Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

BACKGROUND: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

Development of population-level colon cancer pathway concordance measures and association with survival.

Clinical cancer pathways help standardize healthcare delivery to optimize patient outcomes and health system costs. However, population-level measurement of concordance between standardized pathways and actual care received is lacking. Two measures of pathway concordance were developed for a simplified colon cancer pathway map for Stage II-III colon cancer patients in Ontario, Canada: a cumulative count of concordant events (CCCE) and the Levenshtein algorithm. Associations of concordance with patient survival were estimated using Cox proportional hazards models adjusted for patient characteristics and time-dependent cancer-related activities. Models were compared and the impact of including concordance scores was quantified using the likelihood ratio chi-squared test. The ability of the measures to discriminate between survivors and decedents was compared using the C-index. Normalized concordance scores were significantly associated with patient survival in models for cancer stage-a 10% increase in concordance for Stage II patients resulted in a CCCE score adjusted hazard ratio (aHR) of death of 0.93, 95% CI 0.88-0.98 and a Levenshtein score aHR of 0.64, 95% CI 0.60-0.67. A similar relationship was found for Stage III patients-a 10% increase in concordance resulted in a CCCE aHR of 0.85, 95% CI 0.81-0.88 and a Levenshtein aHR of 0.78, 95% CI, 0.74-0.81. Pathway concordance can be used as a tool for health systems to monitor deviations from established clinical pathways. The Levenshtein score better characterized differences between actual care and clinical pathways in a population, was more strongly associated with survival and demonstrated better patient discrimination.

Computational fluid dynamics modeling approaches to assess lower urinary tract hydraulic dynamics in posterior urethral valve before and after endoscopic valve ablation: a pilot study.

PURPOSE: Computational fluid dynamics (CFD) has been used successfully in cardiovascular system research to analyze the physiological processes inside vessels. We evaluated the hydraulic information of urine through the lower urinary tract in a patient with posterior urethral valve (PUV) before and after valve ablation by CFD. METHODS: A set of models of the lower urinary tract were developed based on geometrical data obtained by cystoscopy and voiding cystourethrography. Simulated assumptions and conditions were applied according to prior studies and urodynamic results. We used Fluent CFD 19.0 (Ansys Inc., USA) to compute the velocity and pressure of the fluid regions. The simplification of Bernoulli's formula was applied afterward to calculate the hydraulic energy of different positions. RESULTS: The urine flow rates of the NORMALst, the PUVst, and the POSTst at 5000 Pa were 18.08 ml/s, 11.14 ml/s, and 12.16 ml/s, respectively. Precipitous pressure change was observed around the valve in the PUVst, and the abnormal change was concentrated in the dilated urethra in the POSTst. Major energy dissipations were generated around the valve and the dilated urethra in the PUVst. The energy loss that occurred in the dilated urethra did not improve after the operation. CONCLUSIONS: Our findings are probably indicative of the hydrodynamics changes in the dilated urethra in PUV and need to be confirmed through more improved CFD models in the future. CFD may revolutionize pediatric urologists' perception in the management of urinary disease.

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

OBJECTIVES: Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. METHODS: A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. RESULTS: We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. CONCLUSIONS: Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent. KEY POINTS: • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.

Computational Fluid Dynamic Modeling of Urethral Strictures.

PURPOSE: Computational fluid dynamics have paradigm shifting potential in understanding the physiological flow of fluids in the human body. This translational branch of engineering has already made an important clinical impact on the study of cardiovascular disease. We evaluated the feasibility and applicability of computational fluid dynamics to model urine flow. MATERIALS AND METHODS: We prepared a computational fluid dynamics model using an idealized male genitourinary system. We created 16 hypothetical urethral stricture scenarios as a test bed. Standard parameters of urine such as pressure, temperature and viscosity were applied as well as typical assumptions germane to fluid dynamic modeling. We used ABAQUS/CAE 6.14 (Dassault Systèmes®) with a direct unsymmetrical solver with standard (FC3D8) 3D brick 8Node elements for model generation. RESULTS: The average flow rate in urethral stricture disease as measured by our model was 5.97 ml per second (IQR 2.2-10.9). The model predicted a flow rate of 2.88 ml per second for a single 5Fr stricture in the mid bulbar urethra when assuming all other variables constant. The model demonstrated that increasing stricture diameter and bladder pressure strongly impacted urine flow while stricture location and length, and the sequence of multiple strictures had a weaker impact. CONCLUSIONS: We successfully created a computational fluid dynamics model of an idealized male urethra with varied types of urethral strictures. The resultant flow rates were consistent with the literature. The accuracy of modeling increasing bladder pressure should be improved by future iterations. This technology has vast research and clinical potential.

Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform.

BACKGROUND: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy.

INTRODUCTION: The primary aim of this study is to evaluate the temporal correlation between Google Trends and the data on measles infection arising from the conventional surveillance system, reported by the Istituto Superiore di Sanità's (ISS) bulletin. Moreover, this study is also aimed at forecasting the trends of the reported infectious diseases cases over time. MATERIAL AND METHODS: The reported cases of measles were selected from January 2013 until October 2018. The data on Internet searches have been obtained from Google Trends; the research data referred to the first 48 weeks of year 2017 have been aggregated on a weekly basis. The search volume provided by Google Trends has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. The statistical analyses have been performed by using the Spearman's rank correlation coefficient (rho). The statistical significance level for such analyses has been fixed in 0.05. OUTCOMES: We have observed a strong correlation at a lag of 0 to -4 weeks (rho > 0.70) with the cases reported by ISS with the strongest correlation at a lag of -3 weeks (rho > 0.80 both for measles than for the symptoms of the measles). The database containing monthly data has shown a moderate correlation at a lag of -1 to +1 months and a strongest correlation at a lag of -1 (rho = 0.6152 for measles and rho = 0.5039 for symptoms of the measles). CONCLUSIONS: The surveillance systems based on Google Trends have a potential role in public health in order to provide near real-time indicators of the spread of infectious diseases. Therefore the huge potential of this approach could be used in the immediate future as a support of the traditional surveillance systems.

Hypotension during induction of anaesthesia is neither a reliable nor a useful quality measure for comparison of anaesthetists' performance.

BACKGROUND: Identification of statistically reliable outcomes for comparison among anaesthetists is challenging. Time-weighted intraoperative mean arterial pressure <65 mm Hg (AUC 65 ) is associated with increased odds for myocardial damage. We explored retrospectively whether such hypotension before incision was statistically reliable for peer comparison. METHODS: We retrieved electronic data between 2006 and 2015 at a tertiary care, academic hospital in the USA for patients at risk for myocardial damage (inpatient after surgery, ASA physical status ≥III, ≥50 yr of age, and case duration ≥60 min). We determined the percentage of anaesthetists comparable based on caseload and case-mix. The AUC 65 was compared amongst anaesthetists supervising ≥100 cases involving at-risk patients during the last 12 months. RESULTS: Only 14.1% [95% confidence interval (CI) 13.6-14.5%] of cases involved patients who were 'at risk' during the 10 yr study period. A yearly average of 49 ( sd 6) anaesthetists supervised ≥100 cases of any type, of whom only 52% (95% CI 47.1-56.0%) supervised ≥100 cases involving at-risk patients. Thus, nearly half the anaesthetists would have been excluded from peer comparison. During the last 12 months, there were two outliers among 34 evaluable anaesthetists ( P <0.05, controlling for false discovery). However, their contribution to total hypotension amongst cases for all patients was small, because hypotension was widely distributed (e.g. 80% of hypotension attributable to 61.8% of anaesthetists, 95% CI 59.8-63.7%). There was no relationship between the AUC 65 and propofol induction dose. CONCLUSIONS: The AUC 65 of time-weighted pre-incision hypotension is not a suitable metric for comparing anaesthetists. There were few at-risk patients, half the anaesthetists were not evaluable because of their case-mix and caseload, and hypotension was widely distributed.

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

BACKGROUND: Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. AIMS: To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. METHODS: Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a "calendar year" based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. RESULTS: Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. CONCLUSIONS: ColonFlag® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

Practical guide to the use of digital slides in histopathology education.

Digital pathology (the technology whereby glass histology slides are scanned at high resolution, digitised, stored and shared with pathologists, who can view them using microscopy software on a screen) is transforming the delivery of clinical diagnostic pathology services around the world. In addition to adding value to clinical histopathology practice, digital histology slides provide a versatile medium to achieve the educational needs of a variety of learners including undergraduate students, postgraduate doctors in training and those pursuing continuing professional development portfolios. In this guide, we will review the principal use cases for digital slides in training and education and I will share tips for successful use of digital pathology to support a range of learners based on experience gathered at Leeds Teaching Hospitals National Health Service Trust and the National Pathology Imaging Co-Operative during the last 5 years of digital slide usage.

Towards Structuring Clinical Texts: Joint Entity and Relation Extraction from Japanese Case Report Corpus.

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy.

Using Systemised Nomenclature of Medicine (SNOMED) codes to select digital pathology whole slide images for long-term archiving.

The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.Our results show that 0.2% of cases are recalled after 12 months, and SNOMED code combinations can be used to identify which cases are likely to be recalled and which are not. This approach could reduce the number of cases archived by 62% and still ensure all cases likely to be recalled remain in the archive.

Using Google Trends and Wikipedia to Investigate the Global Public's Interest in the Pancreatic Cancer Diagnosis of a Celebrity.

A cross-sectional study was designed to assess the impact of a celebrity's announcement of having been diagnosed with pancreatic cancer on the volume of cancer-related research on the Internet. Global searches were carried out on Google Trends (GT) for the period from 1 January 2004 to 20 November 2022 (since data prior to 2004 were not available) using the search words Tumore del Pancreas (pancreatic cancer), Tumore neuroendocrino (neuroendocrine tumor), and Fedez (the name of a popular Italian rapper). The frequency of specific page views for Fedez, Tumore del pancreas, and Tumore neuroendocrino was collected via Wikipedia Trends data. Statistical analyses were carried out using the Pearson correlation coefficient (r). The GT data revealed a strong correlation (r = 0.83) while the Wikipedia Trends data indicated a moderate correlation (r = 0.37) for Tumore neuroendocrino and Tumore del pancreas. The search peaks for the GT and Wikipedia pages occur during the same time period. An association was found between the celebrity's announcement of his pancreatic cancer diagnosis and the volume of pancreatic-cancer-related online searches. Our findings demonstrate that media events and media coverage of health-related news can raise people's curiosity and desire for health information.

Signal processing and machine learning algorithm to classify anaesthesia depth.

BACKGROUND: Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures. METHODS: Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1. RESULTS: A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. CONCLUSION: Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.

Impact on online research on celebrities' uncommon diseases: the curious case of Justin Bieber and Ramsay Hunt syndrome.

Aim: We investigated how to use Internet user searches to gauge the impact of a celebrity illness on global public interest. Methods: The study design is cross-sectional. Data on Internet searches were obtained from Google Trends (GT) for the period between 2017-2022 using the search words "Ramsay Hunt syndrome" (RHS), "Ramsay Hunt syndrome type 2," "Herpes zoster," and "Justin Bieber." The frequency of specific page views for "Ramsay Hunt syndrome," "Ramsay Hunt syndrome type 1," Ramsay Hunt syndrome type 2," Ramsay Hunt syndrome type 3," "Herpes zoster," and "Justin Bieber" were collected via a Wikipedia analysis tool that shows the number of times a specific page is viewed. Statistical analyses were performed using the Pearson (r) and Spearman's rank correlation coefficient (rho). Results: GT data, in 2022, show a strong correlation for Justin Bieber and RHS or RHS type 2 (r = 0.75); similarly, Wikipedia data show a strong correlation for Justin Bieber and the others explored terms (r > 0.75). Furthermore, the correlation was strong between GT and Wikipedia for RHS (rho = 0.89) and RHS type 2 (rho = 0.88). Conclusions: The peak search times for the GT and Wikipedia pages were during the same period. Useful new tools and analyses of Internet traffic data may be effective in assessing the impact of announced celebrity uncommon illnesses on global public interest.

Fostering intrinsic motivation in remote undergraduate histopathology education.

AIMS: The levels of abstraction, vast vocabulary and high cognitive load present significant challenges in undergraduate histopathology education. Self-determination theory describes three psychological needs which promote intrinsic motivation. This paper describes, evaluates and justifies a remotely conducted, post-COVID-19 histopathology placement designed to foster intrinsic motivation. METHODS: 90 fourth-year medical students took part in combined synchronous and asynchronous remote placements integrating virtual microscopy into complete patient narratives through Google Classroom, culminating in remote, simulated multidisciplinary team meeting sessions allowing participants to vote on 'red flag' signs and symptoms, investigations, histological diagnoses, staging and management of simulated virtual patients. The placement was designed to foster autonomy, competence and relatedness, generating authenticity, transdisciplinary integration and clinical relevance. A postpositivistic evaluation was undertaken with a validated preplacement and postplacement questionnaire capturing quantitative and qualitative data. RESULTS: There was a significant (p<0.001) improvement in interest, confidence and competence in histopathology. Clinical integration and relevance, access to interactive resources and collaborative learning promoted engagement and sustainability post-COVID-19. Barriers to online engagement included participant lack of confidence and self-awareness in front of peers. CONCLUSIONS: Fostering autonomy, competence and relatedness in post-COVID-19, remote educational designs can promote intrinsic motivation and authentic educational experiences. Ensuring transdisciplinary clinical integration, the appropriate use of novel technology and a focus on patient narratives can underpin the relevance of undergraduate histopathology education. The presentation of normal and diseased tissue in this way can serve as an important mode for the acquisition and application of clinically relevant knowledge expected of graduates.

Initial study of anaemia profile for primary care centres with automated laboratory algorithms reduces the demand for ferritin, iron, transferrin, vitamin B12 and folate tests.

AIM: To evaluate the influence of an algorithm designed to incorporate reflex testing according to haemogram results for analytical tests ordered to investigate anaemia. METHODS: In 2020, a new request for 'initial study of anaemia' was created in three primary care pilot centres for suspected anaemia or new anaemias. A haemogram was ordered and the remainder of the tests were created in a reflex manner according to an algorithm integrated in the laboratory information system that also generates a comment that is completed and validated by a haematologist. The demand for tests was evaluated over three time periods. RESULTS: Of 396 requests, anaemia was detected in 80 (20.2%), with 26 microcytic anaemias (6.57%), 20 iron deficiency anaemias, 41 (10.3%) normocytic anaemias and 13 macrocytic anaemias (3.28%); 4 with folate deficiency; and 1 haemolytic anaemia. No haematological diseases were detected. Twenty-four (6.06%) cases exhibited microcytosis/hypochromia without anaemia, 12 of which exhibited iron deficiency. Four young women exhibiting within-limit haemoglobin levels had iron deficiency. There were 56 (14.1%) cases of macrocytosis without anaemia.With the new profile of 'initial study of anaemia', the demand for tests was reduced and was significantly lower than in the remainder of primary centres for iron, transferrin, ferritin, vitamin B12 and folate. CONCLUSIONS: A new profile of 'initial study of anaemia' in the request form with algorithms integrated in the laboratory information system enabled submission of orders and decreased the demand for unnecessary iron, transferrin, ferritin, vitamin B12 and folate tests.

New life for old cellular pathology: a transformational approach to the upcycling of historic e-pathology records for contemporary clinical uses.

AIMS: Cellular pathology ('e-pathology') record sets are a rich data resource with which to populate the electronic patient record (EPR). Accessible reports, even decades old, can be of great value in contemporary clinical decision making and as a resource for longitudinal clinical research. The aim of this short paper is to describe a solution in a major UK University Hospital which gives immediate visibility and clinical utility to 30 years of e-pathology records METHODS: Over the past decade, we have created a timeline structured and iconographic data framework for the 'whole-of-life' visualisation of the entirety of an EPR. We have enhanced this interface with the sequential extraction of 373 342 e-pathology reports from legacy Ferranti (1990-1997) and Masterlab (1997-2004) files. They have been uploaded into our SQL file servers, following appropriate data quality and patient identity reconciliation checks. RESULTS: We have restored a large repository of previously inaccessible e-pathology records to clinical use and to immediacy of access as a foundation element of our timeline structured EPR. This process has also allowed us to populate and validate an EPR-integral breast cancer data system of 20 000 cases with e-pathology records dating back to 1990. CONCLUSIONS: The revitalisation of old e-pathology reports into a timeline structured EPR creates preserves and upcycles the investment in pathology reporting which is otherwise progressively lost to clinical use. E-pathology records provide reliable, life-long evidence of critical transition points in individual lives and disease progression for clinical and research use, when they can be instantly accessed.

Influence of Single Stent Size and Tandem Stents Subject to Extrinsic Ureteral Obstruction and Stent Occlusion on Stent Failure.

Background and Purpose: Drainage of obstructed kidney attributable to extrinsic ureteral obstruction (EUO), required to prevent renal damage, is often achieved using Double-J ureteral stents. However, these stents fail frequently, and there is considerable debate regarding what stent size, type, and configuration offer the best option for sustained drainage. In this study, we examine the impact of stent diameter and choice of single/tandem configuration, subject to EUO and various degrees of stent occlusion, on stent failure. Materials and Methods: Computational fluid dynamics simulations and an in vitro ureter-stent experiment enabled quantification of flow behavior in stented ureters subject to EUO and stent occlusions. Various single and tandem stents under EUO were considered. In each simulation and experiment, changes in renal pressure were monitored for different degrees of stent lumen occlusion, and onset of stent failure as well as simulated distributions of fluid flow between stent and ureter lumina were determined. Results: For an encircling EUO that completely obstructs the ureter lumen, with or without partial stent occlusion, the choice of stent size/configuration has little effect on renal pressure. The pressure increases significantly for ∼90% stent lumen occlusion, with failure at >95% occlusion, independent of stent diameter or a tandem configuration, and with little influence of occlusion length along the stent. Conclusions: Stent failure rate is independent of stent diameter or single/tandem configuration, for the same percentage of stent lumen occlusion, in this model. Stent failure incidence may decrease for larger diameter stents and tandem configurations, because of the larger luminal area.

Infodemiology of flu: Google trends-based analysis of Italians' digital behavior and a focus on SARS-CoV-2, Italy.

INTRODUCTION: The aim of the current study was to assess if the frequency of internet searches for influenza are aligned with Italian National Institute of Health (ISS) cases and deaths. Also, we evaluate the distribution over time and the correlation between search volume of flu and flu symptoms with reported new cases of SARS-CoV-2. MATERIALS AND METHODS: The reported cases and deaths of flu and the reported cases of SARS-CoV-2 were selected from the reports of ISS, the data have been aggregated by week. The search volume provided by Google Trends (GT) has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. RESULTS: The strongest correlation between GT search and influenza cases was found at a lag of +1 week particularly for the period 2015-2019. A strong correlation was also found at a lag of +1 week between influenza death and GT search. About the correlation between GT search and SARS-CoV-2 new cases the strongest correlation was found at a lag of +3 weeks for the term flu. CONCLUSION: In the last years research in health care has used GT data to explore public interest in various fields of medicine. Caution should be used when interpreting the findings of digital surveillance.