Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis

Sepsis is a severe and potentially life-threatening condition resulting from a dysregulated immune response to infection [1]. Early detection and prompt treatment are crucial for improving patient outcomes and reducing health care costs. In recent years, machine learning (ML) models have emerged as promising tools for detecting and managing sepsis in the intensive care unit (ICU) [2]. These models use complex algorithms and statistical methods to learn from large volumes of patient data, including vital signs, laboratory results, and electronic health records, and to predict the onset of sepsis before its clinical manifestations become apparent [3]. The early identification and treatment of sepsis are related to the improvement of patient prognosis. Machine learning-based warning systems may shorten recognition time. Adams R et al. [4] set up a system called the “Targeted Real-time Early Warning System”, and they found that early warning systems have the potential to identify sepsis patients early and improve their prognosis and can identify and prioritize sepsis patients who would benefit the most from early treatment. By enabling early detection, ML models hold tremendous potential for enhancing patient care and reducing the burden of sepsis on health care systems worldwide.

The Sepsis-3 definitions suggest that patients with at least two of the following three clinical variables may be prone to the poor outcomes typical of sepsis: (1) a low blood pressure (SBP ≤ 100 mmHg), (2) a high respiratory rate (≥ 22 breaths per min), or (3) altered mentation (Glasgow coma scale score < 15). Machine learning can utilize computers to review a large number of clinical cases, and mature machine learning models can be used to make real-time evaluations of whether patients will develop sepsis, allowing for immediate intervention.

In this study, we aimed to explore the use of ML models for predicting the onset of sepsis in the ICU. Specifically, we reviewed the literature on ML models for sepsis prediction, highlighting their strengths and limitations. Additionally, in this article, we discuss the potential impact of these models on patient outcomes, clinical decision-making, and health care costs. Through this meta-analysis, we hope to shed light on the promise of ML models as tools for improving the management of sepsis in the ICU and beyond.

The present study investigated 68 prognostic prediction models across 23 studies to assess the potential of machine learning models for predicting sepsis in the ICU. However, the risk of bias assessment revealed a high risk of bias in the analysis domain, which may be attributed to the small sample size, the processing of missing data, and the interpretation of complex data. Therefore, the research findings may have some deviation due to the insufficient sample size.

Sepsis is a severe medical condition that can cause widespread inflammation and damage to vital organs. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing health care costs. ML models can analyse large amounts of patient data, including vital signs, laboratory results, and electronic health records, to detect early signs of sepsis. ML algorithms can provide physicians with real-time recommendations for patient treatment and management based on the latest medical knowledge and patient data. The use of ML models for predicting the onset of sepsis in the ICU has the potential to revolutionize the way in which sepsis is detected, treated, and managed, leading to better patient outcomes and reduced health care costs.

Several studies have explored the potential of machine learning algorithms for predicting sepsis. Heather M et al. [28] developed a machine learning algorithm to predict severe sepsis and septic shock, which can predict, with high specificity, the impending occurrence of severe sepsis and septic shock. Lucas M Fleuren et al. designed a meta-analysis that found that individual machine learning models can accurately predict sepsis onset early, similar to the present study. Nianzong Hou et al. [29] developed an XGBoost model to predict 30-day mortality, which can assist clinicians in tailoring precise management and therapy for patients with sepsis. Dong Wang et al. [13] developed an artificial intelligence algorithm to predict sepsis early, which has shown good predictive ability in Chinese sepsis patients. However, external validation studies are necessary to confirm the universality of this method for the population and in treatment practice.

In this study, we concluded that two machine learning algorithms, the XGBoost and random forest, showed significant advantages in predicting sepsis incidence in ICU patients with higher ACC and c-index values compared to other models in this study, specifically the random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models. Compared to other studies, this study compared all previous machine learning models for predicting sepsis incidence in ICU patients, including 4,314,145 patients and 26 different machine learning models. This was a large, comprehensive study that strictly followed the PRISMA requirements for systematic evaluation and was methodologically rigorous and scientific. Based on this, we believe that our study is more accurate than previous studies.

The XGBoost and random forest are two machine learning algorithms that showed significant advantages compared to other models in the present study. XGBoost is a popular open-source software library for machine learning that is optimized for speed and scalability, making it one of the most efficient gradient boosting algorithms available. It can handle missing data and noisy data, making it a robust solution for real-world data problems. Random forest is a widely used ensemble machine learning algorithm that combines multiple trees to form a forest and produces a final prediction by aggregating the results from all the trees. These algorithms have been applied in various industries, including finance, health care, and marketing, and have won several machine learning competitions [30]. In our research, the random forest and XGBoost models showed significant advantages compared to other models. We also found other studies using machine learning to predict the incidence of sepsis. Bloch et al. [31] conducted a study using machine learning to predict the onset of sepsis. They found that the support vector machine (SVM) model had the best performance in predicting the onset of sepsis. Compared with this study, the study conducted by Bloch et al. focused on the data of a single medical centre and did not evaluate the data of other medical centres; therefore, the results can only reflect the situation of their single centre, lacking reference value for other regions.

Machine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that XGBoost and random forest models are the most commonly used models for predicting sepsis incidence in ICU patients, and they exhibit significant performance and accuracy compared to other models. The use of predictive models for early risk assessment has relatively ideal effects in preventing sepsis incidence in ICU patients; however, it still needs further improvement. Therefore, we look forward to more validated machine learning methods based on convenient, noninvasive, or minimally invasive predictive indicators, which may have significant performance and accuracy in predicting sepsis incidence in ICU patients.

This study also has some limitations. First, this study focused on the accuracy of machine learning models and did not include risk factors that lead to the high incidence rate of sepsis in ICU patients. Second, some included models contained special variables related to the diagnosis of sepsis (such as infection indicators), which are valuable for further validation and research in subsequent studies.