Development and Validation of a Nomogram Incorporating Colloid Osmotic Pressure for Predicting Mortality in Critically Ill Neurological Patients

Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis

INTRODUCTION

Brain metastasis (BM) is an aggressive complication with an extremely poor prognosis in patients with small-cell lung cancer (SCLC). A well-constructed prognostic model could help in providing timely survival consultation or optimizing treatments.

METHODS

We analyzed clinical data from SCLC patients between 2000 and 2018 based on the Surveillance, Epidemiology, and End Results (SEER) database. We identified significant prognostic factors and integrated them using a multivariable Cox regression approach. Internal validation of the model was performed through a bootstrap resampling procedure. Model performance was evaluated based on the area under the curve (AUC) and calibration curve.

RESULTS

A total of 2,454 SCLC patients' clinical data was collected from the database. It was determined that seven clinical parameters were associated with prognosis in SCLC patients with BM. A satisfactory level of discrimination was achieved by the predictive model, with 6-, 12-, and 18-month AUC values of 0.726, 0.707, and 0.737 in the training cohort; and 0.759, 0.742, and 0.744 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. Furthermore, prognostic scores were found to significantly alter the survival curves of different risk groups. We then deployed the prognostic model onto a website server so that users can access it easily.

CONCLUSIONS

In this study, a nomogram and a web-based predictor were developed to predict overall survival in SCLC patients with BM. It may assist physicians in making informed clinical decisions and determining the best treatment plan for each patient.

Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study

Background

The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery.

Methods

Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved.

Conclusions

FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Objective

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.

Method

Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Result

The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.

Conclusion

The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Objective

Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods

From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results

A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion

Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.