Transfusion and Management of Surgical Patients with Hematologic Disorders

Analysis of the effect and influencing factors of a clinical competency-oriented prospective pre-job training programme on the comprehensive ability of new employees in the department of transfusion medicine

BACKGROUND

The subject of pre-job training for transfusion service laboratory technicians is very important. The key is how to make a reasonable systematic training programme to improve the effectiveness of training.

METHODS

A prospective training programme was conducted and an assessment was performed at enrollment (baseline) and reassessment after 3-months training, using the same tools with a validated questionnaire.

RESULTS

Clinical competency-oriented prospective pre-job training significantly improves the clinical transfusion-related comprehensive skills of new employees. The post-training assessment score was significantly affected by undergraduate major.

CONCLUSION

This study provided a clinical competency-oriented training programme for new employees in the department of transfusion medicine that could effectively enhance their comprehensive abilities.

Incidence and risk factors of postoperative pulmonary complications following total hip arthroplasty revision: a retrospective Nationwide Inpatient Sample database study

BACKGROUND

Postoperative pulmonary complications (PPCs) are among the most severe complications following total hip arthroplasty revision (THAR), imposing significant burdens on individuals and society. This study examined the prevalence and risk factors of PPCs following THAR using the NIS database, identifying specific pulmonary complications (SPCs) and their associated risks, including pneumonia, acute respiratory failure (ARF), and pulmonary embolism (PE).

METHODS

The National Inpatient Sample (NIS) database was used for this cross-sectional study. The analysis included patients undergoing THAR based on NIS from 2010 to 2019. Available data include demographic data, diagnostic and procedure codes, total charges, length of stay (LOS), hospital information, insurance information, and discharges.

RESULTS

From the NIS database, a total of 112,735 THAR patients in total were extracted. After THAR surgery, there was a 2.62% overall incidence of PPCs. Patients with PPCs after THAR demonstrated increased LOS, total charges, usage of Medicare, and in-hospital mortality. The following variables have been determined as potential risk factors for PPCs: advanced age, pulmonary circulation disorders, fluid and electrolyte disorders, weight loss, congestive heart failure, metastatic cancer, other neurological disorders (encephalopathy, cerebral edema, multiple sclerosis etc.), coagulopathy, paralysis, chronic pulmonary disease, renal failure, acute heart failure, deep vein thrombosis, acute myocardial infarction, peripheral vascular disease, stroke, continuous trauma ventilation, cardiac arrest, blood transfusion, dislocation of joint, and hemorrhage.

CONCLUSIONS

Our study revealed a 2.62% incidence of PPCs, with pneumonia, ARF, and PE accounting for 1.24%, 1.31%, and 0.41%, respectively. A multitude of risk factors for PPCs were identified, underscoring the importance of preoperative optimization to mitigate PPCs and enhance postoperative outcomes.

RBC Inventory-Management System Based on XGBoost Model

It is difficult to predict RBC consumption accurately. This paper aims to use big data to establish a XGBoost Model to understand the trend of RBC accurately, and forecast the demand in time. XGBoost, which implements machine learning algorithms under the Gradient Boosting framework can provide a parallel tree boosting. The daily RBC usage and inventory (May 2014-September 2017) were investigated, and rules for RBC usage were analysed. All data were divided into training sets and testing sets. A XGBoost Model was established to predict the future RBC demand for durations ranging from a day to a week. In addition, the alert range was added to the predicted value to ensure RBC demand of emergency patients and surgical accidents. The gap between RBC usage and inventory was fluctuant, and had no obvious rule. The maximum residual inventory of a certain blood group was up to 700 units one day, while the minimum was nearly 0 units. Upon comparing MAE (mean absolute error):A:10.69, B:11.19, O:10.93, and AB:5.91, respectively, the XGBoost Model was found to have a predictive advantage over other state-of-the-art approaches. It showed the model could fit the trend of daily RBC usage. An alert range could manage the demand of emergency patients or surgical accidents. The model had been built to predict RBC demand, and the alert range of RBC inventory is designed to increase the safety of inventory management.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.