Differential impact of transfusion guidelines on blood transfusion practices within a health network

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

Nurses' self-efficiency levels in safe blood and blood component transfusion: The case of Turkey

OBJECTIVE

The study aimed to determine nurses' self-efficacy levels for safe transfusion of blood and blood components.

METHOD

The design of this study is descriptive and cross-sectional. Before starting the study, ethics committee approval and institution approval was obtained. The participants were informed about the purpose of the study, and their written consent was obtained. The research was conducted between the dates 01 March 2022 and 01 May 2022, a private hospital in Turkey. The study sample consisted of 482 nurses. Data were collected using descriptive characteristics form and the Safe Blood and Blood Products Transfusion Self-Efficacy Scale (SBT-SES).

RESULTS

The total SBT-SES scores of the nurses were high (202.7 ± 50.1), and the behavioral sub-factor self-efficacy scores were moderate (48.2 ± 19.5). When the SBT-SES scores were analyzed based on demographic characteristics, it was found that those who had received previous safe blood transfusion training scored higher than those who had not, and women scored higher than men (p < 0.05). In addition, no relationship was found between age, working time, number of weekly blood transfusions, and self-efficacy levels.

DISCUSSION

As a result, nurses' self-efficacy levels towards blood transfusion are high. However, the behavioral sub-factor self-efficacy level is not sufficient.

CONCLUSION

Based on these results, in order to increase the behavioral self-efficacy levels of nurses, our recommendations are as follows: investigating appropriate training methods, considering the sex factor when choosing training methods and techniques, investigating the barriers to safe transfusion behaviors, and measuring self-efficacy levels at regular intervals.