Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

Zhao, Lina; Li, Yun; Wang, Yunying; Gao, Qian; Ge, Zengzheng; Sun, Xibo; Li, Yang

doi:10.3389/fmicb.2021.737066

Cited by 13 publications

(6 citation statements)

References 52 publications

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“…Result from previous study indicated that temperature and lactate were significantly correlated with mortality in SAE patients [ 33 ], these findings have also been confirmed in our research. As is well known, lactate was an important indictor which reflected the prognosis of sepsis patients [ 34 , 35 ].…”

Section: Discussionsupporting

confidence: 91%

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: 91%

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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“…These are advantages of the nomogram. Therefore, nomograms are accepted by a significant number of researchers involved in clinical medical studies and they are widely applied as a visual statistical model for the purpose of determining the prognosis and risk of disease [ 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a Novel Nomogram Incorporating Red Blood Cell Distribution Width-Albumin Ratio for the Prediction of 30-day Mortality in Acute Pancreatitis Patients

Pan

Lei

et al. 2022

Emergency Medicine International

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Purpose. The available nomograms used to predict acute pancreatitis (AP) are not comprehensive. We sought to investigate the effect of red blood cell distribution width (RDW)-albumin ratio (RA) on prognosis of patients with AP and develop a new nomogram to identify AP patients at high risk for mortality. Methods. We used data from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0). A total of 487 patients with acute pancreatitis were included. Patients enrolled in the study were randomly assigned to the training set and validation set at a 7 : 3 ratio. According to the 30-day mortality rate, the data were divided into a survival group and a death group. Multivariate logistic regression was used to establish a prognostic nomogram for predicting the 30-day mortality in AP patients. The area under the receiver operating characteristic curve (AUC), calibration curve, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA) are used to verify the overall performance of the model. Results. Among 487 patients, 54 patients died (11.1%). 338 patients were assigned to the training cohort and 149 were assigned to the validation cohort. The multivariate analysis results showed that RA, age, heart rate, temperature, AST/ALT, BUN, hemoglobin, potassium, and bilirubin were independent risk factors. The prediction performance of the newly established nomogram was better than those of other common scoring systems (including SOFA, OASIS, and APSIII). The nomogram suggests that RA (OR = 1.706, 95% CI: 1.367–2.185) is the most significant laboratory test indicator influencing prognosis. Conclusion. The new nomogram incorporating RA performed well in predicting AP short-term mortality. A prospective study with a larger sample is needed to validate our findings.

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“…(3) In patients with sepsis, traumatic brain injury, encephalitis, intracranial infection, ischemic stroke, and metabolic encephalopathy caused by severe electrolyte imbalances or glycemic disturbances, pulmonary encephalopathy caused by excessive carbon dioxide partial pressure, hepatic encephalopathy, hypertensive encephalopathy, and other liver disease or kidney disease is affecting consciousness; mental disorders and neurological disease; chronic alcohol or drug abuse were excluded by us. The diagnosis of SAE is defined according to the following three aspects: the patient's Glasgow coma scale (GCS) score of <15, patients diagnosed with delirium according to the ICD code, and patients treated with haloperidol during hospitalization ( 4 , 14 , 15 ).…”

Section: Methodsmentioning

confidence: 99%

Prognostic role of serum ammonia in patients with sepsis-associated encephalopathy without hepatic failure

Zhao

Hou²,

Na³

et al. 2023

Front. Public Health

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ObjectivesOur previous study shows that serum ammonia in sepsis patients without hepatic failure is associated with a poor prognosis. The relationship between serum ammonia level and the prognosis of sepsis-associated encephalopathy (SAE) patients without hepatic failure remains unclear. We aimed to explore the relationship between serum ammonia levels and the prognosis of patients with SAE.Materials and methodsThis study is a retrospective cohort study. We collected 465 patients with SAE admitted to the intensive care unit (ICU) from Medical Information Mart for Intensive Care IV (MIMIC IV) from 2008 to 2019. Patients with SAE were divided into a survival group (369 patients) and a non-survival group (96 patients). We used the Wilcoxon signed-rank test and the multivariate logistic regression analysis to analyze the relationship between serum ammonia levels and the prognosis of patients with SAE. R software was used to analyze the dataset.ResultsThe primary outcome was the relationship between serum ammonia level and hospital mortality of SAE. The secondary outcomes were the relationship between serum ammonia level and hospital stays, simplified acute physiology score (SAPS II), Charlson, Glasgow coma scale (GCS), sequential organ failure assessment (SOFA), and lactate level of SAE. The mortality of patients with SAE was 20.6%. The serum ammonia level was not significantly associated with hospital mortality, longer hospital stays, higher SAPS II and Charlson scores, and lower GCS of patients with SAE. The serum ammonia level was associated with higher SOFA scores and lactate levels in patients with SAE. The SAPS II and Charlson scores were independent risk factors for death in patients with SAE.ConclusionSerum ammonia level was associated with higher SOFA scores and lactate levels in patients with SAE. In addition, the SAPS II and Charlson scores can be used to assess the prognosis of patients with SAE. Therefore, we should closely monitor serum ammonia, SAPS II, and Charlson levels in patients with SAE.

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Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

Cited by 13 publications

References 52 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

Development of a Novel Nomogram Incorporating Red Blood Cell Distribution Width-Albumin Ratio for the Prediction of 30-day Mortality in Acute Pancreatitis Patients

Prognostic role of serum ammonia in patients with sepsis-associated encephalopathy without hepatic failure

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