A Randomized Noninferiority Trial to Compare Enteral to Parenteral Phosphate Replacement on Biochemistry, Waste, and Environmental Impact and Healthcare Cost in Critically Ill Patients With Mild to Moderate Hypophosphatemia.

OBJECTIVES: Hypophosphatemia occurs frequently. Enteral, rather than IV, phosphate replacement may reduce fluid replacement, cost, and waste. DESIGN: Prospective, randomized, parallel group, noninferiority clinical trial. SETTING: Single center, 42-bed state trauma, medical and surgical ICUs, from April 20, 2022, to July 1, 2022. PATIENTS: Patients with serum phosphate concentration between 0.3 and 0.75 mmol/L. INTERVENTIONS: We randomized patients to either enteral or IV phosphate replacement using electronic medical record-embedded program. MEASUREMENT AND MAIN RESULTS: Our primary outcome was serum phosphate at 24 hours with a noninferiority margin of 0.2 mmol/L. Secondary outcomes included cost savings and environmental waste reduction and additional IV fluid administered. The modified intention-to-treat cohort comprised 131 patients. Baseline phosphate concentrations were similar between the two groups. At 24 hours, mean (sd) serum phosphate concentration were enteral 0.89 mmol/L (0.24 mmol/L) and IV 0.82 mmol/L (0.28 mmol/L). This difference was noninferior at the margin of 0.2 mmol/L (difference, 0.07 mmol/L; 95% CI, -0.02 to 0.17 mmol/L). When assigned IV replacement, patients received 408 mL (372 mL) of solvent IV fluid. Compared with IV replacement, the mean cost per patient was ten-fold less with enteral replacement ($3.7 [$4.0] vs. IV: $37.7 [$31.4]; difference = $34.0 [95% CI, $26.3-$41.7]) and weight of waste was less (7.7 g [8.3 g] vs. 217 g [169 g]; difference = 209 g [95% CI, 168-250 g]). CO2 emissions were 60-fold less for comparable phosphate replacement (enteral: 2 g producing 14.2 g and 20 mmol of potassium dihydrogen phosphate producing 843 g of CO2 equivalents). CONCLUSIONS: Enteral phosphate replacement in ICU is noninferior to IV replacement at a margin of 0.2 mmol/L but leads to a substantial reduction in cost and waste.

Feasibility of Embedding a Randomised Clinical Trial (RCT) into an Electronic Medical Record (EMR) for Patients Admitted to an Intensive Care Unit (ICU).

To establish the feasibility of embedding an RCT into EMR in the ICU, we evaluated the route of phosphate replacement. The EMR screened 207 patients who met the inclusion criteria from 20 April 2022 to 30 June 2022. 162 patients were randomised and 145 patients allocated to treatment. Our study showed that it was feasible to embed screening, randomisation, and treatment allocation for an RCT within an EMR in the ICU.

Health-related quality of life in patients with extremity bone sarcoma after surgical treatment: a systematic review.

PURPOSE: We conducted a systematic review of studies reporting on measurement of health-related quality of life (HRQoL), with a special focus on the use of the preference-weighted instruments, in patients with extremity bone sarcoma treated with limb-salvage surgery or amputation. METHODS: We searched MedLine, Embase, Cochrane Library and Web of Science for English-language studies reporting on HRQoL of patients with bone sarcoma from inception to 28 August 2023. All records found were independently reviewed by two reviewers. We used the Newcastle-Ottawa Scale (NOS) and the CONSORT 2010 checklist to assess the quality of the cohort and randomised studies, respectively. RESULTS: The search identified 1225 records, of which 16 studies were included for data extraction. Only one study used a preference-weighted instrument for measuring HRQoL in a small sample of patients (n = 28). Ten studies used the generic SF-36 questionnaire, but no preference-weighted HRQoL based on SF-6D was derived from the SF-36 scores. Most studies comparing HRQoL between amputation and limb-salvage surgery reported no significant differences. Twelve cohort studies scored six or more out of nine points based on the NOS. The only randomised study scored 54% on the CONSORT 2010 checklist. CONCLUSIONS: The approaches used to measure HRQoL were inconsistent and outcome scores varied substantially. Only one study used preference-weighted instruments for HRQoL measurement. Future research into the surgical treatment of extremity bone sarcoma should consider the use of preference-weighted instruments to measure HRQoL, which will therefore enable economic evaluation for the growing orthopaedic armamentarium of novel surgical interventions. REGISTRATION: This systematic review was registered with the PROSPERO International prospective register of systematic reviews (CRD42021282380).

Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis.

Background: Health risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia. Methods: We used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status. Results: After adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66-2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21-1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45-0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97-2.29, p < 0.001) after adjusting for all other health risk factors and covariates. Conclusion: Our study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.

Telehealth in oncology: a cost analysis to evaluate the financial impact of implementing regional trial hubs within a phase 3 cancer clinical trial.

This cost analysis, from a societal perspective, compared the cost difference of a networked teletrial model (NTTM) with four regional hubs versus conventional trial operation at a single metropolitan specialist centre. The Australian phase 3 cancer interventional randomised controlled trial included 152 of 328 regional participants (patient enrolment 2018-2021; 6-month primary end point). The NTTM significantly reduced (AU$2155 per patient) patient travel cost and time and lost productivity.

Trial of Vancomycin and Cefazolin as Surgical Prophylaxis in Arthroplasty.

BACKGROUND: The addition of vancomycin to beta-lactam prophylaxis in arthroplasty may reduce surgical-site infections; however, the efficacy and safety are unclear. METHODS: In this multicenter, double-blind, superiority, placebo-controlled trial, we randomly assigned adult patients without known methicillin-resistant Staphylococcus aureus (MRSA) colonization who were undergoing arthroplasty to receive 1.5 g of vancomycin or normal saline placebo, in addition to cefazolin prophylaxis. The primary outcome was surgical-site infection within 90 days after surgery. RESULTS: A total of 4239 patients underwent randomization. Among 4113 patients in the modified intention-to-treat population (2233 undergoing knee arthroplasty, 1850 undergoing hip arthroplasty, and 30 undergoing shoulder arthroplasty), surgical-site infections occurred in 91 of 2044 patients (4.5%) in the vancomycin group and in 72 of 2069 patients (3.5%) in the placebo group (relative risk, 1.28; 95% confidence interval [CI], 0.94 to 1.73; P = 0.11). Among patients undergoing knee arthroplasty, surgical-site infections occurred in 63 of 1109 patients (5.7%) in the vancomyin group and in 42 of 1124 patients (3.7%) in the placebo group (relative risk, 1.52; 95% CI, 1.04 to 2.23). Among patients undergoing hip arthroplasty, surgical-site infections occurred in 28 of 920 patients (3.0%) in the vancomyin group and in 29 of 930 patients (3.1%) in the placebo group (relative risk, 0.98; 95% CI, 0.59 to 1.63). Adverse events occurred in 35 of 2010 patients (1.7%) in the vancomycin group and in 35 of 2030 patients (1.7%) in the placebo group, including hypersensitivity reactions in 24 of 2010 patients (1.2%) and 11 of 2030 patients (0.5%), respectively (relative risk, 2.20; 95% CI, 1.08 to 4.49), and acute kidney injury in 42 of 2010 patients (2.1%) and 74 of 2030 patients (3.6%), respectively (relative risk, 0.57; 95% CI, 0.39 to 0.83). CONCLUSIONS: The addition of vancomycin to cefazolin prophylaxis was not superior to placebo for the prevention of surgical-site infections in arthroplasty among patients without known MRSA colonization. (Funded by the Australian National Health and Medical Research Council; Australian New Zealand Clinical Trials Registry number, ACTRN12618000642280.).

Study protocol for TARGET protein: The effect of augmented administration of enteral protein to critically ill adults on clinical outcomes: A cluster randomised, cross-sectional, double cross-over, clinical trial.

Background: It is unknown whether increasing dietary protein to 1.2-2.0 g/kg/day as recommended in international guidelines compared to current practice improves outcomes in intensive care unit (ICU) patients. The TARGET Protein trial will evaluate this. Objective: To describe the study protocol for the TARGET Protein trial. Design setting and participants: TARGET Protein is a cluster randomised, cross-sectional, double cross-over, pragmatic clinical trial undertaken in eight ICUs in Australia and New Zealand. Each ICU will be randomised to use one of two trial enteral formulae for three months before crossing over to the other formula, which is then repeated, with enrolment continuing at each ICU for 12 months. All patients aged ≥16 years in their index ICU admission commencing enteral nutrition will be eligible for inclusion. Eligible patients will receive the trial enteral formula to which their ICU is allocated. The two trial enteral formulae are isocaloric with a difference in protein dose: intervention 100g/1000 ml and comparator 63g/1000 ml. Staggered recruitment commenced in May 2022. Main outcomes measures: The primary outcome is days free of the index hospital and alive at day 90. Secondary outcomes include days free of the index hospital at day 90 in survivors, alive at day 90, duration of invasive ventilation, ICU and hospital length of stay, incidence of tracheostomy insertion, renal replacement therapy, and discharge destination. Conclusion: TARGET Protein aims to determine whether augmented enteral protein delivery reduces days free of the index hospital and alive at day 90. Trial registration: Australian New Zealand Clinical Trials Registry (ACTRN12621001484831).

Understanding the measurement relationship between EQ-5D-5L, PROMIS-29 and PROPr.

PURPOSE: Many generic patient-reported instruments are available for the measurement of health outcomes, including EQ-5D-5L, and the Patient-Reported Outcome Measurement Information System (PROMIS). Assessing their measurement characteristics informs users about the consistency between, and limits of, evidence produced. The aim was to assess the measurement relationship between the EQ-5D-5L descriptive system and value sets, the PROMIS-29 and PROPr (PROMIS value set). METHODS: Data were extracted from a cross-sectional survey administering measures of quality of life online in Australia. Descriptive analysis, agreement and construct validity assessment methods were used to compare instruments at the item, domain and value set level. RESULTS: In total, 794 Australians completed the survey. Convergent validity analysis found that similar dimensions across instruments were highly correlated (> 0.50), but the PROMIS-29 assesses additional health concepts not explicitly covered by EQ-5D (sleep and fatigue). Known-group assessment found that EQ-5D-5L and PROPr were able to detect those with and without a condition (ES range 0.78-0.83) but PROPr could more precisely detect differing levels of self-reported health. Both instruments were sensitive to differences in levels of pain. DISCUSSION: There is some consistency in what the EQ-5D-5L, PROMIS-29 and PROPr measure. Differences between value set characteristics can be linked to differences what is measured and the valuation approaches used. This has implications for the use of each in assessing health outcomes, and the results can inform decisions about which instrument should be used in which context.

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.

OBJECTIVES: To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD). METHODS: We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models. RESULTS: The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6-9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. CONCLUSIONS: This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.

The comparative mortality of an elite group in the long run of history: an observational analysis of politicians from 11 countries.

This study aims to compare the mortality rate and life expectancy of politicians with those of the age and gender-matched general populations. This was an observational analysis of mortality rates of politicians (i.e. members of national parliaments with available data on dates of birth, death and election, gender, and life tables) in 11 developed countries. Politicians were followed from date of first election until either death or the last available year with life table data. Relative mortality differences were estimated using standardised mortality ratios (SMRs). Absolute inequalities were quantified as the difference in survival by deducting a population's remaining life expectancy from politicians' remaining life expectancy at age 45, estimated using Gompertz parametric proportional hazards models. We included 57,561 politicians (with follow-up ranging from 1816-2016 for France to 1949-2017 for Germany). In almost all countries politicians had similar rates of mortality to the general population in the early part of the twentieth century. Relative mortality and survival differences (favouring politicians) increased considerably over the course of the twentieth century, with recent SMRs ranging from 0.45 (95%CI 0.41-0.50) in Italy to 0.82 (95%CI 0.69-0.95) in New Zealand. The peak life expectancy gaps ranged from 4.4 (95% CI, 3.5-5.4) years in the Netherlands to 7.8 (95% CI, 7.2-8.4) years in the US. Our results show large relative and absolute inequalities favouring politicians in every country. In some countries, such as the US, relative inequalities are at the greatest level in over 150 years.

Evidence on the relationship between PROMIS-29 and EQ-5D: a literature review.

PURPOSE: EQ-5D and PROMIS-29 are both concise, generic measures of patient-reported outcomes accompanied by preference weights that allow the estimation of quality-adjusted life years (QALYs). Both instruments are candidates for use in economic evaluation. However, they have different features in terms of the domains selected to measure respondents' self-perceived health and the characteristics of (and methods used to obtain) the preference weights. It is important to understand the relationship between the instruments and the implications of choosing either for the evidence used in decision-making. This literature review aimed to synthesise existing evidence on the relationship between PROMIS-29 (and measures based on it, such as PROMIS-29+2) and EQ-5D (both EQ-5D-3L and EQ-5D-5L). METHODS: A literature review was conducted in PubMed and Web of Science to identify studies investigating the relationship between PROMIS-29 and EQ-5D-based instruments. RESULTS: The literature search identified 95 unique studies, of which nine studies met the inclusion criteria, i.e. compared both instruments. Six studies examined the relationship between PROMIS-29 and EQ-5D-5L. Three main types of relationship have been examined in the nine studies: (a) comparing PROMIS-29 and EQ-5D as descriptive systems; (b) mapping PROMIS-29 domains to EQ-5D utilities; and (c) comparing and transforming PROMIS-29 utilities to EQ-5D utilities. CONCLUSION: This review has highlighted the lack of evidence regarding the relationship between PROMIS-29 and EQ-5D. The impact of choosing either instrument on the evidence used in cost-effectiveness analysis is currently unclear. Further research is needed to understand the relationship between the two instruments.

Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge.

BACKGROUND: Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. METHODS: Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. RESULTS: Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049). CONCLUSIONS: Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Development of a life expectancy table for individuals with type 1 diabetes.

AIMS/HYPOTHESIS: Tables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes. METHODS: The simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort. RESULTS: There was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors. CONCLUSIONS/INTERPRETATION: The life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors.

Cost-Effectiveness of Combination Therapy for Patients With Systemic Sclerosis-Related Pulmonary Arterial Hypertension.

Background To evaluate the cost-effectiveness of combination pulmonary arterial hypertension specific therapy in systemic sclerosis-related PAH. Methods and Results Health outcomes and costs were captured through data linkage. Health utility was derived from Medical Outcomes Study Short Form-36 scores. A probabilistic discrete-time model was developed to simulate lifetime changes in costs and health utility. Mortality was predicted using a Gompertz parametric survival model. For both treatment arms, the simulations were started using the same cohort of 10 000 patients. Probabilistic sensitivity analysis was performed using the Monte Carlo simulation with 1000 sets of sampled parameter values. Of 143 patients with systemic sclerosis-related pulmonary arterial hypertension, 89 were on monotherapy and 54 on combination therapy. Mean simulated costs per patient per year in monotherapy and combination therapy groups were AU$23 411 (US$16 080) and AU$29 129 (US$19 982), respectively. Mean life years and quality-adjusted life years from pulmonary arterial hypertension diagnosis to death of patients receiving monotherapy were 7.1 and 3.0, respectively, and of those receiving combination therapy were 9.2 and 3.9, respectively. Incremental costs per life year and quality-adjusted life year gained of combination therapy compared with monotherapy were AU$47 989 (US$32 920) and AU$113 823 (US$78 082), respectively. At a willingness-to-pay threshold of AU$102 000 (US$69 972) per life year gained, and of AU$177 222 (US$121 574) per quality-adjusted life year gained, the probability of combination therapy being cost-effective was 0.95. Conclusions The incremental cost per quality-adjusted life year gained of combination therapy compared with monotherapy was substantial in the base case analysis. Given the fatal prognosis of systemic sclerosis-related pulmonary arterial hypertension and the incremental cost per life year of AU$47 989 (US$32 920), combination therapy could be considered cost-effective in systemic sclerosis-related pulmonary arterial hypertension.

Impact of Comorbid Conditions on Healthcare Expenditure and Work-related Outcomes in Patients With Rheumatoid Arthritis.

OBJECTIVE: To evaluate the effect of comorbid conditions on direct healthcare expenditure and work-related outcomes in patients with rheumatoid arthritis (RA). METHODS: This is a retrospective analysis of the Medical Expenditure Panel Survey from 2006 to 2015 in 4967 adults with RA in the United States. Generalized linear models were used for healthcare expenditure and income, logistic models for employment status, and zero-inflated negative binomial models for absenteeism. Thirteen comorbid conditions were included as potential predictors of direct cost- and work-related outcomes. The models were adjusted for sociodemographic factors including sex, age, region, marital status, race/ethnicity, income, education, and smoking status. RESULTS: Patients with RA with heart failure (HF) had the highest incremental annual healthcare expenditure (US$8205, 95% CI $3683-$12,726) compared to those without the condition. Many comorbid conditions including hypertension (HTN), diabetes, depression, chronic obstructive pulmonary disease, cancer, stroke, and HF reduced the chance of patients with RA aged between 18-64 years being employed. Absenteeism of employed patients with RA was significantly affected by HTN, depression, disorders of the eye and adnexa, or stroke. On average, RA patients with HF earned US$15,833 (95% CI $4435-$27,231) per year less than RA patients without HF. CONCLUSION: Comorbid conditions in patients with RA were associated with higher annual healthcare expenditure, lower likelihood of employment, higher rates of absenteeism, and lower income. Despite its low prevalence, HF was associated with the highest incremental healthcare expenditure and the lowest likelihood of being employed compared to other common comorbid conditions.

Measuring the economic impact of hospital-acquired complications on an acute health service.

Objective This study determined the economic impact of 16 'high-priority' hospital-acquired complications (HACs), as defined by the Australian Commission on Safety and Quality in Health Care, from the perspective of an individual Australian health service. Methods A retrospective cohort study was performed using a deidentified patient dataset containing 93056 in-patient separations in Northern Health (Victoria, Australia) from 1 July 2016 to 30 June 2017. Two log-linked generalised linear regression models were used to obtain additional costs and additional length of stay (LOS) for 16 different HACs, with the main outcome measures being the additional cost and LOS for all 16 HACs. Results In all, 1700 separations involving HACs (1.83%) were identified. The most common HAC was health care-associated infections. Most HACs were associated with a statistically significant risk of increased cost (15/16 HACs) and LOS (11/16 HACs). HACs involving falls resulting in fracture or other intracranial injury were associated with the highest additional cost (A$17173). The biggest increase in additional LOS was unplanned admissions to the intensive care unit (5.42 days). Conclusions This study shows the economic impact of HACs from the perspective of an individual health service. The methodology used demonstrates how other health services could determine safety priorities corresponding to their own casemix. What is known about the topic? HACs are a major issue in Australian health care; however, their effect on cost and LOS at the individual health service level is not well quantified. What does this paper add? Additional cost and LOS implications for 16 high-priority HACs have been quantified within an Australian health service. There is substantial variation in terms of the number of HACs and the economic impact of each HAC. What are the implications for practitioners? This study provides a template for other health services to assess the economic impact of HACs corresponding to their own casemix and to inform targeted patient safety programs.

A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes.

OBJECTIVE: To develop a patient-level simulation model for predicting lifetime health outcomes of patients with type 1 diabetes and as a tool for economic evaluation of type 1 diabetes treatment based on data from a large, longitudinal cohort. RESEARCH DESIGN AND METHODS: Data for model development were obtained from the Swedish National Diabetes Register. We derived parametric proportional hazards models predicting the absolute risk of diabetes complications and death based on a wide range of clinical variables and history of complications. We used linear regression models to predict risk factor progression. Internal validation was performed, estimates of life expectancies for different age-sex strata were computed, and the impact of key risk factors on life expectancy was assessed. RESULTS: The study population consisted of 27,841 patients with type 1 diabetes with a mean duration of follow-up of 7 years. Internal validation showed good agreement between the predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was ∼13 years lower than that of the sex- and age-matched general population, and patients with type 1 diabetes could expect to live with one or more complications for ∼40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycemia, and hyperglycemia as well as lowering HbA1c in reducing the risk of complications and death. CONCLUSIONS: Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes and to project events occurring over patients' lifetimes. The model can serve as a tool to estimate the impact of changing clinical risk factors on health outcomes to inform economic evaluations of interventions in type 1 diabetes.

Development and Use of Prediction Models for Classification of Cardiovascular Risk of Remote Indigenous Australians.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death for Indigenous Australians. There is widespread belief that current tools have deficiencies for assessing CVD risk in this high-risk population. We sought to develop a 5-year CVD risk score using a wide range of known risk factors to further improve CVD risk prediction in this population. METHODS: We used clinical and demographic information on Indigenous people aged between 30 and 74 years without a history of CVD events who participated in the Well Person's Health Check (WPHC), a community-based survey. Baseline assessments were conducted between 1998 and 2000, and data were linked to administrative hospitalisation and death records for identification of CVD events. We used Cox proportional hazard models to estimate the 5-year CVD risk, and the Harrell's c-statistic and the modified Hosmer-Lemeshow (mH-L) χ2 statistic to assess the model discrimination and calibration, respectively. RESULTS: The study sample consisted of 1,583 individuals (48.1% male; mean age 45.0 year). The risk score consisted of sex, age, systolic blood pressure, diabetes mellitus, waist circumference, triglycerides, and albumin creatinine ratio. The bias-corrected c-statistic was 0.72 and the bias-corrected mH-L χ2 statistic was 12.01 (p-value, 0.212), indicating good discrimination and calibration, respectively. Using our risk score, the CVD risk of the Indigenous Australians could be stratified to a greater degree compared to a recalibrated Framingham risk score. CONCLUSIONS: A seven-factor risk score could satisfactorily stratify 5-year risk of CVD in an Indigenous Australian cohort. These findings inform future research targeting CVD risk in Indigenous Australians.

Multicentre randomised double-blind placebo controlled trial of combination vancomycin and cefazolin surgical antibiotic prophylaxis: the Australian surgical antibiotic prophylaxis (ASAP) trial.

INTRODUCTION: Resistant Gram-positive organisms, such as methicillin-resistant staphylococci, account for a significant proportion of infections following joint replacement surgery. Current surgical antimicrobial prophylaxis guidelines recommend the use of first-generation or second-generation cephalosporin antibiotics, such as cefazolin. Cefazolin, however, does not prevent infections due to these resistant organisms; therefore, new prevention strategies need to be examined. One proposed strategy is to combine a glycopeptide antibiotic with cefazolin for prophylaxis. The clinical benefit and cost-effectiveness of this combination therapy compared with usual therapy, however, have not been established. METHODS AND ANALYSIS: This randomised, double-blind, parallel, superiority, placebo-controlled, phase 4 trial will compare the incidence of all surgical site infections (SSIs) including superficial, deep and organ/space (prosthetic joint) infections, safety and cost-effectiveness of surgical prophylaxis with cefazolin plus vancomycin to that with cefazolin plus placebo. The study will be performed in patients undergoing joint replacement surgery. In the microbiological sub-studies, we will examine the incidence of SSIs in participants with preoperative staphylococci colonisation (Sub-Study 1) and incidence of VRE acquisition (Sub-Study 2). The trial will recruit 4450 participants over a 4-year period across 13 orthopaedic centres in Australia. The primary outcome is the incidence of SSI at 90 days post index surgery. Secondary outcomes include the incidence of SSI according to joint and microorganism and other healthcare associated infections. Safety endpoints include the incidence of acute kidney injury, hypersensitivity reactions and all-cause mortality. The primary and secondary analysis will be a modified intention-to-treat analysis consisting of all randomised participants who undergo eligible surgery. We will also perform a per-protocol analysis. ETHICS AND DISSEMINATION: The study protocol was reviewed and approved by The Alfred Hospital Human Research Ethics Committee (HREC/18/Alfred/102) on 9 July 2018. Study findings will be disseminated in the printed media, and learnt forums. TRIAL REGISTRATION NUMBER: ACTRN12618000642280.

The Challenge of Transparency and Validation in Health Economic Decision Modelling: A View from Mount Hood.

Transparency in health economic decision modelling is important for engendering confidence in the models and in the reliability of model-based cost-effectiveness analyses. The Mount Hood Diabetes Challenge Network has taken a lead in promoting transparency through validation with biennial conferences in which diabetes modelling groups meet to compare simulated outcomes of pre-specified scenarios often based on the results of pivotal clinical trials. Model registration is a potential method for promoting transparency, while also reducing the duplication of effort. An important network initiative is the ongoing construction of a diabetes model registry (https://www.mthooddiabeteschallenge.com). Following the 2012 International Society for Pharmacoeconomics and Outcomes Research and the Society of Medical Decision Making (ISPOR-SMDM) guidelines, we recommend that modelling groups provide technical and non-technical documentation sufficient to enable model reproduction, but not necessarily provide the model code. We also request that modelling groups upload documentation on the methods and outcomes of validation efforts, and run reference case simulations so that model outcomes can be compared. In this paper, we discuss conflicting definitions of transparency in health economic modelling, and describe the ongoing development of a registry of economic models for diabetes through the Mount Hood Diabetes Challenge Network, its objectives and potential further developments, and highlight the challenges in its construction and maintenance. The support of key stakeholders such as decision-making bodies and journals is key to ensuring the success of this and other registries. In the absence of public funding, the development of a network of modellers is of huge value in enhancing transparency, whether through registries or other means.