Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers.

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Uromodulin in sepsis and severe pneumonia: a two-sample Mendelian randomization study.

The outcome for patients with sepsis-associated acute kidney injury in the intensive care unit (ICU) remains poor. Low serum uromodulin (sUMOD) protein levels have been proposed as a causal mediator of this effect. We investigated the effect of different levels of sUMOD on the risk of sepsis and severe pneumonia and outcomes in these conditions. A two-sample Mendelian randomization (MR) study was performed. Single-nucleotide polymorphisms (SNPs) associated with increased levels of sUMOD were identified and used as instrumental variables for association with outcomes. Data from different cohorts were combined based on disease severity and meta-analyzed. Five SNPs associated with increased sUMOD levels were identified and tested in six datasets from two biobanks. There was no protective effect of increased levels of sUMOD on the risk of sepsis [two cohorts, odds ratio (OR) 0.99 (95% confidence interval 0.95-1.03), P = 0.698, and OR 0.95 (0.91-1.00), P = 0.060, respectively], risk of sepsis requiring ICU admission [OR 1.04 (0.93-1.16), P = 0.467], ICU mortality in sepsis [OR 1.00 (0.74-1.37), P = 0.987], risk of pneumonia requiring ICU admission [OR 1.05 (0.98-1.14), P = 0.181], or ICU mortality in pneumonia [OR 1.17 (0.98-1.39), P = 0.079]. Meta-analysis of hospital-admitted and ICU-admitted patients separately yielded similar results [OR 0.98 (0.95-1.01), P = 0.23, and OR 1.05 (0.99-1.12), P = 0.86, respectively]. Among patients with sepsis and severe pneumonia, there was no protective effect of different levels of sUMOD. Results were consistent regardless of geographic origins and not modified by disease severity. NEW & NOTEWORTHY The presence of acute kidney injury in severe infections increases the likelihood of poor outcome severalfold. A decrease in serum uromodulin (sUMOD), synthetized in the kidney, has been proposed as a mediator of this effect. Using the Mendelian randomization technique, we tested the hypothesis that increased sUMOD is protective in severe infections. Analyses, however, showed no evidence of a protective effect of higher levels of sUMOD in sepsis or severe pneumonia.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children.

OBJECTIVE: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children. STUDY DESIGN: Retrospective observational study. SETTING: Tertiary referral center. PATIENTS: Rural and remote Aboriginal and Torres Strait Islander children who underwent tele-otology ear health screening in the Northern Territory, Australia between 2010 and 2018. INTERVENTIONS: Otoscopic images were labeled by otolaryngologists to classify the ground truth. Deep and transfer learning methods were used to develop an image classification algorithm. MAIN OUTCOME MEASURES: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC) of the resultant algorithm compared with the ground truth. RESULTS: Six thousand five hundred twenty seven images were used (5927 images for training and 600 for testing). The algorithm achieved an accuracy of 99.3% for acute otitis media, 96.3% for chronic otitis media, 77.8% for otitis media with effusion (OME), and 98.2% to classify wax/obstructed canal. To differentiate between multiple diagnoses, the algorithm achieved 74.4 to 92.8% accuracy and an AUC of 0.963 to 0.997. The most common incorrect classification pattern was OME misclassified as normal tympanic membranes. CONCLUSIONS: The paucity of access to tertiary otolaryngology care for rural and remote Aboriginal and Torres Strait Islander communities may contribute to an under-identification of ear disease. Computer vision image classification algorithms can accurately classify ear disease from otoscopic images of Indigenous Australian children. In the future, a validated algorithm may integrate with existing telemedicine initiatives to support effective triage and facilitate early treatment and referral.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Identifying 'at-risk' critically ill patients who present to the emergency department and require intensive care unit admission: A retrospective observational cohort study.

BACKGROUND: Emergency department (ED) triage is the process of prioritising patients by medical urgency. Delays in intensive care unit (ICU) admission can adversely affect patients. OBJECTIVES: This study aimed to identify characteristics associated with ICU admission for patients triaged as Australasian Triage Scale (ATS) 3 but subsequently admitted to the ICU within 24 h of triage. METHODS: This retrospective, observational cohort study was conducted in a public teaching hospital in Queensland, Australia. Patients older than 18 y triaged with an ATS 3 and admitted to the ICU within 24 h of triage or admitted to the ward between January 1, 2012, and December 31, 2012, were included. The demographic and clinical profiles of ICU admissions vs. all other ward admissions for patients triaged an ATS of 3 were compared. Multivariable regression analysis compared characteristics of patients triaged with an ATS of 3 who did and did not require ICU transfer. Descriptive data are reported as n (%) and median and interquartile range (IQR). Regression analysis is reported as adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs). RESULTS: Of the 27 454 adult ED presentations triaged with an ATS of 3, 22.4% (n = 6138) required hospital admission, comprising 5302 individuals, 2.1% of whom (n = 110) were admitted to the ICU within 24 h of triage. Age- and sex-adjusted predictors of ICU admission for patients triaged with an ATS of 3 included infectious (aOR: 3.7; 95% CI: 2.0-6.9), neurological (aOR: 2.8; 95% CI: 1.6-5.0), and gastrointestinal disorders (aOR: 2.2; 95% CI 1.2-3.5); arriving by ambulance; arriving after hours; or arriving on weekends. Regardless of diagnosis or sex, persons older than 80 y were less likely to be admitted to the ICU (aOR: 0.4; 95% CI: 0.2-0.8). CONCLUSIONS: Patients triaged as ATS 3 presenting on weekends or after hours, and those with infectious, gastrointestinal, or neurological conditions warrant careful attention as these factors were associated with higher odds of ICU admission. Ongoing staff education regarding triage and signs of deterioration are important to prevent avoidable outcomes.

The Effect of Renal Replacement Therapy and Antibiotic Dose on Antibiotic Concentrations in Critically Ill Patients: Data From the Multinational Sampling Antibiotics in Renal Replacement Therapy Study.

BACKGROUND: The optimal dosing of antibiotics in critically ill patients receiving renal replacement therapy (RRT) remains unclear. In this study, we describe the variability in RRT techniques and antibiotic dosing in critically ill patients receiving RRT and relate observed trough antibiotic concentrations to optimal targets. METHODS: We performed a prospective, observational, multinational, pharmacokinetic study in 29 intensive care units from 14 countries. We collected demographic, clinical, and RRT data. We measured trough antibiotic concentrations of meropenem, piperacillin-tazobactam, and vancomycin and related them to high- and low-target trough concentrations. RESULTS: We studied 381 patients and obtained 508 trough antibiotic concentrations. There was wide variability (4-8-fold) in antibiotic dosing regimens, RRT prescription, and estimated endogenous renal function. The overall median estimated total renal clearance (eTRCL) was 50 mL/minute (interquartile range [IQR], 35-65) and higher eTRCL was associated with lower trough concentrations for all antibiotics (P < .05). The median (IQR) trough concentration for meropenem was 12.1 mg/L (7.9-18.8), piperacillin was 78.6 mg/L (49.5-127.3), tazobactam was 9.5 mg/L (6.3-14.2), and vancomycin was 14.3 mg/L (11.6-21.8). Trough concentrations failed to meet optimal higher limits in 26%, 36%, and 72% and optimal lower limits in 4%, 4%, and 55% of patients for meropenem, piperacillin, and vancomycin, respectively. CONCLUSIONS: In critically ill patients treated with RRT, antibiotic dosing regimens, RRT prescription, and eTRCL varied markedly and resulted in highly variable antibiotic concentrations that failed to meet therapeutic targets in many patients.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury.

BACKGROUND: Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising 'electronic biomarker' of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system. RESULTS: A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application. CONCLUSIONS: The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Ticarcillin and piperacillin adsorption on to polyethersulfone haemodiafilter membranes in an ex-vivo circuit.

OBJECTIVES: To describe the adsorption of ticarcillin and piperacillin on to polyethersulfone (PES) membranes using the recirculation function on an ex-vivo renal replacement circuit. METHODS: Low (4-8 mg) or high (35-45 mg) doses of ticarcillin and low (4-8 mg) or high (70-80 mg) doses of piperacillin were added to 1 L of human blood-crystalloid mixture and circulated around an ex-vivo modified continuous renal replacement therapy machine at three different blood flow settings (150, 300 and 450 mL/min). Plasma samples were collected from the pre-filter port of the haemodiafilter circuit at consecutive timepoints for a total duration of 4 h. Plasma samples were measured using a validated ultra high performance liquid chromatography-tandem mass spectrometry method. RESULTS: Eighty-one samples including both drugs were collected from 18 experimental runs. Overall, the percentage of piperacillin adsorption for the low and high doses ranged from 21.3% to 27.1% and from 11.5% to 23%, and the percentage of ticarcillin adsorption for the low and high doses ranged from 4.2% to 14.3% and from 3.7% to 15.1%, respectively. The low dose of piperacillin consistently yielded more than 20% adsorption of dose for all blood flow rates. This decreased with high blood flow rates when the high dose of piperacillin was used. Ticarcillin generally displayed ≤5% adsorption, with the exceptions being the high dose at 150 mL/min and the low dose at 300 mL/min, which displayed ~15% adsorption. CONCLUSIONS: Adsorption of both drugs tended to be higher at the lowest blood flow rates and lowest doses. This is likely due to saturation of parts of the filter that have a chemical attraction to both piperacillin and ticarcillin. At low doses at all three blood flow rates, piperacillin demonstrated >20% adsorption, whereas ticarcillin tended to have low rates (up to ~≤15%) of adsorption on to PES membrane filters.

Going digital: a narrative overview of the effects, quality and utility of mobile apps in chronic disease self-management.

Objective Smartphone health applications (apps) are being increasingly used to assist patients in chronic disease self-management. The effects of such apps on patient outcomes are uncertain, as are design features that maximise usability and efficacy, and the best methods for evaluating app quality and utility. Methods In assessing efficacy, PubMed, Cochrane Library and EMBASE were searched for systematic reviews (and single studies if no systematic review was available) published between January 2007 and January 2018 using search terms (and synonyms) of 'smartphone' and 'mobile applications', and terms for each of 11 chronic diseases: asthma, chronic obstructive lung disease (COPD), diabetes, chronic pain, serious mental health disorders, alcohol and substance addiction, heart failure, ischaemic heart disease, cancer, cognitive impairment, chronic kidney disease (CKD). With regard to design features and evaluation methods, additional reviews were sought using search terms 'design', 'quality,' 'usability', 'functionality,' 'adherence', 'evaluation' and related synonyms. Results Of 13 reviews and six single studies assessing efficacy, consistent evidence of benefit was seen only with apps for diabetes, as measured by decreased glycosylated haemoglobin levels (HbA1c). Some, but not all, studies showed benefit in asthma, low back pain, alcohol addiction, heart failure, ischaemic heart disease and cancer. There was no evidence of benefit in COPD, cognitive impairment or CKD. In all studies, benefits were clinically marginal and none related to morbid events or hospitalisation. Twelve design features were identified as enhancing usability. An evaluation framework comprising 32 items was formulated. Conclusion Evidence of clinical benefit of most available apps is very limited. Design features that enhance usability and maximise efficacy were identified. A provisional 'first-pass' evaluation framework is proposed that can help decide which apps should be endorsed by government agencies following more detailed technical assessments and which could then be recommended with confidence by clinicians to their patients. What is known about the topic? Smartphone health apps have attracted considerable interest from patients and health managers as a means of promoting more effective self-management of chronic diseases, which leads to better health outcomes. However, most commercially available apps have never been evaluated for benefits or harms in clinical trials, and there are currently no agreed quality criteria, standards or regulations to ensure health apps are user-friendly, accurate in content, evidence based or efficacious. What does this paper add? This paper presents a comprehensive review of evidence relating to the efficacy, usability and evaluation of apps for 11 common diseases aimed at assisting patients in self-management. Consistent evidence of benefit was only seen for diabetes apps; there was absent or conflicting evidence of benefit for apps for the remaining 10 diseases. Benefits that were detected were of marginal clinical importance, with no reporting of hard clinical end-points, such as mortality or hospitalisations. Only a minority of studies explicitly reported using behaviour change theories to underpin the app intervention. Many apps lacked design features that the literature identified as enhancing usability and potential to confer benefit. Despite a plethora of published evaluation tools, there is no universal framework that covers all relevant clinical and technical attributes. An inclusive list of evaluation criteria is proposed that may overcome this shortcoming. What are the implications for practitioners? The number of smartphone apps will continue to grow, as will the appetite for patients and clinicians to use them in chronic disease self-management. However, the evidence to date of clinical benefit of most apps already available is very limited. Design features that enhance usability and clinical efficacy need to be considered. In making decisions about which apps should be endorsed by government agencies and recommended with confidence by clinicians to their patients, a comprehensive but workable evaluation framework needs to be used by bodies assuming the roles of setting and applying standards.

RR-APET - Heart rate variability analysis software.

BACKGROUND AND OBJECTIVES: Heart rate variability (HRV) has increasingly been linked to medical phenomena and several HRV metrics have been found to be good indicators of patient health. This has enabled generalised treatment plans to be developed in order to respond to subtle personal differences that are reflected in HRV metrics. There are several established HRV analysis platforms and methods available within the literature; some of which provide command line operation across databases but do not offer extensive graphical user interface (GUI) and editing functionality, while others offer extensive ECG editing but are not feasible over large datasets without considerable manual effort. The aim of this work is to provide a comprehensive open-source package, in a well known and multi-platform language, that offers considerable graphical signal editing features, flexibility within the algorithms used for R-peak detection and HRV quantification, and includes graphical functionality for batch processing. Thereby, providing a platform suited to either physician or researcher. METHODS: RR-APET's software was developed in the Python language and is modular in format, providing a range of different modules for established R-peak detection algorithms, as well as an embedded template for alternate algorithms. These modules also include several easily adjustable features, allowing the user to optimise any of the algorithms for different ECG signals or databases. Additionally, the software's user-friendly GUI platform can be operated by both researchers or medical professionals to accomplish different tasks, such as: the in-depth visual analysis of a single ECG, or the analysis multiple signals in a single iteration using batch processing. RR-APET also supports several popular data formats, including text, HDF5, Matlab, and Waveform Database (WFDB) files. RESULTS: The RR-APET platform presents multiple metrics that quantify the heart rate variability features of an R-to-R interval series, including time-domain, frequency-domain, and nonlinear metrics. When known R-peak annotations are available, positive predictability, sensitivity, detection error rate, and accuracy measures are also provided to assess the validity of the implemented R-peak detection algorithm. RR-APET scored an overall usability rating of 4.16 out of a possible 5, when released on a trial basis for user evaluation. CONCLUSIONS: With its unique ability to both create and operate on large databases, this software provides a strong platform from which to conduct further research in the field of HRV analytics and its correlation to patient healthcare outcomes. This software is available free of charge at https://gitlab.com/MegMcC/rr-apet-hrv-analysis-software and can be operated as an executable file within Windows, Mac and Linux systems.

Improving mortality models in the ICU with high-frequency data.

BACKGROUND: Assessment of the performance of Intensive Care Units (ICU) is of vital importance for an effective healthcare system. Such assessment ensures that the limited resources of the healthcare system are allocated where they are most needed. Severity scoring systems are employed for this purpose and improving these systems is a continuing area of research which has focused on the use of more complex techniques and new variables. OBJECTIVES: This paper investigates whether scoring systems could be improved through use of metrics which better summarise the high frequency data collected by automated systems for patients in the ICU. METHODS AND DATA: 3128 admissions to the Gold Coast University Hospital ICU are used to construct three logistic regressions based on the most widely used scoring system (APACHE III) to compare performance with and without predictors leveraging available high frequency information. Performance is assessed based on model accuracy, calibration, and discrimination. High frequency information was considered for existing pulse and mean arterial pressure physiology fields and resulting models compared against a baseline logistic regression using only APACHE III physiology variables. RESULTS: Model discrimination and accuracy were better for models which included high frequency predictors, with calibration remaining good in all cases. The most influential high frequency summaries were the number of turning points in a patient's mean arterial pressure or pulse in the first 24 h of ICU admission. CONCLUSIONS: The findings indicate that scoring systems can be improved by better accounting for high frequency data.

Population pharmacokinetics of ticarcillin in critically ill patients receiving extended daily diafiltration.

The aim of this study was to describe the population pharmacokinetics of ticarcillin during extended daily diafiltration (EDDf) in critically ill patients with acute kidney injury. Blood samples were collected from critically ill patients prescribed ticarcillin during one to two dosing intervals during which EDDf was performed. Plasma samples were measured using a validated ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. Concentration-time data were analysed using a population pharmacokinetics approach with Pmetrics®. A total of 53 blood samples were collected from six critically ill patients (three male). The mean ± standard deviation patient age, weight and body mass index (BMI) was 43 ± 22 years, 88 ± 14 kg and 31 ± 5 kg/m2, respectively. A two-compartment linear model adequately described the data. Median population pharmacokinetic parameter estimates were as follows: clearance in the presence of EDDf (CLEDDf), 6.41 L/h; clearance of EDDf (CLnon-EDDf), 4.97 L/h; volume of distribution of the central compartment (Vc), 56.46 L; intercompartmental clearance from the central to peripheral compartment (kCP), 13.54 L/h; and intercompartmental clearance from the peripheral to central compartment (kPC), 21.93 L/h. This is the first population pharmacokinetic model of ticarcillin in patients receiving EDDf. Large pharmacokinetic variability was found, supporting further investigation of the pharmacokinetics of less-studied ß-lactam antibiotics in prolonged intermittent renal replacement therapy.

Investigating healthcare contacts of Dialysis patients by age and gender.

BACKGROUND: The objective of this paper is to utilise a clinical costing system to investigate differences in the patient journey, defined as the sequence and timing of contacts with the Gold Coast Hospital and Health Services (GCHHS), for four dialysis patient groups defined based on age and gender. It is hypothesised that frequency of contact and form of contact will differ based on both gender and age. METHODS: Data were provided for 393 patients discharged from the GCHHS facility with dialysis treatment between the 1st of January 2015 and the 31st of December 2016. Features extracted from the data included the number and type of contacts (inpatient admissions, outpatient appointments, and emergency department presentations), the likelihood of subsequent contact types, and time spent in and between contact types. Likelihoods of subsequent contact types were estimated by treating the sequence of contacts observed for each patient as a Markov chain and estimating transition probabilities. RESULTS: Differences in patient journey were most prominent when considering age differences, with older patients being characterised by a greater volume of average contacts over the two-year period. The larger volume of average contacts was attributable to shorter times between all types of contacts with the GCHHS as well as an increased volume of inpatient admissions for older patients. Patient journeys did not consistently differ by gender, though some isolated differences were noted for older female patients relative to older male patients. CONCLUSIONS: Different patient groups are characterised by different patient journeys, and better understanding these differences will facilitate improved management of the resources required to service these patients. Clinical costing systems represent a valuable and easily accessible source of data for formulating institution-specific expectations of healthcare utilisation for different groups.

Health Care Provision During a Sporting Mass Gathering: A Structure and Process Description of On-Site Care Delivery.

INTRODUCTION: Mass gatherings such as marathons are increasingly frequent. During mass gatherings, the provision of timely access to health care services is required for the mass-gathering population, as well as for the local community. However, the nature and impact of health care provision during sporting mass gatherings is not well-understood. PURPOSE: The aim of this study was to describe the structures and processes developed for an emergency health team to operate an in-event, acute health care facility during one of the largest mass-sporting participation events in the southern hemisphere, the Gold Coast Marathon (Queensland, Australia). METHODS: A pragmatic, qualitative methodology was used to describe the structures and processes required to operate an in-event, acute health care facility providing services for marathon runners and spectators. Content analysis from 12 semi-structured interviews with emergency department (ED) clinical staff working during the two-day event was undertaken in 2016. FINDINGS: Important structural elements of the in-event health care facility included: physical spaces, such as the clinical zones in the marathon health tent and surrounding area, and access and egress points; and resources such as bilingual staff, senior medical staff, and equipment such as electrocardiograms (ECGs) and intravenous fluids. Process elements of the in-event health care facility included clear communication pathways, as well as inter-professional care coordination and engagement involving shared knowledge of and access to resources, and distinct but overlapping clinical scope between nurses and doctors. This was seen to be critical for timely care provision and appropriate case management. Staff reported many perceived benefits and opportunities of in-event health care delivery, including ED avoidance and disaster training. CONCLUSIONS: This in-event model of emergency care delivery, established in an out-of-hospital location, enabled the delivery of acute health care that could be clearly described and defined. Staff reported satisfaction with their ability to provide a meaningful contribution to hospital avoidance and to the local community. With the number of sporting mass gatherings increasing, this temporary, in-event model of health care provision is one option for event and health care planners to consider.JohnstonANB, WadhamJ, Polong-BrownJ, AitkenM, RanseJ, HuttonA, RichardsB, CrillyJ.Health care provision during a sporting mass gathering: a structure and process description of on-site care delivery. Prehosp Disaster Med. 2019;34(1):62-71.