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

Abstract

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.

Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

“…Several studies have reported models for predicting the outcomes of CA patients [ 8 ]. In a systematic review of current prediction models, the median AUROC value was 0.84 with an IQR of 0.80 to 0.89 [ 8 ].…”

“…Prognostic factors and prediction tools for survivors of IHCA and their neurologic outcomes have been identified in previous studies [ 3 - 8 ]. However, the evidence of an early warning system for predicting the mortality of IHCA survivors is limited.…”

Investigating Novel and Conventional Biomarkers for Post-resuscitation Prognosis: The Role of Cytokeratin-18, Neuron-specific Enolase, and Lactate

“…Bisbal et al reported the SAPS III as less accurate in determining the in-hospital mortality of post-CA patients compared to other general prognostic scores 39 . Signi cant efforts have been made in recent years to develop scoring systems that can be used to estimate the outcome of resuscitated patients 40 , and these often require background information about the patient and about often missing peri-arrest circumstances. Though promising, these scores are not as widely used as the classic general prognostic scores.…”

Evidence-Based Approach to Out-of-Hospital Cardiac Arrest

“…These include old age, unwitnessed cardiac arrest, lack of bystander CPR, achievement of return of spontaneous circulation (ROSC) after 30 min of CPR, initial non-shockable rhythm, elevated lactate level, and low pH (< 7.2) [ 13 •]. The 3 most commonly used predictors of outcome are initial cardiac rhythm, age, and CPR duration [ 14 ]. Some of the predictive models used in clinical practice are discussed below.…”