Survival after in-hospital cardiac arrest is highly associated with the Age-combined Charlson Co-morbidity Index in a cohort study from a two-site Swedish University hospital

Piscator, Eva; Hedberg, Pontus; Göransson, Katarina E.; Djärv, Therese

doi:10.1016/j.resuscitation.2015.11.023

Cited by 42 publications

(31 citation statements)

References 27 publications

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“…These pre-and periarrest factors have previously been reported to be associated with hospital-or short-term survival in univariate or multivariate analyses. 7,10,20,21,24 Our study also confirmed that these variables are associated with long-term survival, even when adjusted for gender and, for the first time, for preceding derangements and aetiology. One could have suspected that cardiac aetiology would have been associated with improved outcome, but this was not the case even with the other outcomes (ROSC, 24 h survival).…”

Section: Discussionsupporting

confidence: 80%

“…26,27 We used age-adjusted charlson comorbidity index as a continuous variable to take into consideration the possible negative effect of cumulative age-related comorbidity in our analyses. 24 Third, despite a single centre design, TAYS is one of the five tertiary referral centres in Finland providing the most advanced care and the general wards represent all major specialties and our study population was very heterogeneous.…”

Section: Discussionmentioning

confidence: 94%

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Aetiology of in-hospital cardiac arrest on general wards

et al. 2016

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a b s t r a c tAim: Aetiology of in-hospital cardiac arrests (IHCAs) on general wards has not been studied. We aimed to determine the underlying causes for IHCAs by the means of autopsy records and clinical judgement of the treating consultants. Furthermore, we investigated whether aetiology and preceding vital dysfunctions are associated with long-term survival. Design and setting: Prospective observational study between 2009-2011 including 279 adult IHCA patients attended by medical emergency team in a Finnish university hospital's general wards. Results: The median age of the patients was 72 (64, 80) years, 185 (66%) were male, 178 (64%) of events were monitored/witnessed, first rhythm was shockable in 42 (15%) cases and 53 (19%) patients survived six months. Aetiology was determined as cardiac in 141 events, 73 of which were due to acute myocardial infarction. There were 138 non-cardiac IHCAs; most common causes were pneumonia (39) and exsanguination (16). No statistical difference was observed in the incidence of objective vital dysfunctions preceding the event between the cardiac and non-cardiac groups (40% vs. 44%, p = 0.448). Subjective antecedents were more common in the cardiac cohort (47% vs. 32%, p = 0.022), chest pain being an example (11% vs. 0.7%, p < 0.001). Reviewing all 279 IHCAs, only shockable primary rhythm, monitored/witnessed event and low comorbidity score were independently associated with 180-day survival. Conclusions: Cardiac aetiology underlies half of the IHCAs on general wards. Both objective and subjective antecedents are common. However, neither the cardiac aetiology nor the absence of preceding deterioration of vital signs were factors independently associated with a favourable outcome.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 94%

Aetiology of in-hospital cardiac arrest on general wards

et al. 2016

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“…( P > .05), however, age, diabetes, CCI and culprit artery were significantly different in the two groups ( P < .05). According to previous reports, age, diabetes, and CCIwere risk factors in predicting CA, so these may also be candidate predictors in our further analysis. The culprit artery of ACS cannot be clearly defined until an angiocardiography is done, which takes time and is unsuitable for early prediction, and so was not included in our analysis.…”

Section: Resultsmentioning

confidence: 90%

Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

Wu²,

Yang³

et al. 2019

Clinical Cardiology

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Background In‐hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS). Hypothesis A predicting model could help to identify the risk of IHCA among patients admitted with ACS. Methods We conducted a case‐control study and analyzed 21 337 adult ACS patients, of whom 164 had experienced CA. Vital signs, demographic, and laboratory data were extracted from the electronic health record. Decision tree analysis was applied with 10‐fold cross‐validation to predict the risk of IHCA. Results The decision tree analysis detected seven explanatory variables, and the variables' importance is as follows: VitalPAC Early Warning Score (ViEWS), fatal arrhythmia, Killip class, cardiac troponin I, blood urea nitrogen, age, and diabetes. The development decision tree model demonstrated a sensitivity of 0.762, a specificity of 0.882, and an area under the receiver operating characteristic curve (AUC) of 0.844 (95% CI, 0.805 to 0.849). A 10‐fold cross‐validated risk estimate was 0.198, while the optimism‐corrected AUC was 0.823 (95% CI, 0.786 to 0.860). Conclusions We have developed and internally validated a good discrimination decision tree model to predict the risk of IHCA. This simple prediction model may provide healthcare workers with a practical bedside tool and could positively impact decision‐making with regard to deteriorating patients with ACS.

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“…The reason for this is because TH was administered to patients with a high probability to develop poor outcomes such as those patients with relative contraindication for TH (e.g., sepsis), patients with prolonged time from collapse to ROSC (more than 30 min) and in-hospital patients who had poor cardiopulmonary reserve. [ 38 ] Another explanation for high unfavorable outcomes is that the time from ROSC to cooling induction took a long time to accomplish because there were insufficient number of ICU beds and the primary physicians lacked knowledge of TH. The other explanation is that we included patients with nonshockable rhythm, especially those patients who may have had prolonged time from collapse to CPR initiation or had irreversible diseases.…”

Section: Discussionmentioning

confidence: 99%

Neurological prognostications for the therapeutic hypothermia among comatose survivors of cardiac arrest

Kongpolprom¹,

Cholkraisuwat²

2018

Indian Journal of Critical Care Medicine

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Background:Currently, there are limited data of prognostic clues for neurological recovery in comatose survivors undergoing therapeutic hypothermia (TH). We aimed to evaluate clinical signs and findings that could predict neurological outcomes, and determine the optimal time for the prognostication.Materials and Methods:We retrospectively reviewed database of postarrest survivors treated with TH in our hospital from 2006 to 2014. Cerebral performance category (CPC), neurological signs and findings in electroencephalography (EEG) and brain computed tomography (CT) were evaluated. In addition, the optimal time to evaluate neurological status was analyzed.Results:TH was performed in 51 postarrest patients. Approximately 53% of TH patients survived at discharge and 33% of the hospital survivors had favorable outcome (CPC1-2). The prognostic clues for unfavorable outcome (CPC3-5) at discharge were lack of pupillary light response (PLR) and/or gag reflex after rewarming, and the absence of at least one of the brainstem reflexes, no eye-opening, or abnormal motor response on the 7th day. Myoclonus and seizure could not be used to indicate poor prognosis. In addition, prognostic values of EEG and CT findings were inconclusive.Conclusions:Our study showed the simple neurological signs helped predict short-term neurological prognosis. The most reliable sign determining unfavorable outcome was the lack of PLR. The optimal time to assess prognosis was either at 48–72 h or 7 days after return of spontaneous circulation.

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Survival after in-hospital cardiac arrest is highly associated with the Age-combined Charlson Co-morbidity Index in a cohort study from a two-site Swedish University hospital

Cited by 42 publications

References 27 publications

Aetiology of in-hospital cardiac arrest on general wards

Aetiology of in-hospital cardiac arrest on general wards

Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

Neurological prognostications for the therapeutic hypothermia among comatose survivors of cardiac arrest

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