A novel model to optimize multiple imputation algorithm for missing data using evolution methods

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

BACKGROUND

The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis.

METHODS

This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer-Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

RESULTS

The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853-0.961) and 0.856 (95% CI = 0.746-0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147-0.940) and 0.784 (95% CI = 0.282-1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004-0.123; P = 0.037) and 0.103 (95% CI = 0.002-0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system.

CONCLUSION

We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient's status and implement interventions to improve prognosis accordingly.

Exploring Predictors of Social Media Use for Health and Wellness during COVID-19 among Adults in the US: A Social Cognitive Theory Application

During COVID-19, SM media was relied upon for health-related information-seeking and activity support. This study uses the social cognitive theory (SCT) and a representative dataset of the population in the US to explore the factors influencing patients' perceptions of SM for health-related activities. As per SCT, consolidated factors comprised personal factors (sociodemographic, health perception, self-efficacy) and environmental factors (social isolation, purpose in life). Multivariate logistic regression analysis was conducted. Among the 6252 respondents, 95.15% rarely use SM to share personal health-related information, and 90.44% rarely use it to share general health-related information. Older individuals and Whites are less likely to consider SM for healthcare decisions. Education levels influence SM's perceived reliability. Those with positive health perceptions find SM more suitable for healthcare discussions. Socially-isolated individuals are less likely to use SM for healthcare. Those with a strong sense of purpose are less inclined to trust it for health decisions and may question its accuracy. SM-based interventions should address sociodemographic differences. Our findings contribute to the literature by SCT relevance validation in identifying the antecedents of SM use in healthcare. Our results also help to understand the challenges to its adoption. This can help enhance SM-based communication strategies and interventions.

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.