Clinical Algorithm Research Articles

Managing Urticarial Vasculitis: A Clinical Decision-Making Algorithm Based on Expert Consensus.

Urticarial vasculitis (UV) is a rare and difficult-to-treat, small-vessel leukocytoclastic vasculitis presenting with recurrent long-lasting wheals. So far, no guidelines and treatment algorithms exist that could help clinicians with the management of UV. In this review, we describe evidence on systemic treatments used for UV and propose a clinical decision-making algorithm for UV management based on the Urticarial Vasculitis Activity Score assessed for 7 days (UVAS7). Patients with occasional UV-like urticarial lesions and patients with UV with skin-limited manifestations and/or mild arthralgia/malaise (total UVAS7 ≤7 of 70) can be initially treated using the step-wise algorithm for chronic urticaria including second-generation H1-antihistamines, omalizumab, and cyclosporine A. Patients with UV with more severe symptoms (UVAS7 >7), especially those with hypocomplementemic UV, may require a multidisciplinary approach, particularly if underlying diseases, for example, systemic lupus erythematosus, cancer, or infection, are present. Immunomodulatory therapy is based on clinical signs and symptoms, and the drug availability and safety profile, and includes systemic corticosteroids, dapsone, hydroxychloroquine, anti-interleukin-1 agents, and other therapies. The level of evidence for all UV treatments is low. Prospective studies with current and novel drugs are needed and could provide further insights into UV pathogenesis and treatment.

FET PET to differentiate between post-treatment changes and recurrence in high-grade gliomas: a single center multidisciplinary clinic controlled study.

The clinico-radiological dilemma in post-treatment high-grade gliomas, between disease recurrence (TR) and treatment-related changes (TRC), still persists. FET (Fluoro-ethyl-tyrosine) PET has been extensively used as problem-solving modality for cases where MR imaging is inconclusive. We incorporated a systematic imaging and clinical follow-up algorithm in a multi-disciplinary clinic (MDC) setting to analyse our cohort of FET PET in post-treatment gliomas. We retrospectively analyzed 171 patients of post-treatment grade III and IV glioma with equivocal findings on MRI. 185-222 MBq of 18F-FET was injected and dedicated static imaging of brain was performed at 20min. TBR (Tumor to background ratio) was used as semi-quantitative parameter. Cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed with histopathological diagnosis, wherever available or in a multidisciplinary joint clinic based on serial imaging. 121 of 171 patients showed recurrent disease on FET PET, on follow up, 109 were confirmed with recurrence; 7 patients showed TRC, whereas 5 were treated with bevacizumab, with no further clinico-radiological deterioration, thus confirming TRC. 50 patients showed TRC on FET PET, on follow up on follow up, 40 were confirmed as true-negative. 10 patients who showed TBR less than 2.5 had confirmed TR on subsequent MR imaging. The overall sensitivity and specificity was 91.6 and 76.9% respectively, with a diagnostic accuracy of 87.13%. There is potential for FET PET to be used along with MRI in the post treatment algorithm of high-grade glial tumors.

Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea

Background/Objectives: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. Our aims are (1) to validate OSABayes prospectively, (2) to build a smartphone app based on the proposed model, and (3) to evaluate app usability. Methods: We prospectively included adult patients suspected of OSA, without suspicion of other sleep disorders, who underwent level I or III diagnostic PSG. Apnea–hypopnea index (AHI) and OSABayes probabilities were obtained and compared using the area under the ROC curve (AUC [95%CI]) for OSA diagnosis (AHI ≥ 5/h) and higher severity levels (AHI ≥ 15/h) prediction. We built the OSABayes app on ‘App Inventor 2’, and the usability was assessed with a cognitive walkthrough method and a general evaluation. Results: 216 subjects were included in the validation cohort, performing PSG levels I (34%) and III (66%). OSABayes presented an AUC of 83.6% [77.3–90.0%] for OSA diagnosis and 76.3% [69.9–82.7%] for moderate/severe OSA prediction, showing good response for both types of PSG. The OSABayes smartphone application allows one to calculate the probability of having OSA and consult information about OSA and the tool. In the usability evaluation, 96% of the proposed tasks were carried out. Conclusions: These results show the good discrimination power of OSABayes and validate its applicability in identifying patients with a high pre-test probability of OSA. The tool is available as an online form and as a smartphone app, allowing a quick and accessible calculation of OSA probability.

Construction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score.

The original STONE score was designed to predict the presence of uncomplicated renal colic and the corresponding absence of alternate serious etiologies. It was retrospectively derived and prospectively validated and resulted in five variables: Sex (male gender), Timing (acute onset of pain), "Origin" (non-Black race), Nausea/vomiting (present), and Erythrocytes (microscopic hematuria). With recent increased awareness of the potential adverse impacts of including race (a socially constructed identity) in clinical prediction rules, we sought to determine if a revised STONE score without race could be constructed with similar diagnostic accuracy. We used data from the original STONE score that utilized retrospective data on patients with confirmed kidney stone by computed tomography (CT) to derive a clinical prediction rule as well as prospective data to validate the score. These data were used to construct a revised STONE score after removing race as a variable. We performed univariate and multivariable logistic regression and compared the old and new STONE scores (including multivariable, integral, and three-level risk) using the area under the receiver operating characteristic curve (AUC) and misclassification rates. After the elimination of race, multivariable logistic regression revealed that gross hematuria was the next strongest feasible variable for the prediction of ureteral stone. This was incorporated into a revised STONE score by substituting "obvious hematuria" for "origin" (formerly race). The revised STONE score had similar predictive accuracy to the original STONE score: AUC 0.85 versus 0.86 (95% confidence interval [CI]: 0.82-0.87 and 0.79-0.93); misclassification rates were also unchanged, 0.23 versus 0.23 (95% CI: 0.20-0.25 and 0.20-0.25). We modified the STONE score to remove race and include "obvious hematuria" without losing clinical accuracy. Considering the potential adverse effects of propagating racial bias in clinical algorithms, we recommend using the revised STONE score. Future research could investigate the potential contributions of social drivers of health (SDOH) to the diagnosis of kidney stone.

Test Smart, Treat Smart-using clinician feedback to adapt a catheter-associated urinary tract infection intervention for spinal cord injury.

Catheter-associated urinary tract infection (CAUTI) prevention is a major target for hospital quality metrics because it is linked to increased morbidity, mortality, and health care costs. Health care systems use strict protocols surrounding catheterization and maintenance, which often disregard the clinical needs of special populations (eg, spinal cord injury [SCI]). However, for populations that rely on chronic instrumentation of the bladder, asymptomatic (ie, nonpathogenic) bacterial colonization in the bladder is common but not linked to adverse outcomes. Additionally, alterations in neurologic and sensory function after SCI make it difficult for clinicians to discern asymptomatic bacteriuria from acute urinary tract infection requiring antibiotics. Institutional policies for screening urine during SCI annual exams often lead to detection of bacteriuria but create a clinical decision-making challenge when determining whether antibiotic treatment is appropriate. To conduct preimplementation SCI-focused adaptations to the evidence-based practice (EBP) "Kicking CAUTI"-to develop a guide for SCI providers surrounding testing and treatment of CAUTI. Four 1-hour focus groups were conducted with SCI clinicians (prescribers and nursing staff) to assess insights on needed modifications to adapt Kicking CAUTI for SCI. The Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (FRAME-IS) was used to plan and report adaptations in this work and the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to guide adaptation of the EBP for the SCI population. Content and thematic analysis guided our process. The clinical algorithm was simplified, a urinary symptom assessment added, and provider- and patient-facing educational materials were developed to support implementation efforts to create the Test Smart Treat Smart Intervention. Traditional CAUTI protocols do not adequately address the needs of those with SCI and modifications are needed. Provider feedback provides valuable insights when adapting population appropriate interventions.

Facial Nerve Reanimation Using Free-functioning Gracilis Muscle Transfer in Adolescent Patients. Long-term Follow-up With Outcome Assessment Using House-Brackmann Facial Nerve Grading System.

Facial nerve palsy is a condition that carries significant consequences, especially when it occurs during adolescence. It represents a considerable clinical problem due to limited treatment options, as well as the fact that it has significant cosmetic and functional implications. In adolescents with persistent facial palsy, microsurgical procedures for dynamic facial reanimation may effectively restore facial symmetry. A commonly performed intervention is free-functioning muscle transfer. The study group includes 10 pediatric patients aged 11 to 17 with diagnosed persistent facial nerve palsy, surgically treated for the restoration of mimic muscle function between 2018 and 2022. All patients included in the study underwent surgical treatment using free-functioning muscle transfer. The average observation period was 38 months (range: 21-54mo). To objectively assess the return of function of the transferred innervated muscle flap during treatment and postoperative recovery, the House-Brackmann Facial Nerve Grading System was used. At an average observation period of 38 months, at least grade 3 according to the HBGS scoring system was demonstrated in all cases. Twelve months after the procedure, 6 patients (60%) were classified as grade 3, and in 4 cases (40%), the achieved symmetry was sufficient to obtain grade 2. No patient reported limitations in the adduction of the lower limb resulting from the harvest of the gracillis muscle. Further research should focus on patient-reported outcomes, conducting studies on a large study group could lead to the development of a clinical algorithm that could facilitate the assessment and effectively assist in the treatment of facial palsy.

Implementing Screening for Neonatal Delirium in the Neonatal Intensive Care Unit: A Quality Improvement Initiative.

Delirium is not commonly diagnosed in neonatal intensive care units and can adversely impact patient outcomes in the ICU setting. Recognition of delirium in the NICU is a necessary first step to address the potential impact on neonatal outcomes. We conducted a quality improvement initiative implementing screening for neonatal delirium. We aimed to increase screening in NICU patients from 0% to 85% by March 2022. Interdisciplinary meetings were held with key stakeholders to develop a clinical algorithm. We used standardized tools for delirium screening. Our process measures included weekly nursing compliance with Richmond Agitation Sedation Scale/Cornell Assessment of Pediatric Delirium/ scoring documentation (Fig. 1) and patients referred to psychiatry. Outcome measures included the percentage of patients screened for delirium before discharge. We conducted Plan-Do-Study Act cycles to optimize the screening process in the electronic medical record (EMR). This included creating an order set, documentation flowsheets, and prompts in the EMR for patients. After initial implementation, we achieved an average weekly screening compliance of 76% (Fig. 1). Inclusion criteria expansion resulted in a downward compliance shift to 59%. Subsequently, the addition of the EMR checklist resulted in a center-line shift to a sustained average weekly screening compliance of 77%. An average of 82% of all eligible NICU patients received delirium screening before discharge (Fig. 2). Using quality improvement methodology, there was increased screening and recognition of delirium in our NICU. Future research efforts could focus on assessing preventive measures and the impact of neonatal delirium on patient outcomes.

The Role of CT and MR Imaging in Stereotactic Body Radiotherapy of the Spine: From Patient Selection and Treatment Planning to Post-Treatment Monitoring.

Spine metastases (SMs) are common, arising in 70% of the cases of the most prevalent malignancies in males (prostate cancer) and females (breast cancer). Stereotactic body radiotherapy, or SBRT, has been incorporated into clinical treatment algorithms over the past decade. SBRT has shown promising rates of local control for oligometastatic spinal lesions with low radiation dose to adjacent critical tissues, particularly the spinal cord. Imaging is critically important in SBRT planning, guidance, and response monitoring. This paper reviews the roles of imaging in spine SBRT, including conventional and advanced imaging approaches for SM detection, treatment planning, and post-SBRT follow-up.

A clinical algorithm for diagnosis and treatment of insomnia in adults: an updated review.

Difficulty falling asleep and/or maintaining sleep are common complaints in patients visiting medical clinics. Insomnia can occur alone or in combination with other medical or psychiatric disorders. Diagnosis and management of insomnia at times are perplexing. This updated study review aimed at a clinical algorithm for diagnosis and treatment of insomnia in adults. We developed an easy-to-apply algorithm to diagnose and manage insomnia that can be used by general practitioners and non-sleep specialists. To this end, our team reviewed the previous studies to determine the prevalence, evaluation, and treatment of insomnia. We used the results to develop a clinical algorithm for diagnosing and managing insomnia.Insomnia occurs in a short (less than 3months duration) or chronic form (≥3months duration). Insomnia management includes both pharmacological and non-pharmacological interventions. There is ample research evidence for the impact of a variety of non-pharmacological treatments, but both types of treatments can be used for each patient. If there are any contradictions in the diagnosis process, therapists should use objective instruments, such as polysomnography, but they should not be in a hurry to use these instruments.

Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism.

To train and validate machine learning-derived clinical decision algorithm (MLCDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning. This retrospective study included 458 consecutive primary hyperparathyroidism (PHPT) patients who underwent combined 4D-CT and sestamibi SPECT/CT (MIBI) with subsequent parathyroidectomy from February 2013 to September 2016. The study cohort was divided into training (first 400 patients) and validation sets (remaining 58 patients). Sixteen clinical, laboratory, and imaging variables were evaluated. A random forest algorithm selected the best predictor variables and generated a clinical decision algorithm with the highest performance (MLCDA). The MLCDA was trained to predict the probability of a hyperfunctioning vs normal gland for each of the four parathyroid glands in a patient. The reference standard was a four-quadrant location on operative reports and pathology. The accuracy of MLCDA was prospectively validated. Of 16 variables, the algorithm selected 3 variables for optimal prediction: combined 4D-CT and MIBI using (1) sensitive reading, (2) specific reading, and (3) cross-product of serum calcium and parathyroid hormone levels and outputted an MLCDA using five probability categories for hyperfunctioning glands. The MLCDA demonstrated excellent accuracy for correct classification in the training (4D-CT + MIBI: 0.91 [95% CI: 0.89-0.92]) and validation sets (4D-CT + MIBI: 0.90 [95% CI: 0.86-0.94]. Machine learning generated a clinical decision algorithm that accurately diagnosed hyperfunctioning parathyroid glands through classification into probability categories, which can be implemented for improved preoperative planning and convey diagnostic certainty. Question Can an MLCDA use preoperative variables for the diagnosis of hyperfunctioning parathyroid glands to facilitate surgical planning? Findings The developed MLCDA demonstrated excellent accuracy for correct classification in the training (0.91 [95% CI: 0.89-0.92]) and validation sets (0.90 [95% CI: 0.86-0.94]). Clinical relevance Using standard preoperative variables, an MLCDA for diagnosing hyperfunctioning parathyroid glands can be implemented to improve preoperative parathyroid localization and included in radiology reports forsurgical planning.

Phase II study of uPAR-PET/CT for staging of primary breast cancer in comparison with ultrasound and fine needle biopsies

Accurate initial staging of patients with breast cancer is essential for planning optimal treatment strategies. However, currently, no imaging modality is able to detect lymph node metastases preoperatively with sufficient reliability; therefore, the N status depends on the sentinel node procedure for ~ 70% of patients. In a prospective clinical trial of breast cancer patients, we compared head-to-head uPAR-PET/CT with current standard-of-care, ultrasound (US) and fine needle biopsy (FNB) as staging methods. Forty-nine patients (48 women and 1 man) with biopsy-proven early breast cancer underwent uPAR-PET/CT prior to surgery. All image data were analyzed by two separate teams, each consisting of a highly experienced certified specialist in nuclear medicine and a highly experienced certified specialist in radiology for visualization of primary tumor lesions and detection of lymph node and distant metastases. Histopathological assessment and verification of malignancy in the excised tissues (primary tumors and lymph nodes) were considered standard-of-truth. On a per patient basis, uPAR PET/CT demonstrated a sensitivity of 94% [CI: 83–99%] for detecting the primary tumor (both teams). For the detection of axillary lymph nodes the pooled sensitivity of uPAR PET/CT was 33.3% [CI: 16.5–54.0%], specificity 87.0% [CI: 66.4–97.2%] and accuracy 58.0% [CI: 43.2–71.8%]. In comparison, the standard-of-care preoperative clinical staging algorithm with US and FNB had a sensitivity of 41% [CI: 22–61%] and specificity of 100% [CI: 85–100%] for axillary lymph node metastases. We conclude that the results do not support the use of uPAR PET/CT for staging in breast cancer patients. However, the finding that 94% of primary tumors were uPAR-PET positive may be encouraging for pursuing uPAR theranostics in localized disease. Additionally, other potential applications, such as using uPAR-PET as a prognostic imaging biomarker of tumor aggressiveness, remain to be investigated.

Assessing the need for coronary angiography in high-risk acute coronary syndrome patients using artificial intelligence and computed tomography

Abstract Background/Introduction The use of coronary computed tomography angiography (CCTA) in managing high-risk acute coronary syndrome (ACS) patients is increasingly common. Yet, its role remains unvalidated in this context, posing challenges to traditional clinical decision-making algorithms. Physicians, accustomed to a hierarchical and structured approach involving symptom assessment, electrocardiograms, and biomarkers for deciding on the necessity of invasive coronary angiography (ICA), now face dilemmas when CCTA results challenge established diagnostic pathways and contradict clinical decisions based on usual criteria. Meanwhile, the potential of artificial intelligence (AI) which processes information in a way fundamentally different from human clinical reasoning, has been shown to aid decision-making in cardiovascular medicine. Yet, it has not been tested in this acute setting. Purpose This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for ICA in high-risk ACS patients based on both standard clinical data and CCTA findings. Methods This investigation is a sub-study of a larger research project where high-risk ACS patients (all having a theoretical guideline-based indication for ICA) underwent CCTA prior to ICA. ChatGPT analyzed clinical vignettes containing patient data, electrocardiograms, troponin levels, and CCTA results to determine the necessity of ICA. The AI's recommendations were then compared to actual ICA findings to assess its decision-making accuracy. Results Among 86 patients with a clear indication for ICA, ChatGPT recommended against it for 19 patients, 16 of whom indeed had no significant findings at ICA (Figure). For 67 patients, ChatGPT advised proceeding with ICA, and a significant lesion was confirmed in 58 of them. Consequently, ChatGPT's overall accuracy stood at 86%, with a sensitivity of 95% and a specificity of 64%. The model's negative predictive value was 84%, and its positive predictive value was 87%. Conclusion These preliminary data suggest that ChatGPT can effectively assist in guiding the need for ICA among high-risk ACS patients, potentially reducing unnecessary procedures but further clinical studies to refine AI's role in clinical decision-making are required.ChatGPT's decision as per ICA's results

The Challenge of Treating Infections Caused by Metallo-β-Lactamase-Producing Gram-Negative Bacteria: A Narrative Review.

Gram-negative multidrug-resistant (MDR) bacteria, including Enterobacterales, Acinetobacter baumannii, and Pseudomonas aeruginosa, pose a significant challenge in clinical practice. Infections caused by metallo-β-lactamase (MBL)-producing Gram-negative organisms, in particular, require careful consideration due to their complexity and varied prevalence, given that the microbiological diagnosis of these pathogens is intricate and compounded by challenges in assessing the efficacy of anti-MBL antimicrobials. We discuss both established and new approaches in the treatment of MBL-producing Gram-negative infections, focusing on 3 strategies: colistin; the recently approved combination of aztreonam with avibactam (or with ceftazidime/avibactam); and cefiderocol. Despite its significant activity against various Gram-negative pathogens, the efficacy of colistin is limited by resistance mechanisms, while nephrotoxicity and acute renal injury call for careful dosing and monitoring in clinical practice. Aztreonam combined with avibactam (or with avibactam/ceftazidime if aztreonam plus avibactam is not available) exhibits potent activity against MBL-producing Gram-negative pathogens. Cefiderocol in monotherapy is effective against a wide range of multidrug-resistant organisms, including MBL producers, and favorable clinical outcomes have been observed in various clinical trials and case series. After examining scientific evidence in the management of infections caused by MBL-producing Gram-negative bacteria, we have developed a comprehensive clinical algorithm to guide therapeutic decision making. We recommend reserving colistin as a last-resort option for MDR Gram-negative infections. Cefiderocol and aztreonam/avibactam represent favorable options against MBL-producing pathogens. In the case of P. aeruginosa with MBL-producing enzymes and with difficult-to-treat resistance, cefiderocol is the preferred option. Further research is needed to optimize treatment strategies and minimize resistance.

Adult emergency resuscitative thoracotomy: A Western Trauma Association clinical decisions algorithm.

Adult emergency resuscitative thoracotomy: A Western Trauma Association clinical decisions algorithm.

Determinants of symptom presentation and resolution following concussions in high school sports

ABSTRACT Sport-related concussions (SRCs) are prevalent in high school (HS) sports, though the determinants of, and relationships between concussion symptoms in this population remain relatively unknown. We analysed SRC data captured within the HS RIO injury surveillance system during 2014/15-2018/19. We used Generalized Estimating Equations to simultaneously assess covariate predictors of symptom presentations and identify pairwise symptom associations and employed multivariable ordinal logistic regressions to determine symptom resolution time (SRT) predictors. Among the 8,969 concussions assessed, headaches (94.3%) and dizziness (73.3%) were the most prevalently reported symptoms. In 5,953 boys’ concussion assessed, class year, event type and injury mechanism emerged as determinants of various concussion symptoms, while in 3,016 girls’ concussions assessed, class year, event type, injury mechanism, and sport type emerged as significant symptom determinants. In both groups, difficulty concentrating, sensitivity to light, sensitivity to noise, and disorientation were symptoms with the strongest associations with other symptoms. Odds of longer SRT were higher with greater endorsement (i.e. counts) of the subset of symptoms with strongest cross-domain associations (ORadj. = 1.35; 95% CI = [1.30, 1.40]). Our findings provide valuable information for informing sideline and follow-up clinical concussion assessment algorithms in high school athletes.

"SWALLOWING AND ASPIRATION: HOW TO EVALUATE AND TREAT SWALLOWING DISORDERS ASSOCIATED WITH ASPIRATION PNEUMONIA IN OLDER PERSONS".

BACKGROUND Aspiration pneumonia (AP) is the most severe complication of oropharyngeal dysphagia (OD). It is highly underdiagnosed and undertreated among older patients hospitalized with community-acquired pneumonia (CAP). Our aim is to review the state of the art in the diagnosis and treatment of swallowing disorders associated with AP. METHODOLOGY We performed a narrative review, including our experience with prior studies at Hospital de Mataró, on the diagnosis and treatment of AP. RESULTS AP refers to pneumonia occurring in patients with swallowing disorders, frequently coinciding with poor oral health and vulnerability. Its main risk factors include oropharyngeal aspiration, impaired health status, malnutrition, frailty, immune dysfunction and oral colonization by respiratory pathogens. Incidence is estimated at between 5%-15% of cases of CAP but it is highly underdiagnosed. Diagnostic criteria for AP have not been standardized but should include its main pathophysiological element, oropharyngeal aspiration. Recently, a clinical algorithm was proposed, based on the recommendations of the Japanese Respiratory Society (JRS), that includes aspiration risk factors and clinical evaluation of OD. To facilitate the task for healthcare professionals, new AI-based screening tools for OD combined with validated clinical methods such as the volume-viscosity swallowing test (V-VST) for the detection of AP are being validated. Prevention and treatment of AP require multimodal interventions aimed to cover the main risk factors: textural adaptation of fluids and diets to avoid oropharyngeal aspiration; nutritional support to avoid malnutrition; and oral hygiene to reduce oral bacterial load. CONCLUSIONS The diagnosis of AP must be based on standardized criteria providing evidence on the main etiological factor, oropharyngeal aspiration. Clinical algorithms are valid in the diagnosis of AP and the identification of its main risk factors. Combination of AI-based tools with V-VST can lead to massive screening of OD and save resources and improve efficiency in the detection AP.

Reliability and validation of an electronic penicillin allergy risk-assessment tool in a pregnant population

BackgroundPenicillin allergy adversely impacts patient care, yet most cases do not have true allergies. Clinicians require efficient, reliable clinical tools to identify low risk patients who can be safely de-labeled. Our center implemented the FIRSTLINE electronic point-of-care decision support tool to help non-allergist practitioners risk stratify patients with penicillin allergy. We sought to explore the reliability and validity of this tool in relation to allergist assessment and actual patient outcomes. We additionally compared it with two other published stratification tools, JAMA and PENFAST, to assess ability to accurately identify low risk patients appropriate for direct oral challenge.MethodsIn this single-center, retrospective, observational study, 181 pregnant females with self-reported penicillin allergy between July 2019 to June 2021 at BC Women’s Hospital, Vancouver, Canada were used to assess the reliability and validity of all three tools. Physician-guided history of penicillin use and symptoms were used for scoring. Results and recommendations were compared to actual patient outcomes after clinician decision for direct oral challenge or intradermal tests. We compared the performance of JAMA, PENFAST and FIRSTLINE.Results181 patients were assessed. 176/181 (97.2%) patients were deemed not allergic. Each risk stratification tool labelled majority of patients as low risk with 88.4% of patients PENFAST 0–2, 60.2% of patients JAMA low risk, 86.7% of patients FIRSTLINE very low risk.ConclusionWe demonstrate that our point-of-care electronic algorithm is reliable in identifying low risk pregnant patients, as compared to an allergist assessment. To our knowledge, this is the first study to provide direct comparison between multiple decision support tools using the same population, minimizing participant bias. Providing clinical algorithms to risk stratify patients, can enable healthcare professionals to safely identify individuals who may be candidates for direct penicillin oral challenges versus needing referral to specialists. This increases the generalizability and efficiency of penicillin allergy de-labeling.

Disparities in screening and risk stratification for hispanic adults with metabolic dysfunction-associated steatotic liver disease.

Cut-points for non-invasive tests (NITs) for risk stratification in metabolic dysfunction-associated steatotic liver disease (MASLD) were derived from predominantly non-Hispanic populations. It is unknown if these cut-points perform adequately in Hispanic individuals. We assessed the performance characteristics of current NIT cut-points among Hispanic patients and determined whether they could be further optimized. We prospectively enrolled 244 adults with biopsy-proven MASLD. Participants underwent a research visit with magnetic resonance elastography (MRE) and vibration controlled transient elastography (VCTE). Histology and imaging assessments were conducted centrally. Diagnostic performance was evaluated by area under the receiver-operating curve (AUROC) and optimal cut-points were identified by Youden J analysis. The mean (±SD) age and body mass index were 52.6 (±13) and 31.6 (±4.6) kg/m2. Overall, 40% had diabetes, 31% (N=75) were Hispanic. 40% of Hispanic and 28.4% of non-Hispanic patients had significant fibrosis. To detect significant fibrosis, MRE and VCTE exhibited significantly lower accuracy in Hispanic versus non-Hispanic participants (AUROC: MRE, 0.87 vs. 0.98, p=0.01; VCTE, 0.78 vs. 0.92, p=0.02). Clinical care algorithms yielded high false-negative rates among Hispanic participants (14% with low-risk FIB-4 and 21% with low-risk VCTE had advanced fibrosis on biopsy). Cut-points of 2.73kPa for MRE and 6.9kPa for VCTE were optimal to detect significant fibrosis in Hispanic individuals. Findings were validated in a Latin American cohort. Lower NIT cut-points may be needed to optimize surveillance for significant fibrosis due to MASLD in Hispanic populations commensurate with their higher burden and severity of disease.

New York City’s Public Health Approach to Reexamining Race-Based Clinical Algorithms

Clinical algorithms are utilized in clinical decision-making — whether for diagnosis or treatment — with the aim of accounting for pertinent biological factors. However, the use of race in some clinical algorithms disproportionately harms patients of color, often delaying necessary care. The erroneous use of race as a biological variable perpetuates the antiquated notion of race essentialism and demonstrates how medical racism is embedded in health care. In 2021, the New York City (NYC) Department of Health and Mental Hygiene launched the Coalition to End Racism in Clinical Algorithms (CERCA). To the authors’ knowledge, CERCA was the first citywide effort to convene seven safety-net and four private health care systems for the elimination of harmful race-based clinical algorithms, representing the health institutions in NYC serving the most patients. Focusing on estimated glomerular filtration rate, vaginal birth after cesarean, and pulmonary function testing, CERCA serves as a platform for health systems to discuss and strategize how to effectively de-implement these harmful race-based algorithms. NYC CERCA serves as a model for how a public health department can leverage its role as a convener by collaborating with health systems to address matters of racial inequities caused by systemic racism in medicine and supporting the swift implementation of reparative initiatives on a systems level.

The Power of Collaboration to Improve Care Delivery

This issue of NEJM Catalyst Innovations in Care Delivery includes articles, case studies, and research reports on a multinational registry, a culture of respect, eliminating race-based clinical algorithms, improving racial health equity, complex care for hard-to-reach patients, engaging pharmacists in preventive care, and health policy.