Relationship between stress hyperglycaemic ratio and incidence of in-hospital cardiac arrest in patients with acute coronary syndrome: a retrospective cohort study.

BACKGROUND: The stress hyperglycaemic ratio (SHR), a new marker that reflects the true hyperglycaemic state of patients with acute coronary syndrome (ACS), is strongly associated with adverse clinical outcomes in these patients. Studies on the relationship between the SHR and in-hospital cardiac arrest (IHCA) incidence are limited. This study elucidated the relationship between the SHR and incidence of IHCA in patients with ACS. METHODS: In total, 1,939 patients with ACS who underwent percutaneous coronary intervention (PCI) at the Affiliated Hospital of Zunyi Medical University were included. They were divided into three groups according to the SHR: group T1 (SHR ≤ 0.838, N = 646), group T2 (0.838< SHR ≤ 1.140, N = 646), and group T3 (SHR3 > 1.140, N = 647). The primary endpoint was IHCA incidence. RESULTS: The overall IHCA incidence was 4.1% (N = 80). After adjusting for covariates, SHR was significantly associated with IHCA incidence in patients with ACS who underwent PCI (odds ratio [OR] = 2.6800; 95% confidence interval [CI] = 1.6200-4.4300; p<0.001), and compared with the T1 group, the T3 group had an increased IHCA risk (OR = 2.1800; 95% CI = 1.2100-3.9300; p = 0.0090). In subgroup analyses, after adjusting for covariates, patients with ST-segment elevation myocardial infarction (STEMI) (OR = 3.0700; 95% CI = 1.4100-6.6600; p = 0.0050) and non-STEMI (NSTEMI) (OR = 2.9900; 95% CI = 1.1000-8.1100; p = 0.0310) were at an increased IHCA risk. After adjusting for covariates, IHCA risk was higher in patients with diabetes mellitus (DM) (OR = 2.5900; 95% CI = 1.4200-4.7300; p = 0.0020) and those without DM (non-DM) (OR = 3.3000; 95% CI = 1.2700-8.5800; p = 0.0140); patients with DM in the T3 group had an increased IHCA risk compared with those in the T1 group (OR = 2.4200; 95% CI = 1.0800-5.4300; p = 0.0320). The restriction cubic spline (RCS) analyses revealed a dose-response relationship between IHCA incidence and SHR, with an increased IHCA risk when SHR was higher than 1.773. Adding SHR to the baseline risk model improved the predictive value of IHCA in patients with ACS treated with PCI (net reclassification improvement [NRI]: 0.0734 [0.0058-0.1409], p = 0.0332; integrated discrimination improvement [IDI]: 0.0218 [0.0063-0.0374], p = 0.0060). CONCLUSIONS: In patients with ACS treated with PCI, the SHR was significantly associated with the incidence of IHCA. The SHR may be a useful predictor of the incidence of IHCA in patients with ACS. The addition of the SHR to the baseline risk model had an incremental effect on the predictive value of IHCA in patients with ACS treated with PCI.

Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest.

BACKGROUND: The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE). METHODS: Retrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014-2018. Blood samples were collected at ICU admission, 12 h, and 48 h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3-5 at 2-6 months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC). RESULTS: Of the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12 h and 48 h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70-0.82), 0.86 (0.81-0.90) and 0.91 (0.87-0.96), and after IHCA with AUC (95% CI) 0.77 (0.66-0.87), 0.83 (0.74-0.92) and 0.83 (0.71-0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66-0.79), 0.75 (0.69-0.81), and 0.93 (0.89-0.96) and after IHCA with AUC (95% CI) 0.61 (0.49-0.74), 0.68 (0.56-0.79), and 0.77 (0.65-0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12 h and 48 h, as well as tau at 48 h, offered similar predictive value as NSE at 48 h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA. CONCLUSION: GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.

In-hospital cardiac arrest characteristics, causes and outcomes in patients with cardiovascular disease across different departments: a retrospective study.

BACKGROUND: Cardiac etiologies arrest accounts for almost half of all in-hospital cardiac arrest (IHCA), and previous studies have shown that the location of IHCA is an important factor affecting patient outcomes. The aim was to compare the characteristics, causes and outcomes of cardiovascular disease in patients suffering IHCA from different departments of Fuwai hospital in Beijing, China. METHODS: We included patients who were resuscitated after IHCA at Fuwai hospital between March 2017 and August 2022. We categorized the departments where cardiac arrest occurred as cardiac surgical or non-surgical units. Independent predictors of in-hospital survival were assessed by logistic regression. RESULTS: A total of 119 patients with IHCA were analysed, 58 (48.7%) patients with cardiac arrest were in non-surgical units, and 61 (51.3%) were in cardiac surgical units. In non-surgical units, acute myocardial infarction/cardiogenic shock (48.3%) was the main cause of IHCA. Cardiac arrest in cardiac surgical units occurred mainly in patients who were planning or had undergone complex aortic replacement (32.8%). Shockable rhythms (ventricular fibrillation/ventricular tachycardia) were observed in approximately one-third of all initial rhythms in both units. Patients who suffered cardiac arrest in cardiac surgical units were more likely to return to spontaneous circulation (59.0% vs. 24.1%) and survive to hospital discharge (40.0% vs. 10.2%). On multivariable regression analysis, IHCA in cardiac surgical units (OR 5.39, 95% CI 1.90-15.26) and a shorter duration of resuscitation efforts (≤ 30 min) (OR 6.76, 95% CI 2.27-20.09) were associated with greater survival rate at discharge. CONCLUSION: IHCA occurring in cardiac surgical units and a duration of resuscitation efforts less than 30 min were associated with potentially increased rates of survival to discharge.

Aetiology and predictors of outcome in non-shockable in-hospital cardiac arrest: A retrospective cohort study from the Swedish Registry for Cardiopulmonary Resuscitation.

BACKGROUND: Non-shockable in-hospital cardiac arrest (IHCA) is a condition with diverse aetiology, predictive factors, and outcome. This study aimed to compare IHCA with initial asystole or pulseless electrical activity (PEA), focusing specifically on their aetiologies and the significance of predictive factors. METHODS: Using the Swedish Registry of Cardiopulmonary Resuscitation, adult non-shockable IHCA cases from 2018 to 2022 (n = 5788) were analysed. Exposure was initial rhythm, while survival to hospital discharge was the primary outcome. A random forest model with 28 variables was used to generate permutation-based variable importance for outcome prediction. RESULTS: Overall, 60% of patients (n = 3486) were male and the median age was 75 years (IQR 67-81). The most frequent arrest location (46%) was on general wards. Comorbidities were present in 79% of cases and the most prevalent comorbidity was heart failure (33%). Initial rhythm was PEA in 47% (n = 2702) of patients, and asystole in 53% (n = 3086). The most frequent aetiologies in both PEA and asystole were cardiac ischemia (24% vs. 19%, absolute difference [AD]: 5.4%; 95% confidence interval [CI] 3.0% to 7.7%), and respiratory failure (14% vs. 13%, no significant difference). Survival was higher in asystole (24%) than in PEA (17%) (AD: 7.3%; 95% CI 5.2% to 9.4%). Cardiopulmonary resuscitation (CPR) durations were longer in PEA, 18 vs 15 min (AD 4.9 min, 95% CI 4.0-5.9 min). The duration of CPR was the single most important predictor of survival across all subgroup and sensitivity analyses. Aetiology ranked as the second most important predictor in most analyses, except in the asystole subgroup where responsiveness at cardiac arrest team arrival took precedence. CONCLUSIONS: In this nationwide registry study of non-shockable IHCA comparing asystole to PEA, cardiac ischemia and respiratory failure were the predominant aetiologies. Duration of CPR was the most important predictor of survival, followed by aetiology. Asystole was associated with higher survival compared to PEA, possibly due to shorter CPR durations and a larger proportion of reversible aetiologies.

Blood laboratory analyses preceding in-hospital cardiac arrest: A matched case-control study.

BACKGROUND: Whether blood laboratory analyses differ in patients who later suffer in-hospital cardiac arrest (IHCA) compared to other hospitalised patients remains unknown. The aim of this study was to describe pre-arrest sampling frequencies, results, and trends in blood laboratory analyses in patients with IHCA compared to controls. METHODS: This study was a matched case-control study using national registries in Denmark. Cases were defined as patients with IHCA from 2017 to 2021. Controls were defined as hospitalised patients and were matched on age, sex, and date and length of admission. Data on a total of 51 different blood laboratory analyses were obtained. The laboratory analyses of primary interest were lactate, sodium, potassium, and haemoglobin. The index time for cases was defined as the time of cardiac arrest, and a corresponding index time was defined for controls based on the time to cardiac arrest for their corresponding case. Blood sampling frequencies were reported for blood laboratory analyses obtained either within the last 24 h before the index time or between the time of hospital admission and the index time. Blood sampling results were reported for blood laboratory analyses obtained within the last 24 h before the index time. RESULTS: A total of 9268 cases and 92,395 controls were included in this study. Cases underwent more frequent sampling of all blood laboratory analyses compared to controls. This higher sampling frequency was more pronounced for lactate compared to sodium, potassium, or haemoglobin. The last measured lactate was higher in cases (median [IQR]: 2.3 [1.3, 4.9]) compared to controls (median [IQR]: 1.3 [0.9, 2.0]). Differences in sodium, potassium, and haemoglobin were negligible. The proportion of abnormally elevated levels of lactate and potassium increased as time to cardiac arrest decreased; no such effect was seen in controls. No temporal trend was evident for sodium or haemoglobin. CONCLUSIONS: Patients with IHCA undergo more frequent blood sampling prior to IHCA and have higher levels of lactate compared to matched controls.

Critical Event Checklists for Simulated In-Hospital Dysrhythmias in Children with Heart Disease.

Children with heart disease are at increased risk of unstable dysrhythmias and in-hospital cardiac arrest (IHCA). Clinician adherence to lifesaving processes of care is an important contributor to improving patient outcomes. This study evaluated whether critical event checklists improve adherence to lifesaving processes during simulated acute events secondary to unstable dysrhythmias. A randomized controlled trial was conducted in a cardiac ward in a tertiary care, academic children's hospital. Unannounced simulated emergencies involving dysrhythmias in pediatric patients with underlying cardiac disease were conducted weekly. Responders were pediatric and anesthesiology residents, respiratory therapists, and bedside registered nurses. Six teams were randomized into two groups-three received checklists (intervention) and three did not (control). Each team participated in four simulated scenarios over a 4-week pediatric cardiology rotation. Participants received a brief slideshow presentation, which included a checklist orientation, at the start of their rotation. Simulations were video and audio recorded and those with three or more participants were included for analysis. The primary outcome was team adherence to lifesaving processes, expressed as the percentage of completed critical management steps. Secondary outcomes included participant perceptions of the checklist usefulness in identifying and managing dysrhythmias. We used generalized estimating equations (GEE) models, which accounted for clustering within groups, to evaluate the effects of the intervention. A total of 24 simulations were conducted; one of the 24 simulations was excluded due to an insufficient number of participants. In our GEE analysis, 81.21% (78.96%, 83.47%) of critical steps were completed with checklists available versus 68.06% (59.38%, 76.74%) without checklists (p = 0.004). Ninety-three percent of study participants reported that they would use the checklists during an unstable dysrhythmia of a child with underlying cardiac disease. Checklists were associated with improved adherence to lifesaving processes during simulated resuscitations for unstable pediatric dysrhythmias. These findings support the use of scenario specific checklists for the management of unstable dysrhythmias in simulations involving pediatric patients with underlying cardiac disease. Future studies should investigate whether checklists are as effective in actual pediatric in-hospital emergencies.

Prognostic effects of cardiopulmonary resuscitation (CPR) start time and the interval between CPR to extracorporeal cardiopulmonary resuscitation (ECPR) on patient outcomes under extracorporeal membrane oxygenation (ECMO): a single-center, retrospective observational study.

BACKGROUND: The impact of the chronological sequence of events, including cardiac arrest (CA), initial cardiopulmonary resuscitation (CPR), return of spontaneous circulation (ROSC), and extracorporeal cardiopulmonary resuscitation (ECPR) implementation, on clinical outcomes in patients with both out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA), is still not clear. The aim of this study was to investigate the prognostic effects of the time interval from collapse to start of CPR (no-flow time, NFT) and the time interval from start of CPR to implementation of ECPR (low-flow time, LFT) on patient outcomes under Extracorporeal Membrane Oxygenation (ECMO). METHODS: This single-center, retrospective observational study was conducted on 48 patients with OHCA or IHCA who underwent ECMO at Hamad General Hospital (HGH), the tertiary governmental hospital of Qatar, between February 2016 and March 2020. We investigated the impact of prognostic factors such as NFT and LFT on various clinical outcomes following cardiac arrest, including 24-hour survival, 28-day survival, CPR duration, ECMO length of stay (LOS), ICU LOS, hospital LOS, disability (assessed using the modified Rankin Scale, mRS), and neurological status (evaluated based on the Cerebral Performance Category, CPC) at 28 days after the CA. RESULTS: The results of the adjusted logistic regression analysis showed that a longer NFT was associated with unfavorable clinical outcomes. These outcomes included longer CPR duration (OR: 1.779, 95%CI: 1.218-2.605, P = 0.034) and decreased survival rates for ECMO at 24 h (OR: 0.561, 95%CI: 0.183-0.903, P = 0.009) and 28 days (OR: 0.498, 95%CI: 0.106-0.802, P = 0.011). Additionally, a longer LFT was found to be associated only with a higher probability of prolonged CPR (OR: 1.818, 95%CI: 1.332-3.312, P = 0.006). However, there was no statistically significant connection between either the NFT or the LFT and the improvement of disability or neurologically favorable survival after 28 days of cardiac arrest. CONCLUSIONS: Based on our findings, it has been determined that the NFT is a more effective predictor than the LFT in assessing clinical outcomes for patients with OHCA or IHCA who underwent ECMO. This understanding of their distinct predictive abilities enables medical professionals to identify high-risk patients more accurately and customize their interventions accordingly.

Comparing characteristics and outcomes of in-hospital cardiac arrest during the early and late COVID-19 pandemic at an academic institution in Saudi Arabia.

BACKGROUND: The COVID-19 pandemic has introduced major changes in the resuscitation practices of cardiac arrest victims. AIM: We aimed to compare the characteristics and outcomes of patients who sustained in-hospital cardiac arrest (IHCA) during the early COVID-19 pandemic period (2020) with those during the late COVID-19 pandemic period (2021). METHODS: This was a retrospective review of adult patients sustaining IHCA at a single academic centre. We compared characteristics and outcomes of IHCA for 5 months in 2020 with those experiencing IHCA for 5 months in 2021. RESULTS: Patients sustaining IHCA during the early COVID-19 pandemic period had higher rates of delayed epinephrine administration of more than 5 min (13.4% vs. 1.9%; p < 0.01), more frequent delays in the initiation of chest compressions (55.6% vs. 17.9%; p < 0.01), and were intubated less often (23.0% vs. 59.3%; p < 0.01). In terms of outcomes, both return of spontaneous circulation (35.8% vs. 51.2%; p < 0.01) and survival to hospital discharge rates (13.9% vs. 30.2%; p < 0.01) were lower during the early COVID-19 pandemic period. CONCLUSIONS: The early COVID-19 pandemic period was associated with delays in epinephrine administration and chest compression initiation for IHCA. Moreover, both return of spontaneous circulation and survival to hospital discharge were lower during the early COVID-19 pandemic period.

Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records.

Background: Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored. This study aims to evaluate the performance of Deep SHapley Additive exPlanations (D-SHAP) model in accurately identifying the diagnosis code associated with the highest mortality risk. Methods: Incidences of at least one in-hospital cardiac arrest (IHCA) for 168,693 patients as well as 1,569,478 clinical records were extracted from Taiwan's National Health Insurance Research Database. We propose a D-SHAP model to provide insights into deep learning model predictions. We trained a deep learning model to predict the 30-day mortality likelihoods of IHCA patients and used D-SHAP to see how the diagnosis codes affected the model's predictions. Physicians were asked to annotate a cardiac arrest dataset and provide expert opinions, which we used to validate our proposed method. A 1-to-4-point annotation of each record (current decision) along with four previous records (historical decision) was used to validate the current and historical D-SHAP values. Results: A subset consisting of 402 patients with at least one cardiac arrest record was randomly selected from the IHCA cohort. The median age was 72 years, with mean and standard deviation of 69 ± 17 years. Results indicated that D-SHAP can identify the cause of mortality based on the diagnosis codes. The top five most important diagnosis codes, namely respiratory failure, sepsis, pneumonia, shock, and acute kidney injury were consistent with the physician's opinion. Some diagnoses, such as urinary tract infection, showed a discrepancy between D-SHAP and clinical judgment due to the lower frequency of the disease and its occurrence in combination with other comorbidities. Conclusions: The D-SHAP framework was found to be an effective tool to explain deep neural networks and identify most of the important diagnoses for predicting patients' 30-day mortality. However, physicians should always carefully consider the structure of the original database and underlying pathophysiology.

Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal.

BACKGROUND: Several prediction models of survival after in-hospital cardiac arrest (IHCA) have been published, but no overview of model performance and external validation exists. We performed a systematic review of the available prognostic models for outcome prediction of attempted resuscitation for IHCA using pre-arrest factors to enhance clinical decision-making through improved outcome prediction. METHODS: This systematic review followed the CHARMS and PRISMA guidelines. Medline, Embase, Web of Science were searched up to October 2021. Studies developing, updating or validating a prediction model with pre-arrest factors for any potential clinical outcome of attempted resuscitation for IHCA were included. Studies were appraised critically according to the PROBAST checklist. A random-effects meta-analysis was performed to pool AUROC values of externally validated models. RESULTS: Out of 2678 initial articles screened, 33 studies were included in this systematic review: 16 model development studies, 5 model updating studies and 12 model validation studies. The most frequently included pre-arrest factors included age, functional status, (metastatic) malignancy, heart disease, cerebrovascular events, respiratory, renal or hepatic insufficiency, hypotension and sepsis. Only six of the developed models have been independently validated in external populations. The GO-FAR score showed the best performance with a pooled AUROC of 0.78 (95% CI 0.69-0.85), versus 0.59 (95%CI 0.50-0.68) for the PAM and 0.62 (95% CI 0.49-0.74) for the PAR. CONCLUSIONS: Several prognostic models for clinical outcome after attempted resuscitation for IHCA have been published. Most have a moderate risk of bias and have not been validated externally. The GO-FAR score showed the most acceptable performance. Future research should focus on updating existing models for use in clinical settings, specifically pre-arrest counselling. Systematic review registration PROSPERO CRD42021269235. Registered 21 July 2021.

Plasma neurofilament light is a predictor of neurological outcome 12 h after cardiac arrest.

BACKGROUND: Previous studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24-72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes. METHODS: Observational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1-2 was considered a good outcome and CPC 3-5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC). RESULTS: Of 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p ≤ 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%. CONCLUSIONS: The prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA.

Triage body temperature and its influence on patients with acute myocardial infarction.

BACKGROUND: Fever can occur after acute myocardial infarction (MI). The influence of body temperature (BT) after hospital arrival on patients with acute MI has rarely been investigated. METHODS: Patients who were diagnosed with acute MI in the emergency department (ED) of a tertiary teaching hospital between 1 January 2020 and 31 December 2020 were enrolled. Based on the tympanic temperature obtained at the ED triage, patients were categorized into normothermic (35.5°C-37.5°C), hypothermic (< 35.5°C), or hyperthermic (> 37.5°C) groups. The primary outcome was in-hospital cardiac arrest (IHCA), while the secondary outcomes were adverse events. Statistical significance was set at p < 0.05. RESULTS: There were 440 enrollees; significant differences were found among the normothermic (n = 369, 83.9%), hypothermic (n = 27, 6.1%), and hyperthermic (n = 44, 10.0%) groups in the triage respiratory rate (median [IQR]) (20.0 [4.0] cycles/min versus 20.0 [4.0] versus 20.0 [7.5], p = 0.009), triage heart rate (88.0 [29.0] beats/min versus 82.0 [28.0] versus 102.5 [30.5], p < 0.001), presence of ST-elevation MI (42.0% versus 66.7% versus 31.8%, p = 0.014), need for cardiac catheterization (87.3% versus 85.2% versus 72.7%, p = 0.034), initial troponin T level (165.9 [565.2] ng/L versus 49.1 [202.0] versus 318.8 [2002.0], p = 0.002), peak troponin T level (343.8 [1405.9] ng/L versus 218.7 [2318.2] versus 832.0 [2640.8], p = 0.003), length of ICU stay (2.0 [3.0] days versus 3.0 [8.0] versus 3.0 [9.5], p = 0.006), length of hospital stay (4.0 [4.5] days versus 6.0 [15.0] versus 10.5 [10.8], p < 0.001), and infection during hospitalization (19.8% versus 29.6% versus 63.6%, p < 0.001) but not in IHCA (7.6% versus 14.8% versus 11.4%, p = 0.323) or any adverse events (50.9% versus 48.1% versus 63.6%, p = 0.258). Multivariable analysis showed no significant association of triage BT with IHCA or any major complication. CONCLUSION: Triage BT did not show a significant association with IHCA or adverse events in patients with acute MI. However, triage BT could be associated with different clinical presentations and should warrant further investigation.

Association between cardiopulmonary resuscitation audit results with in-situ simulation and in-hospital cardiac arrest outcomes and key performance indicators.

INTRODUCTION: In-situ simulation (ISS) is a method to evaluate the performance of hospital units in performing cardiopulmonary resuscitation (CPR). It is conducted by placing a high-fidelity mannequin at hospital units with simulated scenarios and having each unit's performance evaluated. However, little is known about its impact on actual patient outcomes. Therefore, we aimed to evaluate the association between the ISS results and actual outcomes of patients with in-hospital cardiac arrest (IHCA). METHODS: This retrospective study was conducted by reviewing Siriraj Hospital's CPR ISS results in association with the data of IHCA patients between January 2012 and January 2019. Actual outcomes were determined by patients' outcomes (sustained return of spontaneous circulation (ROSC) and survival to hospital discharge) and arrest performance indicators (time-to-first-epinephrine and time-to-defibrillation). These outcomes were investigated for association with the ISS scores in multilevel regression models with hospital units as clusters. RESULTS: There were 2146 cardiac arrests included with sustained ROSC rate of 65.3% and survival to hospital discharge rate of 12.9%. Higher ISS scores were significantly associated with improved sustained ROSC rate (adjusted odds ratio 1.32 (95%CI 1.04, 1.67); p = 0.01) and a decrease in time-to-defibrillation (-0.42 (95%CI -0.73, -0.11); p = 0.009). Although higher scores were also associated with better survival to hospital discharge and a decrease in time-to-first-epinephrine, most models for these outcomes failed to reach statistical significance. CONCLUSION: CPR ISS results were associated with some important patient outcomes and arrest performance indicators. Therefore, it may be an appropriate performance evaluation method that can guide the direction of improvement.

Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters.

BACKGROUND: In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. METHODS: This retrospective cohort study examined all rescue-treated patients hospitalized at the First Medical Center of the PLA General Hospital in Beijing, China, from January 2016 to December 2020. Five machine learning algorithms, including support vector machine, random forest, extra trees classifier (ETC), decision tree, and logistic regression algorithms, were trained to develop models for predicting IHCA. We included blood counts, biochemical markers, and coagulation markers in the model development. We validated model performance using fivefold cross-validation and used the SHapley Additive exPlanation (SHAP) for model interpretation. RESULTS: A total of 11,308 participants were included in the study, of which 7779 patients remained. Among these patients, 1796 (23.09%) cases of IHCA occurred. Among five machine learning models for predicting IHCA, the ETC algorithm exhibited better performance, with an AUC of 0.920, compared with the other four machine learning models in the fivefold cross-validation. The SHAP showed that the top ten factors accounting for cardiac arrest in rescue-treated patients are prothrombin activity, platelets, hemoglobin, N-terminal pro-brain natriuretic peptide, neutrophils, prothrombin time, serum albumin, sodium, activated partial thromboplastin time, and potassium. CONCLUSIONS: We developed a reliable machine learning-derived model that integrates readily available laboratory parameters to predict IHCA in patients treated with rescue therapy.

Survey of hospital practitioners: common understanding of cardiopulmonary resuscitation definition and outcomes.

BACKGROUND: Cardiopulmonary resuscitation (CPR) is internationally defined as chest compressions and rescue breaths, and is a subset of resuscitation. First used for out-of-hospital cardiac arrest, CPR is now frequently used for in-hospital cardiac arrest (IHCA) with different causes and outcomes. AIMS: This paper aims to describe clinical understanding of the role of in-hospital CPR and perceived outcomes for IHCA. METHODS: An online survey of a secondary care staff involved in resuscitation was conducted, focussing on definitions of CPR, features of do-not-attempt-CPR conversations with patients and clinical case scenarios. Data were analysed using a simple descriptive approach. RESULTS: Of 652 responses, 500 were complete and used for analysis. Two hundred eleven respondents were senior medical staff covering acute medical disciplines. Ninety-one percent of respondents agreed or strongly agreed that defibrillation is part of CPR, and 96% believed CPR for IHCA included defibrillation. Responses to clinical scenarios were dissonant, with nearly half of respondents demonstrating a pattern of underestimating survival and subsequently showing a desire to offer CPR in similar scenarios with poor outcomes. This was unaffected by seniority and level of resuscitation training. CONCLUSIONS: The common use of CPR in hospital reflects the broader definition of resuscitation. Recapturing the CPR definition for clinicians and patients as only chest compressions and rescue breaths may allow clinicians to better discuss individualised resuscitation care to aide meaningful shared decision-making around patient deterioration. This may involve reframing current in-hospital algorithms and uncoupling CPR from wider resuscitative measures.

Epidemiology of in-hospital cardiac arrest patients admitted to the intensive care unit in Australia: a retrospective observational study.

BACKGROUND: In-hospital cardiac arrest (IHCA) affects approximately 3000 patients annually in Australia. Introduction of the National Standard for Deteriorating Patients in 2011 was associated with reduced IHCA-related intensive care unit (ICU) admissions and reduced in-hospital mortality of such patients. AIMS: To assess whether the reduction in IHCA-related ICU admissions from hospital wards seen following the implementation of the national standard (baseline period 2013-2014) was sustained over the follow-up period (2015-2019) in Australia. METHODS: A multi-centre retrospective cohort study to compare the characteristics and outcomes of IHCA admitted to the ICU between baseline and follow-up periods. The primary outcome was the proportion of patients admitted to ICU from the ward following IHCA. Secondary outcomes included ICU and hospital mortality of IHCA-related ICU admissions. Data were analysed using hierarchical multivariable logistic regression. RESULTS: The proportion of cardiac arrest-related admissions from the ward was lower in the follow-up period when compared to baseline (4.1 vs 3.8%; P = 0.04). Such patients had lower illness severity and were more likely to have limitations of medical treatment at admission. However, after adjustment for severity of illness, the likelihood of being admitted to ICU following cardiac arrest on the ward increased in the follow-up period (odds ratio (OR) 1.13 (1.05-1.22); P = 0.001). Hospital mortality was lower in the follow-up period (50.3 vs 46.3%; P = 0.02), but after adjustment the likelihood of death did not differ between the periods (OR 1.0 (0.86-1.17); P = 0.98). CONCLUSION: After adjustment for the severity of illness, the likelihood of being admitted to ICU after IHCA slightly increased in the follow-up period.

A simulation-enhanced, spaced learning, interprofessional "code blue" curriculum improves ACLS algorithm adherence and trainee resuscitation skill confidence.

In-hospital cardiac arrest resuscitation training often happens in silos, with minimal interprofessional training. The aim of this study was to implement and evaluate a simulation-enhanced, interprofessional cardiac arrest curriculum in a university hospital. The curriculum ran monthly for 12 months, training interprofessional teams of internal medicine residents, nurses, respiratory therapists, and pharmacy residents. Teams participated in a 90-min high-fidelity simulation including "code blue" (30 min) followed by a 30-min debriefing and a repeat identical simulated "code blue" scenario. Teams were tested in an unannounced mock Code Blue the following month. Advanced Cardiac Life Support (ACLS) algorithm adherence was assessed using a standardized checklist. In-hospital cardiac arrest (IHCA) incidence and survival was tracked for 2 years prior, during, and 1 year after curriculum implementation. Team ACLS-algorithm adherence at baseline varied from 47% to 90% (mean of 71 ± 11%) and improved immediately following training (mean 88 ± 4%, range 80-93%, p = .011). This improvement persisted but decreased in magnitude over 1 month (mean 81 ± 7%, p = .013). Medical resident self-reported comfort levels with resuscitation skills varied widely at baseline, but improved for all skills post-curriculum. This simulation-enhanced, spaced practice, interprofessional curriculum resulted in a sustained improvement in team ACLS algorithm adherence.

Impact of Coronavirus Disease 2019 Pandemic on Cardiac Arrest and Emergency Care.

The incidence of both out-of-hospital and in-hospital cardiac arrest increased during the coronavirus disease 2019 (COVID-19) pandemic. Patient survival and neurologic outcome after both out-of-hospital and in-hospital cardiac arrest were reduced. Direct effects of the COVID-19 illness combined with indirect effects of the pandemic on patient's behavior and health care systems contributed to these changes. Understanding the potential factors offers the opportunity to improve future response and save lives.

Antecedents to and outcomes for in-hospital cardiac arrests in Australian hospitals with mature medical emergency teams: A multicentre prospective observational study.

BACKGROUND: The epidemiology and predictability of in-hospital cardiac arrests (IHCAs) in hospitals with established medical emergency teams (METs) is underinvestigated. OBJECTIVES: We categorised IHCAs into three categories: "possible suboptimal end-of-life planning" (possible SELP), "potentially predictable", or "sudden and unexpected" using age, Charlson Comorbidity Index, place of residence, functional independence, along with documented vital signs, K+ and HCO3 in the period prior to the IHCA. We also described the differences in characteristics and outcomes amongst these three categories of IHCAs. METHODS: This was a prospective observational study (1st July 2017 to 9th August 2018) of adult (18 years) IHCA patients in wards of seven Australian hospitals with well-established METs. RESULTS: Amongst 152 IHCA patients, 145 had complete data. The number (%) classified as possible SELP, potentially predictable, and sudden and unexpected IHCA was 50 (34.5%), 52 (35.8%), and 43 (29.7%), respectively. Amongst the 52 potentially predictable patients, six (11.5%) had missing vital signs in the preceding 6 hr, 18 (34.6%) breached MET criteria in the prior 24 hr but received no MET call, and 6 (11.5%) had a MET call but remained on the ward. Abnormal K+ and HCO3 was present in 15 of 51 (29.5%) and 13 of 51 (25.5%) of such patients, respectively. The 43 sudden and unexpected IHCA patients were mostly (97.6%) functionally independent and had the lowest median Charlson Comorbidity Index. In-hospital mortality for IHCAs classified as possible SELP, potentially predictable, and sudden and unexpected was 76.0%, 61.5%, and 44.2%, respectively (p = 0.007). Only four of 12 (33.3%) possible SELP survivors had a good functional outcome. CONCLUSIONS: In seven Australian hospitals with mature METs, only one-third of IHCAs were sudden and unexpected. Improving end-of-life care in elderly comorbid patients and enhancing the response to objective signs of deterioration may further reduce IHCAs in the Australian context.

Arrest Outcome Consortium Registry Analysis [AOCRA 2022]: Outcome Statistics of Cardiac Arrest in Tertiary Care Hospitals in India, Analysis of Five Year Data of Indian Online Cardiac Arrest Registry, www.aocregistry.com.

Aim and background: To publish data with outcome statistics from our online cardiac arrest (CA) outcome consortium (AOC) online registry. Materials and methods: Data on cardiac arrest (CA) from tertiary care hospitals were collected on the AOC registry online portal from January 2017 to May 2022. Survival endpoints from cardiac arrest events like ROSC, and survival at hospital discharge with neurological status at discharge were analyzed and presented. Studies of demographics, the association of outcome with age, gender, bystander CPR, low and no flow times, and admission lactate were also done along with suitable statistical analysis. Results: Out of 2,235 CA, 2,121 received CPR (1,998 IHCA, 123 Out of hospital Cardiac Arrest (OHCA)) as 114 were DNR. The males-female ratio was 70:30. Average age at arrest was 58.7 years. 26% OHCA received bystander CPR but survival advantage was not significant. (with 16%, without 14% p = 0.78). Asystole (67.7%), Pulseless Electrical Activity (PEA) (25.6%), and VF/pVT (6.7%) as first rhythm significantly influence survival (4.9, 8.6 and 39.4%: p < 0.001) ROSC was achieved in 355 (16.7%), with 173 (8.2%) alive and 141 (6.6%) having good (CPC ≤ 2) neurological state at discharge. At discharge, survival as well as CPC ≤ 2 outcomes were significantly better in females. On multivariate regression analysis, first rhythm and low flow time influence survival at discharge. Admission lactate (available only in 102 OHCA) was lower in survivors than non-survivors 10.3 vs 11.5 mmol/L but the difference was not statistically significant (p = 0.397]. Conclusion: Data from our AOC registry shows poor overall survival from CA. The Female gender had a higher survival rate. Ventricular Fibrillation/Pulseless Ventricular Tachycardia (VF/pVT) as first rhythm and low flow time influence the survival to discharge (CTRI/2022/11/047140). How to cite this article: Clerk AM, Patel K, Shah BA, Prajapati D, Shah RJ, Rachhadia J, et al. Arrest Outcome Consortium Registry Analysis [AOCRA 2022]: Outcome Statistics of Cardiac Arrest in Tertiary Care Hospitals in India, Analysis of Five Year Data of Indian Online Cardiac Arrest Registry, www.aocregistry.com. Indian J Crit Care Med 2023;27(5):322-329.