Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study

Abstract

Background: Sepsis-associated encephalopathy (SAE) is related to increased short-term mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of 30-day risk of mortality in patients with SAE. Methods: Data were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by Glasgow Coma Score (GCS) < 15 or delirium at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by logistic regression analysis and then undergone internal validation and sensitivity analysis. Results: SAE accounted for about 50% in patients with sepsis and was independently associated with the 30-day mortality of sepsis. Variables eligible for the nomogram included patient's age and clinical parameters on the first day of ICU admission including the GCS score, lactate, bilirubin, red blood cell distribution width (RDW), mean value of respiratory rate and temperature, and the use of vasopressor. Compared with Sequential Organ Failure Assessment (SOFA) and Logistic Organ Dysfunction System (LODS), the nomogram exhibited better discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.763 (95%CI 0.736-0.791, p < 0.001) and 0.753 (95%CI 0.713-0.794, p < 0.001) in the training and validation sets, respectively. The calibration plot revealed an adequate fit of the nomogram for predicting the risk of 30-day mortality in both sets. Regarding to clinical usefulness, the DCA of the nomogram exhibited greater net benefit than SOFA and LODS in both of the training and validation sets. Besides, the nomogram exhibited acceptable discrimination, calibration, and clinical usefulness in sensitivity analysis. Conclusions: SAE is related to increased 30-day mortality of patients with sepsis. The nomogram presents excellent performance in predicting 30-day risk of mortality in SAE patients, which can be used to evaluate the prognosis of patients with SAE and may be more beneficial once specific treatments towards SAE are developed.

Development and Validation of a Risk Factor-Based Nomogram to Early Predict in-Hospital Mortality in Adult Patients With Suspected Infection Admitted to the Intensive Care Unit: a Retrospective Cohort Study

“…[3] Fourth, except for lactate, RDW has the highest OR value in the multivariate logistic regression, indicating that it may signi cantly affect the prognosis of patients with suspected infection in ICU. This nding is consistent with some previous studies that RDW can be used to estimate the short-term mortality of nonhematologic diseases, such as sepsis-associated encephalopathy, [23] stroke, [24] cardiovascular diseases, [25,26] and liver diseases. [27,28] Studies had indicated that the in ammatory response and oxidative stress during sepsis may contribute to the adverse effect of RDW, [29][30][31][32] but the exact mechanisms under the relationship between RDW and the in-hospital mortality of patients with suspected infection are still unclear.…”

Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection

“…Other study developed a predictive model that could provide an early risk assessment of sepsis in patients undergoing major hepatobiliary and pancreatic surgery (33). In addition, there are many prognostic models for sepsis patients, for example, one study constructed a predictive model of the 30-day risk of death in patients with sepsis-associated encephalopathy that could be used to assess their prognosis (34). Unlike these studies, to the best of our knowledge, we have constructed the first model that can predict the probability of sepsis in patients with UTI, based on the outcome of the patient's first laboratory examination and comorbidities.…”

Optimal Oxygen Saturation Targets in Patients with Sepsis- Associated Encephalopathy: A Cohort Study from the MIMIC-IV Database

“…SAE in the study was de ned Glasgow Coma Scale (GCS) < 15, diagnosed delirium, or use of haloperidol in sepsis patients [3,12,13]. Consciousness disorder with clear causes were excluded.…”

“…Excluded were patients [3,12]: 1) with brain injury (traumatic brain injury, meningitis, encephalitis intracerebral hemorrhage, cerebral embolism, ischemic stroke, epilepsy, or intracranial infection and another cerebrovascular disease) (Supplementary materials1-Supplementary materials5); 2) mental disorders and neurological disease (Supplementary materials6); 3) chronic alcohol or drug abuse (Supplementary materials7); 4) metabolic encephalopathy, hepatic encephalopathy, hypertensive encephalopathy, hypoglycemic coma, and other liver disease or kidney disease is affecting consciousness (Supplementary materials8); 5) severe electrolyte imbalances or glycemic disturbances, including hyponatremia (< 120 mmol/l), hyperglycemia (> 180 mg/dl), or hypoglycemia (< 54 mg/dl); 6) partial pressure of carbon dioxide (PaCO 2 ) ≥ 80mmHg; PaO 2 , S P O 2 have a nonlinearity relationship with the incidence and mortality of SAE patients. We used generalized additive models [15] to estimate the association between median PaO 2 , S P O 2 and sepsis with SAE, and elucidate the optimal PaO 2 , S P O 2 target in reducing the incidence of SAE patients and hospital mortality.…”