Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Hypocapnia is associated with increased in-hospital mortality and 1 year mortality in acute heart failure patients

AIMS

Both hypercapnia and hypocapnia are common in patients with acute heart failure (AHF), but the association between partial pressure of arterial carbon dioxide (PaCO2) and AHF prognosis remains unclear. The objective of this study was to investigate the connection between PaCO2 within 24 h after admission to the intensive care unit (ICU) and mortality during hospitalization and at 1 year in AHF patients.

METHODS AND RESULTS

AHF patients were enrolled from the Medical Information Mart for Intensive Care IV database. The patients were divided into three groups by PaCO2 values of <35, 35-45, and >45 mmHg. The primary outcome was to investigate the connection between PaCO2 and in-hospital mortality and 1 year mortality in AHF patients. The secondary outcome was to assess the prediction value of PaCO2 in predicting in-hospital mortality and 1 year mortality in AHF patients. A total of 2374 patients were included in this study, including 457 patients in the PaCO2 < 35 mmHg group, 1072 patients in the PaCO2 = 35-45 mmHg group, and 845 patients in the PaCO2 > 45 mmHg group. The in-hospital mortality was 19.5%, and the 1 year mortality was 23.9% in the PaCO2 < 35 mmHg group. Multivariate logistic regression analysis showed that the PaCO2 < 35 mmHg group was associated with an increased risk of in-hospital mortality [hazard ratio (HR) 1.398, 95% confidence interval (CI) 1.039-1.882, P = 0.027] and 1 year mortality (HR 1.327, 95% CI 1.020-1.728, P = 0.035) than the PaCO2 = 35-45 mmHg group. The PaCO2 > 45 mmHg group was associated with an increased risk of in-hospital mortality (HR 1.387, 95% CI 1.050-1.832, P = 0.021); the 1 year mortality showed no significant difference (HR 1.286, 95% CI 0.995-1.662, P = 0.055) compared with the PaCO2 = 35-45 mmHg group. The Kaplan-Meier survival curves showed that the PaCO2 < 35 mmHg group had a significantly lower 1 year survival rate. The area under the receiver operating characteristic curve for predicting in-hospital mortality was 0.591 (95% CI 0.526-0.656), and the 1 year mortality was 0.566 (95% CI 0.505-0.627) in the PaCO2 < 35 mmHg group.

CONCLUSIONS

In AHF patients, hypocapnia within 24 h after admission to the ICU was associated with increased in-hospital mortality and 1 year mortality. However, the increase in 1 year mortality may be influenced by hospitalization mortality. Hypercapnia was associated with increased in-hospital mortality.

A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus

Background

A high risk of developing mild cognitive impairment (MCI) is faced by elderly patients with type 2 diabetes mellitus (T2DM). In this study, independent risk factors for MCI in elderly patients with T2DM were investigated, and an individualized nomogram model was developed.

Methods

In this study, clinical data of elderly patients with T2DM admitted to the endocrine ward of the hospital from November 2021 to March 2023 were collected to evaluate cognitive function using the Montreal Cognitive Assessment scale. To screen the independent risk factors for MCI in elderly patients with T2DM, a logistic multifactorial regression model was employed. In addition, a nomogram to detect MCI was developed based on the findings of logistic multifactorial regression analysis. Furthermore, the accuracy of the prediction model was evaluated using calibration and receiver operating characteristic curves. Finally, decision curve analysis was used to evaluate the clinical utility of the nomogram.

Results

In this study, 306 patients were included. Among them, 186 patients were identified as having MCI. The results of multivariate logistic regression analysis demonstrated that educational level, duration of diabetes, depression, glycated hemoglobin, walking speed, and sedentary duration were independently correlated with MCI, and correlation analyses showed which influencing factors were significantly correlated with cognitive function (p <0.05). The nomogram based on these factors had an area under the curve of 0.893 (95%CI:0.856-0.930)(p <0.05), and the sensitivity and specificity were 0.785 and 0.850, respectively. An adequate fit of the nomogram in the predictive value was demonstrated by the calibration plot.

Conclusions

The nomogram developed in this study exhibits high accuracy in predicting the occurrence of cognitive dysfunction in elderly patients with T2DM, thereby offering a clinical basis for detecting MCI in patients with T2DM.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.

Prediction model of central nervous system infections in patients with severe traumatic brain injury after craniotomy

OBJECTIVE

The aim of this study was to develop and evaluate a nomogram to predict CNS infections in patients with severe traumatic brain injury (sTBI) after craniotomy.

METHODS

This retrospective study was conducted in consecutive adult patients with sTBI who were admitted to the neurointensive care unit (NCU) between January 2014 and September 2020. We applied the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis to construct the nomogram, and k-fold cross-validation (k=10) to validate it. The receiver operator characteristic area under the curve (AUC) and calibration curve were applied to evaluate the predictive effect of the nomogram. The clinical usefulness was investigated by decision curve analysis (DCA).

RESULTS

A total of 471 patients with sTBI who underwent surgical treatment were included, of whom 75 patients (15.7%) were diagnosed with CNS infections. The serum level of albumin, cerebrospinal fluid (CSF) otorrhoea at admission, CSF leakage, CSF sampling, and postoperative re-bleeding were associated with CNS infections and incorporated into the nomogram. The results showed that our model yielded satisfactory prediction performance with an AUC value of 0.962 in the training set and 0.942 in the internal validation. The calibration curve exhibited satisfactory concordance between the predicted and actual outcomes. The model had good clinical use since the DCA covered a large threshold probability.

CONCLUSION

We established a straightforward individualized nomogram for CNS infections in sTBI patients in the NCU, which could help physicians screen high-risk patients to perform early interventions to reduce the incidence of CNS infections in sTBI patients.