The protein kinase IKKepsilon regulates energy balance in obese mice.

Obesity is associated with chronic low-grade inflammation that negatively impacts insulin sensitivity. Here, we show that high-fat diet can increase NF-kappaB activation in mice, which leads to a sustained elevation in level of IkappaB kinase epsilon (IKKepsilon) in liver, adipocytes, and adipose tissue macrophages. IKKepsilon knockout mice are protected from high-fat diet-induced obesity, chronic inflammation in liver and fat, hepatic steatosis, and whole-body insulin resistance. These mice show increased energy expenditure and thermogenesis via enhanced expression of the uncoupling protein UCP1. They maintain insulin sensitivity in liver and fat, without activation of the proinflammatory JNK pathway. Gene expression analyses indicate that IKKepsilon knockout reduces expression of inflammatory cytokines, and changes expression of certain regulatory proteins and enzymes involved in glucose and lipid metabolism. Thus, IKKepsilon may represent an attractive therapeutic target for obesity, insulin resistance, diabetes, and other complications associated with these disorders.

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.

BACKGROUND: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. METHODS: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal-external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. RESULTS: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. CONCLUSION: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

Impact of a prospective feedback loop aimed at reducing non-beneficial treatments in older people admitted to hospital and potentially nearing the end of life. A cluster stepped-wedge randomised controlled trial.

OBJECTIVES: To investigate if a prospective feedback loop that flags older patients at risk of death can reduce non-beneficial treatment at end of life. DESIGN: Prospective stepped-wedge cluster randomised trial with usual care and intervention phases. SETTING: Three large tertiary public hospitals in south-east Queensland, Australia. PARTICIPANTS: 14 clinical teams were recruited across the three hospitals. Teams were recruited based on a consistent history of admitting patients aged 75+ years, and needed a nominated lead specialist consultant. Under the care of these teams, there were 4,268 patients (median age 84 years) who were potentially near the end of life and flagged at risk of non-beneficial treatment. INTERVENTION: The intervention notified clinicians of patients under their care determined as at-risk of non-beneficial treatment. There were two notification flags: a real-time notification and an email sent to clinicians about the at-risk patients at the end of each screening day. The nudge intervention ran for 16-35 weeks across the three hospitals. MAIN OUTCOME MEASURES: The primary outcome was the proportion of patients with one or more intensive care unit (ICU) admissions. The secondary outcomes examined times from patients being flagged at-risk. RESULTS: There was no improvement in the primary outcome of reduced ICU admissions (mean probability difference [intervention minus usual care] = -0.01, 95% confidence interval -0.08 to 0.01). There were no differences for the times to death, discharge, or medical emergency call. There was a reduction in the probability of re-admission to hospital during the intervention phase (mean probability difference -0.08, 95% confidence interval -0.13 to -0.03). CONCLUSIONS: This nudge intervention was not sufficient to reduce the trial's non-beneficial treatment outcomes in older hospital patients. TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry, ACTRN12619000675123 (registered 6 May 2019).

Hemorrhage, Disseminated Intravascular Coagulopathy, and Thrombosis Complications Among Critically Ill Patients with COVID-19: An International COVID-19 Critical Care Consortium Study.

OBJECTIVES: To determine the prevalence and outcomes associated with hemorrhage, disseminated intravascular coagulopathy, and thrombosis (HECTOR) complications in ICU patients with COVID-19. DESIGN: Prospective, observational study. SETTING: Two hundred twenty-nine ICUs across 32 countries. PATIENTS: Adult patients (≥ 16 yr) admitted to participating ICUs for severe COVID-19 from January 1, 2020, to December 31, 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: HECTOR complications occurred in 1,732 of 11,969 study eligible patients (14%). Acute thrombosis occurred in 1,249 patients (10%), including 712 (57%) with pulmonary embolism, 413 (33%) with myocardial ischemia, 93 (7.4%) with deep vein thrombosis, and 49 (3.9%) with ischemic strokes. Hemorrhagic complications were reported in 579 patients (4.8%), including 276 (48%) with gastrointestinal hemorrhage, 83 (14%) with hemorrhagic stroke, 77 (13%) with pulmonary hemorrhage, and 68 (12%) with hemorrhage associated with extracorporeal membrane oxygenation (ECMO) cannula site. Disseminated intravascular coagulation occurred in 11 patients (0.09%). Univariate analysis showed that diabetes, cardiac and kidney diseases, and ECMO use were risk factors for HECTOR. Among survivors, ICU stay was longer (median days 19 vs 12; p < 0.001) for patients with versus without HECTOR, but the hazard of ICU mortality was similar (hazard ratio [HR] 1.01; 95% CI 0.92-1.12; p = 0.784) overall, although this hazard was identified when non-ECMO patients were considered (HR 1.13; 95% CI 1.02-1.25; p = 0.015). Hemorrhagic complications were associated with an increased hazard of ICU mortality compared to patients without HECTOR complications (HR 1.26; 95% CI 1.09-1.45; p = 0.002), whereas thrombosis complications were associated with reduced hazard (HR 0.88; 95% CI 0.79-0.99, p = 0.03). CONCLUSIONS: HECTOR events are frequent complications of severe COVID-19 in ICU patients. Patients receiving ECMO are at particular risk of hemorrhagic complications. Hemorrhagic, but not thrombotic complications, are associated with increased ICU mortality.

Race and ethnicity in the COVID-19 Critical Care Consortium: demographics, treatments, and outcomes, an international observational registry study.

BACKGROUND: Improving access to healthcare for ethnic minorities is a public health priority in many countries, yet little is known about how to incorporate information on race, ethnicity, and related social determinants of health into large international studies. Most studies of differences in treatments and outcomes of COVID-19 associated with race and ethnicity are from single cities or countries. METHODS: We present the breadth of race and ethnicity reported for patients in the COVID-19 Critical Care Consortium, an international observational cohort study from 380 sites across 32 countries. Patients from the United States, Australia, and South Africa were the focus of an analysis of treatments and in-hospital mortality stratified by race and ethnicity. Inclusion criteria were admission to intensive care for acute COVID-19 between January 14th, 2020, and February 15, 2022. Measurements included demographics, comorbidities, disease severity scores, treatments for organ failure, and in-hospital mortality. RESULTS: Seven thousand three hundred ninety-four adults met the inclusion criteria. There was a wide variety of race and ethnicity designations. In the US, American Indian or Alaska Natives frequently received dialysis and mechanical ventilation and had the highest mortality. In Australia, organ failure scores were highest for Aboriginal/First Nations persons. The South Africa cohort ethnicities were predominantly Black African (50%) and Coloured* (28%). All patients in the South Africa cohort required mechanical ventilation. Mortality was highest for South Africa (68%), lowest for Australia (15%), and 30% in the US. CONCLUSIONS: Disease severity was higher for Indigenous ethnicity groups in the US and Australia than for other ethnicities. Race and ethnicity groups with longstanding healthcare disparities were found to have high acuity from COVID-19 and high mortality. Because there is no global system of race and ethnicity classification, researchers designing case report forms for international studies should consider including related information, such as socioeconomic status or migration background. *Note: "Coloured" is an official, contemporary government census category of South Africa and is a term of self-identification of race and ethnicity of many citizens of South Africa.

Cost-effectiveness of an Environmental Cleaning Bundle for Reducing Healthcare-associated Infections.

BACKGROUND: Healthcare-associated infections (HAIs) remain a significant patient safety issue, with point prevalence estimates being ~5% in high-income countries. In 2016-2017, the Researching Effective Approaches to Cleaning in Hospitals (REACH) study implemented an environmental cleaning bundle targeting communication, staff training, improved cleaning technique, product use, and audit of frequent touch-point cleaning. This study evaluates the cost-effectiveness of the environmental cleaning bundle for reducing the incidence of HAIs. METHODS: A stepped-wedge, cluster-randomized trial was conducted in 11 hospitals recruited from 6 Australian states and territories. Bundle effectiveness was measured by the numbers of Staphylococcus aureus bacteremia, Clostridium difficile infection, and vancomycin-resistant enterococci infections prevented in the intervention phase based on estimated reductions in the relative risk of infection. Changes to costs were defined as the cost of implementing the bundle minus cost savings from fewer infections. Health benefits gained from fewer infections were measured in quality-adjusted life-years (QALYs). Cost-effectiveness was evaluated using the incremental cost-effectiveness ratio and net monetary benefit of adopting the cleaning bundle over existing hospital cleaning practices. RESULTS: Implementing the cleaning bundle cost $349 000 Australian dollars (AUD) and generated AUD$147 500 in cost savings. Infections prevented under the cleaning bundle returned a net monetary benefit of AUD$1.02 million and an incremental cost-effectiveness ratio of $4684 per QALY gained. There was an 86% chance that the bundle was cost-effective compared with existing hospital cleaning practices. CONCLUSIONS: A bundled, evidence-based approach to improving hospital cleaning is a cost-effective intervention for reducing the incidence of HAIs.

Estimating the costs of genomic sequencing in cancer control.

BACKGROUND: Despite the rapid uptake of genomic technologies within cancer care, few studies provide detailed information on the costs of sequencing across different applications. The objective of the study was to examine and categorise the complete costs involved in genomic sequencing for a range of applications within cancer settings. METHODS: We performed a cost-analysis using gross and micro-costing approaches for genomic sequencing performed during 2017/2018 across different settings in Brisbane, Australia. Sequencing was undertaken for patients with lung, breast, oesophageal cancers, melanoma or mesothelioma. Aggregated resource data were captured for a total of 1433 patients and point estimates of per patient costs were generated. Deterministic sensitivity analyses addressed the uncertainty in the estimates. Estimated costs to the public health system for resources were categorised into seven distinct activities in the sequencing process: sampling, extraction, library preparation, sequencing, analysis, data storage and clinical reporting. Costs were also aggregated according to labour, consumables, testing, equipment and 'other' categories. RESULTS: The per person costs were AU$347-429 (2018 US$240-297) for targeted panels, AU$871-$2788 (2018 US$604-1932) for exome sequencing, and AU$2895-4830 (2018 US$2006-3347) for whole genome sequencing. Cost proportions were highest for library preparation/sequencing materials (average 76.8% of total costs), sample extraction (8.1%), data analysis (9.2%) and data storage (2.6%). Capital costs for the sequencers were an additional AU$34-197 (2018 US$24-67) per person. CONCLUSIONS: Total costs were most sensitive to consumables and sequencing activities driven by commercial prices. Per person sequencing costs for cancer are high when tumour/blood pairs require testing. Using the natural steps involved in sequencing and categorising resources accordingly, future evaluations of costs or cost-effectiveness of clinical genomics across cancer projects could be more standardised and facilitate easier comparison of cost drivers.

INTEGRATE: gene fusion discovery using whole genome and transcriptome data.

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.

Concentrations of organochlorine pesticides in pooled human serum by age and gender.

Organochlorine pesticides (OCPs) have been used for many decades in Australia with cessation of selected persistent and bioaccumulative OCPs ranging from the 1970s to as recently as 2007. The specific aims of this study were to use samples representative of an Australian population to assess age and gender differences in the concentration of OCPs in human blood sera and to investigate temporal trends in these chemicals. Serum was collected from de-identified, surplus pathology samples over five time periods (2002/03, 2006/07, 2008/09, 2010/11 and 2012/13), with 183 serum pools made from 12,175 individual samples; 26 pools in 2002/03, 85 pools in 2006/07 and 24 pools each in 2008/09, 2010/11 and 2012/13. Samples were analyzed for hexachlorobenzene (HCB), ß-hexachlorocyclohexane (ß-HCH), γ -hexachlorocyclohexane (lindane) (γ-HCH), oxy-chlordane, trans-nonachlor, p,p'-DDE, o,p'-DDT, p,p'-DDT and Mirex. Stratification criteria included gender and age (0-4; 5-15; 16-30; 31-45; 46-60; and >60 years) with age additionally stratified by adults >16 years and children 0-4 and 5-15 years. All pools from all collection periods had detectable concentrations of OCPs with a detection frequency of >60% for HCB, ß-HCH, trans-nonachlor, p,p'-DDT and p,p'-DDE. The overall OCP concentrations increased with age with the highest concentrations in the >60 years groups. Females did not have higher mean OCP concentrations than males except for HCB concentrations (p=0.0006). Temporal trends showed overall decreasing serum concentrations by collection period with the exception of an increase in OCP concentrations between 2006/07 and 2008/09. Excluding this data point, HCB decreased from year to year by 7-76%; ß-HCH concentrations decreased by 14 - 38%; trans-nonachlor concentrations decreased by 10 - 65%; p,p'-DDE concentrations decreased by 6 - 52%; and p,p'-DDT concentrations decreased by 7 - 30%. The results indicate that OCP concentrations have decreased over time as is to be expected following the phase out of these chemicals in Australia.

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

BACKGROUND: When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. METHODS: The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. RESULTS: The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. CONCLUSIONS: The specification of the spatial weights matrix can have a colossal impact on model fit and parameter estimation. The results provide some evidence that a smaller number of neighbours used in defining the spatial weights matrix yields a better model fit, and may provide a more accurate representation of the underlying spatial random field. The commonly used binary, first-order adjacency weights still appear to be a good choice for implementing spatial smoothing.

Pan-cancer transcriptome analysis reveals long noncoding RNAs with conserved function.

A growing number of gene-centric studies have highlighted the emerging significance of lncRNAs in cancer. However, these studies primarily focus on a single cancer type. Therefore, we conducted a pan-cancer analysis of lncRNAs comparing tumor and matched normal expression levels using RNA-Seq data from ∼ 3,000 patients in 8 solid tumor types. While the majority of differentially expressed lncRNAs display tissue-specific expression we discovered 229 lncRNAs with outlier or differential expression across multiple cancers, which we refer to as 'onco-lncRNAs'. Due to their consistent altered expression, we hypothesize that these onco-lncRNAs may have conserved oncogenic and tumor suppressive functions across cancers. To address this, we associated the onco-lncRNAs in biological processes based on their co-expressed protein coding genes. To validate our predictions, we experimentally confirmed cell growth dependence of 2 novel oncogenic lncRNAs, onco-lncRNA-3 and onco-lncRNA-12, and a previously identified lncRNA CCAT1. Overall, we discovered lncRNAs that may have broad oncogenic and tumor suppressor roles that could significantly advance our understanding of cancer lncRNA biology.

Quantitative proteomic analysis in metastatic renal cell carcinoma reveals a unique set of proteins with potential prognostic significance.

Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant malignancies, and patients have a dismal prognosis, with a <10% five-year survival rate. The identification of markers that can predict the potential for metastases will have a great effect in improving patient outcomes. In this study, we used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify proteins that are differentially expressed in metastatic and primary RCC. We identified 1256 non-redundant proteins, and 456 of these were quantified. Further analysis identified 29 proteins that were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic and primary RCC. Dysregulated protein expressions of profilin-1 (Pfn1), 14-3-3 zeta/delta (14-3-3ζ), and galectin-1 (Gal-1) were verified on two independent sets of tissues by means of Western blot and immunohistochemical analysis. Hierarchical clustering analysis showed that the protein expression profile specific for metastatic RCC can distinguish between aggressive and non-aggressive RCC. Pathway analysis showed that dysregulated proteins are involved in cellular processes related to tumor progression and metastasis. Furthermore, preliminary analysis using a small set of tumors showed that increased expression of Pfn1 is associated with poor outcome and is a potential prognostic marker in RCC. In addition, 14-3-3ζ and Gal-1 also showed higher expression in tumors with poor prognosis than in those with good prognosis. Dysregulated proteins in metastatic RCC represent potential prognostic markers for kidney cancer patients, and a greater understanding of their involved biological pathways can serve as the foundation of the development of novel targeted therapies for metastatic RCC.

The chromatin remodeling gene ARID1A is a new prognostic marker in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common tumor of the adult kidney, with an increasing rate of incidence. Recently, exome sequencing studies have revealed that the SWI/SNF (switch/sucrose nonfermentable) members PBRM1 and ARID1A are mutated in ccRCC, and it has also been suggested that aberrant chromatin regulation is a key step in kidney cancer pathogenesis. Herein, we show that down-regulation of another SW/SNF component, ARID1A, occurs frequently in ccRCC. We detected copy number loss of ARID1A in 16% of patients with ccRCC. Immunohistochemistry indicated that 67% of ccRCC (53 of 79) had significantly lower expression of BAF250a, the protein product of ARID1A, than did the matched normal kidney cortex. In parallel, we conducted in silico mRNA expression analysis on 404 ccRCC tumors and 167 normal kidney cortex samples using publicly available databases and confirmed significant down-regulation of ARID1A in 68.8% of patients. We also show that decreased BAF250a protein and ARID1A mRNA expression correlate with tumor stage and grade. Our results indicate that both the protein and mRNA levels of ARID1A are statistically significant prognostic markers for ccRCC. Even after controlling for other confounders in the multivariate analysis, BAF250 retained its prognostic significance. BAF250a IHC is easy to perform and represents a potential biomarker that could be incorporated in laboratory practice to enhance the accuracy of the existing prognostic models.

Identification and validation of dysregulated metabolic pathways in metastatic renal cell carcinoma.

Metastatic renal cell carcinoma (mRCC) is a devastating disease with a 5-year survival rate of approximately 9 % and low response to chemotherapy and radiotherapy. Targeted therapies have slightly improved patient survival, but are only effective in a small subset of patients, who eventually develop resistance. A better understanding of pathways contributing to tumor progression and metastasis will allow for the development of novel targeted therapies and accurate prognostic markers. We performed extensive bioinformatics coupled with experimental validation on proteins dysregulated in mRCC. Gene ontology analysis showed that many proteins are involved in oxidation reduction, metabolic processes, and signal transduction. Pathway analysis showed metabolic pathways are altered in mRCC including glycolysis and pyruvate metabolism, the citric acid cycle, and the pentose phosphate pathway. RT-qPCR analysis showed that genes involved in the citric acid cycle were downregulated in metastatic RCC while genes of the pentose phosphate pathway were overexpressed. Protein-protein interaction analysis showed that most of the 198 proteins altered in mRCC clustered together and many were involved in glycolysis and pyruvate metabolism. We identified 29 reported regions of chromosomal aberrations in metastatic disease that correlate with the direction of protein dysregulation in mRCC. Furthermore, 36 proteins dysregulated in mRCC are predicted to be targets of metastasis-related miRNAs. A more comprehensive understanding of the pathways dysregulated in metastasis can be useful for the development of new therapies and novel prognostic markers. Also, multileveled analyses provide a unique "snapshot" of the molecular "environment" in RCC with prognostic and therapeutic implications.

BCAR4 Expression as a Predictive Biomarker for Endocrine Therapy Resistance in Breast Cancer.

BACKGROUND: Breast cancer, particularly the estrogen receptor positive (ER+) subtype, remains a leading cause of cancer-related death among women. Endocrine therapy is the most effective treatment for ER+ breast cancer; however, the development of resistance presents a significant challenge. This study explored the role of the breast cancer antiestrogen resistance 4 (BCAR4) gene as a potential driver of resistance and a pivotal biomarker in breast cancer. PATIENTS AND METHODS: The researchers undertook a comprehensive analysis of 1743 patients spanning 6 independent cohorts. They examined the association of BCAR4 expression with patient outcomes across all breast cancer types and the PAM50 molecular subtypes. The relationship between elevated BCAR4 expression and resistance to endocrine therapy including AIs, the prevailing standard-of-care for endocrine therapy, was also investigated. RESULTS: This meta-analysis corroborated the link between BCAR4 expression and adverse outcomes as well as resistance to endocrine therapy in breast cancer. Notably, BCAR4 expression is clinically significant in luminal A and B subtypes. Additionally, an association between BCAR4 expression and resistance to AI treatment was discerned. CONCLUSION: This study expands on previous findings by demonstrating that BCAR4 expression is associated with resistance to newer therapies. The identification of patients with intrinsic resistance to hormone therapy is crucial to avoid ineffective treatment strategies. These findings contribute to our understanding of endocrine therapy resistance in breast cancer and could potentially guide the development of more effective treatment strategies.

Stroke in critically ill patients with respiratory failure due to COVID-19: Disparities between low-middle and high-income countries.

PURPOSE: We aimed to compare the incidence of stroke in low-and middle-income countries (LMICs) versus high-income countries (HICs) in critically ill patients with COVID-19 and its impact on in-hospital mortality. METHODS: International observational study conducted in 43 countries. Stroke and mortality incidence rates and rate ratios (IRR) were calculated per admitted days using Poisson regression. Inverse probability weighting (IPW) was used to address the HICs vs. LMICs imbalance for confounders. RESULTS: 23,738 patients [20,511(86.4 %) HICs vs. 3,227(13.6 %) LMICs] were included. The incidence stroke/1000 admitted-days was 35.7 (95 %CI = 28.4-44.9) LMICs and 17.6 (95 %CI = 15.8-19.7) HICs; ischemic 9.47 (95 %CI = 6.57-13.7) LMICs, 1.97 (95 %CI = 1.53, 2.55) HICs; hemorrhagic, 7.18 (95 %CI = 4.73-10.9) LMICs, and 2.52 (95 %CI = 2.00-3.16) HICs; unspecified stroke type 11.6 (95 %CI = 7.75-17.3) LMICs, 8.99 (95 %CI = 7.70-10.5) HICs. In regression with IPW, LMICs vs. HICs had IRR = 1.78 (95 %CI = 1.31-2.42, p < 0.001). Patients from LMICs were more likely to die than those from HICs [43.6% vs 29.2 %; Relative Risk (RR) = 2.59 (95 %CI = 2.29-2.93), p < 0.001)]. Patients with stroke were more likely to die than those without stroke [RR = 1.43 (95 %CI = 1.19-1.72), p < 0.001)]. CONCLUSIONS: Stroke incidence was low in HICs and LMICs although the stroke risk was higher in LMICs. Both LMIC status and stroke increased the risk of death. Improving early diagnosis of stroke and redistribution of healthcare resources should be a priority. TRIAL REGISTRATION: ACTRN12620000421932 registered on 30/03/2020.

Effects of transfusing older red blood cells and platelets on obstetric patient outcomes: A retrospective cohort study.

OBJECTIVE: To investigate associations between transfusion of blood products close to the end of shelf-life and clinical outcomes in obstetric inpatients. METHODS: Mortality and morbidity were compared in patients transfused exclusively with red blood cells (RBC) stored for less than 21 days (fresh) versus RBC stored for 35 days or longer (old), and platelets (PLT) stored for 3 days or fewer (fresh) versus 4 days or longer (old) in Queensland, Australia from 2007 to 2013. Multivariable models were used to examine associations between these groups of blood products and clinical end points. RESULTS: There were 3371 patients who received RBC and 280 patients who received PLT of the eligible storage durations. Patients transfused with old RBC received fewer transfusions (2.7 ± 1.8 vs. 2.3 ± 1.0 units; P < 0.001). However, a higher rate of single-unit transfusions was also seen in those patients who exclusively received old RBC (252 [9.3%] vs. 92 [13.7%]; P = 0.003). Comparison of fresh vs. old blood products revealed no differences in the quantities of transfused RBC (9.5 ± 5.9 vs. 9.1 ± 5.2 units; P = 0.680) or PLT (1.5 ± 0.8 vs. 1.4 ± 1.1 units; P = 0.301) as well as the length of hospital stay for RBC (3 [2-5] vs. 3 [2-5] days; P = 0.124) or PLT (5 [4-8] vs. 6 [4-9] days; P = 0.120). CONCLUSION: Transfusing exclusively older RBC or PLT was not associated with increased morbidity or mortality.

Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance.

OBJECTIVE: Vital signs-based models are complicated by repeated measures per patient and frequently missing data. This paper investigated the impacts of common vital signs modeling assumptions during clinical deterioration prediction model development. STUDY DESIGN AND SETTING: Electronic medical record (EMR) data from five Australian hospitals (1 January 2019-31 December 2020) were used. Summary statistics for each observation's prior vital signs were created. Missing data patterns were investigated using boosted decision trees, then imputed with common methods. Two example models predicting in-hospital mortality were developed, as follows: logistic regression and eXtreme Gradient Boosting. Model discrimination and calibration were assessed using the C-statistic and nonparametric calibration plots. RESULTS: The data contained 5,620,641 observations from 342,149 admissions. Missing vitals were associated with observation frequency, vital sign variability, and patient consciousness. Summary statistics improved discrimination slightly for logistic regression and markedly for eXtreme Gradient Boosting. Imputation method led to notable differences in model discrimination and calibration. Model calibration was generally poor. CONCLUSION: Summary statistics and imputation methods can improve model discrimination and reduce bias during model development, but it is questionable whether these differences are clinically significant. Researchers should consider why data are missing during model development and how this may impact clinical utility.

The bias for statistical significance in sport and exercise medicine.

OBJECTIVES: We aimed to examine the bias for statistical significance using published confidence intervals in sport and exercise medicine research. DESIGN: Observational study. METHODS: The abstracts of 48,390 articles, published in 18 sports and exercise medicine journals between 2002 and 2022, were searched using a validated text-mining algorithm that identified and extracted ratio confidence intervals (odds, hazard, and risk ratios). The algorithm identified 1744 abstracts that included ratio confidence intervals, from which 4484 intervals were extracted. After excluding ineligible intervals, the analysis used 3819 intervals, reported as 95 % confidence intervals, from 1599 articles. The cumulative distributions of lower and upper confidence limits were plotted to identify any abnormal patterns, particularly around a ratio of 1 (the null hypothesis). The distributions were compared to those from unbiased reference data, which was not subjected to p-hacking or publication bias. A bias for statistical significance was further investigated using a histogram plot of z-values calculated from the extracted 95 % confidence intervals. RESULTS: There was a marked change in the cumulative distribution of lower and upper bound intervals just over and just under a ratio of 1. The bias for statistical significance was also clear in a stark under-representation of z-values between -1.96 and +1.96, corresponding to p-values above 0.05. CONCLUSIONS: There was an excess of published research with statistically significant results just below the standard significance threshold of 0.05, which is indicative of publication bias. Transparent research practices, including the use of registered reports, are needed to reduce the bias in published research.

Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice.

OBJECTIVE: Sustainable investment in computerized decision support systems (CDSS) requires robust evaluation of their economic impacts compared with current clinical workflows. We reviewed current approaches used to evaluate the costs and consequences of CDSS in hospital settings and presented recommendations to improve the generalizability of future evaluations. MATERIALS AND METHODS: A scoping review of peer-reviewed research articles published since 2010. Searches were completed in the PubMed, Ovid Medline, Embase, and Scopus databases (last searched February 14, 2023). All studies reported the costs and consequences of a CDSS-based intervention compared with current hospital workflows. Findings were summarized using narrative synthesis. Individual studies were further appraised against the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist. RESULTS: Twenty-nine studies published since 2010 were included. Studies evaluated CDSS for adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). All studies evaluated costs from a hospital perspective but varied based on the valuation of resources affected by CDSS implementation, and the measurement of consequences. We recommend future studies follow guidance from the CHEERS checklist; use study designs that adjust for confounders; consider both the costs of CDSS implementation and adherence; evaluate consequences that are directly or indirectly affected by CDSS-initiated behavior change; examine the impacts of uncertainty and differences in outcomes across patient subgroups. DISCUSSION AND CONCLUSION: Improving consistency in the conduct and reporting of evaluations will enable detailed comparisons between promising initiatives, and their subsequent uptake by decision-makers.