Environmental impact of a blood test reduction intervention in adult intensive care units: A before and after quality improvement project.

BACKGROUND: Pathology testing is a very common investigation in the intensive care unit (ICU). Many tests are ordered on a routine basis rather than for a specific clinical indication, resulting in potential patient harm and unnecessary financial and environmental costs. OBJECTIVE: The objective of this study was to determine whether a multifaceted intervention based on the principles of education, audit, and feedback can result in a decrease in unnecessary pathology tests without a commensurate increase in adverse patient outcomes and to measure this decrease in terms of the associated reduction in environmental and financial costs. METHODS: A before and after quality improvement project was conducted between 2017 and 2019 across four ICUs in three 12-month phases, divided according to baseline, intervention implementation, and follow-up. Local clinician champions from each site partnered with the project coordinating centre to develop and implement a range of interventions based on the principles of education, audit, and feedback. Data were collected for the number of pathology tests performed and the clinical characteristics of patients admitted to a participating ICU across the three phases. RESULTS: A total of 196 323 arterial blood gases and 460 258 other tests across eight categories were performed on the 22 210 patients admitted to participating ICUs during the project. A decrease in testing was observed across all but one category, with the greatest reduction seen in arterial blood gases (31.2% reduction in tests per bed-day). Across all categories, this equated to a mean reduction of 1.8 tCO2e (tonnes of carbon dioxide equivalent), a potential estimated total saving of Australian dollar $918 497.50. No increase in adverse clinical outcomes was observed. CONCLUSION: A multifaceted intervention based on the principles of education, audit, and feedback can produce a significant decrease in the number of unnecessary pathology tests performed. This reduction translates to substantial environmental and financial savings without any associated increase in adverse patient outcomes.

Data cleaning for clinician researchers: Application and explanation of a data-quality framework.

BACKGROUND: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures. OBJECTIVES: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures. METHODS: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided. RESULTS: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future. CONCLUSIONS: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.

Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors: A data linkage study.

BACKGROUND: Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service. OBJECTIVES: The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services. METHODS: A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation. RESULTS: 12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40-1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39-1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14-2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67-0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups-high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died). CONCLUSIONS: Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.

Association Between Preexisting Mental Health Disorders and Adverse Outcomes in Adult Intensive Care Patients: A Data Linkage Study.

OBJECTIVES: Mental illness is known to adversely affect the physical health of patients in primary and acute care settings; however, its impact on critically ill patients is less well studied. This study aimed to determine the prevalence, characteristics, and outcomes of patients admitted to the ICU with a preexisting mental health disorder. DESIGN: A multicenter, retrospective cohort study using linked data from electronic ICU clinical progress notes and the Australia and New Zealand Intensive Care Society Adult Patient Database. SETTING/PATIENTS: All patients admitted to eight Australian adult ICUs in the calendar year 2019. Readmissions within the same hospitalization were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Natural language processing techniques were used to classify preexisting mental health disorders in participants based on clinician documentation in electronic ICU clinical progress notes. Sixteen thousand two hundred twenty-eight patients (58% male) were included in the study, of which 5,044 (31.1%) had a documented preexisting mental health disorder. Affective disorders were the most common subtype occurring in 2,633 patients (16.2%), followed by anxiety disorders, occurring in 1,611 patients (9.9%). Mixed-effects regression modeling found patients with a preexisting mental health disorder stayed in ICU 13% longer than other patients (ß-coefficient, 0.12; 95% CI, 0.10-0.15) and were more likely to experience invasive ventilation (odds ratio, 1.42; 95% CI, 1.30-1.56). Severity of illness and ICU mortality rates were similar in both groups. CONCLUSIONS: Patients with preexisting mental health disorders form a significant subgroup within the ICU. The presence of a preexisting mental health disorder is associated with greater ICU length of stay and higher rates of invasive ventilation, suggesting these patients may have a different clinical trajectory to patients with no mental health history. Further research is needed to better understand the reasons for these adverse outcomes and to develop interventions to better support these patients during and after ICU admission.

Pre-existing mental health disorders in patients admitted to the intensive care unit: A systematic review and meta-analysis of prevalence.

AIMS: To determine the reported prevalence rate of pre-existing mental health disorders in patients admitted to adult ICUs and identify the most commonly occurring types of these disorders. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Five electronic databases were searched from 1 January 2000 -15 April 2020. Google Scholar was used to perform forwards citation searching. METHODS: This review was conducted in line with the PRISMA guidelines and protocol registered with PROSPERO CRD42020181818. Meta-analyses were performed using the quality effects model to calculate weighted pooled prevalence estimates and heterogeneity was tested using the I2 statistic. RESULTS: Seven articles were included in the final review and meta-analysis (143,179 participants). Identified prevalence rates varied considerably, ranging from 6.2-28.0%, reflecting variation in each study's clinical context, as well as different patient selection and identification methodologies. The pooled prevalence rate of all pre-existing mental health disorders was 19.4% (95% CI 8.9-32.6%). Depression was the most common subtype, accounting for an estimated 60.5% (95% CI 54.4-66.5%) of identified mental health disorders. All analyses showed significant heterogeneity with I2  > 95%. CONCLUSION: Approximately 19% of adult ICU patients have a history of a mental health disorder, most commonly depression. Further research is needed to improve the accuracy of this estimate as well as determine the best identification method. IMPACT: This study has demonstrated that patients with pre-existing mental health disorders, particularly depression, constitute a significant subgroup in ICU. Given that the presence of a pre-existing mental health disorder appears to confer an increased mortality risk following ICU discharge, clinicians need to be made aware of this group of patients to provide additional support. Further research is needed to more accurately quantify this vulnerable group and establish methods to enable clinicians to readily identify and refer these patients for appropriate follow-up treatment.