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INTRODUCTION: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved. METHOD: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors. RESULTS: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations. PRACTICAL APPLICATIONS: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation.

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BACKGROUND: Diverticular disease is a common gastrointestinal diagnosis with over 2.7 million clinic visits yearly. National guidelines from the American Society of Colon and Rectal Surgeons state that "the decision to recommend elective sigmoid colectomy after recovery from uncomplicated acute diverticulitis should be individualized." However, tools to individualize this decision are lacking. OBJECTIVE: This study aimed to develop an online educational decision aid (DA) to facilitate effective surgeon and patient communication about treatment options for recurrent left-sided diverticulitis. METHODS: We used a modified design sprint methodology to create a prototype DA. We engaged a multidisciplinary team and adapted elements from the Ottawa Personal Decision Guide. We then iteratively refined the prototype by conducting a mixed methods assessment of content and usability testing, involving cognitive interviews with patients and surgeons. The findings informed the refinement of the DA. Further testing included an in-clinic feasibility review. RESULTS: Over a 4-day in-person rapid design sprint, including patients, surgeons, and health communication experts, we developed a prototype of a diverticulitis DA, comprising an interactive website and handout with 3 discrete sections. The first section contains education about diverticulitis and treatment options. The second section clarifies the potential risks and benefits of both clinical treatment options (medical management vs colectomy). The third section invites patients to participate in a value clarification exercise. After navigating the DA, the patient prints a synopsis that they bring to their clinic appointment, which serves as a guide for shared decision-making. CONCLUSIONS: Design sprint methodology, emphasizing stakeholder co-design and complemented by extensive user testing, is an effective and efficient strategy to create a DA for patients living with recurrent diverticulitis facing critical treatment decisions.

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BACKGROUND: Decision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting. METHODS: This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2. RESULTS: First, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models' parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed. CONCLUSION: This conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.

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Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75-0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

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OBJECTIVES: This study attempts to compare the predictive effects of several prediction models on obstructive coronary artery disease (OCAD) in young patients (30-50 years old), with a view to providing a new evaluation tool for the prediction of premature coronary artery disease (PCAD). METHODS: A total of 532 hospitalized patients aged 30-50 were included in the study.All of them underwent coronary computed tomography angiography (CCTA) for suspected symptoms of coronary heart disease.Coronary artery calcium score (CACS) combined with traditional risk factors and pre-test probability models are the prediction models to be compared in this study.The PTP model was selected from the upgraded Diamond-Forrester model (UDFM) and the Duke clinical score (DCS). RESULTS: All patients included in the study were aged 30-50 years. Among them, women accounted for 24.4%, and 355 patients (66.7%) had a CACS of 0. OCAD was diagnosed in 43 patients (8.1%). The CACS combined with traditional risk factors to predict the OCAD area under the curve of receiver operating characteristic (ROC) (AUC = 0.794,p < 0.001) was greater than the PTP models (AUCUDFM=0.6977,p < 0.001;AUCDCS=0.6214,p < 0.001). By calculating the net reclassification index (NRI) and the integrated discrimination index (IDI), the ability to predict the risk of OCAD using the CACS combined with traditional risk factors was improved compared with the PTP models (NRI&IDI > 0,p < 0.05). CONCLUSION: The predictive value of CACS combined with traditional risk factors for OCAD in young patients is better than the PTP models.

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BACKGROUND: Our goal was to develop and validate the Severe Lower-Extremity Trauma decision aid (SLETRA) to help patients make treatment decisions aligned with their values regarding amputation versus reconstruction. METHODS: We recruited 62 adults (41 women) from a foot and ankle practice at a US academic hospital from June to August 2020. We excluded patients who could not read in English and who lacked internet service access. Patients completed an 8-question knowledge test regarding the risks, benefits, and outcomes of treatment options before and after reviewing SLETRA. The survey presented a hypothetical case of severe lower-extremity trauma. Respondents, imagining themselves as patients, indicated whether they would choose amputation or reconstruction. We evaluated knowledge scores (maximum, 8 points); decisional conflict scores (maximum, 400 points, with higher scores indicating greater respondent difficulty in decision making); ratings of factors influencing the decision; and SLETRA helpfulness (maximum, 7 points). Pre- and post-test knowledge scores were compared using paired Student's t-tests; Alpha = 0.05. RESULTS: Mean and standard deviation knowledge scores improved from 5.2 ± 1.6 (pre-test) to 6.7 ± 1.6 (post-test) (p < 0.001). Mean decisional conflict score was 223 ± 16, reflecting moderate difficulty. Factors affecting treatment choice were risk of complications (n = 29), recovery time (n = 27), and future prosthesis use (n = 27). No respondent had difficulty understanding SLETRA. Mean helpfulness score was 5.6 ± 0.16, reflecting considerable benefit. CONCLUSION: The SLETRA decision aid is a helpful, understandable tool that significantly improves patient knowledge regarding treatment options for severe lower-extremity trauma.

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OBJECTIVES: Preoperative and intraoperative diagnostic tools influence the surgical management of primary hyperparathyroidism (PHPT), whereby their performance of classification varies considerably for the two common causes of PHPT: solitary adenomas and multiglandular disease. A consensus on the use of such diagnostic tools for optimal perioperative management of all PHPT patients has not been reached. DESIGN: A decision tree model was constructed to estimate and compare the clinical outcomes and the cost-effectiveness of preoperative imaging modalities and intraoperative parathyroid hormone (ioPTH) monitoring criteria in a 21-year time horizon with a 3% discount rate. The robustness of the model was assessed by conducting a one-way sensitivity analysis and probabilistic uncertainty analysis. SETTING: The US healthcare system. POPULATION: A hypothetical population consisting of 5000 patients with sporadic, symptomatic or asymptomatic PHPT. INTERVENTIONS: Preoperative and intraoperative diagnostic modalities for parathyroidectomy. MAIN OUTCOME MEASURES: Costs, quality-adjusted life-years (QALYs), net monetary benefits (NMBs) and clinical outcomes. RESULTS: In the base-case analysis, four-dimensional (4D) CT was the least expensive strategy with US$10 276 and 15.333 QALYs. Ultrasound and 99mTc-Sestamibi single-photon-emission CT/CT were both dominated strategies while 18F-fluorocholine positron emission tomography was cost-effective with an NMB of US$416 considering a willingness to pay a threshold of US$95 958. The application of ioPTH monitoring with the Vienna criterion decreased the rate of reoperations from 10.50 to 0.58 per 1000 patients compared to not using ioPTH monitoring. Due to an increased rate of bilateral neck explorations from 257.45 to 347.45 per 1000 patients, it was not cost-effective. CONCLUSIONS: 4D-CT is the most cost-effective modality for the preoperative localisation of solitary parathyroid adenomas and multiglandular disease. The use of ioPTH monitoring is not cost-effective, but to minimise clinical complications, the Miami criterion should be applied for suspected solitary adenomas and the Vienna criterion for multiglandular disease.

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OBJECTIVES: Shared Decision Making (SDM) has potential to support Pulmonary Rehabilitation (PR) decision-making when patients are offered a menu of centre- and home-based options. This study sought to evaluate the feasibility and acceptability of a three-component PR SDM intervention for individuals with Chronic Obstructive Pulmonary Disease (COPD) and PR healthcare professionals. METHODS: Participants were recruited from Dec 2021-Sep 2022. Healthcare professionals attended decision coaching training and used the consultation prompt during consultations. Individuals received the Patient Decision Aid (PtDA) at PR referral. Outcomes included recruitment capability, data completeness, intervention fidelity, and acceptability. Questionnaires assessed patient activation and decisional conflict pre and post-PR. Consultations were assessed using Observer OPTION-5. Optional interviews/focus groups were conducted. RESULTS: 13% of individuals [n = 31, 32% female, mean (SD) age 71.19 (7.50), median (IQR) MRC dyspnoea 3.50 (1.75)] and 100 % of healthcare professionals (n = 9, 78% female) were recruited. 28 (90.32%) of individuals completed all questionnaires. SDM was present in all consultations [standardised scores were mean (SD) = 36.97 (21.40)]. Six healthcare professionals and five individuals were interviewed. All felt consultations using the PtDA minimised healthcare professionals' bias of centre-based PR, increased individuals' self-awareness of their health, prompted consideration of how to improve it, and increased involvement in decision-making. DISCUSSION: Results indicate the study processes and SDM intervention is feasible and acceptable and can be delivered with fidelity when integrated into the PR pathway.

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PURPOSE: Research relating to Total Hip Arthroplasty (THA) has found the anterior-based muscle-sparing (ABMS) approach improves both intraoperative and postoperative outcomes when compared to other THA approaches. This study compares the costs and outcomes of the ABMS approach and standard of care (SOC) to determine the relative cost-effectiveness. METHODS: A decision-analytic model was utilized to estimate intraoperative outcomes (i.e. length of procedure, length of stay (LOS), and transfusion rates) and 90-day postoperative complications (deep infection, periprosthetic fracture, and dislocation). Data relating to postoperative complications, intraoperative outcomes, and costs (adjusted to 2023 USD) were obtained from the literature. Model results were presented as incremental costs and complications avoided using a willingness-to-pay threshold of $100,000. We conducted both one-way sensitivity analysis (OWSA), varying each parameter individually within a specific range, and probabilistic sensitivity analysis (PSA) where parameters were varied simultaneously. In scenario analysis, ABMS was also compared to the posterior approach (PA) and direct anterior approach (DAA) individually. RESULTS: ABMS THA was found to have superior results compared to SOC THA over a 90-day time horizon since it decreased major complications by 0.00186 per patient and cost by $3,851 per patient. The PSA found the ABMS approach dominates SOC and is cost-effective in approximately 98.29% and 100% of 10,000 iterations, respectively. Comparing ABMS with only PA procedures increased cost savings per patient to $4,766 while it decreased to $3,242 when comparing ABMS to only DAA procedures. Length of procedure, LOS, and discharge disposition were the main cost drivers. CONCLUSIONS: This analysis demonstrates the ABMS approach for THA is a cost-effective technique when compared to PA and DAA, which may provide an opportunity for cost savings to the healthcare system.

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Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

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BACKGROUND: Use of extended pharmacologic thromboprophylaxis after major abdominopelvic cancer surgery should depend on best-available scientific evidence and patients' informed preferences. We developed a risk-stratified patient decision aid to facilitate shared decision-making and sought to evaluate its effect on decision-making quality regarding use of extended thromboprophylaxis. METHODS: We enrolled patients undergoing major abdominopelvic cancer surgery at an academic tertiary care centre in this pre-post study. We evaluated change in decisional conflict, readiness to decide, decision-making confidence, and change in patient knowledge. Participants were provided the appropriate risk-stratified decision aid (according to their Caprini score) in either the preoperative or postoperative setting. A sample size calculation determined that we required 17 patients to demonstrate whether the decision aid meaningfully reduced decisional conflict. We used the Wilcoxon matched-pairs signed ranks test for interval scaled measures. RESULTS: We included 17 participants. The decision aid significantly reduced decisional conflict (median decisional conflict score 2.37 [range 1.00-3.81] v. 1.3 [range 1.00-3.25], p < 0.01). With the decision aid, participants had high confidence (median 86.4 [range 15.91-100]) and felt highly prepared to make a decision (median 90 [range 55-100]). Median knowledge scores increased from 50% (range 0%-100%) to 75% (range 25%-100%). CONCLUSION: Our risk-stratified, evidence-based decision aid on extended thromboprophylaxis after major abdominopelvic surgery significantly improved decision-making quality. Further research is needed to evaluate the usability and feasibility of this decision aid in the perioperative setting.

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BACKGROUND: Typical bone proteins, such as sclerostin and periostin, have been associated with cardiovascular disease (CVD). Simultaneously, several risk scores have been developed to predict CVD in the general population. Therefore, we aimed to evaluate the association of these bone proteins related to CVD, with the main vascular risk scales: Framingham Risk Score (FRS), REGICOR and SCORE2-Diabetes, in patients with type 2 diabetes. We focus in particular on the SCORE2-Diabetes algorithm, which predicts 10-year CVD risk and is specific to the study population. METHODS: This was a cross-sectional study including 104 patients with type 2 diabetes (62 ± 6 years, 60% males). Clinical data, biochemical measurements, and serum bioactive sclerostin and periostin levels were collected, and different risk scales were calculated. The association between bioactive sclerostin or periostin with the risk scales was analyzed. RESULTS: A positive correlation was observed between circulating levels of bioactive sclerostin (p < 0.001) and periostin (p < 0.001) with SCORE2-Diabetes values. However, no correlation was found with FRS or REGICOR scales. Both serum bioactive sclerostin and periostin levels were significantly elevated in patients at high-very high risk of CVD (score ≥ 10%) than in the low-moderate risk group (score < 10%) (p < 0.001 for both). Moreover, analyzing these proteins to identify patients with type 2 diabetes at high-very high vascular risk using ROC curves, we observed significant AUC values for bioactive sclerostin (AUC = 0.696; p = 0.001), periostin (AUC = 0.749; p < 0.001), and the model combining both (AUC = 0.795; p < 0.001). For diagnosing high-very high vascular risk, serum bioactive sclerostin levels > 131 pmol/L showed 51.6% sensitivity and 78.6% specificity. Similarly, serum periostin levels > 1144 pmol/L had 64.5% sensitivity and 76.2% specificity. CONCLUSIONS: Sclerostin and periostin are associated with vascular risk in the SCORE2-Diabetes algorithm, opening a new line of investigation to identify novel biomarkers of cardiovascular risk in the type 2 diabetes population.

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People are inundated with popular press reports about medical research concerning what is healthy, get advice from doctors, and hear personal anecdotes. How do people integrate conflicting anecdotal and statistical information when making medical decisions? In four experiments (N = 4126), we tested how people use conflicting information to judge the efficacy of artificial and real medical treatments. Participants read an anecdote from someone in a clinical trial, or who had undergone a medical treatment previously, for whom the medical treatment was ineffective. We found that reading anecdotes for either artificial or real medical treatments shifted participants' beliefs about the efficacy of a medical treatment. We observed this result even when the anecdote was uninformative, was paired with an icon array, or when participants were provided with thorough medical decision aids about reproductive health procedures. Our findings highlight the pervasive effect of anecdotes on medical decision making.

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BACKGROUND: Whole genome sequencing (WGS) has transformative potential for blood cancer management, but reimbursement is hindered by uncertain benefits relative to added costs. This study employed scenario planning and multi-criteria decision analysis (MCDA) to evaluate stakeholders' preferences for alternative reimbursement pathways, informing future health technology assessment (HTA) submission of WGS in blood cancer. METHODS: Key factors influencing WGS reimbursement in blood cancers were identified through a literature search. Hypothetical scenarios describing various evidential characteristics of WGS for HTA were developed using the morphological approach. An online survey, incorporating MCDA weights, was designed to gather stakeholder preferences (consumers/patients, clinicians/health professionals, industry representatives, health economists, and HTA committee members) for these scenarios. The survey assessed participants' approval of WGS reimbursement for each scenario, and scenario preferences were determined using the geometric mean method, applying an algorithm to improve reliability and precision by addressing inconsistent responses. RESULTS: Nineteen participants provided complete survey responses, primarily clinicians or health professionals (n = 6; 32 %), consumers/patients and industry representatives (both at n = 5; 26 %). "Clinical impact of WGS results on patient care" was the most critical criterion (criteria weight of 0.25), followed by "diagnostic accuracy of WGS" (0.21), "cost-effectiveness of WGS" (0.19), "availability of reimbursed treatment after WGS" (0.16), and "eligibility criteria for reimbursed treatment based on actionable WGS results" and "cost comparison of WGS" (both at 0.09). Participants preferred a scenario with substantial clinical evidence, high access to reimbursed targeted treatment, cost-effectiveness below $50,000 per quality-adjusted life year (QALY) gained, and affordability relative to standard molecular tests. Reimbursement was initially opposed until criteria such as equal cost to standard tests and better treatment accessibility were met. CONCLUSION: Payers commonly emphasize acceptable cost-effectiveness, but strong clinical evidence for many variants and comparable costs to standard tests are likely to drive positive reimbursement decisions for WGS.

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The COVID-19 pandemic has imposed significant challenges on global health, emphasizing the persistent threat of large-scale infectious diseases in the future. This study addresses the need to enhance pooled testing efficiency for large populations. The common approach in pooled testing involves consolidating multiple test samples into a single tube to efficiently detect positivity at a lower cost. However, what is the optimal number of samples to be grouped together in order to minimize costs? i.e. allocating ten individuals per group may not be the most cost-effective strategy. In response, this paper introduces the hierarchical quotient space, an extension of fuzzy equivalence relations, as a method to optimize group allocations. In this study, we propose a cost-sensitive multi-granularity intelligent decision model to further minimize testing costs. This model considers both testing and collection costs, aiming to achieve the lowest total cost through optimal grouping at a single layer. Building upon this foundation, two multi-granularity models are proposed, exploring hierarchical group optimization. The experimental simulations were conducted using MATLAB R2022a on a desktop with Intel i5-10500 CPU and 8G RAM, considering scenarios with a fixed number of individuals and fixed positive probability. The main findings from our simulations demonstrate that the proposed models significantly enhance the efficiency and reduce the overall costs associated with pooled testing. For example, testing costs were reduced by nearly half when the optimal grouping strategy was applied, compared to the traditional method of grouping ten individuals. Additionally, the multi-granularity approach further optimized the hierarchical groupings, leading to substantial cost savings and improved testing efficiency.

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