Scoring Systems to Predict Survival or Neurological Recovery after Out-of-hospital Cardiac Arrest

Naik, Rishi; Mandal, Indrajeet; Gorog, Diana A.

doi:10.15420/ecr.2022.05

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In determining the prognosis of cardiac arrest, various scoring systems have been designed for different stages of cardiac arrest [29] and for each stage of sudden cardiac arrest. A typical scoring system for predicting ROSC is the ROSC after cardiac arrest (RACA) score, which consists of sex, age, etiology, the witness of arrest, location of arrest, initial shockable rhythm, bystander CPR, and emergency medical system arrival time.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Serum Lactate Dehydrogenase Level Measured in the Emergency Department in Predicting Clinical Outcome in Out-of-Hospital Cardiac Arrest: A Multicenter, Observational Study

Kim

2023

JCM

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Introduction: Out-of-hospital cardiac arrest (OHCA) is complex, and risk stratification tools have the potential to include components other than clinical risk indicators, thus requiring extensive studies. Simple and accurate biomarkers for OHCA patients with poor prognoses are still needed. Serum lactate dehydrogenase (LDH) has been identified as a risk factor in patients with various diseases, such as cancer, liver disease, severe infections, and sepsis. The primary aim of this study was to assess the accuracy of LDH values at initial presentation in the emergency department (ED) in predicting the clinical outcome in OHCA. Methods: This retrospective multicenter observational study was performed in the ED of two tertiary university hospitals and one general hospital between January 2015 and December 2021. All patients with OHCA who visited the ED were included. The primary outcome was the sustained return of spontaneous circulation (ROSC; >20 min) after advanced cardiac life support (ACLS). The secondary outcome was survival to discharge (including home care and nursing care discharge) among patients with ROSC. The neurological prognosis was considered a tertiary outcome in patients who survived to discharge. Results: In total, 759 patients were enrolled in the final analysis. The median LDH level in the ROSC group was 448 U/L (range: 112–4500), which was significantly lower than that in the no-ROSC group (p < 0.001). The median LDH level in the survival-to-discharge group was 376 U/L (range: 171–1620), which was significantly lower than that in the death group (p < 0.001). Using the adjusted model, the odds ratio of the LDH value (≤634 U/L) for primary outcomes was 2.418 (1.665–3.513) and the odds ratio of LDH value (≤553 U/L) for secondary outcomes was 4.961 (2.184–11.269). Conclusions: In conclusion, the serum LDH levels of patients with OHCA measured in the ED can potentially serve as a predictive marker for clinical outcomes such as ROSC and survival to discharge, although it may be difficult to predict neurological outcomes.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Serum Lactate Dehydrogenase Level Measured in the Emergency Department in Predicting Clinical Outcome in Out-of-Hospital Cardiac Arrest: A Multicenter, Observational Study

Kim

2023

JCM

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“…Amacher et al made meta-analysis of Cstatistic for only 3 scores, as OHCA, CAHP and GO-FAR scores. Their analysis showed good prognostic accuracy in predicting poor neurological outcome or mortality when using OHCA and CAHP scores, but no discrimination was made according to the time of the endpoints [77][78][79].…”

Section: Plos Onementioning

confidence: 99%

Prediction performance of scoring systems after out-of-hospital cardiac arrest: A systematic review and meta-analysis

Kiss,

Nagy,

Kói

et al. 2024

PLoS ONE

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Introduction Ongoing changes in post resuscitation medicine and society create a range of ethical challenges for clinicians. Withdrawal of life-sustaining treatment is a very sensitive, complex decision to be made by the treatment team and the relatives together. According to the guidelines, prognostication after cardiopulmonary resuscitation should be based on a combination of clinical examination, biomarkers, imaging, and electrophysiological testing. Several prognostic scores exist to predict neurological and mortality outcome in post-cardiac arrest patients. We aimed to perform a meta-analysis and systematic review of current scoring systems used after out-of-hospital cardiac arrest (OHCA). Materials and methods Our systematic search was conducted in four databases: Medline, Embase, Central and Scopus on 24th April 2023. The patient population consisted of successfully resuscitated adult patients after OHCA. We included all prognostic scoring systems in our analysis suitable to estimate neurologic function as the primary outcome and mortality as the secondary outcome. For each score and outcome, we collected the AUC (area under curve) values and their CIs (confidence iterval) and performed a random-effects meta-analysis to obtain pooled AUC estimates with 95% CI. To visualize the trade-off between sensitivity and specificity achieved using different thresholds, we created the Summary Receiver Operating Characteristic (SROC) curves. Results 24,479 records were identified, 51 of which met the selection criteria and were included in the qualitative analysis. Of these, 24 studies were included in the quantitative synthesis. The performance of CAHP (Cardiac Arrest Hospital Prognosis) (0.876 [0.853–0.898]) and OHCA (0.840 [0.824–0.856]) was good to predict neurological outcome at hospital discharge, and TTM (Targeted Temperature Management) (0.880 [0.844–0.916]), CAHP (0.843 [0.771–0.915]) and OHCA (0.811 [0.759–0.863]) scores predicted good the 6-month neurological outcome. We were able to confirm the superiority of the CAHP score especially in the high specificity range based on our sensitivity and specificity analysis. Conclusion Based on our results CAHP is the most accurate scoring system for predicting the neurological outcome at hospital discharge and is a bit less accurate than TTM score for the 6-month outcome. We recommend the use of the CAHP scoring system in everyday clinical practice not only because of its accuracy and the best performance concerning specificity but also because of the rapid and easy availability of the necessary clinical data for the calculation.

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“…While many intensive care unit (ICU) prediction scores exist (OHCA, CAST, MIRACLE2, etc. ), 10 their performance fluctuates and invariably degrades when applied to populations different from those used to derive the scores. Machine learning (ML) holds the promise of generating new insight into a vast number of medical topics, 11 – 13 including for the prediction of sepsis or renal failure.…”

Section: Introductionmentioning

confidence: 99%

A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest

Pey,

Doumard,

Komorowski

et al. 2024

DIGITAL HEALTH

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Background Out-of-hospital cardiac arrest (OHCA) represents a major burden for society and health care, with an average incidence in adults of 67 to 170 cases per 100,000 person-years in Europe and in-hospital survival rates of less than 10%. Patients and practitioners would benefit from a prognostication tool for long-term good neurological outcomes. Objective We aim to develop a machine learning (ML) pipeline on a local database to classify patients according to their neurological outcomes and identify prognostic features. Methods We collected clinical and biological data consecutively from 595 patients who presented OHCA and were routed to a single regional cardiac arrest centre in the south of France. We applied recursive feature elimination and ML analyses to identify the main features associated with a good neurological outcome, defined as a Cerebral Performance Category score less than or equal to 2 at six months post-OHCA. Results We identified 12 variables 24 h after admission, capable of predicting a six-month good neurological outcome. The best model (extreme gradient boosting) achieved an AUC of 0.96 and an accuracy of 0.92 in the test cohort. Conclusion We demonstrated that it is possible to build accurate, locally optimised prediction and prognostication scores using datasets of limited size and breadth. We proposed and shared a generic machine-learning pipeline which allows external teams to replicate the approach locally.

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Scoring Systems to Predict Survival or Neurological Recovery after Out-of-hospital Cardiac Arrest

Cited by 6 publications

References 27 publications

Diagnostic Value of Serum Lactate Dehydrogenase Level Measured in the Emergency Department in Predicting Clinical Outcome in Out-of-Hospital Cardiac Arrest: A Multicenter, Observational Study

Diagnostic Value of Serum Lactate Dehydrogenase Level Measured in the Emergency Department in Predicting Clinical Outcome in Out-of-Hospital Cardiac Arrest: A Multicenter, Observational Study

Prediction performance of scoring systems after out-of-hospital cardiac arrest: A systematic review and meta-analysis

A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest

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