Computed tomography pulmonary angiogram ordering, adherence to decision rules and yield in the emergency department: An observational study.

OBJECTIVE: Pulmonary embolism (PE) frequently requires diagnosis through CT pulmonary angiogram (CTPA). Appropriate application of evidence-based clinical decision tools can reduce unnecessary CTPAs. This study assessed adherence to and the efficacy of various aspects of the Queensland Health suspected PE diagnostic pathway, including Wells score, PE rule out criteria (PERC) and age-adjusted D-dimer interpretation. METHODS: Retrospective study of CTPAs ordered from 1 January to 30 April 2023 in a tertiary and urban ED in Southeast Queensland. Data on clinical variables, D-dimer and CTPA results were collected through medical record and radiology database review. Descriptive analyses were used to determine adherence to Queensland guidelines and performance of D-dimer interpretation tools (including comparison of age-adjusted PE with a new pre-test probability [PTP]-based model using D-dimer cut-off <1000 ng/mL for Wells score ≤4 and 500 ng/mL for Wells score 4.5-6). RESULTS: A total of 573 CTPAs were available for analysis with a 12.4% (95% confidence interval 10.0-15.4) diagnostic yield. Stratification by Wells score showed yields of 4.0%, 18.5% and 41% for low-, moderate- and high-risk patients, respectively. Twenty-five patients with low-PTP who received CTPA could have been excluded with the PERC rule. Age-adjusted D-dimer interpretation may have prevented 26 CTPAs with no false negatives, whereas PTP approach may have prevented 128 CTPAs with four false negatives. CONCLUSION: Guideline adherence can be improved, and adherence to existing clinical decision tools may reduce unnecessary CTPA ordering and increase diagnostic yield. The use of the age-adjusted D-dimer had good sensitivity, whereas the new PTP approach will require further prospective research.

How Can the Clinical Aptitude of AI Assistants Be Assayed?

Large language models (LLMs) are exhibiting remarkable performance in clinical contexts, with exemplar results ranging from expert-level attainment in medical examination questions to superior accuracy and relevance when responding to patient queries compared to real doctors replying to queries on social media. The deployment of LLMs in conventional health care settings is yet to be reported, and there remains an open question as to what evidence should be required before such deployment is warranted. Early validation studies use unvalidated surrogate variables to represent clinical aptitude, and it may be necessary to conduct prospective randomized controlled trials to justify the use of an LLM for clinical advice or assistance, as potential pitfalls and pain points cannot be exhaustively predicted. This viewpoint states that as LLMs continue to revolutionize the field, there is an opportunity to improve the rigor of artificial intelligence (AI) research to reward innovation, conferring real benefits to real patients.

Using the sociotechnical model to conduct a focused usability assessment of a breast reconstruction decision tool.

INTRODUCTION: BREASTChoice is a web-based breast reconstruction decision aid. The previous clinical trial-prior to the adaptation of this refined tool in which we explored usability-measured decision quality, quality of life, patient activation, shared decision making, and treatment choice. The current usability study was designed to elicit patients' and clinicians' perspectives on barriers and facilitators for implementing BREASTChoice into the clinical workflow. METHODS: We conducted qualitative interviews with patients and clinicians from two Midwestern medical specialty centers from August 2020 to April 2021. Interviews were first double coded until coders achieved a kappa > 0.8 and percent agreement > 95%, then were coded independently. We used a sociotechnical framework to evaluate BREASTChoice's implementation and sustainability potential according to end-users, human-computer interaction, and contextual factors. RESULTS: Twelve clinicians and ten patients completed interviews. Using the sociotechnical framework we determined the following. People Using the Tool: Patients and clinicians agreed that BREASTChoice could help patients make more informed decisions about their reconstruction and prepare better for their first plastic surgery appointment. Workflow and Communications: They felt that BREASTChoice could improve communication and process if the patient could view the tool at home and/or in the waiting room. Clinicians suggested the information from BREASTChoice about patients' risks and preferences be included in the patient's chart or the clinician electronic health record (EHR) inbox for accessibility during the consultation. Human Computer Interface: Patients and clinicians stated that the tool contains helpful information, does not require much time for the patient to use, and efficiently fills gaps in knowledge. Although patients found the risk profile information helpful, they reported needing time to read and digest. CONCLUSION: BREASTChoice was perceived as highly usable by patients and clinicians and has the potential for sustainability. Future research will implement and test the tool after integrating the stakeholder-suggested changes to its delivery process and content. It is critical to conduct usability assessments such as these prior to decision aid implementation to ensure success of the tool to improve risk communication.

The Bariatric Pyramid: A framework for therapeutic targets.

The Bariatric Pyramid is a model which serves to explain the goals of obesity management, act as a milestone for monitoring of therapy, and to benchmark the utility of various anti-obesity medications and interventions. Built upon a base of sustainability, it lists three more desired aspects of care: sufficiency (sufficient strength) safety and security. Each of the four aspects of the pyramid: sustainability, sufficiency safety and security have three angles, which allows the reader to understand them easily. The model that we propose is useful in training health care professionals, in therapeutic patient education, for assessing various anti-obesity therapies, and in reaching the right decision while managing obesity.

Evaluating the use of the ABCD2 score as a clinical decision aid in the emergency department: Retrospective observational study.

OBJECTIVE: Clinical decision aids (CDAs) can help clinicians with patient risk assessment. However, there is little data on CDA calculation, interpretation and documentation in real-world ED settings. The ABCD2 score (range 0-7) is a CDA used for patients with transient ischaemic attack (TIA) and assesses risk of stroke, with a score of 0-3 being low risk. The aim of this study was to describe ABCD2 score documentation in patients with an ED diagnosis of TIA. METHODS: Retrospective observational study of patients with a working diagnosis of a TIA in two Australian EDs. Data were gathered using routinely collected data from health informatics sources and medical records reviewed by a trained data abstractor. ABCD2 scores were calculated and compared with what was documented by the treating clinician. Data were presented using descriptive analysis and scatter plots. RESULTS: Among the 367 patients with an ED diagnosis of TIA, clinicians documented an ABCD2 score in 45% (95% CI 40-50%, n = 165). Overall, there was very good agreement between calculated and documented scores (Cohen's kappa 0.90). The mean documented and calculated ABCD2 score were similar (3.8, SD = 1.5, n = 165 vs 3.7, SD = 1.8, n = 367). Documented scores on the threshold of low and high risk were more likely to be discordant with calculated scores. CONCLUSIONS: The ABCD2 score was documented in less than half of eligible patients. When documented, clinicians were generally accurate with their calculation and application of the ABCD2. No independent predictors of ABCD2 documentation were identified.

[Implementation of an electronic clinical decision aid to support quality of care for COPD in family medicine]. / Implementierung einer elektronischen Entscheidungshilfe zur Unterstützung der Behandlungsqualität der COPD in der Hausarztmedizin.

BACKGROUND: Electronic clinical decision aids (eCDAs) have the potential to improve the quality of chronic disease management (CDM) and, therefore, patient relevant outcomes. However, eCDAs are only sparsely implemented in primary care for chronic obstructive pulmonary disease (COPD). The aim of this pilot study was to develop and implement of an eCDA for COPD primary care in two Swiss primary care practices. METHOD: Two primary care practices, each with five primary care physicians and their assistants, participated in the study. The eCDA was developed in collaboration with one of the two GP practices (Practice 1) following a development cycle encompassing alpha- and beta-testing stages. Long-term testing for one year was conducted in both practices. The implementation of the eCDA was evaluated according to the RE-AIM framework counting occurrences as follows: Reach: the number of patients included in the CDM using the eCDA. EFFECTIVENESS: the number of treatment processes initiated per patient. Adoption: practice utilization of the diverse functions featured in the eCDA. Implementation and Maintenance: health care professionals' attitudes towards the impact of the eCDA on the quality of care and their willingness to continue using the eCDA after long-term testing. Data were collected by the eCDA itself, which was programmed to track user data, and from practice staff using questionnaires. RESULTS: Reach: After the long-term test, the number of patients recorded in the eCDA was 28 in practice 1, and 12 in practice 2. EFFECTIVENESS: The number of evidence-based treatment processes per patient was 14 (IQR 6 to 22) in Practice 1 and 6 (IQR 5 to 8) in Practice 2. Adoption: The utilization profiles of the eCDA differed greatly between practices. Implementation and Maintenance: After the long-term test, respondents were more critical of the quality of the CDM for patients with COPD, and attitudes consistent with interprofessional care were more prevalent compared to baseline. Respondents were optimistic regarding both the potential of the eCDA to improve the quality of CDM and their motivation to continue using the eCDA after long-term testing. CONCLUSION: This pilot study is a roadmap for future projects aiming to develop and implement eCDAs for the CDM of COPD in primary care. Future larger implementation studies in this domain should place greater emphasis on the measurement of structural practice characteristics as potential determinants of patient-relevant outcomes. The modifiable determinants should then be tested for their effects on patient-relevant outcomes in a randomized controlled design.

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design.

BACKGROUND: The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. OBJECTIVES: To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. DESIGN: This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.

The primary aim of this pilot study was to develop a machine learning algorithm to predict and distinguish eight poisoning agents based on clinical symptoms. Data were used from the National Poison Data System from 2014 to 2018, for patients 0-89 years old with single-agent exposure to eight drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium and sulfonylureas). Four classifier prediction models were applied to the data: logistic regression, LightGBM, XGBoost, and CatBoost. There were 201 031 cases used to develop and test the algorithms. Among the four models, accuracy ranged 77%-80%, with precision and F1 scores of 76%-80% and recall of 77%-78%. Overall specificity was 92% for all models. Accuracy was highest for identifying sulfonylureas, acetaminophen, benzodiazepines and diphenhydramine poisoning. F1 scores were highest for correctly classifying sulfonylureas, acetaminophen and benzodiazepine poisonings. Recall was highest for sulfonylureas, acetaminophen, and benzodiazepines, and lowest for bupropion. Specificity was >99% for models of sulfonylureas, calcium channel blockers, lithium and aspirin. For single-agent poisoning cases among the eight possible exposures, machine learning models based on clinical signs and symptoms moderately predicted the causal agent. CatBoost and LightGBM classifier models had the highest performance of those tested.

Classification of clinically relevant intravascular volume status using point of care ultrasound and machine learning.

Purpose: This is a foundational study in which multiorgan system point of care ultrasound (POCUS) and machine learning (ML) are used to mimic physician management decisions regarding the functional intravascular volume status (IVS) and need for diuretic therapy. We present this as an impactful use case of an application of ML in aided decision making for clinical practice. IVS represents complex physiologic interactions of the cardiac, renal, pulmonary, and other organ systems. In particular, we focus on vascular congestion and overload as an evolving concept in POCUS diagnosis and clinical relevance. It is critical for physicians to be able to evaluate IVS without disrupting workflow or exposing patients to unnecessary testing, radiation, or cost. This work utilized a small retrospective dataset as a feasibility test for ML binary classification of diuretic administration validated with clinical decision data. Future work will be directed toward artificial intelligence (AI) delivery at the bedside and assessment of the impact on patient-centered outcomes and physician workflow improvement. Approach: We retrospectively reviewed and processed 1039 POCUS video clips, including cardiac, thoracic, and inferior vena cava (IVC) views. Multiorgan POCUS clips were correlated with clinical data extracted from the electronic health record and deidentified for algorithm training and validation. We implemented a two-stream three-dimensional (3D) deep learning approach that fuses heart and IVC data to perform binary classification of the need for diuretic use. Results: Our proposed approach achieves high classification accuracy (84%) for the determination of diuretic use with 0.84 area under the receiver operating characteristic curve. Conclusions: Our two-stream 3D deep neural network is able to classify POCUS video clips that match physicians' classification for or against diuretic use with high accuracy. This serves as a foundational step in the progress toward AI-aided diagnosis and AI implementation in the field of IVS evaluation by POCUS.

Development of a trail running injury screening instrument: A multiple methods approach.

OBJECTIVE: To develop a trail running injury screening instrument (TRISI) for utilisation as clinical decision aid in determining if a trail runner is at an increased risk for injury. DESIGN: Multiple methods approach. METHODS: The study utilised five phases 1) identification of injury risk factors 2) determining the relevance of each identified risk factor in a trail running context, 3) creating the content of the Likert scale points from 0 to 4, 4) rescaling the Likert scale points to determine numerical values for the content of each Likert scale point, and 5) determining a weighted score for each injury risk factor that contributes to the overall combined composite score. RESULTS: Of the 77 identified injury risk factors, 26 were deemed relevant in trail running. The weighted score for each injury risk factor ranged from 2.21 to 5.53 with the highest calculated score being 5.53. The final TRISI includes risk categories of training, running equipment, demographics, previous injury, behavioural, psychological, nutrition, chronic disease, physiological, and biomechanical factors. CONCLUSION: The developed TRISI aims to assist the clinician during pre-race injury screening or during a training season to identify meaningful areas to target in designing injury risk management strategies and/or continuous health education.

Prediction Aided Tapering In rheumatoid arthritis patients treated with biOlogicals (PATIO): protocol for a randomized controlled trial.

BACKGROUND: Biological disease-modifying anti-rheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis (RA) but are expensive and increase the risk of infection. Therefore, in patients with a stable low level of disease activity or remission, tapering bDMARDs should be considered. Although tapering does not seem to affect long-term disease control, (short-lived) flares are frequent during the tapering process. We have previously developed and externally validated a dynamic flare prediction model for use as a decision aid during stepwise tapering of bDMARDs to reduce the risk of a flare during this process. METHODS: In this investigator-initiated, multicenter, open-label, randomized (1:1) controlled trial, we will assess the effect of incorporating flare risk predictions into a bDMARD tapering strategy. One hundred sixty RA patients treated with a bDMARD with stable low disease activity will be recruited. In the control group, the bDMARD will be tapered according to "disease activity guided dose optimization" (DGDO). In the intervention group, the bDMARD will be tapered according to a strategy that combines DGDO with the dynamic flare prediction model, where the next bDMARD tapering step is not taken in case of a high risk of flare. Patients will be randomized 1:1 to the control or intervention group. The primary outcome is the number of flares per patient (DAS28-CRP increase > 1.2, or DAS28-CRP increase > 0.6 with a current DAS28-CRP ≥ 2.9) during the 18-month follow-up period. Secondary outcomes include the number of patients with a major flare (flare duration ≥ 12 weeks), bDMARD dose reduction, adverse events, disease activity (DAS28-CRP) and patient-reported outcomes such as quality of life and functional disability. Health Care Utilization and Work Productivity will also be assessed. DISCUSSION: This will be the first clinical trial to evaluate the benefit of applying a dynamic flare prediction model as a decision aid during bDMARD tapering. Reducing the risk of flaring during tapering may enhance the safety and (cost)effectiveness of bDMARD treatment. Furthermore, this study pioneers the field of implementing predictive algorithms in clinical practice. TRIAL REGISTRATION: Dutch Trial Register number NL9798, registered 18 October 2021, https://www.trialregister.nl/trial/9798 . The study has received ethical review board approval (number NL74537.041.20).

A clinical decision aid for patients with suspected midfacial and mandibular fractures (the REDUCTION-I study): a prospective multicentre cohort study.

PURPOSE: To assess physical examination findings related to maxillofacial trauma to identify patients at risk of midfacial and mandibular fractures and then to construct a clinical decision aid to rule out the presence of midfacial and mandibular fractures in emergency department patients. METHODS: We performed a prospective multicentre cohort study in four hospitals in the Netherlands, including consecutive patients with maxillofacial trauma. Each patient received a standardized physical examination consisting of 15 and 14 findings for midfacial and mandibular traumas, respectively. Consequently, clinical decision aids were constructed with the focus being on ruling out the presence of midfacial and mandibular fractures, and diagnostic accuracy was calculated. RESULTS: A total of 993 consecutive patients were identified of whom 766 and 280 patients were suspected of midfacial and mandibular fractures, respectively. Midfacial fractures were diagnosed in 339 patients (44.3%), whereas mandibular fractures were observed in 66 patients (23.6%). The decision aid for midfacial trauma consisting of peri-orbital hematoma, epistaxis, ocular movement limitation, infra-orbital nerve paresthesia, palpable step-off and tooth mobility or avulsion, produced a sensitivity of 89.7 (86.0-92.5), a specificity of 42.6 (38.0-47.4), and a negative predictive value of 83.9% (78.4-88.2). The decision aid for mandibular trauma consisting of the angular compression test, axial chin pressure test, objective malocclusion, tooth mobility or avulsion and the tongue blade bite test resulted in a sensitivity of 98.5 (91.9-99.7), a specificity of 34.6 (28.5-41.2), and a negative predictive value of 98.7% (92.8-99.8). CONCLUSION: The constructed clinical decision aids for maxillofacial trauma may aid in stratifying patients suspected for midfacial and mandibular fractures to reduce unnecessary diagnostic imaging. CLINICAL TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov with the identifier NCT03314480.

A clinical decision aid to discern patients without and with midfacial and mandibular fractures that require treatment (the REDUCTION-II study): a prospective multicentre cohort study.

PURPOSE: To assess the diagnostic accuracy of physical examination findings and to construct clinical decision aids to discern emergency department patients without and with midfacial and mandibular fractures that require treatment. METHODS: A prospective multicentre cohort study was conducted in four hospitals in the Netherlands. Consecutive maxillofacial trauma patients were included whereupon each patient underwent a standardized physical examination consisting of 15 and 14 findings for midfacial and mandibular trauma, respectively. The primary outcome was the decision whether to treat during the emergency department stay or within 24 h of admission. The diagnostic accuracy was calculated for the individual physical examination findings and ensuing clinical decision aids with the focus being on detecting midfacial and mandibular fractures that require active treatment. RESULTS: A total of 766 midfacial trauma patients were identified of whom 339 (44.3%) had midfacial fractures. Of those, 74 (21.8%) required active treatment. A total of 280 mandibular trauma patients were identified of whom 66 (23.6%) had mandibular fractures. Of those, 37 (56.0%) required active treatment. The decision aid for midfacial trauma consisting of facial depression, epistaxis, ocular movement limitation, palpable step-off, objective malocclusion and tooth mobility or avulsion had a sensitivity of 97.3 (90.7-99.3), a specificity of 38.6 (35.0-42.3), and a negative predictive value of 99.3 (97.3-99.8). The decision aid for mandibular trauma consisting of mouth opening limitation, jaw movement pain, objective malocclusion and tooth mobility or avulsion resulted in a sensitivity of 100.0 (90.6-100.0), a specificity of 39.1 (33.2-45.4), and a negative predictive value of 100.0 (96.1-100.0). CONCLUSION: The clinical decision aids successfully identified midfacial and mandibular trauma patients requiring active fracture treatment and so may be useful in preventing unnecessary radiological procedures in the future. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov with the identifier NCT03314480.

90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model.

PURPOSE: The purpose of this study was to develop and validate a prediction model for 90-day mortality following a total knee replacement (TKR). TKR is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify high-risk patients who could be targeted with preventative interventions. The aim was to develop and validate a simple model to help inform treatment choices. METHODS: A mortality prediction model for knee OA patients following TKR was developed and externally validated using a US claims database and a UK general practice database. The target population consisted of patients undergoing a primary TKR for knee OA, aged ≥ 40 years and registered for ≥ 1 year before surgery. LASSO logistic regression models were developed for post-operative (90-day) mortality. A second mortality model was developed with a reduced feature set to increase interpretability and usability. RESULTS: A total of 193,615 patients were included, with 40,950 in The Health Improvement Network (THIN) database and 152,665 in Optum. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and 0.70 when externally validated on THIN. The 12 variable model achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. CONCLUSIONS: A simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR was developed that demonstrated good, robust performance. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and targeting prophylaxis for those at high risk. LEVEL OF EVIDENCE: III.

A COVID-19 Test Triage Tool, Predicting Negative Results and Reducing the Testing Burden on Healthcare Systems During a Pandemic.

Background: Detecting and isolating cases of COVID-19 are amongst the key elements listed by the WHO to reduce transmission. This approach has been reported to reduce those symptomatic with COVID-19 in the population by over 90%. Testing is part of a strategy that will save lives. Testing everyone maybe ideal, but it is not practical. A risk tool based on patient demographics and clinical parameters has the potential to help identify patients most likely to test negative for SARS-CoV-2. If effective it could be used to aide clinical decision making and reduce the testing burden. Methods: At the time of this analysis, a total of 9,516 patients with symptoms suggestive of Covid-19, were assessed and tested at Mount Sinai Institutions in New York. Patient demographics, clinical parameters and test results were collected. A robust prediction pipeline was used to develop a risk tool to predict the likelihood of a positive test for Covid-19. The risk tool was analyzed in a holdout dataset from the cohort and its discriminative ability, calibration and net benefit assessed. Results: Over 48% of those tested in this cohort, had a positive result. The derived model had an AUC of 0.77, provided reliable risk prediction, and demonstrated a superior net benefit than a strategy of testing everybody. When a risk cut-off of 70% was applied, the model had a negative predictive value of 96%. Conclusion: Such a tool could be used to help aide but not replace clinical decision making and conserve vital resources needed to effectively tackle this pandemic.

Validation of a preoperative prediction model for mortality within 1 year after endovascular aortic aneurysm repair of intact aneurysms.

OBJECTIVE: Most would agree that at least 1-year survival is necessary after intact abdominal aortic aneurysm (AAA) repair to appropriately justify the cost and risk of the procedure. No validated clinical decision instruments exist to predict survival after endovascular aneurysm repair (EVAR) beyond the perioperative period. The purpose of this analysis was to create a preoperative prediction model for 1-year mortality after EVAR for intact AAA in the Society for Vascular Surgery Vascular Quality Initiative. METHODS: All intact EVARs in the Society for Vascular Surgery Vascular Quality Initiative from 2011 to 2015 were randomly divided into training (n = 17,836) and validation (n = 2500) data sets, and 31 preoperative candidate predictors were identified. A logistic regression model for 1-year mortality was created, and bootstrapped stepwise variable elimination was used to reduce this model to a best subset of predictors. Penalized maximum likelihood estimation was used to correct for potential overfitting. The final model was internally validated by bootstrapping the area under the curve (AUC) and the calibration slope and intercept, and its performance when applied to the training and validation data sets was compared. RESULTS: After elective and nonelective (symptomatic, intact) EVAR, 1-year mortality was 5.5% (n = 900/16,411) and 11.4% (n = 162/1425), respectively. The mean probability of 1-year mortality was 6.0% (n = 1062) in the training set and 5.7% (n = 143) in the validation cohort (P = .12). Significant preoperative predictors of 1-year mortality included chronic obstructive pulmonary disease, age, preoperative renal insufficiency (creatinine concentration ≥1.8 mg/dL or on hemodialysis), ejection fraction <50%, transfer status, body mass index <24 kg/m2, preoperative beta-blocker exposure, larger AAA diameter, and lower admission hemoglobin level. Preoperative statin use was found to be protective. The bias-corrected AUC was 0.759 (Hosmer-Lemeshow goodness-of-fit P value of 0.36; calibration intercept, -0.003; slope, 0.999). When applied to the validation data set, the model had AUC of 0.724 (95% confidence interval, 0.676-0.768; calibration intercept, 0.0009; slope, 0.970), which was in excellent agreement with the original data set bias-corrected AUC. Notably, ∼27.5% (n = 4902) had four or more risk factors with a predicted 1-year post-EVAR mortality risk of 10% to 22% despite that 33.2% of these patients had AAA diameters below recommended treatment guideline minimum thresholds. CONCLUSIONS: This validated preoperative prediction model for 1-year mortality identifies patients less likely to benefit from EVAR. Appropriateness of intact AAA EVAR care delivery can be improved by use of this clinical decision aid to determine which high-risk patients have lower probability of mortality within the first postoperative year relative to their predicted annualized rupture risk.

Outcomes and emergency medical services resource utilization among patients with syncope arriving to the emergency department by ambulance.

OBJECTIVE: Syncope accounts for 1% of emergency department (ED) visits, yet few experience a serious adverse event (SAE). Two-thirds of syncope patients are transported to the ED by ambulance, placing considerable burden on emergency medical services (EMS), and many of these transports may be unnecessary. We estimated the proportion of syncope patients who fell into a low-risk category based on an ED diagnosis of vasovagal syncope and the absence of EMS intervention, hospitalization, or SAE. METHODS: We conducted a multicentre prospective cohort study enrolling adult syncope patients transported to the ED by ambulance over 13 months. We collected demographics and EMS interventions, and followed patients for 30 days to identify all SAE, including death, dysrhythmia, myocardial infarction, aortic dissection, pulmonary embolism, subarachnoid hemorrhage, significant hemorrhage, and related procedural interventions. RESULTS: Of 990 (67.2%) patients transported to the ED by ambulance, 121 had EMS interventions, 137 suffered 30-day SAE, 393 (39.7%; 95%CI 36.6, 42.8) were deemed low risk, 41 patients with vasovagal syncope were lost to follow-up, and 298 patients were diagnosed with non-vasovagal syncope. During transport, 121 (12.2%; 95%CI 10.2, 14.3) patients underwent some EMS intervention, and 137 (14.6%; 95%CI 12.4, 16.9) suffered SAEs within 30 days. CONCLUSION: About 40% of patients transported to the ED by ambulance are at low risk and may not benefit from paramedic care or transport to a hospital. A robust clinical decision tool would help identify patients safe for treat-and-release, diversion to alternative care, or rapid offload into low-acuity ED areas, potentially reducing EMS workload and cost.

OBJECTIF: Les syncopes motivent 1 % des consultations au service des urgences (SU), mais le malaise entraîne peu d'événements indésirables graves (EIG). Ainsi, deux tiers des patients ayant subi une syncope sont transportés en ambulance au SU, ce qui impose un lourd fardeau sur les services médicaux d'urgence (SMU), et pourtant bon nombre de transports effectués seraient non nécessaires. Aussi l'étude visait-elle à estimer la proportion de patients ayant subi une syncope dont l'état serait jugé à faible risque d'après le diagnostic de syncope vasovagale posé au SU ainsi que d'après l'absence d'intervention faite par les SMU, d'hospitalisation ou d'EIG. MÉTHODE: Il s'agit d'une étude prospective de cohortes, multicentrique, menée chez des adultes qui ont subi une syncope et qui ont été transportés en ambulance au SU, sur une période de 13 mois. Ont été recueillies des données démographiques ainsi que les notes sur les interventions effectuées par les SMU; à cela s'ajoute un suivi de 30 jours aux fins de collecte de renseignements sur tout EIG : mort, arythmie, infarctus du myocarde, dissection de l'aorte, embolie pulmonaire, hémorragie sous-arachnoïdienne, hémorragie importante et gestes interventionnels liés aux troubles en question. RÉSULTATS: Au total, 990 patients (67,2 %) ont été transportés en ambulance au SU; sur ce nombre, 121 ont subi des interventions pratiquées par les SMU; 137 ont connu un EIG au cours des 30 jours suivant le malaise; 393 (39,7 %; IC à 95 % : 36,6-42,8) ont été jugés à faible risque; 41 ayant fait une syncope vasovagale ont été perdus de vue durant le suivi; et 298, ont fait une syncope non vasovagale. Durant le transport, 121 patients (12,2 %; IC à 95 % : 10,2-14,3) ont subi une forme quelconque d'intervention par les SMU et, au cours des 30 jours de suivi, 137 (14,6 %; IC à 95 % : 12,4-16,9) ont connu un EIG. CONCLUSION: Environ 40 % des patients transportés en ambulance au SU connaissent un faible risque et, dans leur cas, la prestation de soins paramédicaux ou le transport à l'hôpital pourraient ne pas être nécessaires. Un outil d'aide à la décision clinique qui soit digne de confiance pourrait faciliter le repérage des patients dont l'état se prêterait au traitement suivi du congé, à une orientation vers d'autres types de soins ou à un passage rapide dans des zones de petites urgences, ce qui permettrait à la fois de réduire la charge de travail des SMU ainsi que les coûts.

Assessing the predictability of self-harm in a high-risk adult prisoner population: a prospective cohort study.

BACKGROUND: Prisoners are at increased risk of self-harm and when either intent is expressed, or an act of self-harm carried out, prisoners in the UK are subject to self-harm/suicide monitoring (referred to as "open ACCT" monitoring). However, there is a paucity of validated instruments to identify risk of self-harm in prisoner populations. In response to the need to support prison staff to determine who is at increased risk of self-harm or repeat self-harm, the aim of this study was to determine whether any pre-existing, standardised instruments could usefully identify future self-harm events in prisoners undergoing ACCT monitoring. METHODS: A multi-stage prospective cohort study was conducted, where the Prison Screening Questionnaire (PriSnQuest), a modified Borderline Symptom List-23 (BSL-23), Self-Harm Inventory (SHI), Patient Health Questionnaire-9 (PHQ-9) and Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM) instruments were administered to prisoners aged 18 and above, who were judged to be at an increased risk of self-harm (on open ACCT monitoring) during the recruitment phase. A 6-month follow-up determined self-harm occurrence since baseline, and Area-Under-the-Curve (AUC) analysis examined the ability of the instruments to predict future self-harm. RESULTS: Prison records established that 29.1% self-harmed during the follow up period, involving a total of 423 self-harm events reported from 126 individuals, followed up for 66,789 prisoner days (median 167 days; IQR 71-207.5 days). This translated to an 'event incidence' of 6.33 per 1000 prisoner days of those who had been placed upon an ACCT, or 'prisoner incidence' of 1.89 per 1000 days, with considerable variation for both gender and participating prisons. None of the summary scores derived from the selected instruments showed a meaningful ability to predict self-harm, however, exploratory logistic regression analysis of individual background and instrument items revealed gender-specific item sets which were statistically significant in predicting future self-harm. CONCLUSIONS: Prospective self-harm was not predicted by any of the pre-existing instruments that were under consideration. Exploratory logistic regression analysis did reveal gender-specific item sets, producing predictive algorithms which were statistically significant in predicting future self-harm; however, the operational functionality of these item sets may be limited.

The development and evaluation of a computerized decision aid for the treatment of psychotic disorders.

BACKGROUND: Routinely monitoring of symptoms and medical needs can improve the diagnostics and treatment of medical problems, including psychiatric. However, several studies show that few clinicians use Routine Outcome Monitoring (ROM) in their daily work. We describe the development and first evaluation of a ROM based computerized clinical decision aid, Treatment-E-Assist (TREAT) for the treatment of psychotic disorders. The goal is to generate personalized treatment recommendations, based on international guidelines combined with outcomes of mental and physical health acquired through ROM. We present a pilot study aimed to assess the feasibility of this computerized clinical decision aid in daily clinical practice by evaluating clinicians' experiences with the system. METHODS: Clinical decision algorithms were developed based on international schizophrenia treatment guidelines and the input of multidisciplinary expert panels from multiple psychiatric institutes. Yearly obtained diagnostic (ROM) information of patients was presented to treating clinicians combined with treatment suggestions generated by the algorithms of TREAT. In this pilot study 6 clinicians and 16 patients of Lentis Psychiatric Institute used the application. Clinicians were interviewed and asked to fill out self-report questionnaires evaluating their opinions about ROM and the effectiveness of TREAT. RESULTS: Six clinicians and 16 patients with psychotic disorders participated in the pilot study. The clinicians were psychiatrists, physicians and nurse-practitioners which all worked at least 8 years in mental health care of which at least 3 years treating patients with psychotic illnesses. All Clinicians found TREAT easy to use and would like to continue using the application. They reported that TREAT offered support in using diagnostic ROM information when drafting the treatment plans, by creating more awareness of current treatment options. CONCLUSION: This article presents a pilot study on the implementation of a computerized clinical decision aid linking routine outcome monitoring to clinical guidelines in order to generate personalized treatment advice. TREAT was found to be feasible for daily clinical practice and effective based on this first evaluation by clinicians. However, adjustments have to be made to the system and algorithms of the application. The ultimate goal is to provide appropriate evidence based care for patients with severe mental illnesses.

The conundrum of acute chest pain in general practice: a nationwide survey in The Netherlands.

BACKGROUND: GPs are frequently confronted with patients with acute onset chest pain. Although usually benign, approximately 5% is due to acute coronary syndrome (ACS). Unfortunately, ACS is not always recognised, leading to a missed diagnosis in 2-5% of presentations. AIM: The authors set out to study the level of risk GPs are willing to accept with regards to missing an ACS diagnosis, and the receptiveness of implementing new clinical decision aids. DESIGN & SETTING: This study involved an online survey among GPs in the Netherlands. METHOD: A concept survey was constructed, which was tested among a panel of 24 GPs. The survey was then modified to achieve content validity. This survey was electronically distributed among 1000 GPs. RESULTS: A total of 313 (31.3%) GPs completed the survey. Of those surveyed, the median age was 50 years (interquartile range 41-57), 53.0% were female, and 6.4% were specialist GPs ('kaderarts') in cardiology or acute care. GPs estimated the missed ACS rate to be <5.0% in clinical practice, most often estimating a chance of 1.0-2.5% (35.2%) or 0.5-1.0% (29.7%). For atypical case presentations, 70% of GPs would accept a 0.1-1.0% missed diagnosis rate, while keeping the referral threshold to a maximum of 50 unnecessary referrals for each ACS case (75% of responders). GPs would welcome additional decision aids, with 79.2% favouring a clinical decision aid, 77.1% favouring troponin point-of-care (POC) testing, and 85.5% favoring a combination of a clinical decision aid and a troponin POC test. CONCLUSION: GPs perceive that they miss more ACS cases than they feel comfortable with, which is reflected in a defensive referral strategy. The vast majority of GPs would welcome the use of clinical decision aids and/or cardiac biomarker POC testing for ruling out ACS, if accompanied by more certainty than based on clinical judgment alone.