Validation of the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) score in an East Asian population

Validation of CASPRI, GO-FAR, PIHCA scores in predicting favorable neurological outcomes after in-hospital cardiac arrest; A five-year three center retrospective study in IRAN

BACKGROUND

Predicting neurological outcomes following in-hospital cardiac arrest is crucial for guiding subsequent clinical treatments. This study seeks to validate the effectiveness of the CASPRI, GO-FAR, and PIHCA tools in predicting favorable neurological outcomes after in-hospital cardiac arrest.

METHOD

This retrospective study utilized a Utstein-style structured form to review the medical records of patients who experienced in-hospital cardiac arrest between March 2018 and March 2023. Predictors were examined using multivariable logistic regression, and the validity of the tools was assessed using ROC curves. Statistical analysis was conducted using SPSS version 25 software.

RESULTS

Out of the 1100 patients included in the study, 42 individuals (3.8%) achieved a favorable neurological outcome. multivariable regression analysis revealed that age, respiratory failure, resuscitation shift, duration of renal failure, and CPC score 24 h before cardiac arrest were significantly associated with favorable neurological outcomes. The predictive abilities of the CASPRI, GO-FAR, and PIHCA scores were calculated as 0.99 (95% CI, 0.98-1.00), 0.98 (95% CI, 0.97-0.99), and 0.96 (95% CI, 0.94-0.99) respectively. A statistically significant difference was observed in the predictive abilities of the CASPRI and PIHCA scores (P = 0.001), while the difference between CASPRI and GO-FAR did not reach significance (P = 0.057). Additionally, there was no significant difference between the predictive abilities of GO-FAR and PIHCA scores (P = 0.159).

CONCLUSION

The study concludes that CASPRI and GO-FAR scores show strong potential as objective measures for predicting favorable neurological outcomes post-cardiac arrest. Integrating these scores into clinical decision-making may enhance treatment and care strategies, in the Iranian healthcare context.

Pericardiocentesis, Chest Tube Insertion, and Needle Thoracostomy During Resuscitation of Nontraumatic Adult In-Hospital Cardiac Arrest: A Retrospective Cohort Study

IMPORTANCE

In-hospital cardiac arrest (IHCA) is a significant public health burden. Rates of return of spontaneous circulation (ROSC) have been improving, but the best way to care for patients after the initial resuscitation remains poorly understood, and improvements in survival to discharge are stagnant. Existing North American cardiac arrest databases lack comprehensive data on the postresuscitation period, and we do not know current post-IHCA practice patterns. To address this gap, we developed the Discover IHCA study, which will thoroughly evaluate current post-IHCA care practices across a diverse cohort.

OBJECTIVES

Our study collects granular data on post-IHCA treatment practices, focusing on temperature control and prognostication, with the objective of describing variation in current post-IHCA practices.

DESIGN, SETTING, AND PARTICIPANTS

This is a multicenter, prospectively collected, observational cohort study of patients who have suffered IHCA and have been successfully resuscitated (achieved ROSC). There are 24 enrolling hospital systems (23 in the United States) with 69 individuals enrolling in hospitals (39 in the United States). We developed a standardized data dictionary, and data collection began in October 2023, with a projected 1000 total enrollments. Discover IHCA is endorsed by the Society of Critical Care Medicine.

MAIN OUTCOMES AND MEASURES

The study collects data on patient characteristics, including prearrest frailty, arrest characteristics, and detailed information on postarrest practices and outcomes. Data collection on post-IHCA practice was structured around current American Heart Association and European Resuscitation Council guidelines. Among other data elements, the study captures postarrest temperature control interventions and postarrest prognostication methods.

RESULTS

The majority of participating hospital systems are large, academic, tertiary care centers serving urban populations. The analysis will evaluate variations in practice and their association with mortality and neurologic function.

CONCLUSIONS AND RELEVANCE

We expect this study, Discover IHCA, to identify variability in practice and outcomes following IHCA and be a vital resource for future investigations into best practices for managing patients after IHCA.

Clinical features and outcomes of in-hospital cardiac arrest in code blue events: a retrospective observational study

Background

In-hospital cardiac arrest (IHCA) is a critical medical event with outcomes less researched compared to out-of-hospital cardiac arrest. This retrospective observational study aimed to investigate key aspects of IHCA epidemiology and prognosis in patients with Code Blue activation.

Methods

This retrospective observational study enrolled patients with Code Blue events in our hospital between January 2010 and October 2019. Participant characteristics, including age and sex, and IHCA characteristics, including the time of cardiac arrest, witnessed event, bystander cardiopulmonary resuscitation (CPR), initial shockable rhythm, vital signs at 1 and 6 h before IHCA, survival to hospital discharge (SHD), and the cardiac arrest survival postresuscitation in-hospital (CASPRI) score were included in univariate and multivariate logistic regression analyses with SHD as the primary endpoint.

Results

From the 293 Code Blue events that were activated during the study period, 81 participants were enrolled. Overall, the SHD rate was 28.4%, the median CPR duration was 14 (interquartile range, 6-28) min, and the rate of initial shockable rhythm was 19.8%. There were significant intergroup differences between the SHD and non-SHD groups in the CPR duration, shockable rhythm, and CASPRI score on univariate logistic regression analysis. Multivariate logistic regression analysis showed that the CASPRI score was the most accurate predictive factor for SHD (OR = 0.98, p = 0.006).

Conclusions

The CASPRI score is associated with SHD in patients with IHCA during Code Blue events. Therefore, the CASPRI score of IHCA patients potentially constitutes a simple, useful adjunctive tool for the management of post-cardiac arrest syndrome.

Predicting in-hospital cardiac arrest outcomes: CASPRI and GO-FAR scores

It is important to predict the neurological prognoses of in-hospital cardiac arrest (IHCA) patients immediately after recovery of spontaneous circulation (ROSC) to make further critical management. The aim of this study was to confirm the usefulness of the Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) and Good Outcome Following Attempted Resuscitation (GO-FAR) scores for predicting the IHCA immediately after the ROSC. This is a retrospective analysis of patient data from a tertiary general hospital located in South Korea. A total of 488 adult patients who had IHCA and achieved sustained ROSC from September 2016 to August 2021 were analyzed to compare effectiveness of the CASPRI and GO-FAR scores related to neurologic prognosis. The primary outcome was Cerebral Performance Category (CPC) score at discharge, defined as a CPC score of 1 or 2. The secondary outcomes were survival-to-discharge and normal neurological status or minimal neurological damage at discharge. Of the 488 included patients, 85 (20.8%) were discharged with good prognoses (CPC score of 1 or 2). The area under the receiver operating characteristic curve of CASPRI score for the prediction of a good neurological outcome was 0.75 (95% CI 0.69-0.81), whereas that of GO-FAR score was 0.67 (95% CI 0.60-0.73). The results of this study show that these scoring systems can be used for timely and satisfactory prediction of the neurological prognoses of IHCA patients after ROSC.

Artificial Intelligence in Resuscitation: A Scoping Review

INTRODUCTION

Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation.

METHODS

We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits.

RESULTS

After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance.

CONCLUSION

AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.

Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management

Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest

OBJECTIVES

Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation.

DESIGN

Analysis of the Get With The Guidelines-Resuscitation registry.

SETTING

Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017.

PATIENTS

Adult in-hospital cardiac arrest survivors.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age.

CONCLUSIONS

The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.

Cardiac Arrest Survival Postresuscitation In-Hospital (CASPRI) Score Predicts Neurological Favorable Survival in Emergency Department Cardiac Arrest

Background: This study was conducted to identify the predictive factors for survival and favorable neurological outcome in patients with emergency department cardiac arrest (EDCA). Methods: ED patients who suffered from in-hospital cardiac arrest (IHCA) from July 2014 to June 2019 were enrolled. The electronic medical records were retrieved and data were extracted according to the IHCA Utstein-style guidelines. Results: The cardiac arrest survival post-resuscitation in-hospital (CASPRI) score was associated with survival, and the CASPRI scores were lower in the survival group. Three components of the CASPRI score were associated with favorable neurological survival, and the CASPRI scores were lower in the favorable neurological survival group of patients who were successfully resuscitated. The independent predictors of survival were presence of hypotension/shock, metabolic illnesses, short resuscitation time, receiving coronary angiography, and TTM. Receiving coronary angiography and low CASPRI score independently predicted favorable neurological survival in resuscitated patients. The performance of a low CASPRI score for predicting favorable neurological survival was fair, with an AUROCC of 0.77. Conclusions: The CASPRI score can be used to predict survival and neurological status of patients with EDCA. Post-cardiac arrest care may be beneficial for IHCA, especially in patients with EDCA.

Neuroprognostic Accuracy of Quantitative Versus Standard Pupillary Light Reflex for Adult Postcardiac Arrest Patients: A Systematic Review and Meta-Analysis

OBJECTIVES

An automated infrared pupillometer measures quantitative pupillary light reflex using a calibrated light stimulus. We examined whether the timing of performing quantitative pupillary light reflex or standard pupillary light reflex may impact its neuroprognostic performance in postcardiac arrest comatose patients and whether quantitative pupillary light reflex may outperform standard pupillary light reflex in early postresuscitation phase.

DATA SOURCES

PubMed and Embase databases from their inception to July 2020.

STUDY SELECTION

We selected studies providing sufficient data of prognostic values of standard pupillary light reflex or quantitative pupillary light reflex to predict neurologic outcomes in adult postcardiac arrest comatose patients.

DATA EXTRACTION

Quantitative data required for building a 2 × 2 contingency table were extracted, and study quality was assessed using standard criteria.

DATA SYNTHESIS

We used the bivariate random-effects model to estimate the pooled sensitivity and specificity of standard pupillary light reflex or quantitative pupillary light reflex in predicting poor neurologic outcome during early (< 72 hr), middle (between 72 and 144 hr), and late (≧ 145 hr) postresuscitation periods, respectively. We included 39 studies involving 17,179 patients. For quantitative pupillary light reflex, the cut off points used in included studies to define absent pupillary light reflex ranged from 0% to 13% (median: 7%) and from zero to 2 (median: 2) for pupillary light reflex amplitude and Neurologic Pupil index, respectively. Late standard pupillary light reflex had the highest area under the receiver operating characteristic curve (0.98, 95% CI [CI], 0.97-0.99). For early standard pupillary light reflex, the area under the receiver operating characteristic curve was 0.80 (95% CI, 0.76-0.83), with a specificity of 0.91 (95% CI, 0.85-0.95). For early quantitative pupillary light reflex, the area under the receiver operating characteristic curve was 0.83 (95% CI, 0.79-0.86), with a specificity of 0.99 (95% CI, 0.91-1.00).

CONCLUSIONS

Timing of pupillary light reflex examination may impact neuroprognostic accuracy. The highest prognostic performance was achieved with late standard pupillary light reflex. Early quantitative pupillary light reflex had a similar specificity to late standard pupillary light reflex and had better specificity than early standard pupillary light reflex. For postresuscitation comatose patients, early quantitative pupillary light reflex may substitute for early standard pupillary light reflex in the neurologic prognostication algorithm.

Long-Term Outcomes After In-Hospital Cardiac Arrest: Does Pre-arrest Skeletal Muscle Depletion Matter?

Background: Skeletal muscle depletion is prevalent in elderly patients and is associated with unfavorable outcomes in patients with chronic diseases. However, the relationship between skeletal muscle mass and neurological outcomes following in-hospital cardiac arrest (IHCA) has not been evaluated. The aim of this study was to investigate whether skeletal muscle status before cardiac arrest is an independent factor affecting neurological outcomes in patients with IHCA. Methods: We reviewed a prospectively enrolled registry of IHCA patients. Consecutive adult patients (>18 years) admitted to a tertiary care hospital from 2013 to 2019 were included in the study. Of these, 421 patients who underwent abdominopelvic computed tomography within 3 months of cardiac arrest were included. Skeletal muscle index (SMI) was measured at the third lumbar vertebra, and skeletal muscle depletion was defined using sex- and body mass index-specific cutoffs of SMI. The primary outcome was a Cerebral Performance Category score of 1 or 2 at 6 months after cardiac arrest, which was considered a good neurological outcome. Results: Of the 421 patients, 248 (58.9%) had skeletal muscle depletion before IHCA. The patients without skeletal muscle depletion showed significantly better neurological outcomes at 6 months after cardiac arrest than those with pre-arrest muscle depletion (20.8 vs. 10.9%, p = 0.004). The absence of skeletal muscle depletion was significantly associated with good neurological outcomes in a multivariable logistic analysis (OR = 3.49, 95% confidence intervals: 1.83-6.65, p < 0.001), along with the absence of diabetes, presence of active cancer, shockable rhythm, and short resuscitation duration. Conclusion: Pre-arrest skeletal muscle depletion was associated with long-term mortality and poor neurological outcomes after IHCA. Skeletal muscle depletion may be used as a tool to identify at-risk patients who may benefit from aggressive treatments.

A retrospective study on the therapeutic effects of sodium bicarbonate for adult in-hospital cardiac arrest

To investigate whether the effects of sodium bicarbonate (SB) during cardiopulmonary resuscitation (CPR) would be influenced by blood pH and administration timing. Adult patients experiencing in-hospital cardiac arrest (IHCA) from 2006 to 2015 were retrospectively screened. Early intra-arrest blood gas data were obtained within 10 min of CPR. Multivariable logistic regression analysis and generalised additive models were used for effect estimation and data exploration, respectively. A total of 1060 patients were included. Only 59 patients demonstrated favourable neurological status at hospital discharge. Blood pH ≤ 7.18 was inversely associated with favourable neurological outcome (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.11-0.52; p value < 0.001) while SB use was not. In the interaction analysis for favourable neurological outcome, significant interactions were noted between SB use and time to SB (SB use × time to SB ≥ 20 min; OR 6.16; 95% CI 1.42-26.75; p value = 0.02). In the interaction analysis for survival to hospital discharge, significant interactions were noted between SB use and blood pH (Non-SB use × blood pH > 7.18; OR 1.56; 95% CI 1.01-2.41; p value = 0.05). SB should not be empirically administered for patients with IHCA since its effects may be influenced by blood pH and administration timing.

Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

BACKGROUND

Accurate estimation of neurological outcomes after in-hospital cardiac arrest (IHCA) provides crucial information for clinical management. This study used artificial neural networks (ANNs) to determine the prognostic factors and develop prediction models for IHCA based on immediate preresuscitation parameters.

METHODS

The derived cohort comprised 796 patients with IHCA between 2006 and 2014. We applied ANNs to develop prediction models and evaluated the significance of each parameter associated with favorable neurological outcomes. An independent dataset of 108 IHCA patients receiving targeted temperature management was used to validate the identified parameters. The generalizability of the models was assessed through fivefold cross-validation. The performance of the models was assessed using the area under the curve (AUC).

RESULTS

ANN model 1, based on 19 baseline parameters, and model 2, based on 11 prearrest parameters, achieved validation AUCs of 0.978 and 0.947, respectively. ANN model 3 based on 30 baseline and prearrest parameters achieved an AUC of 0.997. The key factors associated with favorable outcomes were the duration of cardiopulmonary resuscitation; initial cardiac arrest rhythm; arrest location; and whether the patient had a malignant disease, pneumonia, and respiratory insufficiency. On the basis of these parameters, the validation performance of the ANN models achieved an AUC of 0.906 for IHCA patients who received targeted temperature management.

CONCLUSION

The ANN models achieved highly accurate and reliable performance for predicting the neurological outcomes of successfully resuscitated patients with IHCA. These models can be of significant clinical value in assisting with decision-making, especially regarding optimal postresuscitation strategies.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Background

More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known.

Methods and Results

We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c‐statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was −1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out‐of‐hospital cardiac arrest score (9 validations; median c‐statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c‐statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c‐statistic, 0.76).

Conclusions

Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

In-Hospital Cardiac Arrest: A Review

IMPORTANCE

In-hospital cardiac arrest is common and associated with a high mortality rate. Despite this, in-hospital cardiac arrest has received little attention compared with other high-risk cardiovascular conditions, such as stroke, myocardial infarction, and out-of-hospital cardiac arrest.

OBSERVATIONS

In-hospital cardiac arrest occurs in over 290 000 adults each year in the United States. Cohort data from the United States indicate that the mean age of patients with in-hospital cardiac arrest is 66 years, 58% are men, and the presenting rhythm is most often (81%) nonshockable (ie, asystole or pulseless electrical activity). The cause of the cardiac arrest is most often cardiac (50%-60%), followed by respiratory insufficiency (15%-40%). Efforts to prevent in-hospital cardiac arrest require both a system for identifying deteriorating patients and an appropriate interventional response (eg, rapid response teams). The key elements of treatment during cardiac arrest include chest compressions, ventilation, early defibrillation, when applicable, and immediate attention to potentially reversible causes, such as hyperkalemia or hypoxia. There is limited evidence to support more advanced treatments. Post-cardiac arrest care is focused on identification and treatment of the underlying cause, hemodynamic and respiratory support, and potentially employing neuroprotective strategies (eg, targeted temperature management). Although multiple individual factors are associated with outcomes (eg, age, initial rhythm, duration of the cardiac arrest), a multifaceted approach considering both potential for neurological recovery and ongoing multiorgan failure is warranted for prognostication and clinical decision-making in the post-cardiac arrest period. Withdrawal of care in the absence of definite prognostic signs both during and after cardiac arrest should be avoided. Hospitals are encouraged to participate in national quality-improvement initiatives.

CONCLUSIONS AND RELEVANCE

An estimated 290 000 in-hospital cardiac arrests occur each year in the United States. However, there is limited evidence to support clinical decision making. An increased awareness with regard to optimizing clinical care and new research might improve outcomes.

Factors associated with the decision to terminate resuscitation early for adult in-hospital cardiac arrest: Influence of family in an East Asian society

Background

We attempted to identify factors associated with physicians’ decisions to terminate CPR and to explore the role of family in the decision-making process.

Methods

We conducted a retrospective observational study in a single center in Taiwan. Patients who experienced in-hospital cardiac arrest (IHCA) between 2006 and 2014 were screened for study inclusion. Multivariate survival analysis was conducted to identify independent variables associated with IHCA outcomes using the Cox proportional hazards model.

Results

A total of 1525 patients were included in the study. Family was present at the beginning of CPR during 722 (47.3%) resuscitation events. The median CPR duration was significantly shorter for patients with family present at the beginning of CPR than for those without family present (23.5 mins vs 30 min, p = 0.01). Some factors were associated with shorter time to termination of CPR, including arrest in an intensive care unit, Charlson comorbidity index score greater than 2, age older than 79 years, baseline evidence of motor, cognitive, or functional deficits, and vasopressors in place at time of arrest. After adjusting for confounding effects, family presence was associated with shorter time to termination of CPR (hazard ratio, 1.25; 95% confidence interval, 1.06–1.46; p = 0.008).

Conclusion

Clinicians’ decisions concerning when to terminate CPR seemed to be based on outcome prognosticators. Family presence at the beginning of CPR was associated with shorter duration of CPR. Effective communication, along with outcome prediction tools, may avoid prolonged CPR efforts in an East Asian society.

A novel methodological framework for multimodality, trajectory model-based prognostication

INTRODUCTION

Prognostic tools typically combine several time-invariant clinical predictors using regression models that yield a single, time-invariant outcome prediction. This results in considerable information loss as repeatedly or continuously sampled data are aggregated into single summary measures. We describe a method for real-time multivariate outcome prediction that accommodates both longitudinal data and time-invariant clinical characteristics.

METHODS

We included comatose patients treated after resuscitation from cardiac arrest who underwent ≥6 h of electroencephalographic (EEG) monitoring. We used Persyst v13 (Persyst Development Corp, Prescott AZ) to generate quantitative EEG (qEEG) features and calculated hourly summaries of whole brain suppression ratio and amplitude-integrated EEG. We randomly selected half of subjects as a training sample and used the other half as a test sample. We applied group-based trajectory modeling (GBTM) to the training sample to group patients based on qEEG evolution, then estimated the relationship of group membership and clinical covariates with awakening from coma and surviving to hospital discharge using logistic regression. We used these parameters to calculate posterior probabilities of group membership (PPGMs) in the test sample, and built three prognostic models: adjusted logistic regression (no GBTM), unadjusted GBTM (no clinical covariates) and adjusted GBTM (all data). We compared these models performance characteristics.

RESULTS

We included 723 patients. Group-specific outcome estimates from a 7-group GBTM ranged from 0 to 75%. Compared to unadjusted GBTM, adjusted GBTM calibration was significantly improved at 6 and 12 h, and time to an outcome estimate <10% and <5% were significantly shortened. Compared to simple logistic regression, adjusted GBTM identified a substantially larger proportion of subjects with outcome probability <1%.

CONCLUSIONS

We describe a novel methodology for combining GBTM output and clinical covariates to estimate patient-specific prognosis over time. Refinement of such methods should form the basis for new avenues of prognostication research that minimize loss of clinically important information.