Cardiac arrest (CA) is characterized by abrupt cessation of the heart’s pumping function and is associated with a high mortality rate [1, 2]. In the United States, more than 400,000 individuals die from CA annually, with a survival-to-discharge rate of 10% [1]. Despite the widespread implementation of basic life support and substantial progress in therapeutic interventions, the management of CA remains a significant challenge [3].

Increasing the survival rate of CA survivors is a critical issue that warrants immediate attention in the field of critical care medicine. The poor prognosis of CA patients is often attributed to hypoxic-ischemic brain injury and dysfunction of other organs [4]. Prognostic assessment is an essential component of CA management. At present, serum biomarkers, neurophysiological tests, and imaging are commonly used to assess neuroprognosis for CA patients [5]. However, these methods can be influenced by sedation or limited by the unavailability of equipment and life support conditions [6, 7]. Although severity scoring systems, such as Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) II, and Simplified Acute Physiology Score (SAPS) II, are commonly used for prognostic assessment, their accuracy is reported to be moderate [8, 9, 10]. The advent of machine learning (ML)-based prediction models marks an innovative shift in the prognosis assessment of CA. As a specialized branch of artificial intelligence (AI), ML empowers computers to autonomously discern and learn from historical data, identifying underlying rules and patterns. By constructing precise algorithms, ML is capable of predicting targeted events or trends, thus enhancing the efficiency and accuracy of prognosis assessment [11]. However, ML-based models often suffer from the “black box” issue, that is, their internal mechanisms lack transparency, posing certain challenges in practical application [12].

A nomogram is a graphical tool that represents predictive factors in scaled segments to demonstrate the relationship between variables and outcomes [13]. Its intuitive design and ease of use allow clinicians to rapidly comprehend and apply complex prediction models [14]. Nomograms have demonstrated efficacy in predicting the incidence [15], complications [16], and outcomes [17, 18] of cardiovascular diseases. Therefore, in this study, we developed and validated a nomogram for predicting the risk of in-hospital mortality in CA patients.

In this retrospective study, a total of 996 CA patients were included and the efficacy of three methods in selecting variables for prediction models was compared. The LASSO regression approach revealed 10 independent risk factors, demonstrating a superior discriminative ability to predict in-hospital mortality. The results of external validation supported the good performance of the model. Consequently, the LASSO model was represented as a nomogram to improve its clinical applicability.

Redundant variables may introduce multicollinearity and lead to model overfitting, as well as increasing model complexity and reducing the interpretability and operability. Consequently, the process of variable selection is imperative, as it contributes to the creation of a model with both accuracy and efficiency. LASSO regression converges variables based on L1 regularization, reduces model overfitting and enhances its interpretability [21]. RFE offers high flexibility and shows compatibility with various algorithms, identifying the most impactful variable set through continuous iteration [22]. But the results of RFE can be compromised if the algorithm is insensitive to relevant variables [22]. XGBoost effectively quantifies the importance of variables in the ensemble tree models and captures non-linear relationships and variable interactions, showing strong robustness in the computation of large datasets [23]. However, both RFE and XGBoost are susceptible to overfitting, particularly in case of small sample sizes, high noise, or overly complex models. In addition, we found that XGBoost did not consider categorical variables, such as cause of CA, initial rhythm, and vasoactive drugs (Fig. 2C), potentially owing to the reduced accuracy of feature importance after one-hot encoding [23]. Therefore, LASSO regression shows strong potential in screening key features [24, 25]. In this study, the LASSO model had superior discriminative ability and adaptability, which highlighted its advantages in variable selection.

Assessing the prognosis of CA patients has consistently been a subject that requires the attention and resolution for clinicians. Current research has raised a spectrum of evaluation methods, mainly encompassing risk scores derived from multiple regression analysis [26, 27, 28] and prediction models based on ML algorithms [29, 30, 31]. ML-based models have demonstrated promising predictive capabilities; however, nomogram has certain valuable advantages when used for survival analyses, especially for interpreting the outputs. Nomogram facilitates interaction with clinicians through a user-friendly, operable interface. It integrates multidimensional variables into a single chart, simplifying complex calculations and rendering the results intuitive and easy to comprehend. Fig. 6B displayed the actual prediction process for a patient. By merely collecting the values of the required variables, the risk of in-hospital mortality can be intuitively obtained through the nomogram, thereby enabling a personalized assessment of patient prognosis. Consequently, nomograms have been extensively employed in research as an advantageous clinical prediction model.

Chen et al. [32] and Sun et al. [33] both extracted data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to construct nomogram models for predicting the risk of in-hospital mortality in CA patients, with AUC values of 0.801 (0.775, 0.835) and 0.799 (0.762, 0.837) in internal validation, respectively. Both studies used LASSO regression and logistic regression to identify independent risk factors. Chen et al. [32] selected age, malignant cancer, norepinephrine, heart rate, respiratory rate, temperature, peripheral oxygen saturation (SpO2), sodium, BUN, bicarbonate, and lactate, while Sun et al. [33] chose age, gender, heart rate, mean arterial pressure (MAP), respiratory rate, temperature, SpO2, PT, bicarbonate, Glasgow Coma Scale (GCS) score, and SAPS III score. However, considering the differences in the characteristics of the cohorts, the models based on the MIMIC-IV database often exhibit significant limitations in generalizability, with these two studies lacking the support from external validation. Nagy et al. [34] developed a nomogram for predicting 30-day mortality in 103 Out-of-hospital Cardiac Arrest (OHCA) patients, with the AUC value of 0.835 (0.755, 0.907) in internal validation. Variable selection was achieved through restricted cubic splines and the Akaike information criterion, including age, initial rhythm, heart rate, pH, and right ventricular end-diastolic diameter. Zhang et al. [18] developed a nomogram for predicting in-hospital mortality in 561 in-hospital cardiac arrest (IHCA) patients, identifying only rearrest, duration of cardiopulmonary resuscitation (CPR), and length of hospital stay as three independent risk factors through multivariate logistic regression, yet demonstrating good discriminative ability with an AUC value of 0.88 (0.83, 0.93). Both studies constructed robust nomogram models with fewer predictive factors, but faced issues with small sample sizes and a lack of external validation.

In this retrospective study, we initially constructed a nomogram model for predicting in-hospital mortality risk in a relatively large cohort of CA patients with an AUC value of 0.85 (0.80, 0.89). The calibration and decision curve confirmed its clinical applicability. Furthermore, we substantiated the generalizability of the model in the external validation set, with an AUC value of 0.84 (0.78, 0.90). Variable selection was achieved through LASSO regression and multivariate logistic regression, identifying age, hypertension, initial rhythm, cause of CA, CRRT, vasoactive drugs, temperature, BUN, lactate, and SOFA score as independently associated with mortality. These variables are readily accessible in clinical settings, enhancing the practicability and operation convenience of the nomogram.

However, this study has a few limitations that should be acknowledged. First, its retrospective nature might have introduced unavoidable biases. Second, although the final model was validated in an external cohort, the sample size is small and further validation in large-scale cohort is required. Third, OHCA and IHCA were simultaneously included in the analysis; consequently, some significant independent risk factors specifically associated with either of these conditions might have been overlooked.

In this study, we used variables identified via LASSO regression to developed a nomogram for predicting the in-hospital mortality in CA patients. The nomogram demonstrated robust discriminative ability and clinical utility.

The datasets supporting the conclusions of this article are available from the corresponding author on reasonable request.

PN: Conceptualization, Formal analysis, Investigation, Methodology, Writing-Original Draft. SX: Conceptualization, Formal analysis, Methodology, Writing-Original Draft. WZ: Data Curation, Formal analysis, Methodology, Writing-Review & Editing. CW: Data Curation, Formal analysis, Methodology, Writing-Review & Editing. GZ: Conceptualization, Methodology, Supervision, Writing-Review & Editing. QG: Data Curation, Formal analysis, Supervision, Writing-Review & Editing. XH: Data Curation, Formal analysis, Writing-Review & Editing. YZ: Conceptualization, Methodology, Supervision, Writing-Review & Editing. WH: Conceptualization, Methodology, Supervision, Writing-Review & Editing. MD: Conceptualization, Methodology, Supervision, Writing-Review & Editing. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Hangzhou First People’s Hospital, Westlake University School of Medicine (Ethic Approval Number: IIT-20230420-0077-01), and the informed consent was waived because the study was retrospective.

This research was funded by Zhejiang Provincial Medical and Health Technology Project (Grant. WKJ-ZJ-2315), Science and Technology Development Project of Hangzhou (Grant. 202204A10), and Science and Technology Project of Hangzhou (Grant. 20220919Y006).

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/RCM33387.