Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction caused by a dysregulated host response without central nervous system (CNS) infection (Gofton and Young, 2012). Symptoms and signs range from mild inattentiveness or disorientation, agitation, and hypersomnolence to more severe disturbance of consciousness and coma (Chung et al., 2020). Approximately 70% of the patients with bacteremia display neurological symptoms or signs ranging from lethargy to coma (Peidaee et al., 2018). SAE is associated with increased mortality, prolonged hospitalizations, and inpatient costs. It is also associated with higher severity on the Glasgow Coma Scale (GCS), sequential organ failure assessment score (SOFA), quick sequential organ failure assessment (qSOFA), the simplified acute physiology score (APACHE II) of patients followed by persistent cognitive and functional impairments (Iwashyna et al., 2010; Sonneville et al., 2017). Iwashyna et al. (2010) found that up to 70% of sepsis survivors exhibited lasting neurological impairment, including alterations in mood, cognition, and motor function, and up to 45% had neurocognitive impairments 1 year later. With a mortality rate of up to 63% (Eidelman et al., 1996), and morbidities mentioned above, SAE can have a major effect on the healthcare system, the economy, and the society.

Sepsis-associated encephalopathy is a diagnosis of exclusion. It is diagnosed in the absence of direct infection of the central nervous system, multi-organ failure, traumatic brain injury, fat embolism, and ingestion of illicit drugs (Iacobone et al., 2009). Due to sepsis complications and the lack of an early diagnosis system, diagnosis and management of SAE are often delayed, leading to significant morbidity and mortality. Early diagnosis and treatment for brain injury are crucial for the survival and prognosis of sepsis patients. Sonneville et al. (2017) reported that acute renal failure, hypoglycemia (< 3 mmol/L), hyperglycemia (> 10 mmol/L), hypercapnia (> 45 mmHg), hypernatremia (> 145 mmol/L), and Staphylococcus aureus infection were associated with the development of SAE. Recently, Yang et al. (2020) developed a nomogram to forecast mortality in patients with known SAE. However, to the best of our knowledge, there is currently no prediction model for the diagnosis of SAE. This study is the first attempt to establish a predictive nomogram for SAE, based on the sociodemographic and clinical data of 2,535 sepsis patients, to allow for individualized screening for SAE among septic patients.

The study aims to identify early and potential risk factors for SAE by a retrospective analysis of a large clinical database, and establish a comprehensive prediction model for SAE patients. The proposed nomogram can assist in clinical decision-making and identify sepsis patients at high risk for SAE, who should undergo further investigative tests, thus promoting early diagnosis and management of SAE.

The study observed that 41.6% of sepsis patients with SAE were identified during admission to the ICU. A hospital mortality rate of up to 64.7% was observed in patients with SAE. We identified that clinically relevant risk factors for SAE, including age, qSOFA score, and medications such as carbapenem antibiotics, quinolones antibiotics, steroids, midazolam, H₂-antagonist, diphenhydramine hydrochloride, and heparin sodium injection, had a significant impact on the occurrence of SAE. The study has established a comprehensive visual prediction model which can provide a probabilistic estimate of SAE at the earlier stages in individual sepsis patients. Furthermore, the nomogram showed satisfactory validity, discrimination, and clinical utility.

In this study, 41.6% of sepsis patients suffered from SAE. Previous studies have published the rates of SAE in patients with sepsis ranging from 8 to 70% (Bartynski et al., 2006). This could be the result of different diagnostic criteria. Feng et al. (2019) reported a 42.3% incidence of SAE in septic patients, whereas Yang et al. (2020) reported 50% incidence. This study result is consistent with their study results.

Our cohort study showed that SAE patients had higher SOFA, qSOFA, and APACHE II when compared to non-SAE patients and also a high hospital mortality rate of 64.7%. It shows that SAE patients with more severe organ dysfunction are associated with an increased risk of mortality and the related adverse clinical outcomes. The result is consistent with Yang et al.’s study. SAE patients presented significantly high APACHE II, SOFA scores, and 30-day mortality in a recent retrospective analysis involving more than 2,400 SAE patients (Yang et al., 2020). Feng et al. (2019) demonstrated that the incidence of 28-day mortality was 45.95% and 180-day mortality was 55.41%, and the multivariate stepwise regression analysis demonstrated that the risk of death in the SAE group was significantly higher than in the non-SAE group and that SAE was a risk factor for sepsis-related death (OR = 2.868). These results are consistent with our findings.

We identified clinical and potential risk factors for SAE, which confirms that SAE patients were older and had urinary system infection when compared to non-SAE patients. Sonneville et al. (2017) showed that compared to the non-SAE group, the SAE group included patients who were significantly older in age. Presence of comorbid urinary system infection in patients with delirium had been confirmed by many studies (Chae and Miller, 2015; Carson et al., 2017). Previous studies have also reported that urinary system infection is a risk factor for delirium (Gau et al., 2009; Dahl et al., 2010), urinary tract infections (UTI) increase a subject’s risk of developing delirium, or urinary system infection is a “common cause” of delirium (Lerner et al., 1997; Kamel, 2005). The potential mechanisms of the association between urinary system infection and neuropsychiatric disorders are induced by antibiotic treatment for urinary system infection. The antibiotics that were most frequently implicated were macrolides and fluoroquinolones. Mostafa et al. reported 15 cases of antibiotic-induced psychosis during treatment of a urinary system infection, with 60% of the cases determined to be “highly suggestive” of a causal relationship between antibiotic usage and psychosis (Mostafa and Miller, 2014). Contrary to previously published data (Zhang et al., 2012; Sonneville et al., 2017), we did not find any microorganism as the pathogen associated with SAE. It is attributed to different data sources.

The results of our cohort study show that medications were a critical risk factor for SAE. The most significant impact is from the use of antibiotics, followed by analgesics, sedative drugs, and other drugs. The study demonstrated that more SAE patients used antivirals, cephalosporins, penicillin, antifungal, macrolides, aminoglycosides, quinolones, carbapenem in antibiotic, quinolones, and carbapenem are associated with SAE by filtervariables with the LASSO method. This method surpasses the method of choosing predictors based on the strength of their univariable association with the outcome. In the study, the use of quinolones and carbapenem tended to be associated with SAE, in line with the study by Sonneville et al. (2017) and recent reviews highlighting their neurotoxicity (Bhattacharyya et al., 2016). Previous studies have suggested that the pathophysiology of quinolones and carbapenem-associated encephalopathy is associated with a disturbance of gamma-aminobutyric acid-ergic (GABAergic) interneurons. They affect the central nervous system mainly by inhibiting GABA receptors interfering with inhibitory neurotransmission and enhancing bursts of excitatory neurons, which is concentration-dependent (Hori and Shimada, 1993; Munoz-Gomez et al., 2015). Our results also suggested that the use of propofol, midazolam, and opioids was found to be more likely observed in SAE patients. Further analysis found that midazolam is found to be a risk factor with SAE patients. It is widely accepted that midazolam is an independent risk factor for delirium in critically ill patients. In a large population-based cohort study, Zaal et al. found that the risk of delirium occurrence in critically ill adults is related to benzodiazepine use. A daily dose of only 5 mg of midazolam administered to a coma- and delirium-free patient increased the odds of this patient developing delirium in the following day by 4%. It supports the study results. A large number of SAE patients used vasopressors. On the one hand, SAE patients were found to be more critically ill and had a higher incidence of circulatory failure. On the other hand, the use of vasopressor was found to be a risk factor for SAE, although our further analysis has not been confirmed. Vasopressor use is a known risk factor for long-term cognitive impairment after critical illness. However, the specific mechanisms of these factors cannot be individually determined by our data, and this question requires further research.

Besides, our cohort study demonstrated that the use of H₂-antagonist, steroids, and heparin sodium injection were risk factors for SAE. It has been reported by many studies that H₂-antagonist and steroids cause delirium (Nguyen et al., 2011; Mauran et al., 2016). Tawadrous et al. (2014) demonstrated that compared to a lower dose, initiation of the current standard dose of histamine 2 receptor antagonists (H2RA) in older adults is associated with a small absolute increase in the 30-day risk of altered mental status. Yamada et al. (2018) found that steroid use was the determinant of progression to delirium in an intensive care unit, and research by Romain Sonneville et al. also found that steroid use was an independent risk factor for SAE (Yamada et al., 2018). These studies supported our study results. An interesting finding in the study was that heparin sodium injection and diphenhydramine hydrochloride were found to be risk factors for SAE. The result is consistent with several other studies (Shobugawa et al., 2007; Rothberg et al., 2013; Bidaki et al., 2017). However, the study for the first time demonstrated a strong association between heparin sodium injection, diphenhydramine hydrochloride, and SAE in a large cohort of sepsis patients. It is proposed that we pay attention to monitoring the mental changes in patients when using the above drugs.

The lack of validated predictive tools for early-stage SAE in sepsis patients and the equivocal efficacy of SAE interventions prompted us to develop a novel predictive modeling system using the nomogram methodology. For the construction of the clinical features and risk factors, 89 candidate features were reduced to nine potential predictors (carbapenem antibiotics, quinolones antibiotics, steroids, midazolam, H₂-antagonist, diphenhydramine hydrochloride, and heparin sodium injection) by the LASSO method. The nine potential predictors established a comprehensive visual nomogram for predicting SAE patients. The nomogram demonstrated adequate discrimination in the primary cohort (AUC, 0.743; 95% CI: 0.720–0.766), which surprisingly improved in the validation cohort (AUC, 0.762; 95% CI: 0.716–0.807). We developed and validated a nomogram in the study, which could assess clinical variables. Both physicians and patients could perform an individualized prediction of the risk of SAE with this easy-to-use scoring system, which is in line with the current trend toward personalized medicine. The most important reason for using the nomogram is based on the need to interpret the individualized needs for additional treatment and improve patient outcomes. DCA was applied in this study. This novel method offers insight into the clinical consequences based on the threshold probability, from which the net benefit could be derived. The decision curve showed that if the threshold probability of a patient or doctor was > 10%, then using the SAE nomogram to predict SAE added more benefit compared to not using the SAE nomogram.

There were two limitations in the study. Firstly, the study was based on electronic MIMIC-III, whose data were generated during routine clinical practice. Thus, it is possible that the cohort selection is not exactly consistent with the definition of sepsis based on the guidelines, and neuroimaging data were also not included in the database. Besides, the study only conducted internal validation, and thus external validations are needed. Thus, the current nomogram can only provide a certain reference for SAE forecasts, and further modifications may be required once diagnostic methods are developed.

A nomogram was established for individualized prediction of SAE in sepsis patients and it showed satisfactory performance. It can be conveniently used in the clinical setting and may help physicians to identify SAE patients on time. It can also help physicians to take timely intervention measures to reduce the incidence of SAE and improve patient prognosis.

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

LZ and YW developed the central ideas for this work and wrote the first draft of the manuscript. HZ and ZG collected the data regarding the manuscript. YL revised the manuscript, worked on the English, and made the final version of the manuscript. All authors read and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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