Nonoperative management (NOM) of hemodynamically normal patients with blunt splenic injury (BSI) is the standard of care. Guidelines recommend additional splenic angioembolization (SAE) in patients ...with American Association for the Surgery of Trauma (AAST) Grade IV and Grade V BSI, but the role of SAE in Grade III injuries is unclear and controversial. The aim of this systematic review was to compare the safety and effectiveness of SAE as an adjunct to NOM versus NOM alone in adults with BSI.
A systematic literature search (Medline, Embase, and CINAHL) was performed to identify original studies that compared outcomes in adult BSI patients treated with SAE or NOM alone. Primary outcome was failure of NOM. Secondary outcomes included morbidity, mortality, hospital length of stay, and transfusion requirements. Bayesian meta-analyses were used to calculate an absolute (risk difference) and relative (risk ratio RR) measure of treatment effect for each outcome.
Twenty-three studies (6,684 patients) were included. For Grades I to V combined, there was no difference in NOM failure rate (SAE, 8.6% vs NOM, 7.7%; RR, 1.09 0.80-1.51; p = 0.28), mortality (SAE, 4.8% vs NOM, 5.8%; RR, 0.82 0.45-1.31; p = 0.81), hospital length of stay (11.3 vs 9.5 days; p = 0.06), or blood transfusion requirements (1.8 vs 1.7 units; p = 0.47) between patients treated with SAE and those treated with NOM alone. However, morbidity was significantly higher in patients treated with SAE (SAE, 38.1% vs NOM, 18.6%; RR, 1.83 1.20-2.66; p < 0.01). When stratified by grade of splenic injury, SAE significantly reduced the failure rate of NOM in patients with Grade IV and Grade V splenic injuries but had minimal effect in those with Grade I to Grade III injuries.
Splenic angioembolization should be strongly considered as an adjunct to NOM in patients with AAST Grade IV and Grade V BSI but should not be routinely recommended in patients with AAST Grade I to Grade III injuries.
Systematic review and meta-analysis, level III.
•We propose a novel and systematic way of building causal decision support models.•Our approach combines probabilistic and multi-criteria decision making tools.•The resulting models are based on ...knowledge elicited from multiple experts.•The model's consistency with expert knowledge is evaluated by sensitivity analysis.•The proposed approach is applied to a supplier selection case study.
Bayesian Networks (BNs) are effective tools for providing decision support based on expert knowledge in uncertain and complex environments. However, building knowledge-based BNs is still a difficult task that lacks systematic and widely accepted methodologies, especially when knowledge is elicited from multiple experts. We propose a novel method that systematically integrates a widely used Multi Criteria Decision Making (MCDM) approach called Decision Making Trial and Evaluation Laboratory (DEMATEL) in BN construction. Our method elicits causal knowledge from multiple experts based on DEMATEL and transforms it to a BN structure. It then parameterizes the BN by using ranked nodes and evaluates its robustness and consistency by using sensitivity analysis. The proposed method provides a practical and generic way to build probabilistic decision support models by systematically exploiting expert knowledge. Suitable applications of this method include decision problems with multiple criteria, high uncertainty and limited data. We illustrate our method by applying it to a supplier selection case study in a large automobile manufacturer in Turkey.
Agricultural development projects have a poor track record of success mainly due to risks and uncertainty involved in implementation. Cost-benefit analysis can help allocate resources more ...effectively, but scarcity of data and high uncertainty makes it difficult to use standard approaches. Bayesian Networks (BN) offer a suitable modelling technology for this domain as they can combine expert knowledge and data. This paper proposes a systematic methodology for creating a general BN model for evaluating agricultural development projects. Our approach adapts the BN model to specific projects by using systematic review of published evidence and relevant data repositories under the guidance of domain experts. We evaluate a large-scale agricultural investment in Africa to provide a proof of concept for this approach. The BN model provides decision support for project evaluation by predicting the value-measured as net present value and return on investment-of the project under different risk scenarios.
IntroductionSeveral factors have been implicated in child stunting, but the precise determinants, mechanisms of action and causal pathways remain poorly understood. The objective of this study is to ...explore causal relationships between the various determinants of child stunting.Methods and analysisThe study will use data compiled from national health surveys in India, Indonesia and Senegal, and reviews of published evidence on determinants of child stunting. The data will be analysed using a causal Bayesian network (BN)—an approach suitable for modelling interdependent networks of causal relationships. The model’s structure will be defined in a directed acyclic graph and illustrate causal relationship between the variables (determinants) and outcome (child stunting). Conditional probability distributions will be generated to show the strength of direct causality between variables and outcome. BN will provide evidence of the causal role of the various determinants of child stunning, identify evidence gaps and support in-depth interrogation of the evidence base. Furthermore, the method will support integration of expert opinion/assumptions, allowing for inclusion of the many factors implicated in child stunting. The development of the BN model and its outputs will represent an ideal opportunity for transdisciplinary research on the determinants of stunting.Ethics and disseminationNot applicable/no human participants included.
Celecoxib (CXB) is a Biopharmaceutical Classification System (BCS) Class II molecule with high permeability that is practically insoluble in water. Because of the poor water solubility, there is a ...wide range of absorption and limited bioavailability following oral administration. These unfavorable properties can be improved using dry co-milling technology, which is an industrial applicable technology. The purpose of this study was to develop and optimize CXB nanoformulations prepared by dry co-milling technology, with a quality by design approach to maintain enhanced solubility, dissolution rate, and oral bioavailability. The resulting co-milled CXB composition using povidone (PVP), mannitol (MAN) and sodium lauryl sulfate (SLS) showed the maximum solubility and dissolution rate in physiologically relevant media. Potential risk factors were determined with an Ishikawa diagram, important risk factors were selected with Plackett-Burman experimental design, and CXB compositions were optimized with Central Composite design (CCD) and Bayesian optimization (BO). Physical characterization, intrinsic dissolution rate, solubility, and stability experiments were used to evaluate the optimized co-milled CXB compositions. Dissolution and permeability studies were carried out for the resulting CXB nanoformulation. Oral pharmacokinetic studies of the CXB nanoformulation and reference product were performed in rats. The results of in vitro and in vivo studies show that the CXB nanoformulations have enhanced solubility (over 4.8-fold (8.6 ± 1.06 µg/mL vs. 1.8 ± 0.33 µg/mL) in water when compared with celecoxib pure powder), and dissolution rate (at least 85% of celecoxib is dissolved in 20 min), and improved oral pharmacokinetic profile (the relative bioavailability was 145.2%, compared to that of Celebrex
, and faster t
3.80 ± 2.28 h vs. 6.00 ± 3.67 h, indicating a more rapid absorption rate).
Futbol maçları yüksek belirsizliğe sahiptir ve sonuçlarının tahmin edilmesi zordur. Sadece veriye dayalı tahmin ve yapay öğrenme yöntemleri futbol tahminlerinde kısıtlı performans elde ...edebilmektedir. Uzman bilgisine dayalı modeller başarıya sahip olmuştur, fakat bu modellerin başka yerlere uygulanması için yine uzman bilgisi ve analistler tarafından gözden geçirilmesi gerekmektedir. Bu çalışmada Türkiye futbol ligleri için geliştirilmiş özgün bir Bayes ağı modeli önerilmektedir. Önerilen model futbol müsabakası yapan takımların hücum ve savunma gücünü maça ilişkin birçok gözlem ile belirleyerek maç sonucunu tahmin etmeyi amaçlamaktadır. Modelin yapısı ve parametreleri uzman bilgisi ile geliştirilmiştir. Modelden tahmin üretirken geçmiş maç verisi ile maça ilişkin uzman bilgisi girdi olarak kullanılabilmektedir. Önerilen model Türkiye Süper Ligi’nden gerçek maç verisi ile değerlendirilmiştir.
In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual ...variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in hybrid influence diagram (HID) models (these are influence diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a hybrid Bayesian network and makes use of the dynamic discretization and the junction tree algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.
Background
Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag ...questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers.
Objective
We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.
Methods
A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.
Results
The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.
Conclusions
The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.
International Registered Report Identifier (IRRID)
RR2-10.2196/21804
Çok Kriterli Karar Verme (ÇKKV) problemlerindeki temel bir konu, karar vericinin (KV) tercihlerinin problem çözme sürecine dâhil edilmesidir. Bu tercihler birçok yaklaşımda karar vermeye temel ...oluşturan kriterlere atanan ağırlıklar şeklinde kullanılmaktadır. Ancak literatürdeki çoğu ÇKKV yöntemi, ağırlıkların baştan bilindiğini kabul etmekte veya KV’nin bu ağırlıkları doğru bir şekilde doğrudan ifade edebileceğini varsaymaktadır. Kriter ağırlıklarını elde etmek için geliştirilen az sayıdaki yöntem, genellikle kriterlerin direkt olarak birbirleriyle kıyaslanmasını gerektirmekte ve KV’nin çok sayıda değerlendirme yapmasına ihtiyaç duymaktadır. Bu çalışmada geliştirdiğimiz matematiksel programlama tabanlı yöntem, KV için bilişsel zorluk yaratmayacak az sayıda tercih değerlendirmesi ile kriter ağırlıklarını iyi bir şekilde tahmin etmektedir. KV’nin tercihlerini ağırlıklı toplam şeklinde ifade edilen bir fayda fonksiyonuyla yaptığı varsayılmıştır. KV’den direkt olarak kriterleri değerlendirmesi istenmemekte, sınırlı sayıda karar alternatifini tercih sırasına sokması beklenmektedir. Geliştirilen yöntem, beş kriterle değerlendirilen dünya üniversitelerinin sıralanması problemine uygulanmıştır. Karşılaştırma yapmak amacıyla literatürde sıklıkla kullanılan başka bir ağırlık tahmini yöntemi de (Swing yöntemi) aynı probleme uygulanmıştır. Geliştirdiğimiz yaklaşımın bu yöntemden daha iyi sonuçlar verdiği gözlemlenmiştir.
IntroductionA key decision for assessment of Low back pain (LBP) is identifying serious underlying conditions such as Cauda Equina Syndrome, infection, fracture or space-occupying lesions. Previous ...decision support tools for LBP deployed rule-based recommendations, yet Artificial Intelligence has enabled ‘intelligent’ decision support tools, with Bayesian Networks particularly suitable for complex conditions such as LBP. This study aimed to test whether clinical knowledge could be elicited to construct a Bayesian Network to support clinicians’ detection of serious pathology masquerading as LBP.MethodsA modified-RAND appropriateness procedure elicited knowledge from 16 domain experts from General Practice, Rheumatology and Musculoskeletal specialties. This comprised a four-stage process using bespoke online tools interleaved with face-to-face meetings; 1) Variable elicitation, 2) Structure elicitation, 3) Probability elicitation 4) Validation. Independent experts in spinal pathology reviewed the initial tool and its outputs.ResultsThe tool includes background risk factors (e.g. trauma, age), signs and symptoms (e.g. bladder disturbance, inflammatory symptoms) and derived judgement factors (e.g. cord compression, fracture). The tool has an interactive online interface, requiring real-time patient inputs from the subjective assessment, then gives a judgement comparing baseline to the current patient. Content validation suggested no missing elements to the model, but may require more detail for clinical understanding of terms. Face validation exposed some inconsistency in clinical reasoning, particularly for spinal infections and fractures.ConclusionThe structured elicitation method yielded a reasoning model using expert clinician knowledge, establishing consensus amongst participants about its content. Further iterations to expand this to common LBP presentations should follow.
PDF
