A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes

Chen, Jiayi; Shi, Xiaobei; Diao, Mengyuan; Jin, Guangyong; Zhu, Ying; Hu, Wei; Su, Xi

doi:10.1186/s12873-020-00374-3

Cited by 59 publications

(58 citation statements)

References 30 publications

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“…Although the mechanism remains, to a wide extent, unclear, it is estimated that inflammation reaction and oxidative stress might invite an increase in RDW values, and simultaneously, these mechanisms may play a pivotal role in the poor prognosis of SAE [ 27 – 29 ]. Furthermore, in keeping with previous study, we also unearthed that age was independently associated with 30-day mortality [ 17 ]. It was probably attributable to the fact that elderly patients exhibit a higher risk and mortality from sepsis [ 30 ].…”

Section: Discussionsupporting

confidence: 74%

“…Published study conducted by Chen J et al [ 17 ] indicated that APACHE II and SOFA were independent risk factors for 28-day mortality in SAE patients, which was similar to our findings. A range of previous studies also have found that the mortality rate of sepsis patients is related to higher values of the GCS, SOFA, and the APACHE II score [ 10 , 18 , 19 ].…”

Section: Discussionmentioning

confidence: 99%

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Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Peng

Yang

et al. 2022

BMC Med Res Methodol

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Objective Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test. Results Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively. Conclusions The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Peng

Yang

et al. 2022

BMC Med Res Methodol

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“…Our results from multivariate Cox regression analysis demonstrated that baseline APACHE II score, baseline lactic acid level, septic shock, and baseline AFR were independent prognostic factors in septic patients caused by various infection sites. The APACHE II score is extensively used among critically ill patients with sepsis in the ICU, and is accurate for the prognosis, comparable the SOFA score [ 1 , 26 ]. Our results are also consistent with the apparent correlation between the APACHE II score and prognosis of sepsis.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Albumin-to-Fibrinogen Ratio for 28-Day Mortality among Patients with Sepsis from Various Infection Sites

Shen

Chang

et al. 2022

Mediators of Inflammation

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Purpose. This study investigated the prognostic value of the albumin-to-fibrinogen ratio (AFR) in patients with sepsis as a consequence of infection at various sites. Methods. A total of 300 patients with sepsis caused by various infection sites, who met the diagnostic criteria for sepsis hospitalized in the intensive care unit, were enrolled in this study. The observational endpoint was 28-day mortality. Cox proportional hazard regression analysis was performed to determine the potential prognostic factors for 28-day mortality in these septic patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate and compare the prognostic factors for 28-day mortality. Results. Of 300 participants, 147 died, corresponding to a 28-day mortality of 49% (147/300). Baseline Acute Physiology and Chronic Health Evaluation (APACHE II) score (hazard ratio (HR) 1.18 (95% confidence interval (CI) 1.07–1.30); P < 0.001 ), baseline lactic acid level (HR 1.27 (95% CI 1.08–1.50); P = 0.005 ), the presence of septic shock (HR 21.44 (95% CI 2.51–182.76); P = 0.005 ), and baseline AFR (HR 0.70 (95% CI 0.62–0.80); P < 0.001 ) were independent prognostic factors for 28-day mortality in patients with sepsis according to multivariate Cox analysis. Baseline AFR was an effective predictor of 28-day mortality, with an area under the ROC curve (AUC) of 0.700, and a specificity and sensitivity of 90.8% and 42.1%, respectively. A low baseline AFR level was associated with increased 28-day sepsis-related mortality. The quadruple index, which included the APACHE II score, lactic acid, septic shock, and AFR, showed a more accurate predictive value for septic patients than the APACHE II score, lactic acid, septic shock, and AFR alone, with an AUC of 0.922, and specificity and sensitivity of 86.9% and 83.6%, respectively. Moreover, the triple index, which included the APACHE II score, lactic acid, and septic shock, showed a significantly lower prognostic value for 28-day mortality compared with the ROC curve of the quadruple index and triple index, with an AUC of 0.877 and specificity and sensitivity of 77.8% and 82.3%, respectively. Conclusions. The results of this study demonstrate that AFR is an independent protective factor for predicting 28-day mortality in patients with sepsis due to various infection sites. AFR combined with the APACHE II score, lactic acid, and septic shock showed a higher prognostic value for sepsis prognosis.

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“…This association is independent from sedative or analgesic medication use, age, pre-existing comorbidities, ongoing conditions and the duration of mechanical ventilation [45,51]. In most of the previous studies performed in the ICU, a large number of septic patients were included to investigate the link between delirium and long-term cognitive impairment [52].…”

Section: Sepsis-associated Long-term Cognitive Impairmentmentioning

confidence: 99%

Neuropsychological Outcome of Critically Ill Patients with Severe Infection

Cavalli

Mascia

2022

Biomedicines

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Sepsis and septic shock represent important burdens of disease around the world. Sepsis-associated neurological consequences have a great impact on patients, both in the acute phase and in the long term. Sepsis-associated encephalopathy (SAE) is a severe brain dysfunction that may contribute to long-term cognitive impairment. Its pathophysiology recognizes the following two main mechanisms: neuroinflammation and hemodynamic impairment. Clinical manifestations include different forms of altered mental status, from agitation and restlessness to delirium and deep coma. A definite diagnosis is difficult because of the absence of specific radiological and biological criteria; clinical management is restricted to the treatment of sepsis, focusing on early detection of the infection source, maintenance of hemodynamic homeostasis, and avoidance of metabolic disturbances or neurotoxic drugs.

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A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes

Cited by 59 publications

References 30 publications

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Prognostic Value of Albumin-to-Fibrinogen Ratio for 28-Day Mortality among Patients with Sepsis from Various Infection Sites

Neuropsychological Outcome of Critically Ill Patients with Severe Infection

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