Fever

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study

Background

Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.

Methods

A retrospective study of patients’ data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC).

Results

The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= –0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO₂) (coefficient= –0.101, OR = 0.904), and albumin (ALB) (coefficient= –0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure.

Conclusions

The main clinical indicators of concern included CTnT, RR, SPO₂, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

Knowledge, attitudes and misconceptions of primary care physicians regarding fever in children: a cross sectional study

Background

Fever is an extremely common sign in paediatric patients and the most common cause for a child to be taken to the doctor. The literature indicates that physicians and parents have too many misconceptions and conflicting results about fever management. In this study we aim to identify knowledge, attitudes and misconceptions of primary care physicians regarding fever in children.

Methods

This cross-sectional study was conducted in April-May 2010 involving primary care physicians (n=80). The physicians were surveyed using a self-administered questionnaire. Descriptive statistics were used.

Results

In our study only 10% of the physicians knew that a body temperature of above 37.2°C according to an auxiliary measurement is defined as fever. Only 26.2% of the physicians took into consideration signs and symptoms other than fever to prescribe antipyretics. 85% of the physicians prescribed antipyretics to control fever or prevent complications of fever especially febrile seizures. Most of the physicians (76.3%) in this study reported that the height of fever may be used as an indicator for severe bacterial infection. A great majority of physicians (91.3%) stated that they advised parents to alternate the use of ibuprofen and paracetamol.

Conclusions

There were misconceptions about the management and complications of fever. There is a perceived need to improve the recognition, assessment, and management of fever with regards to underlying illnesses in children.