Clinical Aid Research Articles

Egocentric spatial orientation differences between Alzheimer's disease at early stages and mild cognitive impairment: a diagnostic aid.

Alzheimer's disease (AD) is a growing global crisis. Egocentric spatial orientation deteriorates with age and more significantly with AD. A simple and quick virtual reality (VR) localization and target finding technique is presented as a diagnostic aid to screen mild cognitive impairment (MCI) from AD. Spatial orientation data from 93 individuals (65 AD at a mild stage, 20 MCI, and 8 other dementia types) based on VR localization of a target on a landmark-less cubic 3-story building were analyzed. We hypothesize AD and MCI groups' performances are significantly different. AD and MCI spatial performances were statistically significantly (p < 0.001) different. These results plus the longitudinal tracking of three patients who developed AD over a period of 5years suggest the proposed spatial tests may be used as a quick and simple clinical diagnostic aid to separate AD at early to mild stages from MCI.

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

Developing a predictive model for spinal shock in dogs with spinal cord injury.

BackgroundReduced pelvic limb reflexes in dogs with spinal cord injury typically suggests a lesion of the L4‐S3 spinal cord segments. However, pelvic limb reflexes might also be reduced in dogs with a T3‐L3 myelopathy and concurrent spinal shock.Hypothesis/ObjectivesWe hypothesized that statistical models could be used to identify clinical variables associated with spinal shock in dogs with spinal cord injuries.AnimalsCohort of 59 dogs with T3‐L3 myelopathies and spinal shock and 13 dogs with L4‐S3 myelopathies.MethodsData used for this study were prospectively entered by partner institutions into the International Canine Spinal Cord Injury observational registry between October 2016 and July 2019. Univariable logistic regression analyses were performed to assess the association between independent variables and the presence of spinal shock. Independent variables were selected for inclusion in a multivariable logistic regression model if they had a significant effect (P ≤ .1) on the odds of spinal shock in univariable logistic regression.ResultsThe final multivariable model included the natural log of weight (kg), the natural log of duration of clinical signs (hours), severity (paresis vs paraplegia), and pelvic limb tone (normal vs decreased/absent). The odds of spinal shock decreased with increasing weight (odds ratio [OR] = 0.28, P = .09; confidence interval [CI] 0.07‐1.2), increasing duration (OR = 0.44, P = .02; CI 0.21‐0.9), decreased pelvic limb tone (OR = 0.04, P = .003; CI 0.01‐0.36), and increased in the presence of paraplegia (OR = 7.87, P = .04; CI 1.1‐56.62).Conclusions and Clinical ImportanceA formula, as developed by the present study and after external validation, could be useful for assisting clinicians in determining the likelihood of spinal shock in various clinical scenarios and aid in diagnostic planning.

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.

AimsResource‐strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings.MethodsWe used simple information theory calculations on a brain cancer simulation model and real‐world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto‐adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH‐mutant tumors.ResultsInformation theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH‐mutant tumors. The predictive models may facilitate the reduction of false‐positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing.ConclusionsWe posit that this approach provides an improvement on the cIMPACT‐NOW workflow recommendations for IDH‐mutant tumors and a framework for future resource and testing allocation.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity

Study objectiveA multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participantsCore team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. ResultsThe team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. ConclusionLessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

White Blood Cells Image Classification Based on Radiomics and Deep Learning

White blood cell (WBC) identification performance is highly correlated with the quality of the extracted features, with radiomic features having greater resolution and more detailed information and deep features having more robust semantic information. This research integrates these two aspects to creatively suggest a WBC classification model based on radiomics and deep learning. This research suggests a brand-new method for extracting radiomic features from WBC images as well as a dual-branch feature fusion network RCTNet based on CNN and Transformer for extracting deep features. The radiomic feature extraction method not only has a simple segmentation algorithm but also solves the problem of cell adhesion, can obtain higher quality shape, color, and texture features without segmenting intact cells, and is more generalizable. RCTNet can extract more critical and recognizable deep features from WBC nuclei, avoiding the influence of too much redundant and invalid information on the results, and has better performance than several existing CNN models. We compared the classification outcomes based on radiomics with those based on deep learning to confirm the efficacy of the WBC classification model based on radiomics and deep learning suggested in this research. The experimental results demonstrated that combining radiomic features and deep features significantly improved the classification accuracy, with an AUC exceeding 0.9995, accuracy, sensitivity, specificity, precision, and F1-score reaching 0.9880, 0.9823, 0.9883, 0.9968, and 0.9881, respectively. The model has significant research significance in clinical applications and aids physicians in improving diagnosis and screening for diseases of WBCs.

Острые гинекологические состояния в подростковом возрасте. Трудности дифференциальной диагностики (клинические случаи)

Objective of the Paper: To demonstrate possible challenges with differential diagnosis of adenomyosis and abnormal uterus development in practical paediatric gynaecology. Key Points. We presented two cases of teenage girls with similar clinical symptoms in various diseases: nodular adenomyosis, which is rare for adolescents, and abnormal uterus development. Despite a full range of clinical and instrumental examinations (MRI, ultrasound examinations), it is only laparoscopy that allowed identifying the cause of pain. Conclusion. It is advisable to active use minimally invasive surgical procedures in paediatric inpatient units. Clinical recommendations and medical aid procedures for this urgent pathology are required to facilitate decision-making by medical professionals to apply endosurgical methods of therapy. Keywords: paediatric gynaecology, pain syndrome, differential diagnosis, adenomyosis, abnormal uterus development.

Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach

It has been reported that the healthcare industry is the slowest adopter of artificial intelligence methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness. This method paper aims to identify the enablers for adopting ML in supporting clinical decision-making and propose a strategic road map toward boosting the clinicians' intentions to adopt ML as a clinical decision support tool. This paper utilizes the Total Interpretive Structural Modeling (TISM) methodology and the Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision-making. The paper concludes that building an academic foundation, raising awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians' intentions to adopt machine learning in supporting clinical decision-making.

Comparison of Dermoscopic Findings of Chronic Hand Eczema and Palmoplantar Psoriasis: A Cross-sectional Study

Introduction: Differentiation between Chronic Hand Eczema (CHE) and Palmoplantar Psoriasis (PP) is a diagnostic challenge. Dermoscopy can helpto distinguish between the two conditions. Aim: To establish and compare the dermoscopic findings of the CHE and PP using DermLite DL4 dermoscope and to confirm the accuracy of the dermoscopic findings with the histopathological findings. Materials and Methods: A cross-sectional study was conducted at Navodaya Medical College, Hospital and Research Centre, Raichur, Karnataka, India, from January 2017 to June 2018. Fifty patients each of CHE (26 male and 24 female subjects) and PP (25 male and 25 female subjects) were included in the study. A detailed clinical history, cutaneous clinical and dermoscopic findings of each case were noted. A 4 mm punch biopsy was taken from 30 patients for histopathological analysis. The data was tabulated, analysed and tested for significance using the Chi-square test. Results: The mean age of the patients was 46±4 years. On dermoscopic examination, diffuse white scales in 45 (90%) patients, over a red background in 49 (98%) patients with regularly arranged dots and globules in 45 (90%) patients was seen in PP. Patchy yellow scales 46 (90%), yellow dots, globules and yellow to brown crusts against a yellow background was seen in all patients (100%) of CHE (p-value &lt;0.001). Histopathological findings are in agreement with dermoscopic findings in 9/16 and 10/14 cases of CHE and PP respectively. Conclusion: Dermoscope is a useful non invasive clinical aid to differentiate between CHE and PP. The presence of diffuse white scales over a red background and regularly arranged vessels indicates PP, whereas the presence of patchily distributed yellow scales, yellow dots and globules; and yellow to brown crusts against a yellow background is more suggestive of CHE.

Correlation between McIssac Score and Throat Swab Culture in Patient Presenting with Acute Tonsillitis

Introduction: Tonsillitis is inflammation of tonsils which is characterized by various signs and symptoms with sore throat being the most consistent symptom. The condition is mostly caused by bacterial infection with Streptococci being the most common bacteria. McIssac scoring technique is clinical symptoms based scoring method designed for diagnosing streptococcal tonsillitis. Aims: To find out the association between McIssac Score and beta hemolytic streptococcal infection in acute tonsillitis. Methods: This hospital based, prospective study was carried out in the Department of ENT, Nepalgunj Medical College from July 2020 to June 2021. Patients were scored as per McIssac score. The specimens were collected from the tonsillar surface using sterile cotton swabs and subjected for culture and sensitivity. Results:The most common affected age group was 21 to 30 years (46%). Females were affected more commonly (57%). The most common organism isolated in the study was Group A beta hemolyticn Streptococcus (48%), followed by Pseudomonas (10%), Enterococcus (9%) and Klebsiella (8%) and no organisms were isolated in 25% of the patients. It was observed that high McIssac score was associated with higher chance of having positive beta hemolytic streptococcal infection. Out of 48 group A beta hemolytic streptococci culture positive patients 18(37.5%) patients scored 3, 9 (18.75%) patients scored 4 and 7(14.5%) patients scored 5.The most common antibiotic effective against group A beta hemolytic srtreptococci was ceftriaxone in 25(72.9%), followed by amoxyclavulinic acid in 20 isolates (41.6%) and amikacin in seventeen (35.4%) isolates. Conclusion: The correlation between throat swab culture and McIssac score emphasized that this clinical scoring system aid in early diagnosis of group A beta hemolytic streptococci tonsillitis.

Twin Mix Inferior Alveolar Nerve Anaesthesia for Transalveolar Extraction of Mandibular Third Molar Surgery: Review of Current Evidence

Utmost recurrent surgical process executed in oral &amp; maxillofacial surgery is transalveolar extraction (TAE) of mandibular impacted 3rd molar which consequences to surgical trauma to surrounding soft &amp; hard tissues during the process of tooth removal. Post-operative sequalae like pain, facial swelling &amp; sometimes trismus follow TAE. Dexamethasone is commonly used steroid post-operatively and studies have revealed clinical aids in refining patients’ quality of life (QoL). To improve the QoL after TAE of third molars, use of twin-mix anaesthesia is proposed, where a combination of local anaesthetic solution and steroid is administered as a single injection, thus, negating the requirement of additional route of administration for dexamethasone. The advantages for this technique are, solitary method for dual drug delivery, reduced sensation of sting while injecting owing to altered pH of solution, short latency period, prolonged duration of anaesthesia &amp; amended QoL post operatively. Twin Mix inferior alveolar nerve anaesthesia is found beneficial by reducing patient discomfort and enhancing patient compliance for surgeries of the mandible.

The use of anatomical models in an Orthopaedic clinic aids in patient education

Background: One of the World Health Organisation strategies to achieve patient centered care is for patients to be able to effectively give informed consent. Well-informed patients are also likely to be more compliant and have better treatment outcomes. Aims and Objectives: Our study looks at using anatomical models as adjuncts in consultations to determine if they would be useful in improving patient understanding and satisfaction. Study Design/Methods: A survey of patients was carried out at a single Orthopaedic foot and ankle clinic in Singapore, to investigate if patients felt that the use of anatomical models aided in their understanding of their medical condition, foot and ankle anatomy, and surgical procedures offered, if any. 2 different anatomical models were used, depending on the context of the consultation. All conditions encountered were included. Institutional Review Board approval was not required for this study. Results: 30 patients were surveyed in total, all of whom agreed that the models were useful in helping them understand their medical condition and foot and ankle anatomy better. Of the 19 patients who were counselled regarding surgery, 18 felt that the models improved their understanding of the proposed procedures. 26 patients would like similar models to be used in other Orthopaedic clinics, although 3 would not, and 1 was not sure. On the whole, 19 patients found the models very useful, and 11 patients found them useful. Conclusion: Anatomical models are a cost-effective method of contributing to patient education compared to other alternatives, and we would advocate the use of models in other Orthopaedic clinics during consultations for counselling and taking informed consent.

Gut Ruminococcaceae levels at baseline correlate with risk of antibiotic-associated diarrhea

SummaryAntibiotic-associated diarrhea (AAD) affects a significant proportion of patients receiving antibiotics. We sought to understand if differences in the gut microbiome would influence the development of AAD. We administered a 3-day course of amoxicillin-clavulanate to 30 healthy adult volunteers, and analyzed their stool microbiome, using 16S rRNA gene sequencing, at baseline and up to 4 weeks post antibiotic administration. Lower levels of gut Ruminococcaceae were significantly and consistently observed from baseline until day 7 in participants who developed AAD. Overall, participants who developed AAD experienced a greater decrease in microbial diversity. The probability of AAD could be predicted based on qPCR-derived levels of Faecalibacterium prausnitzii at baseline. Our findings suggest that a lack of gut Ruminococcaceae influences development of AAD. Quantification of F. prausnitzii in stool prior to antibiotic administration may help identify patients at risk of AAD, and aid clinicians in devising individualized treatment regimens to minimize such adverse effects.

Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients

Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice. To assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required. This prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021. 258 variables spanning domains of dementia-related clinical measures and risk factors. The main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment. In a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis. These findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.

First aid guidelines for environmental topics: Educational approach and adaptation

Extreme environmental conditions and exposure to very high or low temperatures, particularly when this happens quickly, can lead to severe illness and injury. As the climate changes and temperature differences become more pronounced, these instances are likely to increase. First aid education which deals with environmental conditions is particularly important in circumstances where people are unprepared, more vulnerable due to age or have existing medical conditions. Remoteness or inaccessibility to medical care also increases vulnerability. This paper reviews a selection of the environmental topics covered in the International First Aid, Resuscitation and Education Guidelines 2020 (IFRC, 2020) which seek to blend clinical first aid evidence with educational evidence, principles and expertise. Covering Hyperthermia, Dehydration, Hypothermia, Frostbite, Altitude Sickness, and Motion Sickness, we provide educational and adaptation interpretation in different contexts, stimulate debate and identify gaps. We also review how the 2020 Guidelines viewed these topics through the lens of the Chain of Survival Behaviors and identify the most pertinent stages in that chain to focus learning on these topics. The 2020 Guidelines emphasize the need for content and educational adaptation according to the learner, which is central to these topics. First aid instructors and program designers need to frame action relevant to their audience and local needs. The teaching of environmental first aid topics should be contextualized to the local geography, legal requirements, culture and behaviors of the learners and their context. For these topics, we conclude that facilitation tips for environmental conditions requiring first aid should focus on prevention and helping learners to recognize early warning signs of the different conditions so they can take preventative action before serious illness sets in.

Prediction models of treatment response in lupus nephritis

In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.

Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation

BackgroundAlthough deep neural networks (DNNs) are showing state of the art performance in clinical gait analysis, they are considered to be black-box algorithms. In other words, there is a lack of direct understanding of a DNN’s ability to identify relevant features, hindering clinical acceptance. Interpretability methods have been developed to ameliorate this concern by providing a way to explain DNN predictions.MethodsThis paper proposes the use of an interpretability method to explain DNN decisions for classifying the movement that precedes freezing of gait (FOG), one of the most debilitating symptoms of Parkinson’s disease (PD). The proposed two-stage pipeline consists of (1) a convolutional neural network (CNN) to model the reduction of movement present before a FOG episode, and (2) layer-wise relevance propagation (LRP) to visualize the underlying features that the CNN perceives as important to model the pathology. The CNN was trained with the sagittal plane kinematics from a motion capture dataset of fourteen PD patients with FOG. The robustness of the model predictions and learned features was further assessed on fourteen PD patients without FOG and fourteen age-matched healthy controls.ResultsThe CNN proved highly accurate in modelling the movement that precedes FOG, with 86.8% of the strides being correctly identified. However, the CNN model was unable to model the movement for one of the seven patients that froze during the protocol. The LRP interpretability case study shows that (1) the kinematic features perceived as most relevant by the CNN are the reduced peak knee flexion and the fixed ankle dorsiflexion during the swing phase, (2) very little relevance for FOG is observed in the PD patients without FOG and the healthy control subjects, and (3) the poor predictive performance of one subject is attributed to the patient’s unique and severely flexed gait signature.ConclusionsThe proposed pipeline can aid clinicians in explaining DNN decisions in clinical gait analysis and aid machine learning practitioners in assessing the generalization of their models by ensuring that the predictions are based on meaningful kinematic features.

Primary and recurrent patellar dislocation: recommendation fordiagnostic evaluation, risk stratification and treatment

The dogma that (every) primary patellar dislocation should be treated non-operatively is considered outdated. Therefore, every first-time dislocation, as well as every recurrent dislocation, should be fully diagnosed with regard to the injury pattern and anatomically predisposing risk factors. This enables the use of risk stratification models to assess the risk of recurrent episodes of instability, thereby supporting the clinical decision-making process. The reconstruction of the MPFL can be considered an established and successful treatment strategy for patellar instability. However, the additional correction of bony risk factors is often useful. In this context, the preoperative grading of the Jsign and dynamic assessment of patellar instability (dynamic apprehension test) are of utmost importance. These findings, combined with the radiographic imaging findings, can be used as a clinical decision aid for a bony correction procedure.

A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis

BackgroundPersonalized treatment for clinical T1 renal cortical masses (RCMs) should take into account competing risks related to tumor and patient characteristics. ObjectiveTo develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-d Clavien grade ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS). Design, setting, and participantsPretreatment clinical and radiological features were collected for consecutive adult patients treated with initial RN, PN, TA, or AS for RCMs at four high-volume referral centers (2000–2019). Outcome measurements and statistical analysisPrediction models used competing-risks regression for CSM and OCM and logistic regression for 90-d Clavien grade ≥3 complications. Performance was assessed using bootstrap validation. Results and limitationsThe cohort comprised 5300 patients treated with RN (n = 1277), PN (n = 2967), TA (n = 476), or AS (n = 580). Over median follow-up of 5.2 yr (interquartile range 2.5–8.7), there were 117 CSM, 607 OCM, and 198 complication events. The C index for the predictive models was 0.80 for CSM, 0.77 for OCM, and 0.64 for complications. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-yr-old male with a 4.5-cm RCM, body mass index of 32 kg/m2, estimated glomerular filtration rate of 50 ml/min, Eastern Cooperative Oncology Group performance status of 3, and Charlson comorbidity index of 3 has predicted 5-yr CSM of 2.9–5.6% across treatments, but 5-yr OCM of 29% and risk of 90-d Clavien grade 3–5 complications of 1.9% for RN, 5.8% for PN, and 3.6% for TA. Limitations include selection bias, heterogeneity in practice across treatment sites and the study time period, and lack of control for surgeon/hospital volume. ConclusionsWe present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment selection for RCMs. Patient summaryWe present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and of complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.

RISADA PEGA: A Mnemonic Method in Portuguese for Alarm Criteria in Emergency Headaches

Introduction We present the first mnemonic method in Portuguese, “RISADA PEGA”, for alarm criteria in emergency headaches in order to facilitate their identification and diagnosis. A search was made through different databases for a mnemonic method regarding alarm criteria in emergency headaches, and methods in English and Spanish were found, but there was no mention of one written in Portuguese. Result With that in mind, the method “RISADA PEGA” (“laughter spreads”, in English) was created: Repentina / (Sudden) I - Início depois dos 50 anos / (onset in a patient older than 50) S - Sinais e/ou sintomas de doenças sistêmicas ou neurológicas / (Signs and/or symptoms of systemic or neurologic diseases) A - Anormalidades no exame neurológico/(abnormalities upon neurologic examination) D - Depressão da imunidade(AIDS, Câncer)/Depressed immunity (HIV or cancer) A - Antecedentes: doenças que podem acometer o SNC; número de vindas ao PShistória familiar de cefaleia)/(Prior conditions: diseases affecting the central nervous system; number of visits to the health center, history of headache in the family) P - Padrão alterado (mudança no padrão anterior da cefaleia)/(change in headache pattern) E - Esforço/exertion) G - Gravidez/(pregnancy) A - Autonomia (sinais autonômicos)/(autonomic signs) Conclusion In the emergency department, an acute headache is a symptom presented by 2%-4% of admitted patients. Secondary headaches are associated with high morbidity and mortality. A need exists for the quick identification of the alarm criteria and a correct stratification of the patient's risk, proper diagnosis and treatment. There are also time constraints in the emergency department. This shows the usefulness of a method for quick identification of the alarm criteria. We believe this is a useful tool for medical education and a clinical aid for clinical practitioners, it may also be a way to educate patients regarding the alarm criteria for emergency headaches.