Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems.

BACKGROUND: One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset. MATERIALS AND METHODS: This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts' consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset. RESULTS: The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset. CONCLUSIONS: We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.

Does red blood cell transfusion affect clinical outcomes in critically ill patients? A report from a large teaching hospital in south Iran.

BACKGROUND: Despite the beneficial effects, RBC transfusion can be associated with infectious and non-infectious complications in critically ill patients. OBJECTIVES: Investigate current RBC transfusion practices and their effect on the clinical outcomes of patients in intensive care units (ICUs). DESIGN: Retrospective observational study. SETTING: Three mixed medical-surgical adult ICUs of a large academic tertiary hospital. PATIENTS AND METHODS: From March 2018 to February 2020, all adult patients admitted to medical or surgical ICU. Patients who received one or more RBC transfusions during the first month of ICU admission were included in the "transfusion" group, while the remaining patients were assigned to the "non-transfusion" group. MAIN OUTCOME MEASURES: Mortality and length of ICU and hospital stay. SAMPLE SIZE: 2159 patients. RESULTS: Of 594 patients who recieved transfusions, 27% of patients received red blood cell (RBC) products. The mean pre-transfusion hemoglobin (Hb) level was 8.05 (1.46) g/dL. There was a significant relationship between higher APACHE II scores and ICU mortality in patients with Hb levels of 7-9 g/dL (OR adjusted=1.05). Also, ICU mortality was associated with age (OR adjusted=1.03), APACHE II score (OR adjusted=1.08), and RBC transfusion (OR adjusted=2.01) in those whose Hb levels were >9 (g/dl). CONCLUSION: RBC transfusion was associated with an approximately doubled risk of ICU mortality in patients with Hb>9 g/dL. High APACHE II score and age increase the chance of death in the ICU by 8% and 3%, respectively. Hence, ICU physicians should consider a lower Hb threshold for RBC transfusion, and efforts must be made to optimize RBC transfusion practices. LIMITATIONS: Single-center and retrospective study.

Usability Assessment of an Electronic Medical Record-Embedded Clinical Decision Support System for Arterial Blood Gas Interpretation.

Since usability is considered a significant success factor for Clinical decision support systems (CDSSs), this study seeks to assess the usability of an electronic medical records-embedded CDSS for arterial blood gas (ABG) interpretation and ordering. The current study was conducted in the general ICU of a teaching hospital, using the System Usability Scale (SUS) and interviews with all anesthesiology residents and intensive care fellows in two rounds of CDSS usability testing. The feedback from the participants was discussed with the research team across a series of meetings, and the second version of CDSS was designed and tailored to participants' feedbacks. Subsequently, the CDSS usability score increased from 67.22±4.58 to 80.00±4.84 (P-value<0.001) through participatory, iterative design and the users' usability testing feedbacks.