Shared Decision-Making and Cardiovascular Health: A Scientific Statement From the American Heart Association.

Shared decision-making is increasingly embraced in health care and recommended in cardiovascular guidelines. Patient involvement in health care decisions, patient-clinician communication, and models of patient-centered care are critical to improve health outcomes and to promote equity, but formal models and evaluation in cardiovascular care are nascent. Shared decision-making promotes equity by involving clinicians and patients, sharing the best available evidence, and recognizing the needs, values, and experiences of individuals and their families when faced with the task of making decisions. Broad endorsement of shared decision-making as a critical component of high-quality, value-based care has raised our awareness, although uptake in clinical practice remains suboptimal for a range of patient, clinician, and system issues. Strategies effective in promoting shared decision-making include educating clinicians on communication techniques, engaging multidisciplinary medical teams, incorporating trained decision coaches, and using tools (ie, patient decision aids) at appropriate literacy and numeracy levels to support patients in their cardiovascular decisions. This scientific statement shines a light on the limited but growing body of evidence of the impact of shared decision-making on cardiovascular outcomes and the potential of shared decision-making as a driver of health equity so that everyone has just opportunities. Multilevel solutions must align to address challenges in policies and reimbursement, system-level leadership and infrastructure, clinician training, access to decision aids, and patient engagement to fully support patients and clinicians to engage in the shared decision-making process and to drive equity and improvement in cardiovascular outcomes.

Estimating risk of endometrial malignancy and other intracavitary uterine pathology in women without abnormal uterine bleeding using IETA-1 multinomial regression model: validation study.

OBJECTIVES: To assess the ability of the International Endometrial Tumor Analysis (IETA)-1 polynomial regression model to estimate the risk of endometrial cancer (EC) and other intracavitary uterine pathology in women without abnormal uterine bleeding. METHODS: This was a retrospective study, in which we validated the IETA-1 model on the IETA-3 study cohort (n = 1745). The IETA-3 study is a prospective observational multicenter study. It includes women without vaginal bleeding who underwent a standardized transvaginal ultrasound examination in one of seven ultrasound centers between January 2011 and December 2018. The ultrasonography was performed either as part of a routine gynecological examination, during follow-up of non-endometrial pathology, in the work-up before fertility treatment or before treatment for uterine prolapse or ovarian pathology. Ultrasonographic findings were described using IETA terminology and were compared with histology, or with results of clinical and ultrasound follow-up of at least 1 year if endometrial sampling was not performed. The IETA-1 model, which was created using data from patients with abnormal uterine bleeding, predicts four histological outcomes: (1) EC or endometrial intraepithelial neoplasia (EIN); (2) endometrial polyp or intracavitary myoma; (3) proliferative or secretory endometrium, endometritis, or endometrial hyperplasia without atypia; and (4) endometrial atrophy. The predictors in the model are age, body mass index and seven ultrasound variables (visibility of the endometrium, endometrial thickness, color score, cysts in the endometrium, non-uniform echogenicity of the endometrium, presence of a bright edge, presence of a single dominant vessel). We analyzed the discriminative ability of the model (area under the receiver-operating-characteristics curve (AUC); polytomous discrimination index (PDI)) and evaluated calibration of its risk estimates (observed/expected ratio). RESULTS: The median age of the women in the IETA-3 cohort was 51 (range, 20-85) years and 51% (887/1745) of the women were postmenopausal. Histology showed EC or EIN in 29 (2%) women, endometrial polyps or intracavitary myomas in 1094 (63%), proliferative or secretory endometrium, endometritis, or hyperplasia without atypia in 144 (8%) and endometrial atrophy in 265 (15%) women. The endometrial sample had insufficient material in five (0.3%) cases. In 208 (12%) women who did not undergo endometrial sampling but were followed up for at least 1 year without clinical or ultrasound signs of endometrial malignancy, the outcome was classified as benign. The IETA-1 model had an AUC of 0.81 (95% CI, 0.73-0.89, n = 1745) for discrimination between malignant (EC or EIN) and benign endometrium, and the observed/expected ratio for EC or EIN was 0.51 (95% CI, 0.32-0.82). The model was able to categorize the four histological outcomes with considerable accuracy: the PDI of the model was 0.68 (95% CI, 0.62-0.73) (n = 1532). The IETA-1 model discriminated very well between endometrial atrophy and all other intracavitary uterine conditions, with an AUC of 0.96 (95% CI, 0.95-0.98). Including only patients in whom the endometrium was measurable (n = 1689), the model's AUC was 0.83 (95% CI, 0.75-0.91), compared with 0.62 (95% CI, 0.52-0.73) when using endometrial thickness alone to predict malignancy (difference in AUC, 0.21; 95% CI, 0.08-0.32). In postmenopausal women with measurable endometrial thickness (n = 848), the IETA-1 model gave an AUC of 0.81 (95% CI, 0.71-0.91), while endometrial thickness alone gave an AUC of 0.70 (95% CI, 0.60-0.81) (difference in AUC, 0.11; 95% CI, 0.01-0.20). CONCLUSION: The IETA-1 model discriminates well between benign and malignant conditions in the uterine cavity in patients without abnormal bleeding, but it overestimates the risk of malignancy. It also discriminates well between the four histological outcome categories. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

The Variable Definition of "Negative Appendicitis" Remains a Surgical Challenge.

AIM: Acute appendicitis (AA) is treated primarily surgically with histopathology being the gold standard for confirmation of appendicitis and reported rates of negative appendicectomies (NA) ranging between 3.2% and 19% worldwide and 15.9-20.6% in the UK. NA rates are frequently used to identify poor performing centers as part of a Model Health System and form an integral part of appendicitis scoring systems. This study aims to evaluate the prevalence of negative appendicectomies within our institution and critically analyze the appropriateness of its use as a quality metric and its impact on clinical practice and research. PATIENTS AND METHODS: Data analysis from a prospective dataset of pediatric appendicitis patients between 2015 and 2021 in a tertiary center in the UK was performed. Detailed analysis of negative appendicectomies was performed and further stratified by two distinct age and gender groups looking at the incidence of NA and the classification of non-histologically normal appendix specimens. RESULTS: In our series, 819 patients met inclusion criteria, 736 (89.9%) had acute appendicitis. Our overall institutional negative appendicectomy rate was 10.1% (83 patients) with the breakdown as follows: 65 histologically normal appendix (7.9%), 10 Enterobius vermicularis, 3 eosinophilic appendicitis, 2 neoplasms, 1 isolated faecolith, 1 fibrous obliteration of the lumen, and 1 peri-appendiceal inflammation. CONCLUSION: Our negative appendicectomy rate is below established UK pediatric NA rates. This rate ranges from 7.9% to 10.1% depending on the definition of NA utilized. A single standard pathological definition for histological acute appendicitis is required when being used as a comparative quality metric. Centers engaged in clinical research should be aware of variations in NA definitions both in scoring systems and individual centers to avoid skewing derived results.

Patient values in patient-provider communication about participation in early phase clinical cancer trials: a qualitative analysis before and after implementation of an online value clarification tool intervention.

BACKGROUND: Patients with advanced cancer who no longer have standard treatment options available may decide to participate in early phase clinical trials (i.e. experimental treatments with uncertain outcomes). Shared decision-making (SDM) models help to understand considerations that influence patients' decision. Discussion of patient values is essential to SDM, but such communication is often limited in this context and may require new interventions. The OnVaCT intervention, consisting of a preparatory online value clarification tool (OnVaCT) for patients and communication training for oncologists, was previously developed to support SDM. This study aimed to qualitatively explore associations between patient values that are discussed between patients and oncologists during consultations about potential participation in early phase clinical trials before and after implementation of the OnVaCT intervention. METHODS: This study is part of a prospective multicentre nonrandomized controlled clinical trial and had a between-subjects design: pre-intervention patients received usual care, while post-intervention patients additionally received the OnVaCT. Oncologists participated in the communication training between study phases. Patients' initial consultation on potential early phase clinical trial participation was recorded and transcribed verbatim. Applying a directed approach, two independent coders analysed the transcripts using an initial codebook based on previous studies. Steps of continuous evaluation and revision were repeated until data saturation was reached. RESULTS: Data saturation was reached after 32 patient-oncologist consultations (i.e. 17 pre-intervention and 15 post-intervention). The analysis revealed the values: hope, perseverance, quality or quantity of life, risk tolerance, trust in the healthcare system/professionals, autonomy, social adherence, altruism, corporeality, acceptance of one's fate, and humanity. Patients in the pre-intervention phase tended to express values briefly and spontaneously. Oncologists acknowledged the importance of patients' values, but generally only gave 'contrasting' examples of why some accept and others refuse to participate in trials. In the post-intervention phase, many oncologists referred to the OnVaCT and/or asked follow-up questions, while patients used longer phrases that combined multiple values, sometimes clearly indicating their weighing. CONCLUSIONS: While all values were recognized in both study phases, our results have highlighted the different communication patterns around patient values in SDM for potential early phase clinical trial participation before and after implementation of the OnVaCT intervention. This study therefore provides a first (qualitative) indication that the OnVaCT intervention may support patients and oncologists in discussing their values. TRIAL REGISTRATION: Netherlands Trial Registry: NL7335, registered on July 17, 2018.

Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.

OBJECTIVES: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability. MATERIAL AND METHODS: Using 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS. RESULTS: The ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589. CONCLUSION: AI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information. CLINICAL RELEVANCE: AI could help monitor at-risk teeth and reduce errors in indications for extractions.

Explaining predictors of discharge destination assessed along the patients' acute stroke journey.

INTRODUCTION: Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores. METHODS: Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models. RESULTS: Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4. CONCLUSION: Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

Decisional conflict in parents of children with congenital heart defect: Towards development of a model.

AIM: This study aimed to develop a model to help parents cope with decisional conflict. BACKGROUND: Parents of children with congenital heart defect experience decisional conflict when they are uncertain about treatment decisions for their child, which may lead to delay in seeking care or distress over the decision made. DESIGN: Correlational design with model building and data triangulation was used. METHODS: Data were collected through surveys and interviews with a consecutive sample of 221 parent respondents from June to December 2018. Structural equation modelling and qualitative data analysis were used. RESULTS: Lower decisional conflict was seen in parents with higher income, more nurse support and physician risk communication. Time delay for surgery was correlated with the child's age, social service coverage, and social support. Decisional conflict mediated the influence of income, nurse support and physician risk communication on satisfaction with decision. Based on model fit parameters, the emerging model is a good and parsimonious model of decisional conflict. The overall theme, 'Deciding for Surgery: What Matters Most', described the processes parents went through in making treatment decisions. CONCLUSION: Nurses may help parents feel more certain, less conflicted, and more satisfied with their decision by addressing factors including knowledge gaps, personal values, available support, and resource access.

Modeled residual current cancer risk after clinical investigation of a positive multicancer early detection test result.

BACKGROUND: Positive results of a multi-cancer early detection (MCED) test require confirmatory diagnostic workup. Here, residual current cancer risk (RR) during the process of diagnostic resolution, including situations where the initial confirmatory test does not provide resolution, was modeled. METHODS: A decision-tree framework was used to model conditional risk in a patient's journey through confirmatory diagnostic options and outcomes. The diagnostic journey assumed that cancer signal detection (a positive MCED test result) had already led to a transition from screening to diagnosis and began with an initial positive predictive value (PPV) from the positive result. Evaluation of a most probable (top) predicted cancer signal origin (CSO) and then a second-most probable predicted CSO followed. Under the assumption that the top- and second-predicted CSOs were each followed by a targeted confirmatory test, the RR was estimated for each subsequent scenario. RESULTS: For an initial MCED test result with typical performance characteristics modeled (PPV, 40%; top-predicted CSO accuracy, 90%), after a negative initial confirmatory test (sensitivity, 70%, 90%, or 100%) the RR ranged from 6% to 20%. A second-predicted CSO (accuracy, 50%), after a negative second confirmatory test, still provided a significant RR (3%-18%) in comparison with the National Institute for Health and Care Excellence-recommended cancer risk threshold warranting investigation in symptomatic individuals (3%). With a 40% PPV for an MCED test and 90% specificity for a confirmatory test, the risk of incidental findings after one or two confirmatory tests was 6% and 12%, respectively. CONCLUSIONS: These results may illustrate the impact of a positive MCED test on follow-up decision-making.

Development and testing of "Is Lung Cancer Screening for You?" A computer-based decision aid.

PURPOSE: To reduce lung cancer mortality, individuals at high-risk should receive a low-dose computed tomography screening annually. To increase the likelihood of screening, interventions that promote shared decision-making are needed. The goal of this study was to investigate the feasibility, acceptability, usability, and preliminary effectiveness of a computer-based decision aid. METHODS: Thirty-three participants were recruited through primary-care clinics in a small southeastern-US city. Participants used a computer-based decision aid ("Is Lung Cancer Screening for You?") during a clinic appointment. Paper surveys collected self-reported feasibility, acceptability, and usability data. A research coordinator was present to observe each patient's and health-care provider's interactions, and to assess the fidelity of shared decision-making. RESULTS: The decision aid was feasible, acceptable for use in a clinic setting, and easy for participants to use. Patients had low decisional conflict following use of the decision aid and had high screening intention and actual screening rates. Shared decision-making discussions using the decision aid were nearly 6 min on average. CONCLUSION: Computer-based decision aids are feasible for promoting shared lung cancer-screening decisions. A more robust study is warranted to measure the added value of a computer-based version of this aid versus a paper-based aid.

Nodal infiltration in endometrial cancer: a prediction model using best subset regression.

OBJECTIVES: To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. METHODS: In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. RESULTS: The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient ß values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. CONCLUSIONS: The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. CLINICAL RELEVANCE STATEMENT: The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. KEY POINTS: •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions.

OBJECTIVES: Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions. METHODS: This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation). RESULTS: At multivariable analysis, one of four selected radiomic features (wavelet-HHL_glcm_ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions. CONCLUSION: Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy. KEY POINTS: In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment.

TARGET-HF: developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data.

BACKGROUND: Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data. METHODS: Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor. RESULTS: Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n = 25,524) and validation (n = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P < 0.001). CONCLUSION: The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation.

Concerned friends of intimate partner violence survivors: results from the myPlan randomized controlled trial on college campuses.

BACKGROUND: Nearly half of intimate partner violence (IPV) survivors experience their first abusive relationship at college age (18-24 years). Most often they disclose the violence to friends. Existing college campus "bystander" interventions training peers to safely intervene have been effective in sexual assault prevention; similar interventions have rarely been tested for IPV. Therefore, we evaluated the effectiveness of an interactive, personalized safety decision and planning tool, myPlan app, on decisional conflict, decisional preparedness, confidence in intervening, supportive safety behaviors, and IPV attitudes with concerned friends of abused college women. METHODS: We recruited college students (age 18-24, N = 293) of any gender who had a female-identified friend who had recently experienced IPV ("concerned friends") from 41 Oregon and Maryland colleges/universities. Participants were randomized to myPlan (n = 147) or control (usual web-based resources; n = 146). Outcomes included decisional conflict, decisional preparedness, confidence to intervene, safety/support behaviors, and IPV attitudes. RESULTS: At baseline, concerned friends described the abused person as a close/best friend (79.1%); 93.7% had tried at least one strategy to help. Most (89.2%) reported concerns their friend would be seriously hurt by the abuser; 22.7% reported extreme concern. Intervention participants had greater improvements in decisional conflict (specifically, understanding of their own values around the decision to intervene and help a friend) and decisional preparedness immediately after their first use of myPlan, and a significantly greater increase in confidence to talk with someone about their own relationship concerns at 12 months. At 12-month follow-up, both intervention and control groups reported increased confidence to intervene, and did not differ significantly in terms of percentage of safety/support strategies used, whether strategies were helpful, or IPV attitudes. CONCLUSIONS: A technology-based intervention, myPlan, was effective in reducing one aspect of decisional conflict (improving clarity of values to intervene) and increasing decisional preparedness to support a friend in an unsafe relationship. Information on IPV and related safety strategies delivered through the myPlan app or usual web-based resources both increased confidence to intervene with a friend. College students in the myPlan group were more likely to talk with someone about concerns about their own relationship, demonstrating potential for IPV prevention or early intervention. TRIAL REGISTRATION: Clinicaltrials.gov ID: NCT02236663, registration date 10/09/2014.

The Rapid Arterial oCclusion Evaluation (RACE) scale accuracy for diagnosis of acute ischemic stroke in emergency department - A multicenter study.

OBJECTIVE: It seems that the available data on performance of the Rapid Arterial oCclusion Evaluation (RACE) as a prehospital stroke scale for differentiating all AIS cases, not only large vessel occlusion (LVO), from the stroke mimics is lacking. As a result, we intend to evaluate the accuracy of the RACE criteria in diagnosing of AIS in patients transferred to the emergency department (ED). METHOD: The present study was a diagnostic accuracy cross-sectional study during 2021 in Iran. The study population consist of all suspected acute ischemic stroke (AIS) patients who transferred to the ED by emergency medical services (EMS). A 3-part checklist consisting of the basic and demographic information of the patients, items related to the RACE scale, and the final diagnosis of the patients based on interpretation of patients' brain MRI was used for data collection. All data were entered in Stata 14 software. We used the ROC analysis to evaluate the diagnostic power of the test. RESULT: In this study, data from 805 patients with the mean age of 66.9 ± 13.9 years were studied of whom 57.5% were males. Of all the patients suspected of stroke who transferred to the ED, 562 (69.8%) had a definite final diagnosis of AIS. The sensitivity and specificity of the RACE scale for the recommended cut-off point (score ≥ 5) were 50.18% and 92.18%, respectively. According to the Youden J index, the best cut-off point for this tool for differentiating AIS cases was a score > 2, at which sensitivity and specificity were 74.73% and 87.65%, respectively. CONCLUSION: It seems that, the RACE scale is an accurate diagnostic tool to detect and screen AIS patients in ED, Of course, not at the previously suggested cut-off point (score ≥ 5), but at the score > 2.

Predictive model for temporomandibular disorder in adolescents: Decision tree.

BACKGROUND: Temporomandibular disorders (TMD) do not only occur in adults but also in adolescents, with negative impacts on their development. AIM: To propose a predictive model for TMD in adolescents using a decision tree (DT) analysis and to identify groups at high and low risk of developing TMD in the city of Recife, PE, Brazil. DESIGN: This cross-sectional study was conducted in Recife on 1342 schoolchildren of both sexes aged 10-17 years. The analyses were performed using Pearson's chi-squared test and Fisher's exact test, as well as the CHAID algorithm for the construction of the DT. The SPSS statistical program was used. RESULTS: The prevalence of TMD was 33.2%. Statistically significant associations were observed between TMD and sex, depression, self-reported orofacial pain, and orofacial pain on clinical examination. The DT consisted of self-reported orofacial pain, orofacial pain on physical examination, and depression, with an overall predictive power of 73.0%. CONCLUSION: The proposed tree has a good predictive capacity and permits to identify groups at high risk of developing TMD among adolescents, such as those with self-reported orofacial pain or orofacial pain on examination associated with depression.

Evaluating the impact of multicancer early detection testing on health and economic outcomes: Toward a decision modeling strategy.

Emerging data provide initial support for the concept that a single, minimally invasive liquid biopsy test, performed in conjunction with confirmatory radiologic or other diagnostic testing, when indicated, could be deployed on a broad scale to screen individuals for multiple types of cancer. Ideally, such a test could do this in a way that yields a clinically important percentage of true-positive indications of cancer while minimizing false-positive signals. Modern decision modeling approaches can and should be deployed to investigate the health and economic consequences of such multicancer early detection (MCED) testing within defined at-risk populations. In this paper, through small-scale analyses involving 3 hypothetical MCED-detectible cancers, the authors illustrate the potential for MCED testing to be cost-effective, along with the pivotal role of test-induced stage shift on results. The time is ripe for additional, prospective investigations of the clinical value of MCED testing, the benefits versus the risks for screened populations, and the overall projected impact on health outcomes and costs over time.

Effects of technology-based contraceptive decision aids: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to conduct a systematic review and meta-analysis of the effects of technology-based decision aids on contraceptive use, continuation, and patient-reported and decision-making outcomes. DATA SOURCES: A systematic search was conducted in OVID MEDLINE, Cochrane Database of Systematic Reviews, CENTRAL, CINAHL, Embase, PsycINFO, and SocINDEX databases from January 2005 to April 2022. Eligible references from a concurrent systematic review evaluating contraceptive care were also included for review. STUDY ELIGIBILITY CRITERIA: Studies were included if a contraceptive decision aid was technology-based (ie, mobile/tablet application, web, or computer-based) and assessed contraceptive use and/or continuation or patient-reported outcomes (knowledge, self-efficacy, feasibility/acceptability/usability, decisional conflict). The protocol was registered under the International Prospective Register of Systematic Reviews (CRD42021240755). METHODS: Three reviewers independently performed data abstraction and quality appraisal. Dichotomous outcomes (use and continuation) were evaluated with an odds ratio, whereas continuous outcomes (knowledge and self-efficacy) were evaluated with the mean difference. Subgroup analyses were performed for the mode of delivery (mobile and tablet applications vs web and computer-based) and follow-up time (immediate vs >1 month). RESULTS: This review included 18 studies evaluating 21 decision aids. Overall, there were higher odds of contraceptive use and/or continuation among decision aid users compared with controls (odds ratio, 1.27; 95% confidence interval, [1.05-1.55]). Use of computer and web-based decision aids was associated with higher odds of contraceptive use and/or continuation (odds ratio, 1.36; 95% confidence interval, [1.08-1.72]) than mobile and tablet decision aids (odds ratio, 1.27; 95% confidence interval, [0.83-1.94]). Decision aid users also had statistically significant higher self-efficacy scores (mean difference, 0.09; 95% confidence interval, [0.05-0.13]), and knowledge scores (mean difference, 0.04; 95% confidence interval, [0.01-0.07]), with immediate measurement of knowledge having higher retention than measurement after 1 month. Other outcomes were evaluated descriptively (eg, feasibility, applicability, decisional conflict) but had little evidence to support a definite conclusion. Overall, the review provided moderate-level evidence for contraceptive use and continuation, knowledge, and self-efficacy. CONCLUSION: The use of technology-based contraceptive decision aids to support contraceptive decision-making has positive effects on contraceptive use and continuation, knowledge, and self-efficacy. There was insufficient evidence to support a conclusion about effects on other decision-making outcomes.

Decision aids for localized prostate cancer treatment choice: Systematic review and meta-analysis.

Patients who are diagnosed with localized prostate cancer need to make critical treatment decisions that are sensitive to their values and preferences. The role of decision aids in facilitating these decisions is unknown. The authors conducted a systematic review of randomized trials of decision aids for localized prostate cancer. Teams of 2 reviewers independently identified, selected, and abstracted data from 14 eligible trials (n = 3377 men), of which 10 were conducted in North America. Of these, 11 trials compared decision aids with usual care, and 3 trials compared decision aids with other decision aids. Two trials suggested a modest positive impact on decisional regret. Results across studies varied widely for decisional conflict (4 studies), satisfaction with decision (2 studies), and knowledge (2 studies). No impact on treatment choices was observed (6 studies). In conclusion, scant evidence at high risk of bias suggests the variable impact of existing decision aids on a limited set of decisional processes and outcomes. Because current decision aids provide information but do not directly facilitate shared decision making, subsequent efforts would benefit from user-centered design of decision aids that promote shared decision making.

Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials.

OBJECTIVES: The COVID-19 pandemic necessitates time-sensitive policy and implementation decisions regarding new therapies in the face of uncertainty. This study aimed to quantify consequences of approving therapies or pursuing further research: immediate approval, use only in research, approval with research (eg, emergency use authorization), or reject. METHODS: Using a cohort state-transition model for hospitalized patients with COVID-19, we estimated quality-adjusted life-years (QALYs) and costs associated with the following interventions: hydroxychloroquine, remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, tocilizumab, lopinavir-ritonavir, interferon beta-1a, and usual care. We used the model outcomes to conduct cost-effectiveness and value of information analyses from a US healthcare perspective and a lifetime horizon. RESULTS: Assuming a $100 000-per-QALY willingness-to-pay threshold, only remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, and tocilizumab were (cost-) effective (incremental net health benefit 0.252, 0.164, 0.545, 0.668, and 0.524 QALYs and incremental net monetary benefit $25 249, $16 375, $54 526, $66 826, and $52 378). Our value of information analyses suggest that most value can be obtained if these 5 therapies are approved for immediate use rather than requiring additional randomized controlled trials (RCTs) (net value $20.6 billion, $13.4 billion, $7.4 billion, $54.6 billion, and $7.1 billion), hydroxychloroquine (net value $198 million) is only used in further RCTs if seeking to demonstrate decremental cost-effectiveness and otherwise rejected, and interferon beta-1a and lopinavir-ritonavir are rejected (ie, neither approved nor additional RCTs). CONCLUSIONS: Estimating the real-time value of collecting additional evidence during the pandemic can inform policy makers and clinicians about the optimal moment to implement therapies and whether to perform further research.

A Brief Shared Decision-Making Intervention for Acute Respiratory Infections on Antibiotic Dispensing Rates in Primary Care: A Cluster Randomized Trial.

PURPOSE: To determine whether acute respiratory infection (ARI) decision aids and a general practitioner (GP) training package reduces antibiotic dispensing rate and improves GPs' knowledge of antibiotic benefit-harm evidence. METHODS: A cluster randomized trial of 27 Australian general practices (13 intervention, 14 control) involving 122 GPs. Intervention group GPs were given brief decision aids for 3 ARIs (acute otitis media, acute sore throat, acute bronchitis) and video-delivered training. Primary outcome was dispensing rate of target antibiotic classes (routinely used for ARIs), extracted for 12 months before, and following, randomization. Secondary outcomes were GPs' knowledge of antibiotic benefit-harm evidence; prescribing influences; acceptability, usefulness, and self-reported resource use; and dispensing rate of all antibiotics. RESULTS: The baseline mean dispensing rate of ARI-related antibiotics was 3.5% (intervention GPs) and 3.2% (control GPs) of consultations. After 12 months, mean rates decreased (to 2.9% intervention; 2.6% control): an 18% relative reduction from baseline but similar in both groups (rate ratio 1.01; 95% CI, 0.89-1.15). Greater increases in knowledge were seen in the intervention group than control; a significant increase (average 3.6; 95% CI, 2.4-4.7, P <.001) in the number of correct responses to the 22 knowledge questions. There were no between-group differences for other secondary outcomes. The intervention was well received, perceived as useful, and reported as used by about two-thirds of intervention GPs. CONCLUSIONS: A brief shared decision-making intervention provided to GPs did not reduce antibiotic dispensing more than usual care, although GPs' knowledge of relevant benefit-harm evidence increased significantly.