Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology.

Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.

Predicting antibiotic resistance in Enterobacterales to support optimal empiric treatment of urinary tract infections in outpatient veterans.

Objective: Bacterial resistance is known to diminish the effectiveness of antibiotics for treatment of urinary tract infections. Review of recent healthcare and antibiotic exposures, as well as prior culture results is recommended to aid in selection of empirical treatment. However, the optimal approach for assessing these data is unclear. We utilized data from the Veterans Health Administration to evaluate relationships between culture and treatment history and the subsequent probability of antibiotic-resistant bacteria identified in urine cultures to further guide clinicians in understanding these risk factors. Methods: Using the XGBoost algorithm, a retrospective cohort of outpatients with urine culture results and antibiotic prescriptions from 2017 to 2022 was used to develop models for predicting antibiotic resistance for three classes of antibiotics: cephalosporins, fluoroquinolones, and trimethoprim/sulfamethoxazole (TMP/SMX) obtained from urine cultures. Model performance was assessed using Area Under the Receiver Operating Characteristic curve (AUC) and Precision-Recall AUC (PRAUC). Results: There were 392,647 prior urine cultures identified in 214,656 patients. A history of bacterial resistance to the specific treatment was the most important predictor of subsequent resistance for positive cultures, followed by a history of specific antibiotic exposure. The models performed better than previously established risk factors alone, especially for fluoroquinolone resistance, with an AUC of .84 and PRAUC of .70. Notably, the models' performance improved markedly (AUC = .90, PRAUC = .87) when applied to cultures from patients with a known history of resistance to any of the antibiotic classes. Conclusion: These predictive models demonstrate potential in guiding antibiotic prescription and improving infection management.

Identification, mapping, and self-reported practice patterns of village doctors in Sitakunda subdistrict, Bangladesh.

Background: Informally trained health care providers, such as village doctors in Bangladesh, are crucial in providing health care services to the rural poor in low- and middle-income countries. Despite being one of the primary vendors of antibiotics in rural Bangladesh, village doctors often have limited knowledge about appropriate antibiotic use, leading to varied and potentially inappropriate dispensing and treatment practices. In this study, we aimed to identify, map, and survey village doctors in the Sitakunda subdistrict of Bangladesh to understand their distribution, practice characteristics, clinical behaviours, access to technologies, and use of these technologies for clinical decision-making. Methods: Using a 'snowball' sampling method, we identified and mapped 411 village doctors, with 371 agreeing to complete a structured survey. Results: The median distance between a residential household and the closest village doctor practice was 0.37 km, and over half of the practices (51.2%) were within 100 m of the major highway. Village doctors were predominately male (98.7%), with a median age of 39. After completing village doctor training, 39.4% had completed an internship, with a median of 15 years of practice experience. Village doctors reported seeing a median of 84 patients per week, including a median of five paediatric diarrhoea cases per week. They stocked a range of antibiotics, with ciprofloxacin and metronidazole being the most prescribed for diarrhoea. Most had access to phones with an internet connection and used online resources for clinical decision-making and guidance. Conclusions: The findings provide insights into the characteristics and practices of village doctors and point to the potential for internet and phone-based interventions to improve patient care and reduce inappropriate antibiotic use in this health care provider group.

Trajectories and Transitions in Service Use Among Older Veterans at High Risk of Long-Term Institutional Care.

BACKGROUND: We aimed to identify combinations of long-term services and supports (LTSS) Veterans use, describe transitions between groups, and identify factors influencing transition. METHODS: We explored LTSS across a continuum from home to institutional care. Analyses included 104,837 Veterans Health Administration (VHA) patients 66 years and older at high-risk of long-term institutional care (LTIC). We conduct latent class and latent transition analyses using VHA and Medicare data from fiscal years 2014 to 2017. We used logistic regression to identify variables associated with transition. RESULTS: We identified 5 latent classes: (1) No Services (11% of sample in 2015); (2) Medicare Services (31%), characterized by using LTSS only in Medicare; (3) VHA-Medicare Care Continuum (19%), including LTSS use in various settings across VHA and Medicare; (4) Personal Care Services (21%), characterized by high probabilities of using VHA homemaker/home health aide or self-directed care; and (5) Home-Centered Interdisciplinary Care (18%), characterized by a high probability of using home-based primary care. Veterans frequently stayed in the same class over the three years (30% to 46% in each class). Having a hip fracture, self-care impairment, or severe ambulatory limitation increased the odds of leaving No Services, and incontinence and dementia increased the odds of entering VHA-Medicare Care Continuum. Results were similar when restricted to Veterans who survived during all 3 years of the study period. CONCLUSIONS: Veterans at high risk of LTIC use a combination of services from across the care continuum and a mix of VHA and Medicare services. Service patterns are relatively stable for 3 years.

Sex differences in unmet needs between male and female older veterans.

Aging Veterans face complex needs across multiple domains. However, the needs of older female Veterans and the degree to which unmet needs differ by sex are unknown. We analyzed responses to the HERO CARE survey from 7,955 Veterans aged 55 years and older (weighted N = 490,148), 93.9% males and 6.1% females. We evaluated needs and unmet needs across the following domains: activities of daily living (ADLs), instrumental ADLs (IADLs), health management, and social. We calculated weighted estimates and compared sex differences using age-adjusted prevalence ratios. On average, female Veterans were younger, more were Non-Hispanic Black and unmarried. Females and males reported a similar prevalence of problems across all domains. However, compared to males, female Veterans had a lesser prevalence of missed appointments due to transportation (aPR 0.49; 95% CI: 0.26-0.92), housework unmet needs (aPR: 0.44; 95% CI: 0.20-0.97), and medication management unmet needs (aPR: 0.33; 95% CI: 0.11-0.95) but a higher prevalence of healthcare communication unmet needs (aPR: 2.40; 95% CI: 1.13-5.05) and monitoring health conditions unmet needs (aPR: 2.13, 95% CI: 1.08-4.20). Female Veterans' common experience of unmet needs in communicating with their healthcare teams could result in care that is less aligned with their preferences or needs. As the number of older female Veterans grows, these data and additional work to understand sex-specific unmet needs and ways to address them are essential to providing high-quality care for female Veterans.

Modelling climate impacts on paediatric sepsis incidence and severity in Bangladesh.

Background: Sepsis is a leading cause of paediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries. The impacts of climate change on the burden and outcomes of sepsis in low- and middle-income countries, particularly in paediatric populations, remain poorly understood. We aimed to assess the associations between climate variables (temperature and precipitation) and paediatric sepsis incidence and mortality in Bangladesh, one of the countries most affected by climate change. Methods: We conducted retrospective analyses of patient-level data from the International Centre for Diarrhoeal Disease Research, Bangladesh, and environmental data from the National Oceanic and Atmospheric Administration. Using random forests, we assessed associations between sepsis incidence and sepsis mortality with temperature and precipitation between 2009-22. Results: A nonlinear relationship between temperature and sepsis incidence and mortality was identified. The lowest incidence occurred at an optimum temperature of 26.6°C with a gradual increase below and a sharp rise above this temperature. Higher precipitation levels showed a general trend of increased sepsis incidence. A similar distribution for sepsis mortality was identified with an optimum temperature of 28°C. Conclusions: Findings suggest that environmental temperature and precipitation play a role in paediatric sepsis incidence and sepsis mortality in Bangladesh. As children are particularly vulnerable to climate impacts, it is important to consider climate change in health care planning and resource allocation, especially in resource-limited settings, to allow for surge capacity planning during warmer and wetter seasons. Further prospective research from more globally representative data sets will provide more robust evidence on the nature of the relationships between climate variables and paediatric sepsis worldwide.

Integrating molecular and clinical variables to predict myocardial recovery.

Mechanical unloading and circulatory support with left ventricular assist devices (LVADs) mediate significant myocardial improvement in a subset of advanced heart failure (HF) patients. The clinical and biological phenomena associated with cardiac recovery are under intensive investigation. Left ventricular (LV) apical tissue, alongside clinical data, were collected from HF patients at the time of LVAD implantation (n=208). RNA was isolated and mRNA transcripts were identified through RNA sequencing and confirmed with RT-qPCR. To our knowledge this is the first study to combine transcriptomic and clinical data to derive predictors of myocardial recovery. We used a bioinformatic approach to integrate 59 clinical variables and 22,373 mRNA transcripts at the time of LVAD implantation for the prediction of post-LVAD myocardial recovery defined as LV ejection fraction (LVEF) ≥40% and LV end-diastolic diameter (LVEDD) ≤5.9cm, as well as functional and structural LV improvement independently by using LVEF and LVEDD as continuous variables, respectively. To substantiate the predicted variables, we used a multi-model approach with logistic and linear regressions. Combining RNA and clinical data resulted in a gradient boosted model with 80 features achieving an AUC of 0.731±0.15 for predicting myocardial recovery. Variables associated with myocardial recovery from a clinical standpoint included HF duration, pre-LVAD LVEF, LVEDD, and HF pharmacologic therapy, and LRRN4CL (ligand binding and programmed cell death) from a biological standpoint. Our findings could have diagnostic, prognostic, and therapeutic implications for advanced HF patients, and inform the care of the broader HF population.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and noncancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1-positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time-consuming. Here, we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a 4-step pipeline for measuring TPS that includes the coregistration of hematoxylin and eosin, PD-L1, and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions, and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and NC images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish a mIoU of 0.82 for cancer segmentation in hematoxylin-stained NC cases. For PD-L1-stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved a mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights on when to apply and how to evaluate commercial automated IHC scoring systems.

Etiologies of bloody diarrhea in children presenting with acute gastroenteritis to US emergency departments.

Among 111 children presenting with bloody diarrhea in a multicenter study of molecular testing in US emergency departments, we found viral pathogens in 18%, bacteria in 48%, protozoa in 2%, and no pathogens detected in 38%.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.

Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.

Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.

Derivation and validation of a clinical prediction model for risk-stratification of children hospitalized with severe pneumonia in Bangladesh.

Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.

Derivation and External Validation of a Clinical Prediction Model for Viral Diarrhea Etiology in Bangladesh.

Background: Antibiotics are commonly overused for diarrheal illness in many low- and middle-income countries, partly due to a lack of diagnostics to identify viral cases, in which antibiotics are not beneficial. This study aimed to develop clinical prediction models to predict risk of viral-only diarrhea across all ages, using routinely collected demographic and clinical variables. Methods: We used a derivation dataset from 10 hospitals across Bangladesh and a separate validation dataset from the icddr,b Dhaka Hospital. The primary outcome was viral-only etiology determined by stool quantitative polymerase chain reaction. Multivariable logistic regression models were fit and externally validated; discrimination was quantified using area under the receiver operating characteristic curve (AUC) and calibration assessed using calibration plots. Results: Viral-only diarrhea was common in all age groups (<1 year, 41.4%; 18-55 years, 17.7%). A forward stepwise model had AUC of 0.82 (95% confidence interval [CI], .80-.84) while a simplified model with age, abdominal pain, and bloody stool had AUC of 0.81 (95% CI, .78-.82). In external validation, the models performed adequately although less robustly (AUC, 0.72 [95% CI, .70-.74]). Conclusions: Prediction models consisting of 3 routinely collected variables can accurately predict viral-only diarrhea in patients of all ages in Bangladesh and may help support efforts to reduce inappropriate antibiotic use.

Skin Color Representation in Teaching Photographs: One Institution's Approach to Addressing Visual Racism in Medical Education.

Problem: Visual racism refers to both the underrepresentation and inappropriate representation of darker skin types in medical education. By not teaching medical students and resident physicians to recognize common conditions in darker skin, it perpetuates biases that contribute to healthcare disparities for racial and ethnic minoritized groups. In this paper we describe our efforts to engage in institutional anti-racism work by addressing imbalances in representation of darker skin types in visual teaching images within our institution's curriculum. Intervention: We initially surveyed preclinical medical students regarding their perceptions of skin color representation in two courses. Researchers recorded the skin types of all teaching photographs in these courses in 2020. We then provided feedback and education to faculty, proposing that they increase brown and black skin color representation in educational content. During 2021, we reviewed the same courses and surveyed students again to ascertain the implementation and impact of our proposal. Context: We applied our intervention to two courses, Host & Defense (H&D) and Skin, Muscle, Bone, and Joint (SMBJ) since both courses utilize a large number of teaching images. Impact: From 2020 to 2021, both H&D and SMBJ significantly increased the proportion of visual teaching images that included darker skin types, with an increase from 28% to 42% in H&D and 20% to 30% in SMBJ. Significantly more students in the courses' 2021 iterations (73% in H&D, 93% in SMBJ) felt that lectures had appropriate representations of darker skin types when compared to students who took the course in 2020 (8% in H&D, 51% in SMBJ). Students in 2021 felt more confident in recognizing dermatological signs and symptoms in patients with darker skin than students in 2020. The majority of students in both 2020 and 2021 reported wanting to see a gradient of skin types for every dermatological condition discussed. Lessons learned: Our work suggests that addressing visual racism can be achieved partly by setting expectations for increased visual representation, collaborating across educational departments, and establishing clear metrics for assessing implementation. Future interventions will require a continual feedback loop of monitoring learning material, assessing faculty and student perception, refining resources, and recommending revisions to improve visual representation across the entire curriculum.

Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC = 0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

Structured Case-Based Ethics Discussion for Trainees and Faculty on Dermatopathology.

Introduction: Ethical and professional dilemmas are part of the day-to-day practice of medicine, including within dermatopathology (e.g., ethical implications of self-referring skin biopsies for pathology interpretation). There is a need for teaching aids that dermatology educators can easily access to help provide ethics education. Methods: We held an hour-long, faculty-facilitated, interactive, virtual discussion about ethical issues in dermatopathology. The session followed a structured, case-based format. We administered anonymous online feedback surveys after the session and used the Wilcoxon signed rank test to compare participants' before and after responses. Results: Seventy-two individuals from two academic institutions participated in the session. We collected 35 total responses (49%) from dermatology residents (n = 15), dermatology faculty (n = 14), medical students (n = 2), and other providers and learners (n = 4). Feedback was largely positive, with 21 attendees (60%) indicating they learned a few things and 11 (31%) indicating they learned a great deal. Additionally, 32 participants (91%) indicated they would recommend the session to a colleague. Our analysis showed that attendees had a greater self-perceived level of achievement for each of our three objectives after the session. Discussion: This dermatoethics session is structured so as to be easily shared, deployed, and built on by other institutions. We hope that other institutions will use our materials and results to improve upon the foundation presented here and that this framework will be used by other medical specialties seeking to foster ethics education in their training programs.

Clinical Prediction Rule to Guide Diagnostic Testing for Shigellosis and Improve Antibiotic Stewardship for Pediatric Diarrhea.

Background: Diarrheal diseases are a leading cause of death for children aged <5 years. Identification of etiology helps guide pathogen-specific therapy, but availability of diagnostic testing is often limited in low-resource settings. Our goal is to develop a clinical prediction rule (CPR) to guide clinicians in identifying when to use a point-of-care (POC) diagnostic for Shigella in children presenting with acute diarrhea. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models for diarrhea of Shigella etiology in children aged ≤59 months presenting with moderate to severe diarrhea in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to externally validate our GEMS-derived CPR. Results: Of the 5011 cases analyzed, 1332 (27%) had diarrhea of Shigella etiology. Our CPR had high predictive ability (area under the receiver operating characteristic curve = 0.80 [95% confidence interval, .79-.81]) using the top 2 predictive variables, age and caregiver-reported bloody diarrhea. We show that by using our CPR to triage who receives diagnostic testing, 3 times more Shigella diarrhea cases would have been identified compared to current symptom-based guidelines, with only 27% of cases receiving a POC diagnostic test. Conclusions: We demonstrate how a CPR can be used to guide use of a POC diagnostic test for diarrhea management. Using our CPR, available diagnostic capacity can be optimized to improve appropriate antibiotic use.

Development of a prediction model for the acquisition of extended spectrum beta-lactam-resistant organisms in U.S. international travellers.

BACKGROUND: Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. METHODS: We used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. RESULTS: A CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors. CONCLUSIONS: We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

Predicting IHC staining classes of NF1 using features in the hematoxylin channel.

Immunohistochemistry (IHC) highlights specific cell types in tissues and traditionally involves antibody staining together with a hematoxylin counterstain. The intensity and pattern of hematoxylin staining differs between cell types and reveals morphological characteristics of cells. Here, we propose that features in the hematoxylin stain can be used to predict IHC labels, such as Neurofibromin (encoded by the gene NF1). The dataset consists of 7.2 million cells from benign and kidney cancer cores in a tissue microarray. Morphology and hematoxylin (H&M) features defined within QuPath are subjected to a clustering analysis in CytoMap. H&M features are also used to train 4 different XGBoost models to predict high, low, and negative NF1 stain classes in benign renal tubules, clear cell (ccRCC), papillary (PRCC), and chromophobe (ChRCC) renal carcinoma. The prediction accuracies of NF1 staining classes in benign, ccRCC, ChRCC, and PRCC range between 70% and 90% with areas under the precision recall curve PRAUCNF1-high = 0.82+0.12, PRAUCNF1-low = 0.62+0.25, and PRAUCNF1-negative = 0.83+0.16. The most important feature for predicting the NF1 class involves the minimum cellular hematoxylin staining intensity. Together, these results demonstrate the feasibility to predict NF1 expression solely from features in hematoxylin staining using open source software. Since the hematoxylin features can be obtained from regular H&E and IHC slides, the proposed workflow has broad applicability.