Psychometric evaluation of the Decision Support System (DSS) for municipal nurses encountering health deterioration among older adults.

BACKGROUND: A valid and reliable tool is crucial for municipal registered nurses (RNs) to make quick decisions in older adults who show rapid signs of health deterioration. The aim of this study was to investigate the psychometric properties of the Decision Support System (DSS) among older adults in the municipal healthcare system. METHODS: Firstly, we utilized the Rasch dichotomous model to analyze the DSS assessments (n=281) that were collected from municipal RNs working with older adults in the municipal healthcare system. We examined the properties of the DSS in terms of its unidimensionality, item fit, and separation indices. Secondly, to investigate inter-rater agreement in using the DSS, four experienced municipal RNs used the DSS to assess 60 health deterioration scenarios presented by one human patient simulators. The 60 DSS assessments were then analyzed using the ICC (2,1), percentage agreement, and Cohen κ statistics. RESULTS: The sample of older adults had a mean age of 82.8 (SD 11.7). The DSS met the criteria for unidimensionality, although two items did not meet the item fit statistics when all the DSS items were analyzed together. The person separation index was 0.47, indicating a limited level of separation among the sample. The item separation index was 11.43, suggesting that the DSS has good ability to discriminate between and separate the items. At the overall DSS level, inter-rater agreements were good according to the ICC. At the individual DSS item level, the percentage agreements were 75% or above, while the Cohen κ statistics ranged from 0.46 to 1.00. CONCLUSIONS: The Rasch analysis revealed that the psychometric properties of the instrument were acceptable, although further research with a larger sample size and more items is needed. The DSS has the potential to assist municipal RNs in making clinical decisions regarding health deterioration in older adults, thereby avoiding unnecessary emergency admistion and helping.

Swefoot - The Swedish national quality register for foot and ankle surgery.

BACKGROUND: Population-based register data could be used to improve our knowledge of patients surgically treated for foot and ankle disorders. The quality register Swefoot was recently created to collect surgical and patient-reported data of foot and ankle surgery. This manuscript aims to describe the development and current use of the register. METHODS: The development of Swefoot started in 2014 and currently, data on 16 different diagnoses are collected in 49 units performing foot and ankle surgery. Registrations are performed by the surgeon and the patient. RESULTS: Between 2014 and 2020 approximately 20,000 surgical procedures have been registered. 75.1% of the registered patients were women, 9.3% were smokers, 9.3% had a concomitant rheumatoid disease, and 18.4% a BMI larger than 30 kg/m2. CONCLUSIONS: The Swefoot is a unique national register for foot and ankle surgery. It is by now possible to present demographic, surgical, and outcome parameters based on Swefoot.

Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports.

Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the reporting of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs and adverse events (AEs) in humans from two sources of data: the Side Effect Resource, derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System, derived from postmarketing surveillance. The ratio of a drug's therapeutic unbound plasma concentration over the drug's in vitro potency against a given protein was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the positive predictive value and the fraction of drugs with AEs that can be detected. However, considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 9 carbonic anhydrases, of which CA5B is significantly associated with cholestatic jaundice. We include the full quantitative data on associations between measured and predicted in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.

Deep Learning-Based Conformal Prediction of Toxicity.

Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.

Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods.

Current in vitro models for hepatotoxicity commonly suffer from low detection rates due to incomplete coverage of bioactivity space. Additionally, in vivo exposure measures such as Cmax are used for hepatotoxicity screening and are unavailable early on. Here we propose a novel rule-based framework to extract interpretable and biologically meaningful multiconditional associations to prioritize in vitro end points for hepatotoxicity and understand the associated physicochemical conditions. The data used in this study were derived for 673 compounds from 361 ToxCast bioactivity measurements and 29 calculated physicochemical properties against two lowest effective levels (LEL) of rodent hepatotoxicity from ToxRefDB, namely 15 mg/kg/day and 500 mg/kg/day. To achieve 80% coverage of toxic compounds, 35 rules with accuracies ranging from 96% to 73% using 39 unique ToxCast assays are needed at a threshold level of 500 mg/kg/day, whereas to describe the same coverage at a threshold of 15 mg/kg/day, 20 rules with accuracies of between 98% and 81% were needed, comprising 24 unique assays. Despite the 33-fold difference in dose levels, we found relative consistency in the key mechanistic groups in rule clusters, namely (i) activities against Cytochrome P, (ii) immunological responses, and (iii) nuclear receptor activities. Less specific effects, such as oxidative stress and cell cycle arrest, were used more by rules to describe toxicity at the level of 500 mg/kg/day. Although the endocrine disruption through nuclear receptor activity formulated an essential cluster of rules, this bioactivity was not covered in four commercial assay setups for hepatotoxicity. Using an external set of 29 drugs with drug-induced liver injury (DILI) labels, we found that promiscuity over important assays discriminates between compounds with different levels of liver injury. In vitro-in vivo associations were also improved by incorporating physicochemical properties especially for the potent, 15 mg/kg/day toxicity level as well for assays describing nuclear receptor activity and phenotypic changes. The most frequently used physicochemical properties, predictive for hepatotoxicity in combination with assay activities, are linked to bioavailability, which were the number of rotatable bonds (less than 7) at a of level of 15 mg/kg/day and the number of rings (of less than 3) at level of 500 mg/kg/day. In summary, hepatotoxicity cannot very well be captured by single assay end points, but better by a combination of bioactivities in relevant assays, with the likelihood of hepatotoxicity increasing with assay promiscuity. Together, these findings can be used to prioritize assay combinations that are appropriate to assess potential hepatotoxicity.

Scaffold-hopping identifies furano[2,3-d]pyrimidine amides as potent Notum inhibitors.

The carboxylesterase Notum is a key negative regulator of the Wnt signaling pathway by mediating the depalmitoleoylation of Wnt proteins. Our objective was to discover potent small molecule inhibitors of Notum suitable for exploring the regulation of Wnt signaling in the central nervous system. Scaffold-hopping from thienopyrimidine acids 1 and 2, supported by X-ray structure determination, identified 3-methylimidazolin-4-one amides 20-24 as potent inhibitors of Notum with activity across three orthogonal assay formats (biochemical, extra-cellular, occupancy). A preferred example 24 demonstrated good stability in mouse microsomes and plasma, and cell permeability in the MDCK-MDR1 assay albeit with modest P-gp mediated efflux. Pharmacokinetic studies with 24 were performed in vivo in mouse with single oral administration of 24 showing good plasma exposure and reasonable CNS penetration. We propose that 24 is a new chemical tool suitable for cellular studies to explore the fundamental biology of Notum.

Using Predicted Bioactivity Profiles to Improve Predictive Modeling.

Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

eMolTox: prediction of molecular toxicity with confidence.

Summary: In this work, we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. A total of 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. Availability and implementation: The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. Supplementary information: Supplementary data are available at Bioinformatics online.

Orthologue chemical space and its influence on target prediction.

Motivation: In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice. Results: Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance. Availability and implementation: Orthologue-based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2. Contact: ab454@cam.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Meniscal body extrusion and cartilage coverage in middle-aged and elderly without radiographic knee osteoarthritis.

OBJECTIVES: To determine meniscal extrusion and cartilage coverage on magnetic resonance (MR) images and factors associated with these parameters in knees of middle-aged and elderly persons free from radiographic tibiofemoral osteoarthritis (OA). METHODS: Seven hundred eighteen persons, free of radiographic tibiofemoral OA, aged 50-90 years from Framingham, MA, USA, were included. We measured meniscal extrusion on 1.5 T MRI of both knees to evaluate both medial and lateral meniscal body extrusion and cartilage coverage. We also determined meniscal morphology and structural integrity. The multivariable association with age, body mass index (BMI), and ipsilateral meniscal damage was also evaluated. RESULTS: The mean meniscal body extrusion medially was 2.7 mm and laterally 1.8 mm. The tibial cartilage coverage was about 30% of ipsilateral cartilage surface (both compartments). The presence of ipsilateral meniscal damage was associated with more extrusion in only the medial compartment, 1.0 mm in men and 0.6 mm in women, and less cartilage coverage proportion, -5.5% in men and -4.6% in women. CONCLUSIONS: Mean medial meniscal body extrusion in middle-aged or older persons without radiographic tibiofemoral OA approximates the commonly used cutoff (3 mm) to denote pathological extrusion. Medial meniscal damage is a factor associated with medial meniscal body extrusion and less cartilage coverage. KEY POINTS: • Medial meniscal extrusion in middle-aged/older persons without OA is around 3 mm. • Lateral meniscal extrusion in middle-aged/older persons without OA is around 2 mm. • Meniscal damage is associated with medial meniscal extrusion and less cartilage coverage.

Spring and Late Summer Phytoplankton Biomass Impact on the Coastal Sediment Microbial Community Structure.

Two annual Baltic Sea phytoplankton blooms occur in spring and summer. The bloom intensity is determined by nutrient concentrations in the water, while the period depends on weather conditions. During the course of the bloom, dead cells sink to the sediment where their degradation consumes oxygen to create hypoxic zones (< 2 mg/L dissolved oxygen). These zones prevent the establishment of benthic communities and may result in fish mortality. The aim of the study was to determine how the spring and autumn sediment chemistry and microbial community composition changed due to degradation of diatom or cyanobacterial biomass, respectively. Results from incubation of sediment cores showed some typical anaerobic microbial processes after biomass addition such as a decrease in NO2- + NO3- in the sediment surface (0-1 cm) and iron in the underlying layer (1-2 cm). In addition, an increase in NO2- + NO3- was observed in the overlying benthic water in all amended and control incubations. The combination of NO2- + NO3- diffusion plus nitrification could not account for this increase. Based on 16S rRNA gene sequences, the addition of cyanobacterial biomass during autumn caused a large increase in ferrous iron-oxidizing archaea while diatom biomass amendment during spring caused minor changes in the microbial community. Considering that OTUs sharing lineages with acidophilic microorganisms had a high relative abundance during autumn, it was suggested that specific niches developed in sediment microenvironments. These findings highlight the importance of nitrogen cycling and early microbial community changes in the sediment due to sinking phytoplankton before potential hypoxia occurs.

Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction.

Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach for large scale bioactivity prediction. This approach handles both high degrees of missing data and label imbalances while still producing high quality predictive models. When applied to ten assay end points from PubChem, the models generated valid models with an efficiency of 74.0-80.1% at the 80% confidence level with similar performance both for the minority and majority class. Also when deleting progressively larger portions of the available data (0-80%) the performance of the models remained robust with only minor deterioration (reduction in efficiency between 5 and 10%). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced data sets.

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets.

Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster speed and lower cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm, inherited its high predictivity but resolved its scalability and long computational time by adopting a leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity data sets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity data sets. We recommend LightGBM for applications of in silico safety assessment and also other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related end points of large compound libraries present in the pharmaceutical and chemical industry.

WINDOW consortium: A path towards increased therapy efficacy against glioblastoma.

Glioblastoma is the most common and malignant form of brain cancer, for which the standard treatment is maximal surgical resection, radiotherapy and chemotherapy. Despite these interventions, mean overall survival remains less than 15 months, during which extensive tumor infiltration throughout the brain occurs. The resulting metastasized cells in the brain are characterized by chemotherapy resistance and extensive intratumoral heterogeneity. An orthogonal approach attacking both intracellular resistance mechanisms as well as intercellular heterogeneity is necessary to halt tumor progression. For this reason, we established the WINDOW Consortium (Window for Improvement for Newly Diagnosed patients by Overcoming disease Worsening), in which we are establishing a strategy for rational selection and development of effective therapies against glioblastoma. Here, we overview the many challenges posed in treating glioblastoma, including selection of drug combinations that prevent therapy resistance, the need for drugs that have improved blood brain barrier penetration and strategies to counter heterogeneous cell populations within patients. Together, this forms the backbone of our strategy to attack glioblastoma.

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity.

Adverse events resulting from drug therapy can be a cause of drug withdrawal, reduced and or restricted clinical use, as well as a major economic burden for society. To increase the safety of new drugs, there is a need to better understand the mechanisms causing the adverse events. One way to derive new mechanistic hypotheses is by linking data on drug adverse events with the drugs' biological targets. In this study, we have used data mining techniques and mutual information statistical approaches to find associations between reported adverse events collected from the FDA Adverse Event Reporting System and assay outcomes from ToxCast, with the aim to generate mechanistic hypotheses related to structural cardiotoxicity (morphological damage to cardiomyocytes and/or loss of viability). Our workflow identified 22 adverse event-assay outcome associations. From these associations, 10 implicated targets could be substantiated with evidence from previous studies reported in the literature. For two of the identified targets, we also describe a more detailed mechanism, forming putative adverse outcome pathways associated with structural cardiotoxicity. Our study also highlights the difficulties deriving these type of associations from the very limited amount of data available.

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction.

High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a conformal predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the active compounds could be identified, compared to 13.5% when screening the same number of compounds solely based on docking. The use of conformal predictors also gives a clear indication of the number of compounds to screen in the next iteration. These results indicate that iterative screening based on molecular docking and conformal prediction can be an efficient way to find active compounds while screening only a small part of the compound collection.

A Decision support system (DSS) for municipal nurses encountering health deterioration among older people.

BACKGROUND: This study is part of a larger project called ViSam and includes testing of a decision support system developed and adapted for older people on the basis of M (R) ETTS (Rapid Emergency Triage and Treatment System). The system is designed to allow municipal nurses to determine the optimal level of care for older people whose health has deteriorated. This new system will allow more structured assessment, the patient should receive optimal care and improved data transmission to the next caregiver. METHODS: This study has an explanatory approach, commencing with quantitative data collection phase followed by qualitative data arising from focus group discussions over the RNs professional experience using the Decision Support system. Focus group discussions were performed to complement the quantitative data to get a more holistic view of the decision support system. RESULTS: Using elements of the decision support system (vital parameters for saturation, pain and affected general health) together with the nurses' decision showed that 94 % of the older persons referred to hospital were ultimately hospitalized. Nurses felt that they worked more systematically, communicated more effectively with others and felt more professional when using the decision support system. CONCLUSIONS: The results of this study showed that, with the help of a decision support system, the correct patients are sent to the Emergency Department from municipal home care. Unnecessary referrals of older patients that might lead to poorer health, decreased well-being and confusion can thus be avoided. Using the decision support system means that healthcare co-workers (nurses, ambulance/emergency department/district doctor/SOS alarm) begin to communicate more optimally. There is increased understanding leading to the risk of misinterpretation being reduced and the relationship between healthcare co-workers is improved. However, the decision support system requires more extensive testing in order to enhance the evidence base relating to the vital parameters among older people and the use of the decision support system.