Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves

Background: Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability–weighted survival curves to address this limitation. Methods: A case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability–weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. Results: The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803–3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276–3415]) or when completely ignoring confounding (2838 [95% CI, 2101–3575]). Conclusions: ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability–weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures

Cost of fertility treatment and live birth outcome in women of different ages and BMI

Acknowledgements We thank the Aberdeen Fertility Centre Database Committee and the Aberdeen Maternal and Neonatal Databank Committee for giving us approval to use their databases. We thank the Data Management Team for extracting the required information from these databases. The views expressed in this paper represent the views of the authors and not necessarily the views of the funding bodies. Funding This study was partly funded by an NHS endowment grant (Grant Number 12/48) and DM by a Chief Scientist Office Postdoctoral Fellowship (Ref PDF/12/06).Peer reviewedPostprin

Using orthopaedic health care resources efficiently: a cost analysis of day surgery for unicompartmental knee replacement

Background: Day surgery for unicompartmental knee replacement (UKR) could potentially reduce hospital costs. We aimed to measure the impact of introducing a day surgery UKR pathway on mean length of stay (LOS) and costs for the UK NHS, compared to an accelerated inpatient pathway. Secondly, the study aimed to compare the magnitude of costs using three costing approaches: top-down costing; simple micro-costing; and real-world costing. Methods: We conducted an observational, before-and-after study of 2,111 UKR patients at one NHS hospital: 1,094 patients followed the day surgery pathway between September 2017 and February 2020; and 1,017 patients followed the accelerated inpatient pathway between September 2013 and February 2016. Top-down costs were estimated using Average NHS Costs. Simple micro-costing used the cost per bed-day. Real-world costs for this centre were estimated by costing actual changes in staffing levels. Results: 532 (48.5%) patients in the day surgery pathway were discharged on the day of surgery compared with 36 (3.5%) patients in the accelerated inpatient pathway. The day surgery pathway reduced the mean LOS by 2.2 (95% CI: 1.81, 2.53) nights and was associated with an 18% decrease in Average NHS Costs (p < 0.001). Mean savings were £1,429 per patient with the Average NHS Costs approach, £905 per patient with the micro-costing approach, and £577 per patient with the “real-world” costing approach. Overall, moving NHS UKR surgeries to a day surgery pathway could save the NHS £8,659,740 per year. Conclusion: Day surgery for UKR could produce substantial cost savings for hospitals and the NHS

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based cohort study

Objective: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. Design: Population based cohort study. Setting: Data from the UK Biobank study, collected between March 2006 and July 2010. Participants: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. Main outcome measures: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. Results: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. Conclusions: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population

Overcoming challenges in the economic evaluation of interventions to optimise antibiotic use

Bacteria are becoming increasingly resistant to antibiotics, reducing our ability to treat infections and threatening to undermine modern health care. Optimising antibiotic use is a key element in tackling the problem. Traditional economic evaluation methods do not capture many of the benefits from improved antibiotic use and the potential impact on resistance. Not capturing these benefits is a major obstacle to optimising antibiotic use, as it fails to incentivise the development and use of interventions to optimise the use of antibiotics and preserve their effectiveness (stewardship interventions). Estimates of the benefits of improving antibiotic use involve considerable uncertainty as they depend on the evolution of resistance and associated health outcomes and costs. Here we discuss how economic evaluation methods might be adapted, in the face of such uncertainties. We propose a threshold-based approach that estimates the minimum resistance-related costs that would need to be averted by an intervention to make it cost-effective. If it is probable that without the intervention costs will exceed the threshold then the intervention should be deemed cost-effective

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis: a cohort study in UK Biobank

OBJECTIVE: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. DESIGN: Population based cohort study. SETTING: Data from the UK Biobank study, collected between March 2006 and July 2010. PARTICIPANTS: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. MAIN OUTCOME MEASURES: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. RESULTS: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. CONCLUSIONS: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population

Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling

Objectives: The objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods. Methods: Given the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective. Results: Some of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models. Conclusion: The economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.</p