AI-based pathology predicts origins for cancers of unknown primary.

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4-9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm-Tumour Origin Assessment via Deep Learning (TOAD)-that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

The uncovered biases and errors in clinical determination of bone age by using deep learning models.

OBJECTIVES: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA. METHODS: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM. RESULTS: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists. CONCLUSIONS: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered. KEY POINTS: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films.

Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework.

BACKGROUND: Policy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework. METHODS: We searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity. FINDINGS: We found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates. CONCLUSIONS: While fragments of good practices were widespread across the reviewed PSMs, only a few included a 'critical mass' of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials.

BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.

BACKGROUND & AIMS: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk. METHODS: Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development. RESULTS: In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%-50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up. CONCLUSIONS: This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir. LAY SUMMARY: Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance.

Formation of the Metal-Binding Core of the ZRT/IRT-like Protein (ZIP) Family Zinc Transporter.

Zinc homeostasis in mammals is constantly and precisely maintained by sophisticated regulatory proteins. Among them, the Zrt/Irt-like protein (ZIP) regulates the influx of zinc into the cytoplasm. In this work, we have employed all-atom molecular dynamics simulations to investigate the Zn2+ transport mechanism in prokaryotic ZIP obtained from Bordetella bronchiseptica (BbZIP) in a membrane bilayer. Additionally, the structural and dynamical transformations of BbZIP during this process have been analyzed. This study allowed us to develop a hypothesis for the zinc influx mechanism and formation of the metal-binding site. We have created a model for the outward-facing form of BbZIP (experimentally only the inward-facing form has been characterized) that has allowed us, for the first time, to observe the Zn2+ ion entering the channel and binding to the negatively charged M2 site. It is thought that the M2 site is less favored than the M1 site, which then leads to metal ion egress; however, we have not observed the M1 site being occupied in our simulations. Furthermore, removing both Zn2+ ions from this complex resulted in the collapse of the metal-binding site, illustrating the "structural role" of metal ions in maintaining the binding site and holding the proteins together. Finally, due to the long Cd2+-residue bond distances observed in the X-ray structures, we have proposed the existence of an H3O+ ion at the M2 site that plays an important role in protein stability in the absence of the metal ion.

Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants.

BACKGROUND: Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources. METHODS: Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period. RESULTS: A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3-30.8%) and suicide deaths by 29.3% (95% interval 27.1-31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that 'more is not necessarily better.' CONCLUSION: Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

Assessing Nephrology Fellows' Skills in Communicating About Kidney Replacement Therapy and Kidney Biopsy: A Multicenter Clinical Simulation Study on Breaking Bad News.

RATIONALE & OBJECTIVE: Interpersonal communication skills and professionalism competencies are difficult to assess among nephrology trainees. We developed a formative "Breaking Bad News" simulation and implemented a study in which nephrology fellows were assessed with regard to their skills in providing counseling to simulated patients confronting the need for kidney replacement therapy (KRT) or kidney biopsy. STUDY DESIGN: Observational study of communication competency in the setting of preparing for KRT for kidney failure, for KRT for acute kidney injury (AKI), or for kidney biopsy. SETTING & PARTICIPANTS: 58 first- and second-year nephrology fellows assessed during 71 clinical evaluation sessions at 8 training programs who participated in an objective structured clinical examination of simulated patients in 2017 and 2018. PREDICTORS: Fellowship training year and clinical scenario. OUTCOME: Primary outcome was the composite score for the "overall rating" item on the Essential Elements of Communication-Global Rating Scale 2005 (EEC-GRS), as assessed by simulated patients. Secondary outcomes were the score for EEC-GRS "overall rating" item for each scenario, score < 3 for any EEC-GRS item, Mini-Clinical Examination Exercise (Mini-CEX) score < 3 on at least 1 item (as assessed by faculty), and faculty and fellow satisfaction with simulation exercise (via a survey they completed). ANALYTICAL APPROACH: Nonparametric tests of hypothesis comparing performance by fellowship year (primary goal) and scenario. RESULTS: Composite scores for EEC-GRS overall rating item were not significantly different between fellowship years (P = 0.2). Only 4 of 71 fellow evaluations had an unsatisfactory score for the EEC-GRS overall rating item on any scenario. On Mini-CEX, 17% scored < 3 on at least 1 item in the kidney failure scenario; 37% and 53% scored < 3 on at least 1 item in the AKI and kidney biopsy scenarios, respectively. In the survey, 96% of fellows and 100% of faculty reported the learning objectives were met and rated the experience good or better in 3 survey rating questions. LIMITATIONS: Relatively brief time for interactions; limited familiarity with and training of simulated patients in use of EEC-GRS. CONCLUSIONS: The fellows scored highly on the EEC-GRS regardless of their training year, suggesting interpersonal communication competency is achieved early in training. The fellows did better with the kidney failure scenario than with the AKI and kidney biopsy scenarios. Structured simulated clinical examinations may be useful to inform curricular choices and may be a valuable assessment tool for communication and professionalism.

Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy.

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.

Endoscopic surgical simulation using low-fidelity and virtual reality transurethral resection simulators in urology simulation boot camp course: trainees feedback assessment study.

OBJECTIVES: The objective of our study was to study trainees' feedback and rating of models for training transurethral resection of bladder lesions (TURBT) and prostate (TURP) during simulation. METHODS: The study was performed during the ''Transurethral resection (TUR) module" at the boot camp held in 2019. Prior to the course, all trainees were required to evaluate their experience in performing TURBT and TURP procedures. Trainees simulated resection on two different models; low-fidelity tissue model (Samed, GmBH, Dresden, Germany) and virtual reality simulator (TURPMentor, 3D Systems, Littleton, US). Following the completion of the module, trainees completed a questionnaire using a 5-point Likert scale to evaluate their assessment of the models for surgical training. RESULTS: In total, 174 simulation assessments were performed by 56 trainees (Samed Bladder-40, Prostate-45, TURPMentor Bladder-51, Prostate-37). All trainees reported that they had performed < 50 TUR procedures. The Samed model median scores were for appearance (4/5), texture (5/5), feel (5/5) and conductibility (5/5). The TURPMentor median score was for appearance (4/5), texture and feel (4/5) and conductibility (4/5). The most common criticism of the Samed model was that it failed to mimic bleeding. In contrast, trainees felt that the TURPMentor haptic feedback was inadequate to allow for close resection and did not calibrate movements accurately. CONCLUSIONS: Our results demonstrate that both forms of simulators (low-fidelity and virtual reality) were rated highly by urology trainees and improve their confidence in performing transurethral resection and in fact complement each other in providing lower tract endoscopic resection simulation.

Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure.

OBJECTIVES: Developing and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes. METHODS: The model was developed using patient-level data from the Trans-European Network - Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation. RESULTS: Patients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data. CONCLUSIONS: This study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Applications of interpretability in deep learning models for ophthalmology.

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS: The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.

A new platform for laparoscopic training: initial evaluation of the ex-vivo live multivisceral training device.

BACKGROUND: Various training models have been developed for laparoscopic training. Inanimate models including cadavers, ex-vivo simulator, and virtual reality (VR), are less realistic and often fail to display specific events such as bleeding, bile leakage, etc. Animal models provide more realistic experience, but constraints like cost involved, anesthetic requirement, and ethical approval have limited its application. We have designed a new training ex-vivo simulator-Smagister to address these issues. METHODS: The Smagister consists of a normothermic machine perfusion platform, multivisceral organ of porcine abdominal cavity (liver, gallbladder, pancreas, stomach, intestine, kidney, uterus, bladders, etc.), high-definition display, and software system. Blood gas analysis and number of peristalsis per hour were recorded. A questionnaire was used to subjectively assess vitality of the organ cluster every hour. Three laparoscopic procedures including cholecystectomy (LC), enterotomy closure (LEC) and hepatectomy (LLR) were performed on Smagister, with demonstration of specific events for each procedure. Six experts compared the procedures with actual surgery in terms of feasibility to complete procedures and demonstration of complications. RESULTS: The fluctuation of perfusate glucose (6.1-8.2 mmol/L) and lactate (5.82-6.55 mmol/L) suggested metabolic function of the multivisceral organs. The mean number of peristalsis was 2.2/min. The simulated surgical view and anatomic structures closely resembled actual surgery during continuous perfusion (3.5 ± 1.0, 3.8 ± 0.8, respectively). The evaluation scores of haptic feedbacks were 3.8 ± 0.8, resembling live tissue handling. LC, LEC, and LLR were performed well on the Smagister, with clear display of the specific events. All six experts considered Smagister as a suitable training modality for both basic and advanced laparoscopic surgery. CONCLUSION: The amalgamation of live animal model and ex-vivo simulation in Smagister centralizes the virtue of both modalities, expands the training field, and provides high-fidelity laparoscopic training for both novice and senior surgeons.

Preoperative skill evaluation in transabdominal preperitoneal (TAPP) inguinal hernia repair using a three-dimensional printed TAPP repair simulator.

BACKGROUND: Although transabdominal preperitoneal laparoscopic inguinal hernia repair (TAPP LIHR) is becoming increasingly more critical in the management of hernias, it has a long learning curve. The learning curve is often measured by a shortened operative time without mention of the quality of the procedure. This study was performed to evaluate the efficacy of a three-dimensional printed TAPP LIHR simulator to evaluate the surgeon's preoperative skill before entering the operative theater. METHODS: Fifteen surgeons in our institution were enrolled in this study. They performed simulation TAPP LIHR while being video recorded. The TAPP LIHR simulator allows for the performance of all procedures required in TAPP LIHR. All participants were classified according to several background factors: postgraduate years (PGYs) (1-5, 6-10, or > 10), number of TAPP LIHR procedures performed (< 10, 11-49, or > 50), and number of laparoscopic surgeries performed (≤ 100 or > 100). The correlation among PGYs, the number of TAPP repairs performed, and the checklist score was evaluated. RESULTS: The mean total score and time required to perform TAPP LIHR were significantly different among the three TAPP LIHR experience groups (< 10, 11-49, and > 50 procedures; P < 0.05). The checklist score and time required to perform TAPP LIHR were strongly correlated with the number of TAPP LIHR procedures performed (r = 0.74 and r = 0.69, respectively). However, the checklist score showed a weak correlation with PGY (r = 0.52). CONCLUSIONS: Preoperative skill evaluation using a TAPP LIHR simulator and TAPP repair checklist supports the distinction between novices and experts. Both education systems are a valuable and affordable tool for evaluation and training of TAPP LIHR.

Competence in Decision Making: Setting Performance Standards for Critical Care.

BACKGROUND: Health care professionals must be able to make frequent and timely decisions that can alter the illness trajectory of intensive care patients. A competence standard for this ability is difficult to establish yet assuring practitioners can make appropriate judgments is an important step in advancing patient safety. We hypothesized that simulation can be used effectively to assess decision-making competence. To test our hypothesis, we used a "standard-setting" method to derive cut scores (standards) for 16 simulated ICU scenarios targeted at decision-making skills and applied them to a cohort of critical care trainees. METHODS: Panelists (critical care experts) reviewed digital audio-video performances of critical care trainees managing simulated critical care scenarios. Based on their collectively agreed-upon definition of "readiness" to make decisions in an ICU setting, each panelist made an independent judgment (ready, not ready) for a large number of recorded performances. The association between the panelists' judgments and the assessment scores was used to derive scenario-specific performance standards. RESULTS: For all 16 scenarios, the aggregate panelists' ratings (ready/not ready for independent decision making) were positively associated with the performance scores, permitting derivation of performance standards for each scenario. CONCLUSIONS: Minimum competence standards for high-stakes decision making can be established through standard-setting techniques. We effectively identified "front-line" providers who are, or are not, ready to make independent decisions in an ICU setting. Our approach may be used to assure stakeholders that clinicians are competent to make appropriate judgments. Further work is needed to determine whether our approach is effective in simulation-based assessments in other domains.

Comparative in silico prediction of P-glycoprotein-mediated transport for 2010-2020 US FDA-approved drugs using six Web-tools.

P-glycoprotein (P-gp) is an efflux pump implicated in pharmacokinetics and drug-drug interactions. The identification of its substrates is consequently an important issue, notably for drugs under development. For such a purpose, various in silico methods have been developed, but their relevance remains to be fully established. The present study was designed to get insight about this point, through determining the performance values of six freely accessible Web-tools (ADMETlab, AdmetSAR2.0, PgpRules, pkCSM, SwissADME and vNN-ADMET), computationally predicting P-gp-mediated transport. Using an external test set of 231 marketed drugs, approved over the 2010-2020 period by the US Food and Drug Administration and fully in vitro characterized for their P-gp substrate status, various performance parameters (including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the receiver operating characteristics curve) were determined. They were found to rather poorly meet criteria commonly required for acceptable prediction, whatever the Web-tools were used alone or in combination. Predictions of being P-gp substrate or non-substrate by these online in silico methods may therefore be considered with caution.

A computational framework for modeling and studying pertussis epidemiology and vaccination.

BACKGROUND: Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid19 and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of these diseases and to decide on vaccination policies, human interaction controls, and other social measures to counter, mitigate or simply delay the spread of the infectious diseases. Nevertheless, the construction of mathematical models for these diseases and their solutions remain a challenging tasks due to the fact that little effort has been devoted to the definition of a general framework easily accessible even by researchers without advanced modelling and mathematical skills. RESULTS: In this paper we describe a new general modeling framework to study epidemiological systems, whose novelties and strengths are: (1) the use of a graphical formalism to simplify the model creation phase; (2) the implementation of an R package providing a friendly interface to access the analysis techniques implemented in the framework; (3) a high level of portability and reproducibility granted by the containerization of all analysis techniques implemented in the framework; (4) a well-defined schema and related infrastructure to allow users to easily integrate their own analysis workflow in the framework. Then, the effectiveness of this framework is showed through a case of study in which we investigate the pertussis epidemiology in Italy. CONCLUSIONS: We propose a new general modeling framework for the analysis of epidemiological systems, which exploits Petri Net graphical formalism, R environment, and Docker containerization to derive a tool easily accessible by any researcher even without advanced mathematical and computational skills. Moreover, the framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows.

Rare variant association testing for multicategory phenotype.

Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context. To address this issue, we propose an extension of burden tests by using a multinomial regression model, which enables association tests between rare variants and multicategory phenotypes. We evaluated the type I error and the power of two burden tests, CAST and WSS, by simulating data under different scenarios. In the case of genetic heterogeneity between case subgroups, we showed an advantage of multinomial regression over logistic regression, which considers all the cases against the controls. We replicated these results on real data from Moyamoya disease where the burden tests performed better when cases were stratified according to age-of-onset. We implemented the functions for association tests in the R package "Ravages" available on Github.