The Use of AI to Reduce Cardiogenic Shock in Post-Op Cardiac Surgery Patients

Cardiogenic shock (CS), as one of the postoperative complications of open-heart surgeries, is associated with increased morbidity, mortality rate, and length of critical care stay. Clinical monitoring is an essential component; however, clinical signs may not always give early signs of the start of deterioration. The use of AI, primarily through machine learning in medicine, helps recognize all forms of risk that may not be detected casually: The Use of AI to Reduce Cardiogenic Shock in Post-Op Cardiac Surgery Patients.

In postoperative cardiac care, this technology includes the capacity to identify CS right on its inception so as to initiate relevant interventions that enhance a patient’s well-being. In light of this concept, this paper aims to use available information and research data that can give a concrete answer to the question of whether AI plays a part in decreasing cardiogenic shock in patients who undergo open-heart surgery. It is also accompanied by a clear, evidence-based implication for clinical practice, thus aligning it with the principles of evidence-based practice and having a robust methodological evaluation.

PICOT Question

The guiding question for this synthesis is: Among patients who have undergone open-heart surgery, how does AI assessment, compared to standard assessment, affect the risk of developing cardiogenic shock? This question relates to the clinical need: the existing testing methods can be insufficient when identifying the development of CS for postoperative patients.

CS affects 2 to 6% of cardiac surgery patients and has high mortality rates if left unrecognized; thus, the consequences of timely diagnosis are very high. The involvement of AI in monitoring provides a direction toward potentially decreasing these poor outcomes by detecting clinical deterioration by using enhanced computational modeling.

Assessment of Evidence Quality

Methodological Rigor

All four studies under review employed retrospective cohort studies in evaluating the performance of AI models for prediction. Soltesz et al. (2024) conducted a study involving 1550 high-risk patients of cardiac surgery from a single center where a random forest classifier was applied to the electronic health record data. They conducted internal validation and used multivariable analysis to improve the reliability of the study’s results.

Jajcay et al. (2023) employed a machine learning pipeline on the MIMIC III critical care database. These concepts of the study included data preprocessing and missing value imputation as well as interpretability of the model: including these concepts enriched the methodological dimension of the study. Bohm et al. (2022), on a sample of over 3,200 ACS patients, employed logistic regression and Gaussian classifiers.

Further, Chen et al. (2025) have used major predictions on adverse cardiac events in the context of PCI and used four different kinds of machine learning models with feature selection with the Boruta approach and SHAP interpretability. Finally, although all applied valid statistical models, Soltesz et al. (2024) and Jajcay et al. (2023) had deeper designing the internal structure of the models.

Consistency of Findings

All four studies revealed that the AI-based prediction models were superior to the conventional risk assessment models. Soltesz et al. (2024), in another study, highlighted an AUC of 0.85 with only 80% sensitivity at 75% specificity and, hence, proved the ability of a predictive model to identify the early risk of CS in postoperative patients. Jajcay et al. (2023) got an AUC of 0.805 with the gradient-boosted trees algorithm.

Bohm et al. (2022) and Chen et al. (2025) also reported AUCs of 0.75 to 0.81. All the studies indicate that AI tools increase the identification of early risks regardless of algorithm or target population. Although some variations were observed regarding the architecture of the models and the data entered into algorithms, similarities in predictive results underscore the extensibility of the outcomes.

Effect Sizes, Bias, and Applicability

The studies were statistically and clinically significant. Particularly, the study by Soltesz et al. (2024) pointed to meaningful classification metrics to recognize high-risk patients. Similar to Jajcay et al. (2023), performance metrics were also encouraging, mainly due to the large dataset and number of different clinical inputs. Bias may arise from selective data retrieval as retrospective data in the records may not be complete or may hold other unknown confounding factors.

Single-center data in Soltesz et al. (2024) and Bohm et al. (2022) could have restricted generalization to other centers, whereas multicenter data with public database resources from Jajcay et al. (2023) and Chen et al. (2025) increases generalizability. All the studies incorporated variables that are easily assessable in an ICU setting and also endorsed by post-op cardiac care, such as heart rate, mean arterial pressure, oxygen saturation, and laboratory values.

Synthesis of Findings

The synthesized evidence highlights and proves that the application of clinical models driven by artificial intelligence can enhance the early identification of cardiogenic shock. Specifically, Soltesz et al. (2024) included sample patients who had undergone high-risk cardiac surgery, which relates well to the PICOT question.

They utilized a modified random forest approach that incorporated an intraoperative and postoperative model of key variables such as intraoperative transfusions needed, low cardiac output in the postoperative period, and renal function. It was developed for application soon after surgery and had a high discriminative capacity (AUC 0.85).

The implications of such models are apparent in the clinical setting: the model can pick up on a change in condition before manifested deterioration occurs, meaning clinicians are able to manage the condition at a crucial time. Their study was made practical for implementation in ICU settings due to the use of model transparency and variable weighting.

Jajcay et al. (2023) used the MIMIC III dataset and implemented data cleaning and imputation, and analyzed CS in patients of ACS using machine learning. It was not specific for post-surgical patients; however, the model collected information common to the ICU, including respiratory rate, oxygen, heart rate, and glucose levels.

Their approach was more comprehensive and more generalizable, and their framework can be applied to post-op cardiac patients. The AUC of 0.805 has been obtained without any hyperparameter tuning, which further indicates a high intrinsic worth of the clinical data. It also integrates their model well for future deployment in ICUs of patients who have undergone surgery.

Bohm et al. (2022) and Chen et al. (2025) applied AI to predict more extensive events CS in ACS and MACE in patients undergoing PCI. This model was generated using 3,232 patient records, excluding CS at admission, with regular inputs such as heart rate, oxygen saturation, and blood glucose. As an observational study, it did a good job in risk classification and highlighted the significance of using non-invasive real-time data for risk assessment.

Chen et al.’s (2025) study differs from the others as they used four models in an attempt to build a nomogram based on logistic regression. While specific to STEMI and PCI, the model construction, which involves the Gensini score, Killip class, renal indexes and platelet count, echoes the risk factors used in the surgical model. These studies indirectly support the notion that AI models based on common data elements can accurately identify the high risk of mortality, including CS, across a spectrum of cardiac patient populations.

Practice Recommendation

Based on the evidence, healthcare organizations should include AI-based predictive monitoring as a standard routine for patients who are recovering from open-heart surgery. Such AI models should integrate instant clinical measures, including heart rate, blood pressure, oxygenation, laboratory values, and fluid status, and offer a dynamic risk score for cardiogenic shock on a continual basis (Armoundas et al., 2024). It would act as an early warning system that would inform the clinical decisions of the related teams about the patients’ gradual deterioration before complications set in. It should be integrated into current EHRs and designed with the workflow of the ICU.

For optimal implementation, a multidisciplinary approach should be used to train ICU nurses, cardiac surgeons, and intensivists on the interpretation and clinical integration of AI outputs. Before going for the full implementation, a pilot study should first be conducted in order to assess the alert fatigue, the number of false positives, and the clinical effectiveness of the tool.

These tools should not act as substitutes for human analysis but as agents of increased alertness and prompt action (Dailah et al., 2024). If practiced appropriately, AI monitoring can decrease time spent in the Intensive Care Unit and the death rate among cardiac surgery patients, as well as overall resource utilization.

Justification for Practice Recommendation

The suggestion to implement AI-based predictive systems is based on strong empirical research evidence. Soltesz et al. (2024) analyzed a surgical population in which AI models with perioperative clinical data allow the identification of CS with sensitivity and specificity figures exceeding traditional indicators. Their study has the most significant degree of relevance to addressing the PICOT question.

The clinical relevance is enhanced due to the usage of routine data satisfying indicators and the capacity of the model to predict the initial hemodynamic condition. Since time is of the essence in the management of shock, as it can result in irreversible shock, risk stratification at an early stage is optimal.

Jajcay et al. (2023) and Bohm et al. (2022) show that AI models, when fine-tuned on the real-life data of ICU patients, work with a high degree of accuracy and efficiency. The implications are to extend the application of AI monitoring beyond a particular procedure to other cardiac care contexts. Chen et al. (2025) also highlighted the importance of using clinical variables in combination with interpretable models for timely intervention.

Altogether, these works offer an apparent premise for transitioning away from the mere theoretical to the practical in clinical practice. Using AI, in this case, pushes the option to focus on proactive care instead of a reactive approach, as informed by patient safety goals meant to enhance the delivery of critical care.

Conclusion

Conclusively, literature supports the idea that postoperative cardiac surgery patients are being monitored through artificial intelligence. Notably, using AI models in predicting cardiogenic shock is much more effective than employing conventional tools when applied to different groups of cardiac patients. This is especially the case in the care of high-risk patients, particularly after surgery, since early identification of deterioration can be crucial.

In all the studies, there was methodological quality, cohesion in findings, and relevance with critical care settings. In this regard, while there is a need for future prospective validation, the evidence currently supports pilot implementation. AI’s capacity to facilitate right-time clinical decisions, prevent the risk of adverse events, and enhance the worth of surgical treatment will make it a sound progression in the next advancement of evidenced-based surgical practice.

References

Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., Lett, E., Menon, B. K., Sheehan, K. A., & Al-Zaiti, S. S. (2024). Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association. Circulation, 149(14). https://doi.org/10.1161/cir.0000000000001201

Bohm, A., N Jajcay, J Jankova, K Petrikova, & Bezak, B. (2022). Artificial intelligence model for prediction of cardiogenic shock in patients with acute coronary syndrome. European Heart Journal. Acute Cardiovascular Care, 11(Supplement_1). https://doi.org/10.1093/ehjacc/zuac041.077

Chen, M., Sun, C., Yang, L., Zhang, T., Zhang, J., & Chen, C. (2025). Application of machine learning algorithms in predicting major adverse cardiovascular events after percutaneous coronary intervention in patients with new-onset ST-segment elevation myocardial infarction. Reviews in Cardiovascular Medicine, 26(2). https://doi.org/10.31083/rcm25758

Dailah, H. G., Koriri, M., Sabei, A., Kriry, T., & Zakri, M. (2024). Artificial intelligence in nursing: Technological benefits to nurse’s mental health and patient care quality. Healthcare, 12(24), 2555–2555. https://doi.org/10.3390/healthcare12242555

Jajcay, N., Bezak, B., Segev, A., Matetzky, S., Jankova, J., Spartalis, M., Tahlawi, M. E., Guerra, F., Friebel, J., Thevathasan, T., Berta, I., Pölzl, L., Nägele, F., Pogran, E., Cader, F. A., Jarakovic, M., Gollmann-Tepeköylü, C., Kollarova, M., Petrikova, K., . . . Böhm, A. (2023). Data processing pipeline for cardiogenic shock prediction using machine learning. Frontiers in Cardiovascular Medicine, 10. https://doi.org/10.3389/fcvm.2023.1132680

Soltesz, E. G., Parks, R. J., Jortberg, E. M., & Blackstone, E. H. (2024). Machine learning-derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patients. JTCVS Open, 22, 272–285. https://doi.org/10.1016/j.xjon.2024.10.002

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Question

Summative Assignment Part 3: Synthesis and practice recommendation

Attached Files:

Summative Assignment Part 3

Evidence Synthesis and Practice Recommendation (25% of grade)

Purpose: To synthesize appraised evidence from Part 2, determining if it supports a change in clinical practice based on the PICOT question.

Learner Objectives:

Synthesize evidence, apply critical thinking, and translate research findings into actionable practice recommendations.
Demonstrate critical thinking and analytical skills in evaluating research evidence and formulating practice recommendations.
Apply evidence-based principles to formulate informed clinical decisions and practice recommendations.
Communicate complex information clearly and effectively in writing.

Introduction: The Importance of Evidence Synthesis
In evidence-based practice, we don’t make decisions based on just one study; we need to understand the entire body of evidence. That’s where evidence synthesis comes in. It’s the crucial process of systematically combining findings from multiple individual studies to arrive at a conclusion about a body of evidence.

It involves more than just summarizing; it requires analyzing, integrating, and interpreting the findings of different studies to inform our clinical decisions and ultimately improve patient care.

The document titled “Understanding the Concept of Synthesis” provides a broad overview of what the concept is and how it is conducted and applied to literature review. As that document describes, effective synthesis involves identifying key themes and patterns across studies, evaluating the strength and quality of findings, and addressing inconsistencies or limitations. It is not simply summarizing individual studies or juxtaposing them; it’s about creating new insights and demonstrating a deep understanding of the subject matter.

This assignment directly follows Parts 1 and 2, utilizing your finalized PICOT question and Evidence Review Table. Building on your work, the focus here is to synthesize the evidence you have gathered. This involves identifying similarities and differences across studies, evaluating the strength and quality of findings, to determine if they warrant a practice change.

The Use of AI to Reduce Cardiogenic Shock in Post-Op Cardiac Surgery Patients

Assignment Instructions:

Review Appraisals: Revisit your completed “Evidence Appraisal” assignment (Part 2), focusing on the critical appraisals of each study related to your PICOT question.
Analyze Studies: Analyze the studies from your Evidence Review Table, specifically:

- Identify key similarities and differences in:
  - Study designs
  - Participant characteristics
  - Intervention protocols
  - Outcome measures
  - Findings and conclusions

Evaluate Evidence Quality:

- Assess the overall strength and quality of the evidence base by considering:
  - Methodological rigor of individual studies
  - Consistency of findings across studies
  - Magnitude of effect sizes
  - Potential sources of bias
  - Applicability of findings to your population of interest.

Synthesize Evidence: Develop a coherent synthesis of the evidence, highlighting key findings and addressing any inconsistencies or limitations.
Formulate Recommendation: Based on your synthesis, formulate a clear and concise recommendation regarding whether a change in clinical practice is warranted. Provide a robust justification, directly linking evidence from the appraised studies to your proposed practice change.
Submission: Submit a well-structured written document that includes:

- A brief restatement of your PICOT question.

- A synthesis of the evidence from your Evidence Review Table.

- Your evidence-based practice recommendation.

- A clear justification for your recommendation, citing specific studies.

- Adherence to APA (professional version) formatting guidelines.

- A maximum of five pages long excluding the front page and references.

SafeAssign Information: SafeAssign checks submissions for originality. While some matching content is expected (e.g., quotations, references, common phrases), there is no specific acceptable percentage. You must review all matches and revise your writing to minimize them as much as possible, except for properly cited quotations.

Important Notes:

SafeAssign is a comprehensive tool and may take several hours to generate an originality report during periods of high usage. Plan accordingly.
Submit a draft of your evidence synthesis paper without the reference page to assess the originality of your narrative. This will give you the most accurate evaluation of your writing.
You are responsible for obtaining any necessary contact information before submitting the final assignment.

The Use of AI to Reduce Cardiogenic Shock in Post-Op Cardiac Surgery Patients

The Use of AI to Reduce Cardiogenic Shock in Post-Op Cardiac Surgery Patients

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