The Impact of Restrictive Transfusion Practices on Hemodynamically Stable Critically Ill Children Without Heart Disease: A Secondary Analysis of the Age of Blood in Children in the PICU Trial.

OBJECTIVES: Guidelines recommend against RBC transfusion in hemodynamically stable (HDS) children without cardiac disease, if hemoglobin is greater than or equal to 7 g/dL. We sought to assess the clinical and economic impact of compliance with RBC transfusion guidelines. DESIGN: A nonprespecified secondary analysis of noncardiac, HDS patients in the randomized trial Age of Blood in Children (NCT01977547) in PICUs. Costs analyzed included ICU stay and physician fees. Stabilized inverse propensity for treatment weighting was used to create a cohort balanced with respect to potential confounding variables. Weighted regression models were fit to evaluate outcomes based on guideline compliance. SETTING: Fifty international tertiary care centers. PATIENTS: Critically ill children 3 days to 16 years old transfused RBCs at less than or equal to 7 days of ICU admission. Six-hundred eighty-seven subjects who met eligibility criteria were included in the analysis. INTERVENTIONS: Initial RBC transfusions administered when hemoglobin was less than 7 g/dL were considered "compliant" or "non-compliant" if hemoglobin was greater than or equal to 7 g/dL. MEASUREMENTS AND MAIN RESULTS: Frequency of new or progressive multiple organ system dysfunction (NPMODS), ICU survival, and associated costs. The hypothesis was formulated after data collection but exposure groups were masked until completion of planned analyses. Forty-nine percent of patients (338/687) received a noncompliant initial transfusion. Weighted cohorts were balanced with respect to confounding variables (absolute standardized differences < 0.1). No differences were noted in NPMODS frequency (relative risk, 0.86; 95% CI, 0.61-1.22; p = 0.4). Patients receiving compliant transfusions had more ICU-free days (mean difference, 1.73; 95% CI, 0.57-2.88; p = 0.003). Compliance reduced mean costs in ICU by $38,845 U.S. dollars per patient (95% CI, $65,048-$12,641). CONCLUSIONS: Deferring transfusion until hemoglobin is less than 7 g/dL is not associated with increased organ dysfunction in this population but is independently associated with increased likelihood of live ICU discharge and lower ICU costs.

Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry.

BACKGROUND: There have been multiple efforts toward individual prediction of recurrent strokes based on structured clinical and imaging data using machine learning algorithms. Some of these efforts resulted in relatively accurate prediction models. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs. Therefore, we developed recurrent stroke prediction models based solely on data easily obtained from the patient at home. METHODS: Data from 384 patients with ischemic stroke were obtained from the Erlangen Stroke Registry. Patients were followed at 3 and 12 months after first stroke and then annually, for about 2 years on average. Multiple machine learning algorithms were applied to train predictive models for estimating individual risk of recurrent stroke within 1 year. Double nested cross-validation was utilized for conservative performance estimation and models' learning capabilities were assessed by learning curves. Predicted probabilities were calibrated, and relative variable importance was assessed using explainable artificial intelligence techniques. RESULTS: The best model achieved the area under the curve of 0.70 (95% CI, 0.64-0.76) and relatively good probability calibration. The most predictive factors included patient's family and housing circumstances, rehabilitative measures, age, high calorie diet, systolic and diastolic blood pressures, percutaneous endoscopic gastrotomy, number of family doctor's home visits, and patient's mental state. CONCLUSIONS: Developing fairly accurate models for individual risk prediction of recurrent ischemic stroke within 1 year solely based on registry data is feasible. Such models could be applied in a home setting to provide an initial risk assessment and identify high-risk patients early.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Economic Evaluation of Sequences of Biological Treatments for Patients With Moderate-to-Severe Rheumatoid Arthritis and Inadequate Response or Intolerance to Methotrexate in France.

OBJECTIVES: Biologic disease-modifying antirheumatic drugs (bDMARDs) are prescribed sequentially in the treatment of rheumatoid arthritis (RA). Healthcare decision makers continue to debate their use, mainly because of their high costs. Our aim was to perform an economic evaluation for France of bDMARD sequences for treatment of moderate-to-severe RA after inadequate response or intolerance to conventional DMARDs (eg, methotrexate). METHODS: A discretely integrated condition event simulation was developed to track the course of patients from first bDMARD through switches to further lines in a sequence. The model included 11 events, 91 conditions, and 21 controlling equations. Inputs were obtained from a meta-analysis of clinical trials, a French registry, national drug lists, and databases. Survival, time with minimal activity, quality-adjusted life-years (QALYs), and total costs were output. Structural and probabilistic sensitivity analyses were conducted. RESULTS: Sequences starting with etanercept biosimilars (ETB) cost less, with ETB-abatacept-infliximab the least expensive: the mean lifetime discounted total cost was €116 912 per patient, with a mean of 11.166 QALYs. Most other strategies were dominated or led to small QALY gains (0.0008-0.0329). Only ETB-tocilizumab-abatacept made it onto the efficiency frontier, but at €955 778 per QALY gained. These results were confirmed in several scenarios and uncertainty analyses. CONCLUSION: Given minor differences in QALYs gained between bDMARD sequences with large cost differences, starting with biosimilars was more efficient than starting with branded products. Our model and findings should provide French and other decision makers with useful tools to address the challenges of comparing sequences of treatments for RA.

Leveraging DICE (Discretely-Integrated Condition Event) Simulation to Simplify the Design and Implementation of Hybrid Models.

OBJECTIVES: Using an example of an existing model constructed by the National Institute for Health and Care Excellence (NICE) to inform a real health technology assessment, this study seeks to demonstrate how a discretely integrated condition event (DICE) simulation can improve the implementation of Markov models. METHODS: Using the technical report and spreadsheet, the original model was translated to a standard DICE simulation without making any changes to the design. All original analyses were repeated and the results were compared. Aspects that could have improved the original design were then considered. RESULTS: The original model consisted of 32 copies (8 risk strata × 4 treatments) of the Markov structure, containing more than 6000 Microsoft Excel® formulas (18 MB files). Three aspects (nonadherence, scheduled treatment stop, and end of fracture risk) were handled by incorporating weighted averages into the cycle-specific calculations. The DICE implementation used 3 conditions to represent the states and a single transition event to apply the probabilities; 3 additional events processed the special aspects, and profiles handled the 8 strata (0.12 MB file). One replication took 16 seconds. The original results were reproduced but extensive additional sensitivity analyses, including structural analyses, were enabled. CONCLUSION: Implementing a real Markov model using DICE simulation both preserves the advantages of the approach and expands the available tools, improving transparency and ease of use and review.

Effect of Fresh vs Standard-issue Red Blood Cell Transfusions on Multiple Organ Dysfunction Syndrome in Critically Ill Pediatric Patients: A Randomized Clinical Trial.

Importance: The clinical consequences of red blood cell storage age for critically ill pediatric patients have not been examined in a large, randomized clinical trial. Objective: To determine if the transfusion of fresh red blood cells (stored ≤7 days) reduced new or progressive multiple organ dysfunction syndrome compared with the use of standard-issue red blood cells in critically ill children. Design, Setting, and Participants: The Age of Transfused Blood in Critically-Ill Children trial was an international, multicenter, blinded, randomized clinical trial, performed between February 2014 and November 2018 in 50 tertiary care centers. Pediatric patients between the ages of 3 days and 16 years were eligible if the first red blood cell transfusion was administered within 7 days of intensive care unit admission. A total of 15 568 patients were screened, and 13 308 were excluded. Interventions: Patients were randomized to receive either fresh or standard-issue red blood cells. A total of 1538 patients were randomized with 768 patients in the fresh red blood cell group and 770 in the standard-issue group. Main Outcomes and Measures: The primary outcome measure was new or progressive multiple organ dysfunction syndrome, measured for 28 days or to discharge or death. Results: Among 1538 patients who were randomized, 1461 patients (95%) were included in the primary analysis (median age, 1.8 years; 47.3% girls), in which there were 728 patients randomized to the fresh red blood cell group and 733 to the standard-issue group. The median storage duration was 5 days (interquartile range [IQR], 4-6 days) in the fresh group vs 18 days (IQR, 12-25 days) in the standard-issue group (P < .001). There were no significant differences in new or progressive multiple organ dysfunction syndrome between fresh (147 of 728 [20.2%]) and standard-issue red blood cell groups (133 of 732 [18.2%]), with an unadjusted absolute risk difference of 2.0% (95% CI, -2.0% to 6.1%; P = .33). The prevalence of sepsis was 25.8% (160 of 619) in the fresh group and 25.3% (154 of 608) in the standard-issue group. The prevalence of acute respiratory distress syndrome was 6.6% (41 of 619) in the fresh group and 4.8% (29 of 608) in the standard-issue group. Intensive care unit mortality was 4.5% (33 of 728) in the fresh group vs 3.5 % (26 of 732) in the standard-issue group (P = .34). Conclusions and Relevance: Among critically ill pediatric patients, the use of fresh red blood cells did not reduce the incidence of new or progressive multiple organ dysfunction syndrome (including mortality) compared with standard-issue red blood cells. Trial Registration: ClinicalTrials.gov Identifier: NCT01977547.

The Use of MCDA in HTA: Great Potential, but More Effort Needed.

The potential for multi-criteria decision analysis (MCDA) to support health technology assessment (HTA) has been much discussed, and various HTA agencies are piloting or applying MCDA. Alongside these developments, good practice guidelines for the application of MCDA in health care have been developed. An assessment of current applications of MCDA to HTA in light of good practice guidelines reveals, however, that many have methodologic flaws that undermine their usefulness. Three challenges are considered: the use of additive models, a lack of connection between criteria scales and weights, and the use of MCDA in economic evaluation. More attention needs to be paid to MCDA good practice by researchers, journal editors, and decision makers and further methodologic developments are required if MCDA is to achieve its potential to support HTA.

PATIENT-CENTERED DECISION MAKING: LESSONS FROM MULTI-CRITERIA DECISION ANALYSIS FOR QUANTIFYING PATIENT PREFERENCES.

OBJECTIVES: Patient preferences should be a central consideration in healthcare decision making. However, stories of patients challenging regulatory and reimbursement decisions has led to questions on whether patient voices are being considered sufficiently during those decision making processes. This has led some to argue that it is necessary to quantify patient preferences before they can be adequately considered. METHODS: This study considers the lessons from the use of multi-criteria decision analysis (MCDA) for efforts to quantify patient preferences. It defines MCDA and summarizes the benefits it can provide to decision makers, identifies examples of MCDAs that have involved patients, and summarizes good practice guidelines as they relate to quantifying patient preferences. RESULTS: The guidance developed to support the use of MCDA in healthcare provide some useful considerations for the quantification of patient preferences, namely that researchers should give appropriate consideration to: the heterogeneity of patient preferences, and its relevance to decision makers; the cognitive challenges posed by different elicitation methods; and validity of the results they produce. Furthermore, it is important to consider how the relevance of these considerations varies with the decision being supported. CONCLUSIONS: The MCDA literature holds important lessons for how patient preferences should be quantified to support healthcare decision making.

Applying the win ratio method in clinical trials of orphan drugs: an analysis of data from the COMET trial of avalglucosidase alfa in patients with late-onset Pompe disease.

BACKGROUND: Clinical trials for rare diseases often include multiple endpoints that capture the effects of treatment on different disease domains. In many rare diseases, the primary endpoint is not standardized across trials. The win ratio approach was designed to analyze multiple endpoints of interest in clinical trials and has mostly been applied in cardiovascular trials. Here, we applied the win ratio approach to data from COMET, a phase 3 trial in late-onset Pompe disease, to illustrate how this approach can be used to analyze multiple endpoints in the orphan drug context. METHODS: All possible participant pairings from both arms of COMET were compared sequentially on changes at week 49 in upright forced vital capacity (FVC) % predicted and six-minute walk test (6MWT). Each participant's response for the two endpoints was first classified as a meaningful improvement, no meaningful change, or a meaningful decline using thresholds based on published minimal clinically important differences (FVC ± 4% predicted, 6MWT ± 39 m). Each comparison assessed whether the outcome with avalglucosidase alfa (AVA) was better than (win), worse than (loss), or equivalent to (tie) the outcome with alglucosidase alfa (ALG). If tied on FVC, 6MWT was compared. In this approach, the treatment effect is the ratio of wins to losses ("win ratio"), with ties excluded. RESULTS: In the 2499 possible pairings (51 receiving AVA × 49 receiving ALG), the win ratio was 2.37 (95% confidence interval [CI], 1.30-4.29, p = 0.005) when FVC was compared before 6MWT. When the order was reversed, the win ratio was 2.02 (95% CI, 1.13-3.62, p = 0.018). CONCLUSION: The win ratio approach can be used in clinical trials of rare diseases to provide meaningful insight on treatment benefits from multiple endpoints and across disease domains.

Correction: Chua et al. Cost-Effectiveness Analysis of HPV Extended versus Partial Genotyping for Cervical Cancer Screening in Singapore. Cancers 2023, 15, 1812.

The authors wish to revise two words in Table 1 row 3, and the first paragraph of Section 2 [...].

Cost-Effectiveness Analysis of HPV Extended versus Partial Genotyping for Cervical Cancer Screening in Singapore.

Human papillomavirus (HPV) partial genotyping (PGT) identifies HPV16 and HPV18 individually, alongside 12 other high-risk HPV genotypes (hrHPV) collectively. HPV extended genotyping (XGT) identifies four additional hrHPV individually (HPV31, 45, 51, and 52), and reports the remaining eight in three groups (HPV33|58; 56|59|66; 35|39|68). Quality-adjusted life years (QALY), health care resource use, and costs of XGT were compared to PGT for cervical cancer screening in Singapore using DICE simulation. Women with one of the three hrHPV identified by XGT (HPV35|39|68; 56|59|66; 51), and atypical squamous cells of undetermined significance (ASCUS) on cytology, are recalled for a repeat screening in one year, instead of undergoing an immediate colposcopy with PGT. At the repeat screening, the colposcopy is performed only for persistent same-genotype infections in XGT, while with PGT, all the women with persistent HPV have a colposcopy. Screening 500,122 women, aged 30-69, with XGT, provided an incremental cost-effectiveness ratio (ICER) versus PGT of SGD 16,370/QALY, with 7130 (19.4%) fewer colposcopies, 6027 (7.0%) fewer cytology tests, 9787 (1.6%) fewer clinic consultations, yet 2446 (0.5%) more HPV tests. The XGT ICER remains well below SGD 100,000 in sensitivity analyses, (-SGD 17,736/QALY to SGD 50,474/QALY). XGT is cost-effective compared to PGT, utilizes fewer resources, and provides a risk-based approach as the primary cervical cancer screening method.

Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.

Models--mathematical frameworks that facilitate estimation of the consequences of health care decisions--have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.

Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--4.

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force.

Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--7.

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description--including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations--should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

A Framework for Integrating Continuous Glucose Monitor-Derived Metrics into Economic Evaluations in Type 1 Diabetes.

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.

Adjusting for patient crossover in clinical trials using external data: a case study of lenalidomide for advanced multiple myeloma.

OBJECTIVES: In some trials, particularly in oncology, patients whose disease progresses under the comparator treatment are crossed over into the experimental arm. This unplanned crossover can introduce bias in analyses because patients who crossover likely have a different prognosis than those who do not cross over; for instance, sicker patients not responding to standard therapy or those expected to benefit the most may be selectively chosen to receive the experimental treatment. Standard statistical methods cannot adequately correct for this bias. We describe an approach designed to minimize the impact of crossover, and illustrate this by using data from two randomized trials in multiple myeloma (MM). METHODS: The MM-009/010 trials compared lenalidomide and high-dose dexamethasone (Len+Dex) with dexamethasone alone (Dex). Nearly half (47%) of the patients randomized to Dex crossed over to Len with or without Dex (Len+/-Dex) at disease progression or study unblinding. Data from these trials was used to predict survival in an economic model evaluating the cost-effectiveness of lenalidomide. To adjust for crossover, the prediction equations were calibrated to match survival with Dex or Dex-equivalent therapies in trials conducted by the Medical Research Council (MRC) in the United Kingdom. To adjust for differences between the MM and MRC trial populations, a prediction equation was developed from the MRC data and used to predict survival by setting predictors to mean values for patients in the MM-009/010 trials. The expected survival with Dex without crossover was then predicted from the calibrated MM-009/010 equation (i.e., adjusted to match survival predicted from the MRC equation). RESULTS: The adjusted median overall survival predicted by the MRC equation was 19.5 months (95%CI, 16.6-22.9) for patients with one prior therapy, and 11.6 months (95% CI, 9.5-14.2) for patients with >1 prior therapy. These estimates are considerably shorter than was observed in the clinical trials: 33.6 months (27.1-NE) and 27.3 months (95% CI, 23.3-33.3) as of December 2005. CONCLUSION: The calibration method described here is simple to implement, provided that suitable data are available; it can be implemented with other types of endpoints in any therapeutic area.

Unaltered ethical standards for individual physicians in the face of drastically reduced resources resulting from an improvised nuclear device event.

When disaster disrupts healthcare and other systems, the ethical allocation of resources should follow principles of justice, defined as fairness, established for normal clinical practice. Standards of clinical practice may be altered during disaster, but ethical standards must remain centered on prioritizing the treatment of patients according to need and the effectiveness of treatment. Should resources become extremely limited, it is fair to restrict their use to patients who have the highest needs, provided that the intervention is effective. When resources become more available, patients with lower priority can be increasingly accommodated.

Risk factors in adolescents as predictors of arterial hypertension in adults: Protocol for a systematic review.

This is a protocol of a review paper, and there is no abstract. This review is part of a doctoral project that aims to develop a discrete event simulation model to predict how many adolescents may become hypertensive in adulthood. We will use data from the Brazilian study of cardiovascular risks in adolescents, called ERICA (Portuguese acronym). This study may help promote adherence to disease prevention protocols.