Profiling of circulating glial cells for accurate blood-based diagnosis of glial malignancies.

Here, we describe a blood test for the detection of glial malignancies (GLI-M) based on the identification of circulating glial cells (CGCs). The test is highly specific for GLI-M and can detect multiple grades (II-IV) and subtypes including gliomas, astrocytomas, oligodendrogliomas, oligoastrocytomas and glioblastomas, irrespective of gender and age. Analytical validation of the test was performed as per Clinical and Laboratory Standards Institute (CLSI) guidelines. Real-world performance characteristics of the test were evaluated in four clinical (observational) studies. The test has high analytical sensitivity (95%), specificity (100%) and precision (coefficient of variation [CV] = 13.7% for repeatability and CV = 23.5% for within laboratory precision, both at the detection threshold) and is not prone to interference from common drugs and serum factors. The ability of the test to detect and differentiate GLI-M from non-malignant brain tumours (NBT), brain metastases from primary epithelial malignancies (EPI-M) and healthy individual donors (HD) was evaluated in four clinical cohorts. Across these clinical studies, the test showed 99.35% sensitivity (95% confidence interval [CI]: 96.44%-99.98%) and 100% specificity (95% CI: 99.37%-100%). The performance characteristics of this test support its clinical utility for diagnostic triaging of individuals presenting with intracranial space-occupying lesions (ICSOL).

A deep learning approach to case prioritisation of colorectal biopsies.

AIMS: To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis). MATERIALS AND METHODS: Triagnexia Colorectal, a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)-stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision-making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow. RESULTS: Validation of the model was conducted on two cohorts: the first, on 100 single-slide cases, achieved micro-average model specificity of 0.984, micro-average model sensitivity of 0.949 and micro-average model F1 score of 0.949 across all classes. A secondary multi-institutional validation cohort, of 101 single-slide cases, achieved micro-average model specificity of 0.978, micro-average model sensitivity of 0.931 and micro-average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities. CONCLUSIONS: We have developed a high-performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non-neoplastic biopsies.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

A Symptom-Checker for Adult Patients Visiting an Interdisciplinary Emergency Care Center and the Safety of Patient Self-Triage: Real-Life Prospective Evaluation.

BACKGROUND: Symptom-checkers have become important tools for self-triage, assisting patients to determine the urgency of medical care. To be safe and effective, these tools must be validated, particularly to avoid potentially hazardous undertriage without leading to inefficient overtriage. Only limited safety data from studies including small sample sizes have been available so far. OBJECTIVE: The objective of our study was to prospectively investigate the safety of patients' self-triage in a large patient sample. We used SMASS (Swiss Medical Assessment System; in4medicine, Inc) pathfinder, a symptom-checker based on a computerized transparent neural network. METHODS: We recruited 2543 patients into this single-center, prospective clinical trial conducted at the cantonal hospital of Baden, Switzerland. Patients with an Emergency Severity Index of 1-2 were treated by the team of the emergency department, while those with an index of 3-5 were seen at the walk-in clinic by general physicians. We compared the triage recommendation obtained by the patients' self-triage with the assessment of clinical urgency made by 3 successive interdisciplinary panels of physicians (panels A, B, and C). Using the Clopper-Pearson CI, we assumed that to confirm the symptom-checkers' safety, the upper confidence bound for the probability of a potentially hazardous undertriage should lie below 1%. A potentially hazardous undertriage was defined as a triage in which either all (consensus criterion) or the majority (majority criterion) of the experts of the last panel (panel C) rated the triage of the symptom-checker to be "rather likely" or "likely" life-threatening or harmful. RESULTS: Of the 2543 patients, 1227 (48.25%) were female and 1316 (51.75%) male. None of the patients reached the prespecified consensus criterion for a potentially hazardous undertriage. This resulted in an upper 95% confidence bound of 0.1184%. Further, 4 cases met the majority criterion. This resulted in an upper 95% confidence bound for the probability of a potentially hazardous undertriage of 0.3616%. The 2-sided 95% Clopper-Pearson CI for the probability of overtriage (n=450 cases,17.69%) was 16.23% to 19.24%, which is considerably lower than the figures reported in the literature. CONCLUSIONS: The symptom-checker proved to be a safe triage tool, avoiding potentially hazardous undertriage in a real-life clinical setting of emergency consultations at a walk-in clinic or emergency department without causing undesirable overtriage. Our data suggest the symptom-checker may be safely used in clinical routine. TRIAL REGISTRATION: ClinicalTrials.gov NCT04055298; https://clinicaltrials.gov/study/NCT04055298.

Natural Language Processing of Referral Letters for Machine Learning-Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study.

BACKGROUND: Decision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients' goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment. OBJECTIVE: The objective of this study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP. METHODS: A retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models' accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters. RESULTS: Data from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results. CONCLUSIONS: This study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application.

A Systematic Review of Teleophthalmology Services Post-COVID-19 Pandemic in New Zealand, the United Kingdom, Australia, the United States of America, and Canada.

Background: This systematic review of teleophthalmology services in Australia, the United States of America (USA), Canada, and the United Kingdom (UK) during the COVID-19 pandemic is aimed to evaluate changes in teleophthalmology, comparing them to New Zealand (NZ). Methods: A literature search of electronic databases Scopus, Proquest, PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Google was conducted using search terms: telemedicine, ophthalmology, teleophthalmology/teleophthalmology, and COVID/COVID-19/coronavirus/covid-pandemic. Studies describing teleophthalmology services created in response to COVID-19 restrictions from March 1, 2020, to January 31, 2024, were analyzed. Results: Of the articles, 37 describing 29 discrete teleophthalmology services were included. There were 15 services in the USA, seven in the UK, two in Canada, two in Australia, and three in NZ. The models of care in the USA were well described, and teleophthalmology was used for general, external, anterior segment, neuro-ophthalmology, and oculoplastic consults, as well as for grading of fundus images in the emergency department setting. In the UK, teleophthalmology was used for general eye care, oculoplastics, and pediatric ophthalmology. In Australia, teleophthalmology was used for postglaucoma surgery monitoring of Intraocular Pressure. In NZ, teleophthalmology was used for general eye consults and triaging, but no formal models were described. Conclusion: COVID-19 offered a unique opportunity for re-examination and expansion of teleophthalmology services globally. Video-based and home-screening teleophthalmology services are feasible but have limitations. Investing in multidisciplinary and community-based technology partnerships can create more equitable teleophthalmology care models (to complement and, when necessary, replace traditional in-person consults), within existing frameworks, making eye care more accessible and efficient.

Automated APACHE II and SOFA score calculation using real-world electronic medical record data in a single center.

The integration of illness severity and organ dysfunction scores into clinical practice, including the APACHE II and SOFA scores, has been challenging due to constraints associated to manual score calculation. With electronic medical records (EMR), score calculation automation using data extraction scripts has emerged as a solution. We aimed to demonstrate that APACHE II and SOFA scores calculated with an automated EMR-based data extraction script predict important clinical endpoints. In this retrospective cohort study, every adult patient admitted to one of our three ICUs, between July 1, 2019, and December 31, 2020, were enrolled. For every patient, an automated ICU admission APACHE II score was calculated using EMR data and minimal clinician input. Fully automated daily SOFA scores were calculated for every patient. 4 794 ICU admissions met our selection criteria. Of these ICU admissions, 522 deaths were recorded (10.9% in-hospital mortality rate). The automated APACHE II was discriminant for in-hospital mortality (AU-ROC = 0.83 (95% CI 0.81-0.85)). We observed an association between the APACHE II score and ICU LOS, with a statistically significant mean increase of 1.1 days of ICU LOS (1.1 [1-1.2]; p = < .0001) for each 10 units increase in APACHE score. SOFA score curves did not discrimate significantly between survivors and non-survivors. A partially automated APACHE II score, calculated with real-world EMR data using an extraction script, is associated with in-hospital mortality risk. The automated APACHE II score could potentially constitute an acceptable surrogate of ICU acuity to be used in resource allocation and triaging, especially in time of high demand for ICU beds.

Triage of patients and remote consultations in primary care facilities during the COVID-19 pandemic in France (PRICOV-19 study) / Triage des patients et consultations à distance dans les structures de soins primaires pendant la pandémie de Covid-19 en France (étude PRICOV-19)

Introduction: PRICOV-19 is a European cross-sectional study based on an online questionnaire, describing the impact of the pandemic on primary care structures (PCS). In France, PCS are solo practices (SP), single or multi-professional group practices (GP), coordinated practice structures (CPS): health houses and health centers. Triage, whether it is digital (TD), by telephone (TT) or at the reception (TR), is essential to reduce the risk of infection, and is part of recommended organizational practices. Purpose of research: Based on French data from the PRICOV-19 study, the objective is to describe the frequency and factors associated with triage in PCSs during the COVID 19 pandemic. Results: 1100 structures responded to the survey. The TD was implemented in 64% of PCSs (53.3% of SPs, 64.9% of GPs, 73.2% of CPSs). The TT was implemented in 76% of structures (72.7% of SPs, 75.4% of GPs and 81% of CPSs). Finally, TR was implemented in 52% of structures (37.7% of SPs, 52% of GPs and 67% of CPSs). The other positively associated factors are the urban territory and the lower workload for the TD, and the presence of a receptionist for the TR. Conclusions: Triage practices seem to be clearly associated with the organization and working conditions in the PCSs, and first and foremost with the type of structure.

Introduction: PRICOV-19 est une étude transversale européenne basée sur un questionnaire en ligne, décrivant l'impact de la pandémie sur les structures de soins primaires (SSP). En France, les SSP sont les cabinets solo (CS), les cabinets de groupe mono ou pluriprofessionnels (CG), les structures d'exercice coordonné (SEC) : maisons de santé et centres de santé. Le triage, qu'il soit numérique (TN), téléphonique (TT) ou à l'arrivée (TA) est indispensable pour réduire le risque infectieux, et fait partie des pratiques d'organisation recommandées. But de l'étude: En s'appuyant sur les données françaises de l'étude PRICOV-19, l'objectif est de décrire la fréquence et les facteurs associés au triage dans les SSP pendant la pandémie de COVID 19. Résultats: 1 100 structures ont répondu au questionnaire. Le TN a été mis en œuvre dans 64 % des SSP (53,3 % des CS, 64,9 % des CG, 73,2 % des SEC). Le TT a été mis en œuvre dans 76 % des structures (72,7 % des CS, 75,4 % des CG et 81 % des SEC). Enfin, le TA a été mis en œuvre dans 52 % des structures (37,7 % des CS, 52 % des CG et 67 % des SEC). Les autres facteurs positivement associés sont le territoire urbain et la charge de travail moins importante pour le TN, et la présence d'une réceptionniste pour le TA. Conclusion: Les pratiques de triage semblent clairement associés à l'organisation et aux conditions d'exercice dans les SSP, et en premier lieu au type de structure.

The Montreal Cognitive Assessment (MoCA) with a double threshold: improving the MoCA for triaging patients in need of a neuropsychological assessment.

OBJECTIVES: Diagnosis of patients suspected of mild dementia (MD) is a challenge and patient numbers continue to rise. A short test triaging patients in need of a neuropsychological assessment (NPA) is welcome. The Montreal cognitive assessment (MoCA) has high sensitivity at the original cutoff <26 for MD, but results in too many false-positive (FP) referrals in clinical practice (low specificity). A cutoff that finds all patients at high risk of MD without referring to many patients not (yet) in need of an NPA is needed. A difficulty is who is to be considered at risk, as definitions for disease (e.g. MD) do not always define health at the same time and thereby create subthreshold disorders. DESIGN: In this study, we compared different selection strategies to efficiently identify patients in need of an NPA. Using the MoCA with a double threshold tackles the dilemma of increasing the specificity without decreasing the sensitivity and creates the opportunity to distinguish the clinical (MD) and subclinical (MCI) state and hence to get their appropriate policy. SETTING/PARTICIPANTS: Patients referred to old-age psychiatry suspected of cognitive impairment that could benefit from an NPA (n = 693). RESULTS: The optimal strategy was a two-stage selection process using the MoCA with a double threshold as an add-on after initial assessment. By selecting who is likely to have dementia and should be assessed further (MoCA<21), who should be discharged (≥26), and who's course should be monitored actively as they are at increased risk (21<26). CONCLUSION: By using two cutoffs, the clinical value of the MoCA improved for triaging. A double-threshold MoCA not only gave the best results; accuracy, PPV, NPV, and reducing FP referrals by 65%, still correctly triaging most MD patients. It also identified most MCIs whose intermediate state justifies active monitoring.

Triaging of COVID-19 patients using low dose chest CT: Incidence and factor analysis of lung involvement on CT images.

INTRODUCTION: Despite an increase in CT studies to evaluate patients with coronavirus disease 2019 (COVID-19), their indication in triage is not well-established. The purpose was to investigate the incidence of lung involvement and analyzed factors related to lung involvement on CT images for establishment of the indication for CT scans in the triaging of COVID-19 patients. METHODS: Included were 192 COVID-19 patients who had undergone CT scans and blood tests for triaging. Two radiologists reviewed the CT images and recorded the incidence of lung involvement. The prediction model for lung involvement on CT images using clinico-laboratory variables [age, gender, body mass index, oxygen saturation of the peripheral artery (SpO2), comorbidities, symptoms, and blood data] were developed by multivariate logistic regression with cross-validation. RESULTS: In 120 of the 192 patients (62.5%), CT revealed lung involvement. The patient age (odds ratio [OR]; 4.95, 95% confidence interval [CI]; 0.93-26.49), albumin (OR; 4.66, 95%CI; 1.37-15.84), lactate dehydrogenase (OR; 5.79, 95%CI; 1.43-23.38) and C-reactive protein (OR; 8.93, 95%CI; 4.13-19.29) were selected for the final prediction model for lung involvement on CT images. The cross-validated area under the receiver operating characteristics (ROC) curve was 0.83. CONCLUSIONS: The high incidence of lung involvement (62.5%) was confirmed on CT images. The proposed prediction model that includes the patient age, albumin, lactate dehydrogenase, and C-reactive protein may be useful for predicting lung involvement on CT images and may assist in deciding whether triaged COVID-19 patients should undergo CT.

Racism without racists and consequentialist life-maximizing approaches to triaging.

Consequentialist life-maximizing approaches to triaging prescribe that everyone ought to have an equal chance of living a typical lifespan, through the saving more life-years (or saving most lives) principle, which emphasizes the youngest-first principle and in some cases a lottery approach, often at the expense of the old and the sick. Although this approach has already been criticized by several bioethicists, this article provides a different kind of criticism to the life-cycle viewpoint, one that has not yet been explored at length; namely, we contend that the life-maximizing approach entails a form of racism without racists in its attitude towards Black people. More specifically, we contend that by neglecting the idea that current societies are not post-racial, it privileges White individuals and disadvantages Black people in the triaging process, curtails equal opportunities for Black people, reinforces white normativity, and neglects African culture. We end the article by pointing towards an Afro-communitarian relational triaging approach that does not face the same difficulties as consequentialist life-maximizing approaches do.

Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand.

BACKGROUND: Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. METHODS: Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. RESULTS: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. CONCLUSIONS:  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.

COVID 19: prioritise autonomy, beneficence and conversations before score-based triage.

At the start of the COVID-19 pandemic, mounting demand overwhelmed critical care surge capacities, triggering implementation of triage protocols to determine ventilator allocation. Relying on triage scores to ration care, while relieving clinicians from making morally distressing decisions under high situational pressure, distracts clinicians from what is essentially deeply humanistic issues entrenched in this protracted public health crisis. Such an approach will become increasingly untenable as countries flatten their epidemic curves. Decisions regarding intensive care unit admission are particularly challenging in older people, who are most likely to require critical care, but for whom benefits are most uncertain. Before applying score-based triage, physicians must first discern if older people will benefit from critical care (beneficence) and second, if he wants critical care (autonomy). When deliberating beneficence, physicians should steer away from solely using age-stratified survival probabilities from epidemiological data. Instead, decisions must be based on individualised risk-stratification that encompasses evidence-based predictors of adverse outcomes specific to older adults. Survival will also need to be weighed against burden of treatment, as well as longer term functional deficits and quality-of-life. By identifying the robust older people who may benefit from critical care, clinicians should proceed to elicit his values and preferences that would determine the treatment most aligned with his best interest. During these dialogues, physicians must truthfully convey the emergent clinical reality, discern the older person's therapeutic goals and discuss the feasibility of achieving them. Given that COVID-19 is here to stay, these conversations aimed at achieving goal-cordant care must become a new clinical norm.

Clinical value of the Montreal Cognitive Assessment (MoCA) in patients suspected of cognitive impairment in old age psychiatry. Using the MoCA for triaging to a memory clinic.

Objectives: Diagnostic pathways are limited. A validated instrument that can triage patients when they are suspected of mild dementia (MD) is necessary to optimise referrals. Method: The MoCA is validated for identifying MD and mild cognitive impairment (MCI) in a cohort of patients suspected of cognitive impairment (CI) after initial assessment in old age psychiatry. The reference standard was the consensus-based diagnoses for MD and MCI, adhering to the international criteria and using suspected patients, but without CI as comparisons (NoCI). Results: The mean MoCA scores differ significantly between the groups: 24(SE: .59) in NoCI, 21(SE: .31) in MCI and 16,7(SE: .45) in MD (p < .05). The AUC of MD against non-demented (MCI + NoCI) was 0.83(95%CI: 0.78-0.88) resulting in 90% sensitivity, 65% specificity, 50%PPV and 94%NPV at a "best" cutoff of <21 according the Youden index and respectively 0.77(95%CI: 0.69-0.85), 56%, 73%, 90%, 28% for CI (MD + MCI) against NoCI at <21. Conclusion: 90% of individuals with a MoCA of <21 will have CI (MD + MCI), while 94% with a MoCA of ≥21 will not have dementia. The MoCA can reduce referrals substantially (50%) by selecting who don't need further work up in a memory clinic, even if they were suspected of CI after initial assessment.

Triaging algorithm for head & neck oncology follow-up patients in COVID-19 climate.

The current climate is one of uncertainty and immeasurable tragedy for people afflicted by the pandemic of SARS-CoV-2 virus infection. As professionals, we have a duty of care towards all patients especially the vulnerable and those suffering with life-threatening illnesses such as oral cancer. We present a safe & objective triaging method for afflicted with this disease in the prevailing morbid situation.

Pediatric triage variations among nurses, pediatric and emergency residents using the Canadian triage and acuity scale.

BACKGROUND: Emergency care continues to be a challenge, since patients' arrival is unscheduled and could occur at the same time which may fill the Emergency Department with non-urgent patients. Triaging is an integral part of every busy ED. The Canadian Triage and Acuity Scale (CTAS) is considered an accurate tool to be used outside Canada. This study aims to identify the chosen triage level and compare the variation between registered nurses, pediatric and adult emergency residents by using CTAS cases. METHOD: This study was conducted at King Abdulaziz Medical City,Saudi Arabia. A cross-sectional self-administered questionnaire was used, and which contains 15 case scenarios with different triage levels. All cases were adopted from a Canadian triage course after receiving permission. Each case provides the patient's symptoms, clinical signs and mode of arrival to the ED. The participants were instructed to assign a triage level using the following scale. A non-random sampling technique was used for this study. The rates of agreement between residents were calculated using kappa statistics (weighted-kappa) (95%CI). RESULT: A total of 151 participants completed the study questionnaire which include 15 case scenarios. 73 were nurses and 78 were residents. The results showed 51.3, 56.6, and 59.9% mis-triaged the cases among the nurses, emergency residents, and pediatric residents respectively. Triage scores were compared using the Kruskal Wallis test and were statistically significant with a p value of 0.006. The mean ranks for nurses, emergency residents and pediatric residents were 86.41, 73.6 and 59.96, respectively. The Kruskal Wallis Post-Hoc test was performed to see which groups were statistically significant, and it was found that there was a significant difference between nurses and pediatrics residents (P value = 0.005). Moreover, there were no significant differences found between nurses and ER residents (P value> 0.05). CONCLUSION: The triaging system was found to be a very important tool to prioritize patients based on their complaints. The results showed that nurses had the greatest experience in implementing patients on the right triage level. On the other hand, ER and pediatric residents need to develop more knowledge about CTAS and become exposed more to the triaging system during their training.

A multicentre development and validation study of a novel lower gastrointestinal bleeding score-The Birmingham Score.

PURPOSE: Lower gastrointestinal bleeding (LGIB) is common and risk stratification scores can guide clinical decision-making. There is no robust risk stratification tool specific for LGIB, with existing tools not routinely adopted. We aimed to develop and validate a risk stratification tool for LGIB. METHODS: Retrospective review of LGIB admissions to three centres between 2010 and 2018 formed the derivation cohort. Using regressional analysis within a machine learning technique, risk factors for adverse outcomes were identified, forming a simple risk stratification score-The Birmingham Score. Retrospective review of an additional centre, not included in the derivation cohort, was performed to validate the score. RESULTS: Data from 469 patients were included in the derivation cohort and 180 in the validation cohort. Admission haemoglobin OR 1.07(95% CI 1.06-1.08) and male gender OR 2.29(95% CI 1.40-3.77) predicted adverse outcomes in the derivation cohort AUC 0.86(95% CI 0.82-0.90) which outperformed the Blatchford 0.81(95% CI 0.77-0.85), Rockall 0.60(95% CI 0.55-0.65) and AIM65 0.55(0.50-0.60) scores and in the validation cohort AUC 0.80(95% CI 0.73-0.87) which outperformed the Blatchford 0.77(95% CI 0.70-0.85), Rockall 0.67(95% CI 0.59-0.75) and AIM 65 scores 0.61(95% CI 0.53-0.69). The Birmingham Score also performs well at predicting adverse outcomes from diverticular bleeding AUC 0.87 (95% CI 0.75-0.98). A score of 7 predicts a 94% probability of adverse outcome. CONCLUSION: The Birmingham Score represents a simple risk stratification score that can be used promptly on patients admitted with LGIB.

Human papillomavirus and cervical cancer.

Cervical cancer is by far the most common HPV-related disease. About 99.7% of cervical cancer cases are caused by persistent genital high-risk human papillomavirus (HPV) infection. Worldwide, cervical cancer is one of the most common cancers in women with an estimated 528,000 new cases reported in 2012. Most HPV infections clear spontaneously but persistent infection with the oncogenic or high-risk types may cause cancer of the oropharynx and anogenital regions. The virus usually infects the mucocutaneous epithelium and produces viral particles in matured epithelial cells and then causes a disruption in normal cell-cycle control and the promotion of uncontrolled cell division leading to the accumulation of genetic damage. There are currently two effective prophylactic vaccines against HPV infection, and these comprise of HPV types 16 and 18, and HPV types 6, 11, 16 and 18 virus-like particles. HPV testing in the secondary prevention of cervical cancer is clinically valuable in triaging low-grade cytological abnormalities and is also more sensitive than cytology as a primary screening. If these prevention strategies can be implemented in both developed and developing countries, many thousands of lives could be saved.

Validity and Reliability of Emergency Severity Index and Conventional Three-Tier Triage System in the Emergency Department, Hospital Universiti Sains Malaysia.

BACKGROUND: The study aimed to examine the reliability and validity of the existing three-tier triaging system and a new five-level emergency triaging system, emergency severity index (ESI), in the Emergency Department (ED) of Hospital Universiti Sains Malaysia (HUSM). METHODS: This study was conducted in HUSM's ED over two study periods. In the first three months, 300 patients were triaged under the three-tier triaging system, and, in the subsequent three months, 280 patients were triaged under the ESI. The patients were triaged by junior paramedics and the triage records were retained and later re-triaged by senior paramedics. The inter-rater reliability was evaluated using Cohen's Kappa statistics. The acuity ratings of the junior paramedics were compared with those of the expert panel to determine the sensitivity and specificity of each acuity level for both the ESI and the three-tier triaging system. The over-triage rate, under-triage rate, amount of resources used, admission rate and discharge rate were also determined. RESULTS: The inter-rater agreement for the three-tier triaging system was 0.81 while that of the ESI was 0.75. The ESI had a higher average sensitivity of 74.3% and a specificity of 94.4% while the three-tier system's average sensitivity was 68.5% and its specificity 87.0%. The average under-triage and over-triage rates for the ESI were 10.7% and 6.2%, respectively, which were lower than the three-tier system's average under-triage rate of 13.1% and over-triage rate of 17.1%. The urgency levels of both the ESI and the three-tier system were associated with increased admission rates and resources used in the ED. CONCLUSION: The ESI's inter-rater reliability was comparable to the three-tier triaging system and it demonstrated better validity than the existing three-tier system.

Human papillomavirus and cervical cancer.

Cervical cancer is by far the most common HPV-related disease. About 99.7% of cervical cancer cases are caused by persistent genital high-risk human papillomavirus (HPV) infection. Worldwide, cervical cancer is one of the most common cancer in women with an estimated 528,000 new cases reported in 2012. Most HPV infections clear spontaneously but persistent infection with the oncogenic or high-risk types may cause cancer of the oropharynx and anogenital regions. The virus usually infects the mucocutaneous epithelium and produces viral particles in matured epithelial cells and then causes a disruption in normal cell-cycle control and the promotion of uncontrolled cell division leading to the accumulation of genetic damage. There are currently two effective prophylactic vaccines against HPV infection, and these comprise of HPV types 16 and 18, and HPV types 6, 11, 16 and 18 virus-like particles. HPV testing in the secondary prevention of cervical cancer is clinically valuable in triaging low-grade cytological abnormalities and is also more sensitive than cytology as a primary screening. If these prevention strategies can be implemented in both developed and developing countries, many thousands of lives could be saved.