Comparison of different clinical risk scores to predict long-term survival and neurological outcome in adults after cardiac arrest: results from a prospective cohort study

System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method

BACKGROUND

A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods.

METHODS

The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).

RESULTS

Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).

CONCLUSIONS

The NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.

Association between serum alkaline phosphatase and clinical prognosis in patients with acute liver failure following cardiac arrest: a retrospective cohort study

BACKGROUND

Acute liver failure (ALF) following cardiac arrest (CA) poses a significant healthcare challenge, characterized by high morbidity and mortality rates. This study aims to assess the correlation between serum alkaline phosphatase (ALP) levels and poor outcomes in patients with ALF following CA.

METHODS

A retrospective analysis was conducted utilizing data from the Dryad digital repository. The primary outcomes examined were intensive care unit (ICU) mortality, hospital mortality, and unfavorable neurological outcome. Multivariable logistic regression analysis was employed to assess the relationship between serum ALP levels and clinical prognosis. The predictive value was evaluated using receiver operator characteristic (ROC) curve analysis. Two prediction models were developed, and model comparison was performed using the likelihood ratio test (LRT) and the Akaike Information Criterion (AIC).

RESULTS

A total of 194 patients were included in the analysis (72.2% male). Multivariate logistic regression analysis revealed that a one-standard deviation increase of ln-transformed ALP were independently associated with poorer prognosis: ICU mortality (odds ratios (OR) = 2.49, 95% confidence interval (CI) 1.31-4.74, P = 0.005), hospital mortality (OR = 2.21, 95% CI 1.18-4.16, P = 0.014), and unfavorable neurological outcome (OR = 2.40, 95% CI 1.25-4.60, P = 0.009). The area under the ROC curve for clinical prognosis was 0.644, 0.642, and 0.639, respectively. Additionally, LRT analyses indicated that the ALP-combined model exhibited better predictive efficacy than the model without ALP.

CONCLUSIONS

Elevated serum ALP levels upon admission were significantly associated with poorer prognosis of ALF following CA, suggesting its potential as a valuable marker for predicting prognosis in this patient population.

Factors predicting mortality in the cardiac ICU during the early phase of targeted temperature management in the treatment of post-cardiac arrest syndrome - The RAPID score

Introduction

Survival rates after out-of-hospital cardiac arrest (OHCA) remain low, and early prognostication is challenging. While numerous intensive care unit scoring systems exist, their utility in the early hours following hospital admission, specifically in the targeted temperature management (TTM) population, is questionable. Our aim was to create a score system that may accurately estimate outcome within the first 12 h after admission in patients receiving TTM.

Methods

We analyzed data from 103 OHCA patients who subsequently underwent TTM between 2016 and 2022. Patient demographic data, prehospital characteristics, clinical and laboratory parameters were already available in the first 12 h after admission were collected. Following a bootstrap-based predictor selection, we constructed a nonlinear logistic regression model. Internal validation was performed using bootstrap resampling. Discrimination was described using the c-statistic, whereas calibration was characterized by the intercept and slope.

Results

According to the Akaike Information Criterion (AIC) heart rate (AIC = 9.24, p = 0.0013), age (AIC = 4.39, p = 0.0115), pH (AIC = 3.68, p = 0.0171), initial rhythm (AIC = 4.76, p = 0.0093) and right ventricular end-diastolic diameter (AIC = 2.49, p = 0.0342) were associated with 30-day mortality and were used to build our predictive model and nomogram. The area under the receiver-operating characteristics curve for the model was 0.84. The model achieved a C-statistic of 0.7974, with internally validated acceptable calibration (intercept: -0.0190, slope: 0.7772) and low error rates (mean absolute error: 0.040).

Conclusion

The model we have developed may be suitable for early risk assessment of patients receiving TTM as part of primary post-resuscitation care. The calculator needed for scoring can be accessed at the following link: https://www.rapidscore.eu/.

Impact of cardiopulmonary resuscitation duration on functional outcome, level of independence, and survival among patients with in-hospital cardiac arrests: A pilot study

BACKGROUND

Cardiovascular diseases (CVDs) are the leading cause of cardiac arrest (CA), which are presented as sudden cardiac arrest (SCA) and sudden cardiac death (SCD). To assess the impact of CPR duration on the functional outcome, level of independence, and survival among patients with in-hospital cardiac arrest (IHCA).

MATERIAL AND METHODS

This prospective longitudinal pilot study was conducted at a tertiary care hospital in South India. Data were collected using consecutive sampling techniques from nine patients with IHCA, and outcomes were measured using the cerebral performance category (CPC) and Katz level of independence (LOI) during the immediate post-CPR, 30th day, and 90th day. Based on the principles of pilot study design, descriptive statistics was used to analyze the results. Inferential statistics analysis was not applicable based on the sample size of the pilot study.

RESULTS

Nine patients were included in this pilot study. The mean and median age of the patients were 48.11 ± 8.66 (46, IQR, 32-67 years) and 77.8% were male patients. The primary medical diagnosis was cardiology and neurology conditions among 44.4% and 22.2% of patients. The mean and median CPR duration was 12.11 ± 4.59 minutes (IQR, 8-15.50) and 44.4% achieved a return of spontaneous circulation (ROSC) with a mean ROSC time of 5.56 ± 7.418. The mean CPC score in the immediate post-CPR period and 30th day was 4 ± 1.732 and 4.56 ± 1.33, with mortality of 66.7% and 33.3% survivors in the immediate post-CPR period. While the mean LOI score among the survivors during the immediate post-CPR and 30th day was zero and four. which highlights the complete dependency of patients during the immediate post-CPR with significant improvement by the 30th day and unchanged until the 90th day.

CONCLUSIONS

The overall mortality and survival were 88.8% and 11.1%, respectively, by the 90th day. The pilot study is feasible at the end of the study. However, due to the difficulty in obtaining CA, an additional tertiary hospital was included in the larger study.

Long-term survival following out-of-hospital cardiac arrest in women and men: Influence of comorbidities, social characteristics, and resuscitation characteristics

AIM

We aimed to study sex differences in long-term survival following out-of-hospital cardiac arrest (OHCA) compared to the general population, and determined associations for comorbidities, social characteristics, and resuscitation characteristics with survival in women and men separately.

METHODS

We followed 2,452 Danish (530 women and 1,922 men) and 1,255 Dutch (259 women and 996 men) individuals aged ≥25 years, who survived 30 days post-OHCA in 2009-2015, until 2019. Using Poisson regression analyses we assessed sex differences in long-term survival and sex-specific associations of characteristics mutually adjusted, and compared survival with an age- and sex-matched general population. The potential predictive value was assessed with the Concordance-index.

RESULTS

Post-OHCA survival was longer in women than men (adjusted incidence rate ratio (IRR) for mortality 0.74, 95%CI 0.61-0.89 in Denmark; 0.86, 95%CI 0.65-1.15 in the Netherlands). Both sexes had a shorter survival than the general population (e.g., IRR for mortality 3.07, 95%CI 2.55-3.70 and IRR 2.15, 95%CI 1.95-2.37 in Danish women and men). Higher age, glucose lowering medication, no dyslipidaemia medication, unemployment, and a non-shockable initial rhythm were associated with shorter survival in both sexes. Cardiovascular medication, depression/anxiety medication, living alone, low household income, and residential OHCA location were associated with shorter survival in men. Not living with children and bystander cardiopulmonary resuscitation provision were associated with shorter survival in women. The Concordance-indexes ranged from 0.51 to 0.63.

CONCLUSIONS

Women survived longer than men post-OHCA. Several characteristics were associated with long-term post-OHCA survival, with some sex-specific characteristics. In both sexes, these characteristics had low predictive potential.

Prediction of outcomes after cardiac arrest by a generative artificial intelligence model

Aims

To investigate the prognostic accuracy of a non-medical generative artificial intelligence model (Chat Generative Pre-Trained Transformer 4 - ChatGPT-4) as a novel aspect in predicting death and poor neurological outcome at hospital discharge based on real-life data from cardiac arrest patients.

Methods

This prospective cohort study investigates the prognostic performance of ChatGPT-4 to predict outcomes at hospital discharge of adult cardiac arrest patients admitted to intensive care at a large Swiss tertiary academic medical center (COMMUNICATE/PROPHETIC cohort study). We prompted ChatGPT-4 with sixteen prognostic parameters derived from established post-cardiac arrest scores for each patient. We compared the prognostic performance of ChatGPT-4 regarding the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and likelihood ratios of three cardiac arrest scores (Out-of-Hospital Cardiac Arrest [OHCA], Cardiac Arrest Hospital Prognosis [CAHP], and PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages [PROLOGUE score]) for in-hospital mortality and poor neurological outcome.

Results

Mortality at hospital discharge was 43% (n = 309/713), 54% of patients (n = 387/713) had a poor neurological outcome. ChatGPT-4 showed good discrimination regarding in-hospital mortality with an AUC of 0.85, similar to the OHCA, CAHP, and PROLOGUE (AUCs of 0.82, 0.83, and 0.84, respectively) scores. For poor neurological outcome, ChatGPT-4 showed a similar prediction to the post-cardiac arrest scores (AUC 0.83).

Conclusions

ChatGPT-4 showed a similar performance in predicting mortality and poor neurological outcome compared to validated post-cardiac arrest scores. However, more research is needed regarding illogical answers for potential incorporation of an LLM in the multimodal outcome prognostication after cardiac arrest.

Post-intensive care syndrome and health-related quality of life in long-term survivors of cardiac arrest: a prospective cohort study

Patients discharged from intensive care are at risk for post-intensive care syndrome (PICS), which consists of physical, psychological, and/or neurological impairments. This study aimed to analyze PICS at 24 months follow-up, to identify potential risk factors for PICS, and to assess health-related quality of life in a long-term cohort of adult cardiac arrest survivors. This prospective cohort study included adult cardiac arrest survivors admitted to the intensive care unit of a Swiss tertiary academic medical center. The primary endpoint was the prevalence of PICS at 24 months follow-up, defined as impairments in physical (measured through the European Quality of Life 5-Dimensions-3-Levels instrument [EQ-5D-3L]), neurological (defined as Cerebral Performance Category Score > 2 or Modified Rankin Score > 3), and psychological (based on the Hospital Anxiety and Depression Scale and the Impact of Event Scale-Revised) domains. Among 107 cardiac arrest survivors that completed the 2-year follow-up, 46 patients (43.0%) had symptoms of PICS, with 41 patients (38.7%) experiencing symptoms in the physical domain, 16 patients (15.4%) in the psychological domain, and 3 patients (2.8%) in the neurological domain. Key predictors for PICS in multivariate analyses were female sex (adjusted odds ratio [aOR] 3.17, 95% CI 1.08 to 9.3), duration of no-flow interval during cardiac arrest (minutes) (aOR 1.17, 95% CI 1.02 to 1.33), post-discharge job-loss (aOR 31.25, 95% CI 3.63 to 268.83), need for ongoing psychological support (aOR 3.64, 95% CI 1.29 to 10.29) or psychopharmacologic treatment (aOR 9.49, 95% CI 1.9 to 47.3), and EQ-visual analogue scale (points) (aOR 0.88, 95% CI 0.84 to 0.93). More than one-third of cardiac arrest survivors experience symptoms of PICS 2 years after resuscitation, with the highest impairment observed in the physical and psychological domains. However, long-term survivors of cardiac arrest report intact health-related quality of life when compared to the general population. Future research should focus on appropriate prevention, screening, and treatment strategies for PICS in cardiac arrest patients.

Prospective comparison of prognostic scores for prediction of outcome after out-of-hospital cardiac arrest: results of the AfterROSC1 multicentric study

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is a heterogeneous entity with multiple origins and prognoses. An early, reliable assessment of the prognosis is useful to adapt therapeutic strategy, tailor intensity of care, and inform relatives. We aimed primarily to undertake a prospective multicentric study to evaluate predictive performance of the Cardiac Arrest Prognosis (CAHP) Score as compare to historical dataset systematically collected after OHCA (Utstein style criteria). Our secondary aim was to evaluate other dedicated scores for predicting outcome after OHCA and to compare them to Utstein style criteria.

METHODS

We prospectively collected data from 24 French and Belgium Intensive Care Units (ICUs) between August 2020 and June 2022. All cases of non-traumatic OHCA (cardiac and non-cardiac causes) patients with stable return of spontaneous circulation (ROSC) and comatose at ICU admission (defined by Glasgow coma score ≤ 8) on ICU admission were included. The primary outcome was the modified Rankin scale (mRS) at day 90 after cardiac arrest, assessed by phone interviews. A wide range of developed scores (CAHP, OHCA, CREST, C-Graph, TTM, CAST, NULL-PLEASE, and MIRACLE2) were included, and their accuracies in predicting poor outcome at 90 days after OHCA (defined as mRS ≥ 4) were determined using the area under the receiving operating characteristic curve (AUROC) and the calibration belt.

RESULTS

During the study period, 907 patients were screened, and 658 were included in the study. Patients were predominantly male (72%), with a mean age of 61 ± 15, most having collapsed from a supposed cardiac cause (64%). The mortality rate at day 90 was 63% and unfavorable neurological outcomes were observed in 66%. The performance (AUROC) of Utstein criteria for poor outcome prediction was moderate at 0.79 [0.76-0.83], whereas AUROCs from other scores varied from 0.79 [0.75-0.83] to 0.88 [0.86-0.91]. For each score, the proportion of patients for whom individual values could not be calculated varied from 1.4% to 17.4%.

CONCLUSIONS

In patients admitted to ICUs after a successfully resuscitated OHCA, most of the scores available for the evaluation of the subsequent prognosis are more efficient than the usual Utstein criteria but calibration is unacceptable for some of them. Our results show that some scores (CAHP, sCAHP, mCAHP, OHCA, rCAST) have superior performance, and that their ease and speed of determination should encourage their use. Trial registration https://clinicaltrials.gov/ct2/show/NCT04167891.

External validation of the PROLOGUE score to predict neurological outcome in adult patients after cardiac arrest: a prospective cohort study

BACKGROUND

The PROLOGUE score (PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages) is a novel prognostic model for the prediction of neurological outcome after cardiac arrest, which showed exceptional performance in the internal validation. The aim of this study is to validate the PROLOGUE score in an independent cohort of unselected adult cardiac arrest patients and to compare it to the thoroughly validated Out-of-Hospital Cardiac Arrest (OHCA) and Cardiac Arrest Hospital Prognosis (CAHP) scores.

METHODS

This study included consecutive adult cardiac arrest patients admitted to the intensive care unit (ICU) of a Swiss tertiary teaching hospital between October 2012 and July 2022. The primary endpoint was poor neurological outcome at hospital discharge, defined as a Cerebral Performance Category (CPC) score of 3 to 5 including death.

RESULTS

Of 687 patients included in the analysis, 321 (46.7%) survived to hospital discharge with good neurological outcome, 68 (9.9%) survived with poor neurological outcome and 298 (43.4%) died. The PROLOGUE score showed an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI 0.80 to 0.86) and good calibration for the prediction of the primary outcome. The OHCA and CAHP score showed similar performance (AUROC 0.83 and 0.84 respectively), the differences between the three scores were not significant (p = 0.495). In a subgroup analysis, the PROLOGUE score performed equally in out-of-hospital and in-hospital cardiac arrest patients whereas the OHCA and CAHP score performed significantly better in OHCA patients.

CONCLUSION

The PROLOGUE score showed good prognostic accuracy for the early prediction of neurological outcome in adult cardiac arrest survivors in our cohort and might support early goals-of-care discussions in the ICU. Trial registration Not applicable.