and support were assessed.

RESULTS

Out of 7690 patients, 176 (2%) with ICU-CA were identified during the study period. Male patients comprised 63% and the median age was 70 (58-78) years. The median ICU stay before ICU-CA was 3 (1-8) days. The initial cardiac rhythm was shockable (VT/VF) in 23% of patients; defibrillation during CPR was performed in 19%. The presumed cause of CA was cardiac in 24%, and sustained ROSC was observed in 80% of patients. Before CA 57% (n = 100) of patients were sedated, 63% (n = 110) mechanically ventilated, 70% needed vasopressor therapy and renal replacement therapy was necessary in 27% (n = 48) of patients. Organ failure after ICU-CA was common, 70% suffered from post-CA cardiac failure, renal replacement therapy was newly initiated in 26% of patients and liver failure occurred in 24% of patients. Mortality at ICU-discharge and at hospital discharge was 66 % and 68 %, respectively. Multivariate regression analysis identified the SOFA score [HR 1.09, 95% CI (0.92-3.18); p < 0.05] and liver failure [HR 2.44, 95% CI (1.39-4.26); p < 0.001] after ICU-CA as independent predictors of mortality.

CONCLUSION

The incidence of ICU-CA is rare in critically ill patients. Organ failure before and after ICU-CA is common; liver failure incidence and severity of illness after ICU-CA are independent predictors of mortality and should be considered in further decisions on ICU therapy.

Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study

BACKGROUND

Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs model).

METHODS

All adult patients hospitalized in a tertiary care hospital in Japan between October 2011 and October 2018 were included in this study. Random forest models with/without laboratory results (Vitals+Labs model and Vitals-Only model, respectively) were trained and tested using chronologically divided datasets. Both models use patient demographics and eight-hourly vital signs collected within the previous 48 hours. The primary and secondary outcomes were the occurrence of IHCA in the next 8 and 24 hours, respectively. The area under the receiver operating characteristic curve (AUC) was used as a comparative measure. Sensitivity analyses were performed under multiple statistical assumptions.

RESULTS

Of 141,111 admitted patients (training data: 83,064, test data: 58,047), 338 had an IHCA (training data: 217, test data: 121) during the study period. The Vitals-Only model and Vitals+Labs model performed comparably when predicting IHCA within the next 8 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.862 [95% confidence interval (CI): 0.855-0.868] vs 0.872 [95% CI: 0.867-0.878]) and 24 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.830 [95% CI: 0.825-0.835] vs 0.837 [95% CI: 0.830-0.844]). Both models performed similarly well on medical, surgical, and ward patient data, but did not perform well for intensive care unit patients.

CONCLUSIONS

In this single-center study, the machine learning model predicted IHCAs with good discrimination. The addition of laboratory values to vital signs did not significantly improve its overall performance.

Bradycardia at the onset of pulseless electrical activity arrests in hospitalized patients is associated with improved survival to discharge

Background

Recent studies have suggested that the incidence of in-hospital pulseless electrical activity (PEA) arrests is increasing. Bradycardia in patients with in-hospital PEA is common but it is unknown if it is associated with respiratory arrest or patient outcomes.

Objective

To determine risk factors and outcomes associated with bradycardic-PEA arrests, and relationship between bradycardia and respiratory arrest.

Methods

This was a retrospective cohort study of all inpatient cardiac arrests at an academic medical center over a four-year period. Patient demographics, comorbidities, vital signs, arrest event data, and outcomes were abstracted from the medical record. PEA arrest was defined as a non-shockable rhythm with loss of pulse requiring cardiopulmonary resuscitation and having organized electrocardiographic activity. Bradycardia was classified as a HR < 60 bpm at the time of pulse loss. The primary outcomes were survival of arrest and survival to hospital discharge.

Results

Between July 2013 and August 2017, there were 176 in-hospital patients with PEA arrests. While 105 (59.7%) survived the arrest, only 38 (21.6%) survived to discharge. A total of 66 (37.5%) were bradycardic-PEA arrests. Patients with bradycardic PEA arrests were no more likely to have their arrest precipitated by respiratory failure than non-bradycardic PEA patients (36.4% vs 27.3%, P = 0.24), but patients with non-bradycardic PEA arrests were more likely to have a CIED than non-bradycardic PEA patients (14.5% vs 3.0%, P = 0.02). On multivariate analysis, bradycardic PEA was associated with improved survival to hospital discharge (OR = 3.31, 95% CI: 1.41–7.79, p = 0.006), but not survival of arrest (OR 1.45, 95% CI: 0.68–3.09, p = 0.34). Respiratory arrest was an independent predictor of survival of code (OR 2.62, 95% CI: 1.36–5.47, P = 0.01) and to hospital discharge (OR 3.47, 95% CI: 1.35–8.91, P = 0.01). Other predictors of survival to discharge include history of coronary artery disease, and non-use of epinephrine, atropine, and sodium bicarbonate.

Conclusion

In a retrospective study of hospitalized patients in the intensive care unit and non-intensive care, bradycardia at the time of PEA cardiac arrest was associated with improved survival to hospital discharge but not survival of arrest. Respiratory arrest was an independent predictor of survival, but there was no association between respiratory arrest and bradycardic PEA arrest.

The incidence of cardiac arrest in the intensive care unit: A systematic review and meta-analysis

The incidence of cardiac arrest in the intensive care unit (ICU-CA) has not been widely reported. We undertook a systematic review and meta-analysis of studies reporting the incidence of cardiac arrest in adult, general intensive care units. The review was prospectively registered with PROSPERO (CRD42017079717). The search identified 7550 records, which included 20 relevant studies for qualitative analysis and 16 of these were included for quantitative analyses. The reported incidence of ICU-CA was 22.7 per 1000 admissions (95% CI: 17.4-29.6) with survival to hospital discharge of 17% (95% CI: 9.5-28.5%). We estimate that at least 5446 patients in the UK have a cardiac arrest after ICU admission. There are limited data and significant variation in the incidence of ICU-CA and efforts to synthesise these are limited by inconsistent reporting. Further prospective studies with standardised process and incidence measures are required to define this important patient group.

Comparison of Hospital-Wide Code Rates and Mortality Before and After the Implementation of a Rapid Response Team

Objective

To compare hospital-wide code rates and mortality before and after the implementation of a rapid response team (RRT).

Study design

A prospective cohort design with historical controls.

Place of study

This study was conducted at Shifa International Hospital, Islamabad, from January 21, 2016, to January 20, 2017.

Materials and methods

The triggers for the rapid response team (RRT) were displayed on each floor. The in-house staff was trained on when and how to activate the rapid response team (RRT). Data were collected on a specified data collection form. Mortality and hospital-wide code blue rates were calculated and compared with those from one year before the implementation of the rapid response team (RRT) (i.e., from January 21, 2015, to January 20, 2016).

Results

The total number of admissions during the study period was 40,177. In total, 796 RRTs were activated with a rate of activation of 19.81 per 1000 admissions. The most common activator for RRTs was an altered level of consciousness (24.12%), followed by tachycardia (19.22%), and tachypnea (14.45%). The total number of admissions one year before the implementation of the RRT was 39,460. The total number of mortality events before the implementation of the RRT was 1470 (3.725%) and after the implementation of the RRT was 1529 (3.805%), which was not significantly different (P = .576). The total number of code blues before the implementation of the RRT was 146 (0.369%) and after the implementation of RRT was 148 (0.368%), which was not significantly different (P = .929).

Conclusion

In this large single-institution study, rapid response team implementation was not associated with significant reductions in either hospital-wide code blue or mortality.

Increased need for interventions predicts mortality in the critically ill

INTRODUCTION

The aim of this study was to determine the association of early treatment intensity with hospital mortality of intensive care unit (ICU) patients.

METHODS

We performed an observational study based on a national ICU registry. We included adult patients treated in Finnish ICUs between 2003 and 2013 with the length of ICU stay of more than 3 days. We measured treatment intensity with the Therapeutic Intervention Scoring System (TISS-76). We assessed mean and daily TISS scores. To define the change in treatment intensity during the first days in the ICU, we calculated the difference between the TISS score on day 3 and the score on day 1 (ΔTISS). We used multivariate logistic regression to adjust for baseline differences and continuous net reclassification improvement (NRI) to determine the impact of adding TISS data to the baseline prediction model on its prognostic performance.

RESULTS

We identified 42,493 patients eligible for the study. For 71% of the patients, ΔTISS was ≤ 0 and crude hospital mortality was 18%. ΔTISS > 0 was observed for 29% of the patients, with a hospital mortality of 23%. When compared to the group ΔTISS ≤ 0, the category ΔTISS > 0 was independently associated with substantially increased mortality. Adding TISS data to the prediction model resulted in the improvement of prognostic performance particularly in the patients with the lowest initial baseline risk.

CONCLUSIONS

Early increase in TISS scores was associated with increased risk of death, especially in patients with a lower initial severity of illness.

Monitoring cardiorespiratory instability: Current approaches and implications for nursing practice

Unrecognised in-hospital cardiorespiratory instability (CRI) risks adverse patient outcomes. Although step down unit (SDU) patients have continuous non-invasive physiologic monitoring of vital signs and a ratio of one nurse to four to six patients, detection of CRI is still suboptimal. Telemedicine provides additional surveillance but, due to high costs and unclear investment returns, is not routinely used in SDUs. Rapid response teams have been tested as possible approaches to support CRI patients outside the intensive care unit with mixed outcomes. Technology-enabled early warning scores, though rigorously studied, may not detect subtle instability. Efforts to utilise nursing intuition as a means to promote early identification of CRI have been explored, but the problem still persists. Monitoring systems hold promise, but nursing surveillance remains the key to reliable early detection and recognition. Research directed towards improving nursing surveillance and facilitating decision-making is needed to ensure safe patient outcomes and prevent CRI.

Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians

The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth(®) Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO2 trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1-50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO2 error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error.