Prediction Rule Research Articles

SC-PLR: An Approximate Spiking Neural Network Accelerator with On-Chip Predictive Learning Rule

SC-PLR: An Approximate Spiking Neural Network Accelerator with On-Chip Predictive Learning Rule

Prediction of papillary thyroid cancer recurrence according to preoperative data

To create a formalized method for predicting papillary thyroid cancer recurrence after hemithyroidectomy based on preoperative data. At this stage of the study, we selected 101 patients with papillary thyroid cancer who underwent surgical treatment in 2017-2023. Recurrence was observed in in 47 patients. Fifty-four patients had no recurrence within 5 years after surgical treatment, i.e. these patients underwent surgery in 2017-2018. To find prediction rules, we used original classification method based on searching for subsets of variables and piecewise linear rules separating classes in pairs with subsequent voting of such rules to make a decision. The exam was carried out using a training sample (101 cases) and sliding control method (10 tests on 10 random cases). On the training sample, sensitivity of predictive algorithm was 91%, specificity 78% and error rate 13%. The aggregated result of 10 trials using sliding control method revealed sensitivity of predictive algorithm 86%, specificity 75% and error rate 15%. This result is close to overall sample and confirms the effectiveness of this method for predicting recurrence. The pilot experiments revealed the patterns in data for potential prediction of recurrence based on preoperative indicators. Further study of this problem may be valuable for decision-making and adjustments in the management of patients with papillary thyroid cancer.

A Dynamic Prediction Model for Renal Progression in Primary Membranous Nephropathy.

Objective: This study aimed to build and validate a practical web-based dynamic prediction model for predicting renal progression in patients with primary membranous nephropathy (PMN). Method: A total of 359 PMN patients from The First Affiliated Hospital of Fujian Medical University and 102 patients with PMN from The Second Hospital of Longyan between January 2018 to December 2023 were included in the derivation and validation cohorts, respectively. Renal progression was delineated as a decrease in eGFR of 30% or more from the baseline measurement at biopsy or the onset of End-Stage Renal Disease (ESRD). Multivariable Cox regression analysis was employed to identify independent prognostic factors. A web-based dynamic prediction model for renal progression was built and validated, and the performance was assessed using. An analysis of the receiver operating characteristic and the decision curve analysis. Results: In the derivation cohort, 66 (18.3%) patients experienced renal progression during the follow-up period (37.60 ± 7.95 months). The final prediction rule for renal progression included hyperuricemia (HR=2.20, 95%CI 1.26 to 3.86), proteinuria (HR=2.16, 95%CI 1.47 to 3.18), significantly lower serum albumin (HR=2.34, 95%CI 1.51 to 3.68) and eGFR (HR=1.96, 95%CI 1.47 to 2.61), older age (HR=1.85, 95%CI 1.28 to 2.61), and higher sPLA2R-ab levels (HR=2.08, 95%CI 1.43 to 3.18). Scores for each variable were calculated using the regression coefficients in the Cox model. The developed web-based dynamic prediction model, available online at http://imnpredictmodel1.shinyapps.io/dynnomapp, showed good discrimination (C-statistic = 0.72) and calibration (Brier score, P = 0.155) in the validation cohort. Conclusion: We developed a web-based dynamic prediction model that can predict renal progression in patients with PMN. It may serve as a helpful tool for clinicians to identify high-risk PMN patients and tailor appropriate treatment and surveillance strategies.

Synergy of prediction rule and total synthesis in solving the stereochemical puzzle of eucalactam B

The absolute configurations of the fungal-derived reduced polyketide eucalactam B were initially predicted using the “Biochemistry-based rule” and later confirmed through its first successful total synthesis.

Research inefficiencies in external validation studies of the Framingham Wilson coronary heart disease risk rule: A systematic review.

External validation studies create evidence about a clinical prediction rule's (CPR's) generalizability by evaluating and updating the CPR in populations different from those used in the derivation, and also by contributing to estimating its overall performance when meta-analysed in a systematic review. While most cardiovascular CPRs do not have any external validation, some CPRs have been externally validated repeatedly. Hence, we examined whether external validation studies of the Framingham Wilson coronary heart disease (CHD) risk rule contributed to generating evidence to their full potential. A forward citation search of the Framingham Wilson CHD risk rule's derivation study was conducted to identify studies that evaluated the Framingham Wilson CHD risk rule in different populations. For external validation studies of the Framingham Wilson CHD risk rule, we examined whether authors updated the Framingham Wilson CHD risk rule when it performed poorly. We also assessed the contribution of external validation studies to understanding the Predicted/Observed (P/O) event ratio and c statistic of the Framingham Wilson CHD risk rule. We identified 98 studies that evaluated the Framingham Wilson CHD risk rule; 40 of which were external validation studies. Of these 40 studies, 27 (67.5%) concluded the Framingham Wilson CHD risk rule performed poorly but did not update it. Of 23 external validation studies conducted with data that could be included in meta-analyses, 13 (56.5%) could not fully contribute to the meta-analyses of P/O ratio and/or c statistic because these performance measures were neither reported nor could be calculated from provided data. Most external validation studies failed to generate evidence about the Framingham Wilson CHD risk rule's generalizability to their full potential. Researchers might increase the value of external validation studies by presenting all relevant performance measures and by updating the CPR when it performs poorly.

A Decision Tree Approach to Develop Price Prediction Rules for Korea Allowance Unit and Renewable Energy Certificate

A Decision Tree Approach to Develop Price Prediction Rules for Korea Allowance Unit and Renewable Energy Certificate

A machine learning approach to identifying non-parental caregivers' risk for harsh caregiving towards infants in daycare centers

BackgroundHarsh Caregiving behavior amongst daycare providers (i.e., non-parental Harsh Caregiving) negatively impacts children's development across a variety of domains. As prevalences of non-parental Harsh Caregiving appear to increase worldwide, identifying its predictors is crucial for screening and intervention. ObjectiveThe goal of this study was to identify a set of indicators and predictive rules that may accurately predict women's risk for Harsh Caregiving behavior in daycare environments. Participants and SettingThe study recruited 75 female non-parental caregivers, from the general population, who work with infants aged 0-1. Caregivers filled out self-report questionnaires including a Harsh Caregiving measure as well as a broad variety of potential predictors. MethodsTo elucidate combinations of input variables that are predictive of non-parental Harsh Caregiving, we used machine learning Decision Three Inference and CHAID algorithms. ResultsStudy results revealed a predictive model including 27 questions and four different prediction paths. For example, the first path indicated that women who reported low levels of attention deficit and hyperactivity problems and low levels of rigid-negative caregiving philosophies, had 100 % chance to report low levels of Harsh Caregiving behavior. Overall classification accuracy for "High Harsh Caregiving behavior" was 95.2 %. ConclusionsAfter replication in larger samples, the model can be used as a screening tool for women expressing their wish to work with infants. Women at risk can either be declined employment or alternatively receive targeted supervision throughout their work with small infants.

Investigation of the open-circuit voltage of non-fullerene acceptors-based ternary organic solar cells based on interpretable machine-learning approach and chemically inspired descriptors

Ternary strategy to enhance the photovoltaic performances (e.g., open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (η)) of non-fullerene acceptors (NFAs)-based organic solar cells (OSCs) has exhibited alluring potential for next generation photovoltaic technology. However, the prediction and optimization of Voc in NFA-based ternary OSCs through expensive and time-consuming experiments or a theoretical perspective (e.g., an empirical rule for Voc prediction of the donor–acceptor binary system) is still an open challenge, mainly because of the complicated ternary photoactive layers. In this study, the prominent predictive performance (R2 > 0.7) of Voc is obtained by utilizing the technique of machine-learning combined with specific descriptors (e.g., frontier molecular orbital theory or electrophilicity index (ɷ)), for the first time forNFA-based ternary OSCs. Furthermore, the Shapley additive explanation (SHAP) model is employed to provide both global and local interpretable explanations for exploring the interpretability of the (eXtreme Gradient Boosting (XGBoost) model prediction and extracting the complex correlation between the specific descriptors and the Voc of NFA-based ternary OSCs. The proposed interpretable machine-learning strategy can contribute to the predictive modeling of highly efficient ternary OSCs based on multicomponent photoactive layers.

Diagnostic Utility of Cerebrospinal Fluid White Blood Cell Components for the Identification of Bacterial Meningitis in Infants.

To evaluate the diagnostic and predictive utility of cerebrospinal fluid (CSF) white blood cell (WBC) components in the diagnosis of bacterial meningitis in infants discharged from the neonatal intensive care unit (NICU). We identified a cohort of infants discharged from a Pediatrix NICU between 1997 and 2020 who did not have an immunodeficiency, had at least 1 CSF culture collected within the first 120 days of life, and at least 1 CSF laboratory specimen obtained on the day of culture collection. We only included an infant's first CSF culture and excluded cultures from CSF reservoirs and those growing contaminants or nonbacterial organisms. We examined the utility of CSF WBC components to diagnose or predict bacterial meningitis by calculating sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating curve (AUC) at different cutoff values for each parameter. We performed subgroup analysis excluding infants treated with antibiotics the day before CSF culture collection. Of the 20 756 infants that met the study inclusion criteria, 320 (2%) were diagnosed with bacterial meningitis. We found (AUC [95% CI]) CSF WBC count (0.76 [0.73-0.79]), CSF neutrophil count (0.74 [0.70-0.78]), and CSF neutrophil percent (0.71 [0.67-0.75]) had the highest predictive values for bacterial meningitis, even when excluding infants with early antibiotic administration. No single clinical prediction rule had the optimal discriminatory power for predicting culture-proven bacterial meningitis, and clinicians should be cautious when interpreting CSF WBC parameters in infants with suspected meningitis.

Evaluating Clinical Prediction Rules for Bacteremia Detection in the Emergency Department: A Retrospective Review

BackgroundBacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia. ObjectiveTo compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department. MethodsRetrospective analysis of medical records of patients who had a blood culture performed in a tertiary hospital emergency department in 2020 (12 months). Positive blood cultures were compared with randomly selected negative blood cultures (1:4 ratio). Blood cultures were analyzed per patient presentation. Clinical data from patient presentations were extracted and appraised against the modified-Shapiro (mShapiro) rule and systemic inflammatory response syndrome (SIRS) criteria to calculate diagnostic accuracy to detect bacteremia. ResultsDuring the study period, 3870 blood cultures were taken from 2921 patients: 476 (12.3%) cultures were positive for bacterial growth, from 421 individual patient presentations (10 excluded as incomplete data). Of included patients, 338 were true positives and 73 contaminates, these were compared with 1446 patients with negative blood culture presentations. Evaluating mShapiro's rule and SIRS criteria to detect bacteremia vs. no bacteremia (negative + contaminated cultures) had a sensitivity of 94.4% (95% confidence interval [CI] 91.4–96.4%) and 84.9% (95% CI 80.7–88.3%), respectively, and a specificity of 37.9% (95% CI 35.5–40.1%) and 33.8% (95% CI 31.5–36.3%), respectively. Both had a high negative predictive value for bacteremia of 96.8% (95% CI 95.1–98.0) and 91.0% (95% CI 88.3–93.1) for mShapiro's rule and SIRS criteria, respectively. ConclusionsIn this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.

Feasibility of using chest computed tomography (CT) imaging at the first lumbar vertebra (L1) level to assess skeletal muscle mass: a retrospective study

Background Skeletal muscle mass is an essential parameter for diagnosing sarcopenia. The gold standard for assessing skeletal muscle mass is using computed tomography (CT) to measure skeletal muscle area at the third lumbar vertebra (L3) level. This study aims to investigate whether skeletal muscle mass could be evaluated at the first lumbar vertebra (L1) level using images obtained from routine chest CT scans. Methods Skeletal muscle index (SMI, cm2/m2) and skeletal muscle density (SMD, HU) are commonly used to measure relative muscle mass and the degree of fat infiltration. This study used CT images at the L1 level to measure the skeletal muscle area (SMA, cm2) in 815 subjects from the health examination center. Linear regression analysis was used to explore the association between L1 and L3 measurements. The receiver operating characteristic (ROC) analysis was used to assess the predictive performance of L1 SMI for sarcopenia. The sex-specific cut-off values for low skeletal muscle mass in patients under the age of 60 were determined using the following formula: “mean − 1.28 × standard deviation.” A multivariate linear regression model was established. Results A significantly higher SMI at the L1 level was found in males than in females (43.88 ± 6.33 cm2/m2 vs 33.68 ± 5.03 cm2/m2; P < 0.001). There were strong correlations between measures at the L1 and L3 levels in both the total subject and sex-specific analyses. A negative association was found between age and L3 SMI in males (r = −0.231, P = 0.038). Both body mass index (BMI) and body surface area (BSA) were positively associated with L1 SMI in both males and females. A multivariate analysis was used to establish a prediction rule to predict SMI at the L3 level. The assessment of consistency and interchangeability between predicted and actual SMI at the L3 level yielded moderately good results. Considering the significant differences observed between male and female participants, the sex-specific cut-off values of the L1 SMI for defining low skeletal muscle mass were 36.52 cm2/m2 in males and 27.29 cm2/m2 in females. Conclusions Based on a population from central China, the correlated indicators obtained at the L1 level from routine chest CT scans may serve as effective surrogate markers for those at the L3 level in assessing overall skeletal muscle mass.

Internet Tool to Support Self-Assessment and Self-Swabbing of Sore Throat: Development and Feasibility Study.

Sore throat is a common problem and a common reason for the overuse of antibiotics. A web-based tool that helps people assess their sore throat, through the use of clinical prediction rules, taking throat swabs or saliva samples, and taking throat photographs, has the potential to improve self-management and help identify those who are the most and least likely to benefit from antibiotics. We aimed to develop a web-based tool to help patients and parents or carers self-assess sore throat symptoms and take throat photographs, swabs, and saliva samples for diagnostic testing. We then explored the acceptability and feasibility of using the tool in adults and children with sore throats. We used the Person-Based Approach to develop a web-based tool and then recruited adults and children with sore throats who participated in this study by attending general practices or through social media advertising. Participants self-assessed the presence of FeverPAIN and Centor score criteria and attempted to photograph their throat and take throat swabs and saliva tests. Study processes were observed via video call, and participants were interviewed about their views on using the web-based tool. Self-assessed throat inflammation and pus were compared to clinician evaluation of patients' throat photographs. A total of 45 participants (33 adults and 12 children) were recruited. Of these, 35 (78%) and 32 (71%) participants completed all scoring elements for FeverPAIN and Centor scores, respectively, and most (30/45, 67%) of them reported finding self-assessment relatively easy. No valid response was provided for swollen lymph nodes, throat inflammation, and pus on the throat by 11 (24%), 9 (20%), and 13 (29%) participants respectively. A total of 18 (40%) participants provided a throat photograph of adequate quality for clinical assessment. Patient assessment of inflammation had a sensitivity of 100% (3/3) and specificity of 47% (7/15) compared with the clinician-assessed photographs. For pus on the throat, the sensitivity was 100% (3/3) and the specificity was 71% (10/14). A total of 89% (40/45), 93% (42/45), 89% (40/45), and 80% (30/45) of participants provided analyzable bacterial swabs, viral swabs, saliva sponges, and saliva drool samples, respectively. Participants were generally happy and confident in providing samples, with saliva samples rated as slightly more acceptable than swab samples. Most adult and parent participants were able to use a web-based intervention to assess the clinical features of throat infections and generate scores using clinical prediction rules. However, some had difficulties assessing clinical signs, such as lymph nodes, throat pus, and inflammation, and scores were assessed as sensitive but not specific. Many participants had problems taking photographs of adequate quality, but most were able to take throat swabs and saliva samples.

Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury.

Several clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI). Using the published and validated CPR, a front-end web application called "Ambulation" was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted. After 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%). Ambulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice.

The utility of the digital clock drawing test as an accurate early screening tool for those with preclinical Alzheimer’s disease

AbstractBackgroundWith new drug treatments emerging, the need for early Alzheimer’s disease (AD) detection methods that can be done in a primary care setting has become urgent. Significant advances have been made in validating blood‐based AD biomarkers, but the absence of a validated, non‐invasive clinical indicator remains a barrier for determining those who might be optimal candidates for treatment. The digital clock drawing test (dCDT) has been previously shown to be related to AD PET biomarkers. However, it is unknown whether and the extent to which dCDT would predict AD as compared to blood‐based AD biomarkers. The current study used an interpretable machine learning approach to determine whether the dCDT, independently and in combination with blood‐based AD biomarkers (e.g., amyloid‐β 42 (Aβ42); total tau (T‐tau)), was predictive of incident AD.MethodThis study included participants from the Framingham Heart Study Generation 2 cohort (Gen 2; n = 527) who had dCDT measures, plasma Aβ42, and T‐tau. The fast interpretable greedy‐tree sums method was used to identify clinical decision rules for AD prediction. Synthetic minority oversampling technique was employed to handle the class imbalance. The predictive ability of plasma Aβ42, T‐tau and dCDT measures was evaluated by both area under the curve (AUC) and standardized partial AUC. The permutation feature importance method was used to show the importance of each measure.ResultInterpretable clinical decision rules were generated as multivariate biomarker signatures for AD prediction (Figure 1). The dCDT (AUC 0.859) was better at incident AD prediction than AD blood‐based biomarkers (AUC 0.839). The combination of dCDT, Aβ42, and T‐tau best predicted incident AD (AUC 0.884) (Figure 2). The combination of dCDT and blood‐based biomarkers also has higher partial AUC than individual ones (Table 1). The total score and information processing scores were the most important contributing dCDT measures.ConclusionThese results highlight the efficiency of detecting early AD through the integration of digital measures as a clinical indicator combined with AD specific biomarkers. The interpretable decision rules have the potential to provide automated results that can be used in practical clinical settings, although external validation is warranted.

Development of a clinical prediction rule for mobility status at discharge in patients with total knee arthroplasty: Using a decision tree model

BackgroundTotal knee arthroplasty (TKA) is an effective treatment to improve mobility in patients with severe knee osteoarthritis. However, some patients continue to have poor mobility after surgery. The preoperative identification of patients with poor mobility after TKA allows for better treatment selection and appropriate goal setting. The purpose of this study was to develop a clinical prediction rule (CPR) to predict mobility after TKA. MethodsThis study included patients undergoing primary TKA. Predictors of outcome included patient characteristics, physical function, and psychological factors, which were measured preoperatively. The outcome measure was the Timed Up and Go test, which was measured at discharge. Patients with a score of ≥11 s were considered having a low-level of mobility. The classification and regression tree methodology of decision tree analysis was used for developing a CPR. ResultsOf the 101 cases (mean age, 72.2 years; 71.3 % female), 26 (25.7 %) were classified as low-mobility. Predictors were the modified Gait Efficacy Scale, age, knee pain on the operated side, knee extension range of motion on the non-operated side, and Somatic Focus, a subscale of the Tampa Scale for Kinesiophobia (short version). The model had a sensitivity of 50.0 %, a specificity of 98.7 %, a positive predictive value of 92.9 %, a positive likelihood ratio of 37.5, and an area under the receiver operating characteristic curve of 0.853. ConclusionWe have developed a CPR that, with some accuracy, predicts the mobility outcomes of patients after TKA. This CPR may be useful for predicting postoperative mobility and clinical goal setting.

Confidentiality in Arbitration: A Principled Response to the Opportunity for Codification in England and Wales

Confidentiality is often perceived as a pivotal aspect of arbitration, shielding sensitive information from unauthorized disclosure and preventing potential harm. This article examines the opportunity for codification of the confidentiality obligations in arbitration in England and Wales in light of the Law Commission’s recent review of the Arbitration Act 1996, emphasizing the importance of clear and predictable rules to enhance England and Wales’ status as a favoured arbitral seat. Codification will offer legal certainty and the concern that users from around the world must navigate a complex web of case law to determine the existence and scope of confidentiality duties, making it less accessible. By enshrining confidentiality in the Act, informed decisions can be made without ambiguity. This article is split into four sections, examining the concept and significance of confidentiality, the global development of confidentiality rules, the opportunity for codification, and the potential benefits of harmonizing international arbitration law. For codification to be effective, the drafters must define robust exceptions, such as consent, court orders, protection of legitimate interests, and public interest. Drawing inspiration from other jurisdictions, the Act should allow flexibility for courts to address unforeseen scenarios. By embracing confidentiality and providing clarity, England and Wales can strengthen its position as a leading jurisdiction for international commercial arbitration. Confidentiality, Arbitration, Procedure, Codification, Uniformity, Harmonization, UNCITRAL Model Law, Institutional Rules

Barriers to Implementing Registered Nurse-Driven Clinical Decision Support for Antibiotic Stewardship: Retrospective Case Study.

Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

Design Information Systems for Malnutrition Analysis Apriori Algorithm

Malnutrition drives lasting detriments across individual and community wellbeing, requiring data-informed action. Advanced analytics through information systems present pathways for revelatory pattern detection from multidimensional health data. This paper outlines a system design encompassing preprocessing, modeling, analysis and interpretation techniques for mining malnutrition dataset through Apriori algorithm. The core data mining methodology enables extraction of frequencies, associations and prediction rules linking nutritional status parameters and food intake patterns. Custom algorithms filter results to high-confidence associations via statistical measures before expert evaluation. System testing verifies accurate architecture for surfaced dietary risk factors of malnutrition down to village-level. The systemization and computational augmentation of health insight derivation provides a template for needs-based analytics platforms. By targeting analysis to community data, impactful interventions become possible. The potential of customized information systems with data mining at the core is highlighted alongside domain challenges requiring cross-disciplinary impetus. The data-to-decisions system with embedded Apriori pipelines demonstrates applied informatics transforming malnutrition strategy through unveiling actionable patterns within intricacies of public welfare data.

Modeling Thermodynamic Properties of Mixtures of CO2 + O2 in the Allam Cycle by Equations of State

Different equations of state (EOS) were applied for describing thermodynamic properties of the system CO2+O2,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {CO}_{2} + \ ext {O}_{2},$$\\end{document} which is important for the Allam cycle: cubic EOS (Soave–Redlich–Kwong, Peng–Robinson), molecular-based EOS (PC-SAFT, PCP-SAFT, SAFT-VR Mie, polar soft-SAFT, BACKONE, sCPA), and multiparameter EOS (GERG-2008, EOS-CG). The pure component models were taken from the literature. The results for the mixture were compared to experimental data from the literature for two cases: (i) the thermodynamic properties of the mixture were modeled using predictive mixing and combination rules; (ii) additional binary interaction parameters were fitted to experimental data for improving the performance. In the predictive mode (i), the best results were obtained with molecular-based EOS. In the adjusted mode (ii) the best results were obtained with the multi-parameter GERG-2008 EOS and EOS-CG.

Minor head injury in anticoagulated patients: Outcomes and analysis of clinical predictors. A prospective study

BackgroundThe optimal management of patients taking oral anticoagulants who experience minor head injury (MHI) is unclear. The availability of validated protocols and reliable predictors of prognosis would be of great benefit. We investigated clinical factors as predictors of clinical outcomes and intracranial injury (ICI). MethodsWe conducted a single-cohort, prospective, observational study in an ED. Our structured clinical pathway included a first head CT scan, 24 h observation and a second CT scan. The primary outcome was the occurrence of MHI-related death or re-admission to ED at day +30. The secondary outcome was the rate of delayed ICI (dICI), defined as second positive CT scan after a first negative CT scan. We assessed some clinical predictors derived from guidelines and clinical prediction rules as potential risk factors for the outcomes. Results450 patients with a negative first CT scan who underwent a second CT scan composed our ‘study population’. The rate of the primary outcome was 4%. The rate of the secondary outcome was 4.7%. Upon univariate and multivariate analysis no statistically significant predictors for the outcomes were found. ConclusionsPrevious retrospective studies showed a lot of negative predictive factors for anticoagulated patients suffering a minor head injury. In our prospective study no clinical factors emerged as predictors of poor clinical outcomes and dICI. So, even if we confirmed a low rate of adverse outcomes, the best management of these patients in ED remains not so clear and future trials are needed.