Patient Factors That Influence Reliable Delirium Assessments in a Medical Intensive Care Unit During the Routine Clinical Practice.

This study aimed to determine patient and therapeutic characteristics of patients in the medical intensive care unit (MICU) that contribute to inconsistent results of delirium assessments performed during routine clinical practice. Therefore, electronic health records were reviewed and compared with secondary data collected from the same medical ICU patients who were assessed using the Confusion Assessment Method in the ICU (CAM-ICU). Of 5,241 cases involving 762 patients, 827 (15.78%) cases showed disagreement between assessments. Continuous renal replacement therapy, physical restraint use, and altered mental status were factors that increased the likelihood of inconsistencies between assessments. A significant positive correlation was found between the CAM-ICU disagreement rate and the total number of assessments per month. To maximize the reliability of delirium assessments, individual-targeted approaches considering the patient's level of consciousness and type of treatment implemented are required, along with ensuring a stable, and regulated working environment and customized educational programs.

Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units.

ABSTRACT: BACKGROUND: Biosignal data acquired during quantitative electroencephalography (QEEG) research may ultimately be used to develop algorithms for more accurate detection of delirium. This study investigates the biosignal changes during delirium states by using the QEEG data of patients in a medical intensive care unit. METHODS: This observational study was conducted between September 2018 and December 2019 at a tertiary hospital in South Korea. Delirium was measured using the Korean version of Confusion Assessment Method for the Intensive Care Unit in intensive care unit patients. Quantitative EEG measurements were recorded for 20 minutes in a natural state without external treatment or stimuli, and QEEG data measured in the centroparietal and parietal regions with eyes open were selected for analysis. Power spectrum analysis with a 5-minute epoch was conducted on the selected 65 cases. RESULTS: QEEG changes in the presence of delirium indicated that alpha, beta, gamma, and spectral edge frequency 50% waves showed significantly lower absolute power spectra than the corresponding findings in the absence of delirium. Brain-mapping results showed that these brain waves were inactivated in delirious states. CONCLUSION: QEEG assessments can potentially detect the changes in the centroparietal and parietal regions of delirium patients. QEEG changes, including lower power spectra of alpha, beta, and gamma waves, and spectral edge frequency 50%, can be successfully used to distinguish delirium from the absence of delirium.

Medication Use and Risk of Delirium in Mechanically Ventilated Patients.

One of the principal complications in patients in the intensive care unit, particularly in those receiving mechanical ventilation, is medication-induced delirium. The present study aimed to intensively analyze pharmaceutical factors affecting the development of delirium in mechanically ventilated patients using the electronic health records. The present study was designed as a retrospective case-control study. The delirium group included 500 mechanically ventilated patients. The non-delirium group included 2,000 patients who were hospitalized during the same period as the delirium group and received mechanical ventilation. A total of seven types of medications (narcotic analgesics, non-narcotic analgesics, psychopharmaceuticals, sleep aid medications, anticholinergics, steroids, and diuretics), conventionally used to manage mechanical ventilation, were found to be major risk factors associated with the occurrence of delirium. Since these medications are an integral part of managing mechanically ventilated patients, prudent protocol-based medication approaches are essential to decrease the risk of delirium.

Automated Fall and Pressure Injury Risk Assessment Systems: Nurses' Experiences, Perspectives, and Lessons Learned.

This study examined the clinical usability of two automated risk assessment systems-the Automated Fall Risk Assessment System and Automated Pressure Injury Risk Assessment System. The clinical usability of automated assessment systems was tested in three ways: agreement between the scales that nurses generally use and the automated assessment systems, focus group interviews, and the predicted amount of time saved for risk assessment and documentation. For the analysis of agreement, 1160 patients and 1000 patients were selected for falls and pressure injuries, respectively. A total of 60 nurses participated in focus group interviews. The nurses personally checked the time taken to assess and document the risks of falls and pressure injury for 271 and 251 patient cases, respectively. The results for the agreement showed a κ index of 0.43 and a percentage of agreement of 71.55% between the Automated Fall Risk Assessment System and the Johns Hopkins Fall Risk Assessment Tool. For the agreement between the Automated Pressure Injury Risk Assessment System and the Braden scale, the κ index was 0.52 and the percentage of agreement was 80.60%. The focus group interviews showed that participants largely perceived the automated risk assessment systems positively. The time it took for assessment and documentation were about 5 minutes to administer the Johns Hopkins Fall Risk Assessment Tool and 2 to 3 minutes to administer the Braden scale per day to all patients. Overall, the automated risk assessment systems may help in obtaining time devoted to directly preventing falls and pressure injuries and thereby contribute to better quality care.

Risk factors and outcomes of sepsis-associated delirium in intensive care unit patients: A secondary data analysis.

OBJECTIVE: To identify the risk factors of sepsis-associated delirium and determine their effect on intensive care unit adult patient outcomes. DESIGN: A secondary analysis of data from system development studies. SETTING: Korean intensive care unit patients in a university hospital who were diagnosed with sepsis. METHODS: The risk factors for sepsis-associated delirium were classified into patient factors and sepsis clinical features and were analysed using hierarchical logistic regression analysis. Outcomes included in-hospital mortality, 30-day in-hospital mortality, duration of mechanical ventilation, length of stay in the intensive care unit, length of hospital stay, total medical expenses, discharge placement, re-hospitalisation and visits to the emergency department after discharge. RESULTS: The risk factor for sepsis-associated delirium including patients aged 65 ≥years, dependent activity and high nursing needs (patient factors), low level of consciousness, tachypnoea, and thrombocytopaenia (clinical features of sepsis). Use of vasopressors/inotropes and albumin decreased the risk of sepsis-associated delirium. Mechanical ventilation duration was prolonged and discharge to skilled nursing facilities was increased by sepsis-associated delirium. CONCLUSIONS: The risk factors for sepsis-associated delirium increased as the severity of condition for patients with sepsis increased. Early identification of risk factors associated with sepsis-associated delirium may improve patient outcomes.

Impact of Pressure Injuries on Patient Outcomes in a Korean Hospital: A Case-Control Study.

PURPOSE: The purpose of this study was to compare the effect of pressure injuries on mortality, hospital length of stay, healthcare costs, and readmission rates in hospitalized patients. DESIGN: A case-control study. SUBJECTS AND SETTING: The sample comprised 5000 patients admitted to a tertiary hospital located in Seoul Korea; 1000 patients with pressure injuries (cases) were compared to 4000 patients who acted as controls. METHODS: We retrospectively extracted clinical data from electronic health records. Study outcomes were mortality, hospital length of stay, healthcare costs, and readmission rates. The impact of pressure injuries on death and readmission was analyzed via multiple logistic regression, hospital deaths within 30 days were analyzed using the survival analysis and Cox proportional hazards regression, and impact on the length of hospitalization and medical costs were analyzed through a multiple linear regression. RESULTS: Developing a pressure injury was significantly associated with an increased risk of in-hospital mortality (odds ratio [OR], 3.94; 95% confidence interval [CI], 2.91-5.33), 30-days in-hospital mortality (OR, 2.18; 95% CI, 1.59-3.00), and healthcare cost (ß = 11,937,333; P < .001). Pressure injuries were significantly associated with an extended length of hospitalization (ß = 20.84; P < .001) and length of intensive care unit (ICU) stay (ß = 8.16; P < .001). Having a pressure injury was significantly associated with an increased risk of not being discharged home (OR, 5.55; 95% CI, 4.35-7.08), along with increased risks of readmission (OR, 1.30; 95% CI, 1.05-1.62) and emergency department visits after discharge (OR, 1.70; 95% CI, 1.29-2.23). CONCLUSIONS: Development of pressure injuries influenced mortality, healthcare costs, ICU and hospital length of stay, and healthcare utilization following discharge (ie, readmission or emergency department visits). Hospital-level efforts and interdisciplinary approaches should be prioritized to develop interventions and protocols for pressure injury prevention.

Development and Evaluation of the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection.

Catheter-associated urinary tract infection is one of the most common healthcare-acquired infections. It is important to institute preventive measures such as surveillance of the appropriate use of indwelling urinary catheters and timely removal by identifying patients at high risk for catheter-associated urinary tract infection. The purpose of this study was to develop an Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection and evaluate its predictive validity. This study involved secondary data analysis based on a case-control study and used the data extracted from electronic health records. The Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection was developed using a risk-scoring algorithm that was based on a logistic regression model and integrated into the electronic health records. The following eight risk factors for urinary tract infection were included in the logistic regression model: length of stay, admission to the Intensive Care Unit, dependent physical activity, highest neutrophil level (%), lowest blood sodium level of less than 136 mEq/L, lowest blood albumin level of less than 3.5 g/dL, highest blood urea nitrogen level of greater than 20 mg/dL, and indwelling urinary catheter application period (days). The risk groups classified by the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection were automatically displayed on the patient summary screen of the electronic health record. The predictive validity of the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection gradually increased up to the fifth and sixth assessment data after patients' admission; then, it leveled. It is possible to allocate nurses' time and effort for catheter-associated urinary tract infection risk assessment to surveillance of the use, removal, and management of indwelling urinary catheters and education and training by using the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection in clinical settings.

Operative and anaesthetic factors influencing on delirium in the intensive care unit: An Analysis of electronic health records.

AIMS AND OBJECTIVES: To analyse the operation, anaesthesia and recovery-related factors affecting the occurrence of delirium in the intensive care unit. BACKGROUND: The occurrence rate of postoperative delirium is high in surgical patients. Postoperative delirium most frequently occurs usually within 3 days after an operation. DESIGN: This study used a secondary data analysis based on a case-control study. METHODS: This study analysed data extracted from the electronic health records at a university hospital from October 2009-July 2015. One hundred and eighty patients with delirium admitted to the intensive care unit through the recovery room after surgery, and 720 nondelirium controls were included. A total of 17 variables were selected, and hierarchical logistic regression was performed to identify operative and anaesthetic factors influencing on delirium. STROBE statement was applied for reporting this study. RESULTS: The operation, anaesthesia and recovery-related factors increasing the risk of delirium included Class II or higher in the classification system of American Society of Anesthesiologists physical status, continuous remifentanil infusion and lower than seven-point postanaesthesia recovery score at the time of admission to the recovery room. CONCLUSION: The operative and anaesthetic factors influencing the occurrence of delirium should be assessed when a patient is admitted to the ICU following an operation even if a patient is conscious. RELEVANCE TO CLINICAL PRACTICE: Identifying operative and anaesthetic risk factors for delirium can improve the prevention intervention and the patient outcome in the intensive care unit.

Automated Medication Error Risk Assessment System (Auto-MERAS).

This study developed the Automated Medical Error Risk Assessment System (Auto-MERAS), which was incorporated into the electronic health record system. The system itself maintained high predictive validity for medication errors at the area under the receiver operating characteristic curves of above 0.80 at the time of development and validation. This study has found possibilities to predict the risk of medication errors that are sensitive to situational and environmental risks without additional data entry from nurses.

Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system.

BACKGROUND: A key component of the delirium management is prevention and early detection. OBJECTIVE: To develop an automated delirium risk assessment system (Auto-DelRAS) that automatically alerts health care providers of an intensive care unit (ICU) patient's delirium risk based only on data collected in an electronic health record (EHR) system, and to evaluate the clinical validity of this system. DESIGN: Cohort and system development designs were used. SETTING: Medical and surgical ICUs in two university hospitals in Seoul, Korea. PARTICIPANTS: A total of 3284 patients for the development of Auto-DelRAS, 325 for external validation, 694 for validation after clinical applications. METHODS: The 4211 data items were extracted from the EHR system and delirium was measured using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). The potential predictors were selected and a logistic regression model was established to create a delirium risk scoring algorithm to construct the Auto-DelRAS. The Auto-DelRAS was evaluated at three months and one year after its application to clinical practice to establish the predictive validity of the system. RESULTS: Eleven predictors were finally included in the logistic regression model. The results of the Auto-DelRAS risk assessment were shown as high/moderate/low risk on a Kardex screen. The predictive validity, analyzed after the clinical application of Auto-DelRAS after one year, showed a sensitivity of 0.88, specificity of 0.72, positive predictive value of 0.53, negative predictive value of 0.94, and a Youden index of 0.59. CONCLUSIONS: A relatively high level of predictive validity was maintained with the Auto-DelRAS system, even one year after it was applied to clinical practice.

Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System.

BACKGROUND: Pressure injury risk assessment is the first step toward preventing pressure injuries, but traditional assessment tools are time-consuming, resulting in work overload and fatigue for nurses. OBJECTIVES: The objectives of the study were to build an automated pressure injury risk assessment system (Auto-PIRAS) that can assess pressure injury risk using data, without requiring nurses to collect or input additional data, and to evaluate the validity of this assessment tool. METHODS: A retrospective case-control study and a system development study were conducted in a 1,355-bed university hospital in Seoul, South Korea. A total of 1,305 pressure injury patients and 5,220 nonpressure injury patients participated for the development of a risk scoring algorithm: 687 and 2,748 for the validation of the algorithm and 237 and 994 for validation after clinical implementation, respectively. A total of 4,211 pressure injury-related clinical variables were extracted from the electronic health record (EHR) systems to develop a risk scoring algorithm, which was validated and incorporated into the EHR. That program was further evaluated for predictive and concurrent validity. RESULTS: Auto-PIRAS, incorporated into the EHR system, assigned a risk assessment score of high, moderate, or low and displayed this on the Kardex nursing record screen. Risk scores were updated nightly according to 10 predetermined risk factors. The predictive validity measures of the algorithm validation stage were as follows: sensitivity = .87, specificity = .90, positive predictive value = .68, negative predictive value = .97, Youden index = .77, and the area under the receiver operating characteristic curve = .95. The predictive validity measures of the Braden Scale were as follows: sensitivity = .77, specificity = .93, positive predictive value = .72, negative predictive value = .95, Youden index = .70, and the area under the receiver operating characteristic curve = .85. The kappa of the Auto-PIRAS and Braden Scale risk classification result was .73. DISCUSSION: The predictive performance of the Auto-PIRAS was similar to Braden Scale assessments conducted by nurses. Auto-PIRAS is expected to be used as a system that assesses pressure injury risk automatically without additional data collection by nurses.

Longitudinal Evaluation of Johns Hopkins Fall Risk Assessment Tool and Nurses' Experience.

The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) is relatively new in Korea, and it has not been fully evaluated. This study revealed that the JHFRAT had good predictive validity throughout the hospitalization period. However, 2 items (fall history and elimination patterns) on the tool were not determinants of falls in this population. Interestingly, the nurses indicated those 2 items were the most difficult items to assess and needed further training to develop the assessment skills.

Development and Validation of an Automated Sepsis Risk Assessment System.

Aggressive resuscitation can decrease sepsis mortality, but its success depends on early detection of sepsis. The purpose of this study was to develop and verify an Automated Sepsis Risk Assessment System (Auto-SepRAS), which would automatically assess the sepsis risk of inpatients by applying data mining techniques to electronic health records (EHR) data and provide daily updates. The seven predictors included in the Auto-SepRAS after initial analysis were admission via the emergency department, which had the highest odds ratio; diastolic blood pressure; length of stay; respiratory rate; heart rate; and age. Auto-SepRAS classifies inpatients into three risk levels (high, moderate, and low) based on the predictive values from the sepsis risk-scoring algorithm. The sepsis risk for each patient is presented on the nursing screen of the EHR. The AutoSepRAS was implemented retrospectively in several stages using EHR data and its cut-off scores adjusted. Overall discrimination power was moderate (AUC>.80). The Auto-SepRAS should be verified or updated continuously or intermittently to maintain high predictive performance, but it does not require invasive tests or data input by nurses that would require additional time. Nurses are able to provide patients with nursing care appropriate to their risk levels by using the sepsis risk information provided by the Auto-SepRAS. In particular, with early detection of changes related to sepsis, nurses should be able to help in providing rapid initial resuscitation of high-risk patients. © 2016 Wiley Periodicals, Inc.