Exploring the acceptability of a community-enhanced intervention to improve decision support partnership between patients with chronic kidney disease and their family caregivers.

Patients face numerous health-related decisions once advanced chronic kidney disease (CKD) is diagnosed. Yet, when patients are underprepared to navigate and discuss health-related decisions, they can make choices inconsistent with their expectations for the future. This pilot study, guided by the multiphase optimization strategy and community-engaged research principles, aimed to explore the acceptability of a developed patient component to a decision-support training intervention called ImPart (Improving Decisional Partnership of CKD Dyads). CKD patients and their family caregivers were recruited from an urban, academic medical center. Eligibility criteria for patients included a diagnosis of stage 3 or higher CKD (on chart review), and caregivers participated in interview sessions only. Patients without a caregiver were not eligible. The intervention was lay coach, telephone-delivered, and designed to be administered in 1-2 week intervals for 4 sessions. An interview guide, developed in collaboration with an advisory group, was designed to ascertain participants' experiences with the intervention. Caregiver interviews focused on changes in the patient's decision ability or engagement. Thirteen patients and eleven caregivers were interviewed. The program was viewed as "good" or "beneficial." Three themes capture the intervention's impact- 1) Frequent and deliberate disease-focused communication, 2) Future planning activation, and 3) Coaching relationship. The piloted intervention was successfully delivered, acceptable to use, and found to promote enhanced disease and future planning communication. By undergoing this work, we ensure that the patient component is feasible to use and meets the needs of participants before implementation in a larger factorial trial.

A nomogram for prediction of early mortality in patients undergoing cardiac surgery for infective endocarditis: a retrospective single-center study.

OBJECTIVE: Despite advancements in surgical techniques, operations for infective endocarditis (IE) remain associated with relatively high mortality. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing cardiac surgery for infective endocarditis based on the preoperative clinical features. METHODS: We retrospectively analyzed the clinical data of 357 patients with IE who underwent surgeries at our center between January 2007 and June 2023. Independent risk factors for early postoperative mortality were identified using univariate and multivariate logistic regression models. Based on these factors, a predictive model was developed and presented in a nomogram. The performance of the nomogram was evaluated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Internal validation was performed utilizing the bootstrapping method. RESULTS: The nomogram included nine predictors: age, stroke, pulmonary embolism, albumin level, cardiac function class IV, antibotic use <4weeks, vegetation size ≥1.5 cm, perivalvular abscess and preoperative dialysis. The area under the ROC curve (AUC) of the model was 0.88 (95%CI:0.80-0.96). The calibration plot indicated strong prediction consistency of the nomogram with satisfactory Hosmer-Lemeshow test results (χ2 = 13.490, p = 0.142). Decision curve analysis indicated that the nomogram model provided greater clinical net benefits compared to "operate-all" or "operate-none" strategies. CONCLUSIONS: The innovative nomogram model offers cardiovascular surgeons a tool to predict the risk of early postoperative mortality in patients undergoing IE operations. This model can serve as a valuable reference for preoperative decision-making and can enhance the clinical outcomes of IE patients.

Left atrial remodeling and voltage-guided ablation outcome in persistent atrial fibrillation patients according to CHA2DS2-VASc score.

BACKGROUND: CHA2DS2-VASc score-related differences have been reported in atrial fibrotic remodeling and prognosis of atrial fibrillation (AF) patients after ablation. There are currently no data on the efficacy of low voltage zone (LVZ)-guided ablation in persistent AF patients according to CHA2DS2-VASc score. We assessed in a cohort of persistent AF patients the extent of LVZ, the regional distribution of LA voltage and the outcome of LA voltage-guided substrate ablation in addition to PVI according to CHA2DS2-VASc score. METHODS: 138 consecutive persistent AF patients undergoing a first voltage-guided catheter ablation were enrolled. 58 patients with CHAD2DS2-VASc score ≥ 3 and 80 patients with CHAD2DS2-VASc score ≤ 2 were included. LA voltage maps were obtained using 3D-electroanatomical mapping system in sinus rhythm. LVZ was defined as < 0.5 mV. RESULTS: In the high CHAD2DS2-VASc score group, LA voltage was lower (1.5 [1.1-2.5] vs. 2.3 [1.5-2.8] mV, p = 0.02) and LVZs were more frequently identified (40% vs. 18%), p < 0.01). Female with CHA2DS2-VASc score ≥ 3 (p = 0.031), LA indexed volume (p = 0.009) and P-wave duration ≥ 150 ms (p = 0.001) were predictors of LVZ. After a 36-month follow-up, atrial arrhythmia-free survival was similar between the two groups (logrank test, P = 0.676). CONCLUSIONS: AF patients with CHAD2DS2-VASc score ≥ 3 display more LA substrate remodeling with lower voltage and more LVZs compared with those with CHAD2DS2-VASc score ≤ 2. Despite this atrial remodeling, they had similar and favorable 36 months results after one single procedure. Unlike male with CHAD2DS2-VASc score ≥ 3, female with CHAD2DS2-VASc score ≥ 3 was predictor of LVZ occurrence.

Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach.

OBJECTIVE: To adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan. METHODS: The EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients' cardiovascular risks across institutions. RESULTS: The CHIME risk equations had acceptable discrimination (AUROC: 0.71-0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/- 0.0008, 1.1003/- 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/- 0.0011, 0.8714/0.0030, and 1.0476/- 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05). CONCLUSIONS: We provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.

Exploring decision-makers' challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare.

BACKGROUND: Systematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare decision-makers (eg, practitioners, policymakers, researchers) use SRs to inform decision-making and to explore the potential role of a proposed artificial intelligence (AI) tool to assist in critical appraisal and choosing among SRs. METHODS: We developed a survey with 21 open and closed questions. We followed a knowledge translation plan to disseminate the survey through social media and professional networks. RESULTS: Our survey response rate was lower than expected (7.9% of distributed emails). Of the 684 respondents, 58.2% identified as researchers, 37.1% as practitioners, 19.2% as students and 13.5% as policymakers. Respondents frequently sought out SRs (97.1%) as a source of evidence to inform decision-making. They frequently (97.9%) found more than one SR on a given topic of interest to them. Just over half (50.8%) struggled to choose the most trustworthy SR among multiple. These difficulties related to lack of time (55.2%), or difficulties comparing due to varying methodological quality of SRs (54.2%), differences in results and conclusions (49.7%) or variation in the included studies (44.6%). Respondents compared SRs based on the relevance to their question of interest, methodological quality, and recency of the SR search. Most respondents (87.0%) were interested in an AI tool to help appraise and compare SRs. CONCLUSIONS: Given the identified barriers of using SR evidence, an AI tool to facilitate comparison of the relevance of SRs, the search and methodological quality, could help users efficiently choose among SRs and make healthcare decisions.

Association of diabetes with cardiovascular calcification and all-cause mortality in end-stage renal disease in the early stages of hemodialysis: a retrospective cohort study.

BACKGROUND: The main goal of this study was to examine how diabetes, cardiovascular calcification characteristics and other risk factors affect mortality in end-stage renal disease (ESRD) patients in the early stages of hemodialysis. METHODS: A total of 285 ESRD patients in the early stages of hemodialysis were enrolled in this research, including 101 patients with diabetes. Survival time was monitored, and general data, biochemical results, cardiac ultrasound calcification of valvular tissue, and thoracic CT calcification of the coronary artery and thoracic aorta were recorded. Subgroup analysis and logistic regression were applied to investigate the association between diabetes and calcification. Cox regression analysis and survival between calcification, diabetes, and all-cause mortality. Additionally, the nomogram model was used to estimate the probability of survival for these individuals, and its performance was evaluated using risk stratification, receiver operating characteristic, decision, and calibration curves. RESULTS: Cardiovascular calcification was found in 81.2% of diabetic patients (82/101) and 33.7% of nondiabetic patients (62/184). Diabetic patients had lower phosphorus, calcium, calcium-phosphorus product, plasma PTH levels and lower albumin levels (p < 0.001). People with diabetes were more likely to have calcification than people without diabetes (OR 5.66, 95% CI 1.96-16.36; p < 0.001). The overall mortality rate was 14.7% (42/285). The risk of death was notably greater in patients with both diabetes and calcification (29.27%, 24/82). Diabetes and calcification, along with other factors, collectively predict the risk of death in these patients. The nomogram model demonstrated excellent discriminatory power (area under the curve (AUC) = 0.975 at 5 years), outstanding calibration at low to high-risk levels and provided the greatest net benefit across a wide range of clinical decision thresholds. CONCLUSIONS: In patients with ESRD during the early period of haemodialysis, diabetes significantly increases the risk of cardiovascular calcification, particularly multisite calcification, which is correlated with a higher mortality rate. The risk scores and nomograms developed in this study can assist clinicians in predicting the risk of death and providing individualised treatment plans to lower mortality rates in the early stages of hemodialysis.

A danish healthcare-focused economic evaluation of first-line cryoballoon ablation versus antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation.

INTRODUCTION: Three randomised controlled trials (RCTs) have demonstrated that first-line cryoballoon pulmonary vein isolation decreases atrial tachycardia in patients with symptomatic paroxysmal atrial fibrillation (PAF) compared with antiarrhythmic drugs (AADs). The aim of this study was to develop a cost-effectiveness model (CEM) for first-line cryoablation compared with first-line AADs for the treatment of PAF. The model used a Danish healthcare perspective. METHODS: Individual patient-level data from the Cryo-FIRST, STOP AF and EARLY-AF RCTs were used to parameterise the CEM. The model structure consisted of a hybrid decision tree (one-year time horizon) and a Markov model (40-year time horizon, with a three-month cycle length). Health-related quality of life was expressed in quality-adjusted life years (QALYs). Costs and benefits were discounted at 3% per year. Model outcomes were produced using probabilistic sensitivity analysis. RESULTS: First-line cryoablation is dominant, meaning it results in lower costs (-€2,663) and more QALYs (0.18) when compared to first-line AADs. First-line cryoablation also has a 99.96% probability of being cost-effective, at a cost-effectiveness threshold of €23,200 per QALY gained. Regardless of initial treatment, patients were expected to receive ∼ 1.2 ablation procedures over a lifetime horizon. CONCLUSION: First-line cryoablation is both more effective and less costly (i.e. dominant), when compared with AADs for patients with symptomatic PAF in a Danish healthcare system.

Comparison of warfarin, rivaroxaban, and dabigatran for effectiveness and safety in atrial fibrillation patients with different CHA2DS2-VASc scores: a retrospective cohort study.

BACKGROUND: This retrospective cohort study aims to compare the effectiveness and safety of warfarin, rivaroxaban, and dabigatran in atrial fibrillation (AF) patients with different CHA2DS2-VASc scores in northern China. METHODS: A retrospective cohort study was performed to evaluate anticoagulation in AF patients at the second affiliated hospital of Harbin Medical University from September 2018 to August 2019. Patients included in this study (n = 806) received warfarin (n = 300), or rivaroxaban (n = 203), or dabigatran (n = 303). Baseline characteristics and follow-up data including adherence, bleeding events and ischemic stroke (IS) events were collected. RESULTS: Patients receiving rivaroxaban (73.9%) or dabigatran (73.6%) showed better adherence than those receiving warfarin (56.7%). Compared with warfarin-treated patients, dabigatran-treated patients had lower incidence of bleeding events (10.9% vs 19.3%, χ2 = 8.385, P = 0.004) and rivaroxaban-treated patients had lower incidence of major adverse cardiovascular events (7.4% vs 13.7%, χ2 = 4.822, P = 0.028). We classified patients into three groups based on CHA2DS2-VASc score (0-1, 2-3, ≥ 4). In dabigatran intervention, incidence of bleeding events was higher in patients with score 0-1 (20.0%) than those with score 2-3 (7.9%, χ2 = 5.772, P = 0.016) or score ≥ 4 (8.6%, χ2 = 4.682, P = 0.030). Patients with score 0-1 in warfarin or rivaroxaban therapy had a similar but not significant increase of bleeding compared with patients with score 2-3 or score ≥ 4, respectively. During the follow-up, 33 of 806 patients experienced IS and more than half (19, 57.6%) were patients with score ≥ 4. Comparing patients with score 0-1 and 2-3, the latter had an significant reduction of IS in patients prescribed warfarin and non-significant reduction in rivaroxaban and dabigatran therapy. CONCLUSION: Compared with warfarin therapy, patients with different CHA2DS2-VASc scores receiving either rivaroxaban or dabigatran were associated with higher persistence. AF patients with score ≥ 4 were more likely to experience IS events while hemorrhagic tendency preferred patients with low score 0-1.

A nomogram for predicting CRT response based on multi-parameter features.

OBJECTIVE: To construct a nomogram for predicting the responsiveness of cardiac resynchronization therapy (CRT) in patients with chronic heart failure and verify its predictive efficacy. METHOD: A retrospective study was conducted including 109 patients with chronic heart failure who successfully received CRT from January 2018 to December 2022. According to patients after six months of the CRT preoperative improving acuity in the left ventricular ejection fraction is 5% or at least improve grade 1 NYHA heart function classification, divided into responsive group and non-responsive group. Clinical data of patients were collected, and LASSO regression analysis and multivariate logistic regression analysis were used to explore relative factors. A nomogram was constructed, and the predictive performance of the nomogram was evaluated using the calibration curve and decision curve analysis (DCA). RESULTS: Among the 109 patients, 61 were assigned to the CRT-responsive group, while 48 were assigned to the non-responsive group. LASSO regression analysis showed that left ventricular end-systolic volume, diffuse fibrosis, and left bundle branch block (LBBB) were independent factors for CRT responsiveness in patients with heart failure (P < 0.05). Based on the above three predictive factors, a nomogram was constructed. The ROC curve analysis showed that the area under the curve (AUC) was 0.865 (95% CI 0.794-0.935). The calibration curve analysis showed that the predicted probability of the nomogram is consistent with the actual occurrence rate. DCA showed that the line graph model has an excellent clinical net benefit rate. CONCLUSION: The nomogram constructed based on clinical features, laboratory, and imaging examinations in this study has high discrimination and calibration in predicting CRT responsiveness in patients with chronic heart failure.

Dynamic adaptive decision support for strategic decision-making in healthcare organizations.

PURPOSE: The main objective of this study was to design a dynamic adaptive decision support model for healthcare organizations facing deep uncertainties by considering promising dynamic adaptive approaches. The main argument for this is that healthcare organizations have to make strategic decisions under deep uncertainty, but lack an approach to deal with this. DESIGN/METHODOLOGY/APPROACH: A Dynamic Adaptive Decision Support model (DADS) is designed using the Design Science Research methodology. The evaluation of an initial model leads, through two case studies on ongoing and strategic decision-making, to the final design of this needed model for healthcare organizations. FINDINGS: The research reveals the relevance of the designed dynamic and adaptive tool to support strategic decision-making for healthcare organizations. The final design of DADS innovates Decision Making under Deep Uncertainty (DMDU) approaches in an organizational context for ongoing and strategic decision-making. ORIGINALITY/VALUE: The designed model applies the Dynamic Adaptive Policy Pathways approach in an organizational context and more specifically in health care organizations. It further integrates Corporate Real Estate Management knowledge and experience to develop a most needed tool for decision-makers in healthcare. This is the first DADS designed for an organization facing deep uncertainties in a rapidly changing healthcare environment and dealing with ongoing and strategic decision-making.

A qualitative study on the effect of a decision aid on knowledge of cataract surgery in patients in developing countries.

BACKGROUND: Despite cataract surgery being a safe procedure with a low incidence of perioperative complications rates, poor knowledge, concerns about the effectiveness of treatment and cost-benefit analysis of the procedure significantly hinder cataract surgery uptake rates in Africa. This study describes the effect of a decision aid on knowledge and decision conflict on cataract patients in Africa. METHODS AND ANALYSIS: 120 patients with cataracts reporting to a tertiary hospital in Ghana were randomly assigned to receive a decision aid containing information on the possible outcomes of cataract surgery or a control booklet containing general knowledge about cataracts without information about cataract surgery. The primary outcome measured was the effect of the decision aid on their knowledge of cataract surgery. A score greater than 6/12 (50%) was deemed adequate knowledge. The secondary outcome was the decision conflict experienced by the participants assessed using the Decision Conflict Scale. RESULTS: Compared to the control group, the participants in the intervention group scored higher marks across all sections of the questionnaire (2.92 vs 2.7, p = 0.042 in section "Background"; 2.62 vs 1.77, p < 0.001 in section "Materials"; 1.87 vs 1.55, p = 0.03 in section "Results"). The average total score was higher in the intervention group than in the control (36.7% difference; p < 0.001). Participants in the intervention group also demonstrated lower decision conflict scores than those in the control group (13.00 vs 37.17; p < 0.001). CONCLUSION: The decision aid increased knowledge of cataract surgery and reduced decision conflict among patients in a developing country.

Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia.

AIM: To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms. METHODS: A dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model. RESULTS: According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF. CONCLUSIONS: The present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Parallel multi-criteria decision analysis for sub-national prioritization of zoonoses and animal diseases in Africa: The case of Cameroon.

The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

[Patient decision aids for cancer prevention Synthesis for the francophone community]. / Outils d'aide à la décision pour la prévention des cancers. Synthèse pour la francophonie.

In recent years, the relationship between patients and healthcare professionals, and more broadly between public health actors and citizens, has shifted from a paternalistic, top-down approach to one of increased patient involvement in decision-making. Primary and secondary cancer prevention involve both benefits and risks, underscoring the importance of informed decision-making aligned with each patient and citizen's unique values and preferences. Shared decision-making, supported by decision aids, offers patients and citizens clear and comprehensible information about their options, enabling informed choices. This article aims to compile and define the characteristics of tools developed or translated into French for this purpose.

Depuis plusieurs années, la relation entre les patients et les professionnels de la santé et plus largement entre les acteurs de la santé publique et les citoyens a évolué, passant d'une approche paternaliste et top-down à une implication accrue des patients dans les décisions les concernant. La prévention primaire et secondaire des cancers présente des bénéfices mais parfois également des risques, nécessitant une décision alignée avec les valeurs et les préférences des patients et des citoyens. La prise de décision partagée, via des outils d'aide à la décision, offre aux patients des informations claires et faciles à comprendre à propos des options qui leur sont offertes, afin de prendre une décision éclairée. Cet article vise à recenser les outils créés ou traduits en français et à en définir les caractéristiques.

Association of Life's essential 8 score with the risk of all-cause mortality and cardio-cerebrovascular mortality in patients with stroke.

BACKGROUND: A higher Life's Essential 8 (LE8)-based cardiovascular health (CVH) has been reported to be associated with a lower risk of both all-cause mortality and cardio-cerebrovascular diseases (CCVDs) related mortality in adults in the United States. At the same time, multiple studies have shown a significant negative association of CVH with the risk of stroke and CCVDs. Since no research has investigated the applicability of the LE8 in stroke patients, this study aimed to explore the association of LE8 with all-cause mortality and cardio-cerebrovascular mortality in stroke patients. METHODS: Data of patients were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2007-2018 in this retrospective cohort study. Weighted univariate and multivariate COX regression analyses were utilized to investigate the associations of LE8 with all-cause mortality and cardio-cerebrovascular mortality. We further explored these relationships in subgroups of age, gender, body mass index (BMI), cancer, congestive heart failure (CHF), and coronary heart disease (CHD). The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Among the eligible patients, 278 died from all-cause and 89 (8.38%) of them died due to CCVDs. After adjusting for covariates, patients with LE8 score ≥ 58.75 seemed to have both lower risk of all-cause mortality (HR = 0.46, 95%CI: 0.31-0.69) and cardio-cerebrovascular mortality (HR = 0.51, 95%CI: 0.26-0.98), comparing to those with LE8 score < 48.123. Higher LE8 scores were associated with lower risk of all-cause mortality in patients aged < 65 years old, without cancer, and whatever the gender, BMI, CHF or CHD conditions (all P < 0.05). The relationships between high LE8 scores and low cardio-cerebrovascular mortality risk were only found in age < 65 years old and non-cancer subgroups (all P < 0.05). CONCLUSION: A higher LE8 score was associated with lower risk of both all-cause mortality and cardio-cerebrovascular mortality in patients with stroke, which may provide some reference for risk management and prognosis improvement in stoke. However, more evidences are needed to verify this beneficial role of high LE8 score in stroke prognosis.

Decision aids for female BRCA mutation carriers: a scoping review.

OBJECTIVES: Women who inherit a pathogenic BRCA1 or BRCA2 mutation are at substantially higher risk of developing breast and ovarian cancer than average. Several cancer risk management strategies exist to address this increased risk. Decisions about which strategies to choose are complex, personal and multifactorial for these women. Decision aids (DAs) are tools that assist patients in making health-related decisions. The aim of this scoping review was to map evidence relating to the development and testing of patient DAs for cancer unaffected BRCA mutation carriers. DESIGN: Scoping review conducted according to the Joanna Briggs Institute's (JBI's) scoping review methodological framework. DATA SOURCES: MEDLINE, EMBASE, CINAHL, Web of Science. No restrictions applied for language or publication date. A manual search was also performed. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies on DAs for cancer risk management designed for or applicable to women with a pathogenic BRCA1 or BRCA2 mutation who are unaffected by breast or ovarian cancer. DATA EXTRACTION AND SYNTHESIS: Data were extracted using a form based on the JBI instrument for extracting details of studies' characteristics and results. Data extraction was performed independently by two reviewers. Extracted data were tabulated. RESULTS: 32 evidence sources relating to development or testing of 21 DAs were included. Four DAs were developed exclusively for cancer unaffected BRCA mutation carriers. Of these, two covered all guideline recommended risk management strategies for this population though only one of these was readily available publicly in its full version. All studies investigating DA effectiveness reported a positive effect of the DA under investigation on at least one of the outcomes evaluated, however only six DAs were tested in randomised controlled trials. CONCLUSION: This scoping review has mapped the landscape of the literature relating to developing and testing, DAs applicable to cancer unaffected BRCA mutation carriers.

Development of a nomogram for predicting liver transplantation prognosis in hepatocellular carcinoma.

BACKGROUND: At present, liver transplantation (LT) is one of the best treatments for hepatocellular carcinoma (HCC). Accurately predicting the survival status after LT can significantly improve the survival rate after LT, and ensure the best way to make rational use of liver organs. AIM: To develop a model for predicting prognosis after LT in patients with HCC. METHODS: Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms. RESULTS: The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability. CONCLUSION: The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.

Development and implementation of an online intervention and embedded decision aid to support self-management of acne vulgaris: qualitative interviews with healthcare professionals.

BACKGROUND: Acne is common and has a significant impact on quality of life. Topical treatments are first-line and effective, but non-adherence is common due to slow onset of action, or lack of advice on how to manage side effects. AIM: The Acne Care Online programme is developing an online intervention to support acne self-management and help-seeking. This project aims to explore experiences and views of healthcare professionals (HCPs) treating acne to assess the acceptability and feasibility of implementing this intervention and embedded decision aid in practice. METHOD: This qualitative study recruited HCPs working in general practice. Email invitations were sent to eight research-active practices in Southwest England, and snowball sampling was used. Purposive sampling was used to seek diverse participants. Semi-structured virtual interviews were conducted, audio-recorded, and transcribed. Data were explored using thematic analysis. RESULTS: Nine interviews were conducted, including seven GPs, one nurse, and one pharmacist. Findings highlighted challenges faced by HCPs managing acne in time-limited consultations, particularly around patients' limited understanding of effective treatments, frequent requests from patients for non-first-line treatments, treatment non-adherence, and addressing psychosocial impact. The intervention and embedded decision aid were perceived as beneficial in addressing these challenges, improving shared decision making, and providing a reliable resource for patients. Interviewees suggested integrating the intervention into electronic practice templates for effective implementation. CONCLUSION: HCPs were positive about the potential for the intervention to be implemented as a useful tool in practice. Potential implementation barriers need to be considered, such as making the intervention quick to access in time-limited consultations.

Identification of Preference "Phenotypes" in Men With Prostate Cancer.

INTRODUCTION: Patient preference assessment is key to high-quality decision-making in men with prostate cancer. We aimed to determine if "phenotypes" could be identified among men with prostate cancer, with each phenotype representing a cohort with a distinct combination of preferences. We wished to learn if there was an association between phenotype and treatment selection. METHODS: A prospective cohort of men with prostate cancer received a pre-visit decision aid. This software used conjoint analysis to quantify relative patient preferences for treatment-associated survival, quality of life outcomes, and recovery time. We collected patient clinical data, physician recommendation for active treatment or surveillance, and treatments received. Preferences were analyzed using latent class analysis to identify distinct classes of preference phenotypes. We compared patient characteristics and treatment choice across phenotypes, both univariately and in a multivariable logistic regression. RESULTS: In 250 men who used the decision aid as part of routine care, latent class analysis revealed 3 phenotypic classes. Men in Class 1 had the highest concerns around recovery time and the lowest value on improving lifespan. Men in Class 2 had relatively evenly distributed concerns. Men in Class 3 had the lowest concerns around recovery time and risk of surgical complications. On multivariate analysis, treatment choice was not associated with preference-based phenotype. Only physician recommendation was associated with choice of active treatment. CONCLUSIONS: We identified the existence of 3 patient preference-based phenotypes in men with prostate cancer. Each phenotype had a unique combination of trade-offs when considering competing treatment outcomes. These phenotypes were not associated with treatment. Physician recommendation was the only factor determining treatment choice.