Information For Management Of Patients Research Articles

Integrated immune networks in SARS-CoV-2 infected pregnant women reveal differential NK cell and unconventional T cell activation

Abstract Although pregnancy poses a greater risk for severe COVID-19, the underlying immunological changes associated with SARS-CoV-2 infection during pregnancy are poorly understood. We defined immune responses to SARS-CoV-2 in pregnant and non-pregnant women during acute and convalescent COVID-19 up to 258 days post symptom onset, quantifying 217 immunological parameters. Additionally, matched maternal and cord blood were collected from COVID-19 convalescent pregnancies. Although serological responses to SARS-CoV-2 were similar in pregnant and non-pregnant women, cellular immune analyses revealed marked differences in key NK cell and unconventional T cell responses during COVID-19 in pregnant women. While NK cells, γδ T cells and MAIT cells displayed pre-activated phenotypes in healthy pregnant women when compared to non-pregnant age-matched women, activation profiles of these pre-activated NK and unconventional T cells remained unchanged at acute and convalescent COVID-19 in pregnancy. Conversely, activation dynamics of NK and unconventional T cells were prototypical in non-pregnant women in COVID-19. In contrast, activation of αβ CD4+ and CD8+ T cells, T follicular helper cells and antibody-secreting cells was similar in pregnant and non-pregnant women with COVID-19. Elevated levels of IL-1β, IFN-γ, IL-8, IL-18 and IL-33 were also found in pregnant women in their healthy state, and these cytokine levels remained elevated during acute and convalescent COVID-19. Collectively, our study provides key insight to innate T cell and NK cell perturbations occurring in pregnant women with COVID-19, which will potentially inform patient management and education for those with COVID-19 during pregnancy. Supported by National Health and Medical Research Council, Australia

Institutionally validated nomogram predicting prognosis for older patients with nonmetastatic nasopharyngeal carcinoma.

Aim: Older adult patients with nonmetastatic nasopharyngeal carcinoma (NPC) have poor outcomes relative to younger patients. The authors' groupestablished a nomogram to predict the overall survival of older adults with NPC and inform patient management. Methods: Cases with NPC (n=782) were enrolled in this study; clinical data in the Surveillance, Epidemiology, and End Results database from 2010 to 2015 served as the training cohort (n=657), and patients from Jiangxi Cancer Hospital (n=125) served as the external validation cohort. Results: Training and external validation cohort C-index, receiver operator characteristicscurves and calibration curves showed that our nomogram has great predictive ability. Conclusions: Compared with tumor-node-metastasis staging, this nomogram can help clinicians better predict the prognosis of older adults with nonmetastatic NPC.

Contribution of Uremia to Ureaplasma-Induced Hyperammonemia.

ABSTRACTLung transplant recipients (LTRs) are vulnerable to hyperammonemia syndrome (HS) in the early postoperative period, a condition typically unresponsive to nonantibiotic interventions. HS in LTRs is strongly correlated with Ureaplasma infection of the respiratory tract, although it is not well understood what makes LTRs preferentially susceptible to HS compared to other immunocompromised hosts. Ureaplasma species harbor highly active ureases, and postoperative LTRs commonly experience uremia. We hypothesized that uremia could be a potentiating comorbidity, providing increased substrate for ureaplasmal ureases. Using a novel dialyzed flow system, the ammonia-producing capacities of four isolates of Ureaplasma parvum and six isolates of Ureaplasma urealyticum in media formulations relating to normal and uremic host conditions were tested. For all isolates, growth under simulated uremic conditions resulted in increased ammonia production over 24 h, despite similar endpoint bacterial quantities. Further, transcripts of ureC (from the ureaplasmal urease gene cluster) from U. urealyticum IDRL-10763 and ATCC-27816 rose at similar rates under uremic and nonuremic conditions, with similar endpoint populations under the two conditions (despite markedly increased ammonia concentrations under uremic conditions), indicating that the difference in ammonia production by these isolates is due to increased urease activity, not expression. Lastly, uremic mice infected with an Escherichia coli strain harboring a U. urealyticum serovar 8 gene cluster exhibited higher blood ammonia levels compared to nonuremic mice infected with the same strain. Taken together, these data show that U. urealyticum and U. parvum produce more ammonia under uremic conditions compared to nonuremic conditions. This implies that uremia is a plausible contributing factor to Ureaplasma-induced HS in LTRs.IMPORTANCE Ureaplasma-induced hyperammonemia syndrome is a deadly complication affecting around 4% of lung transplant recipients and, to a lesser extent, other solid organ transplant patients. Understanding the underlying mechanisms will inform patient management, potentially decreasing mortality and morbidity. Here, it is shown that uremia is a plausible contributing factor to the pathophysiology of the condition.

Abstract P2-01-18: Orthogonal assessment of PIK3CA and ESR1 mutation detection in longitudinal cfDNA samples from endocrine-resistant HR+/HER2- advanced breast cancer patients using dPCR and NGS-based SafeSEQ technology

Abstract Background: Indentification of actionable or therapy resistance-associated mutations may guide sequential treatments in Hormone Receptor-positive and HER2-negative (HR+/HER2-) Advanced Breast Cancer (ABC) patients with resistance to prior endocrine therapies. Molecular profiling from cell-free DNA (cfDNA) is progressively permeating the clinical setting as an alternative or adjunct to tissue biopsies. Digital PCR (dPCR) assays permit the detection of genomic alterations with high sensitivity but are associated with cumbersome workflows and limited genomic coverage. NGS-based cfDNA assays are being increasingly adopted, as they can offer broader coverage while maintaining competitive sensitivity. Methods:Mutation testing was performed on plasma samples obtained from patients with HR+/HER2- ABC who had recurred or progressed after receiving an aromatase inhibitor. Samples were collected at the start of new treatment, during therapy, and at progression. dPCR tests were carried out using QuantStudio3D Digital PCR at the Molecular Oncology Laboratory at Clínico San Carlos Hospital. dPCR targets included PIK3CA E542K, E545K, and H1047R, and ESR1 Y537S and D538G. Samples[CdS1] were also tested with the SafeSEQ Breast Cancer Panel to assess clinically relevant genomic regions across PIK3CA, ESR1, AKT1, TP53, KRAS, and ERBB2. SafeSEQ testing was performed in Sysmex Inostics’ CLIA-certified, CAP-accredited laboratory. Results: Mutational data obtained from both dPCR and SafeSEQ testing were available for concordance analysis in 107 samples from 50 patients for PIK3CA, and 86 samples from 41 patients for ESR1. Combined results showed PIK3CA mutations in 47 samples (43.9%), with H1047R (14%), E545K (11.2%), and E542K (9.3%) being the most frequent mutations detected. ESR1 mutations were present in 35 samples (32.7%), where D538G (19.6%) and Y537S (9.3%) were most commonly observed. Among samples with no mutations detected by dPCR, the expanded coverage of SafeSEQ enabled the detection of clinically relevant mutations in PIK3CA and ESR1 in 10 (9.3%) and 3 (2.8%) samples, respectively. The concordance rates for dPCR and SafeSEQ were 93.1% and 88.9% for PIK3CA and ESR1, with positive percent agreements (PPA) of 93.8% and 84.6%, and negative percent agreements (NPA) of 92.8% and 90.9% for PIK3CA and ESR1, respectively. A strong correlation was observed between MAF levels (Spearman's ρ = 0.87 [95% CI 0.79-0.93], p<0.001) as well as number of mutant molecules per mL of plasma (ρ = 0.79 [95% CI 0.66-0.88], p<0.001). When samples with MAFs<0.1% were evaluated, lower concordance rate was detected (38.5%; N=13; 46.2% detected by SafeSEQ and 92.3% by dPCR). Finally, no significant difference between assays was found when comparing concordance rates among samples at different timepoints. Regarding longitudinal ctDNA data, 18 and 16 patients showed mutations in PIK3CA and/or ESR1 in plasma at least in one timepoint. Monitoring results were concordant in 50%/56.3% of patients for PIK3CA/ESR1 (during therapy and/or at progression time, 5.6%/18.8% of patients showed decrease or clearance of ctDNA and 38.9%/25% showed increase of ctDNA).Conclusion:In this cohort of AI-progressing HR+/HER2- ABC patients, dPCR and SafeSEQ methods showed high overall agreement for the detection of mutations in PIK3CA and ESR1, key genes for clinical decision-making and enrollment in clinical trials. The expanded coverage of SafeSEQ makes it a powerful tool to inform patient management considering the growing pool of clinically relevant genomic alterations in this type of patients. Efforts are ongoing to expand the analysis to a larger cohort and incorporate long-term outcomes and multivariate adjustment. Supported by AstraZeneca. Citation Format: Jesus Fuentes Antras, Vanesa García-Barberán, Fernando Moreno, Hillary Sloane, Igor López-Cade, Alicia De Luna, Alejandro Pascual, Carmen Ramirez-Ruda, Victor Lorca, Paloma Flores, Pedro Perez-Segura, Alberto Ocaña, Jennifer Preston, Hannah Quinn, Frederick Jones, Jose Angel Garcia-Saenz. Orthogonal assessment of PIK3CA and ESR1 mutation detection in longitudinal cfDNA samples from endocrine-resistant HR+/HER2- advanced breast cancer patients using dPCR and NGS-based SafeSEQ technology [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-18.

Neuro-ophthalmological evaluation including optical coherence tomography surrounding venous sinus stenting in idiopathic intracranial hypertension with papilledema: a case series

Venous sinus stenting (VSS) for idiopathic intracranial hypertension (IIH) has been demonstrated to achieve significant symptom improvement while harboring a low periinterventional morbidity profile. Comprehensive neuro-ophthalmological monitoring represents a cornerstone of disease monitoring. The value of optical coherence tomography (OCT) requires further exploration. Patients with IIH and papilledema who underwent VSS between 04/2018 and 02/2021 were retrospectively reviewed. Clinical and radiological were analyzed. Neuro-ophthalmological data included visual acuity, visual fields, fundoscopy categorized via Frisén scale, and OCT obtained retinal nerve fiber layer (RNFL) thickness were analyzed. Of 39 IIH patients who underwent cerebral angiography with transverse-sigmoid sinus pressure evaluation, 18 patients with IIH and papilledema underwent 21 transverse-sigmoid sinus stenting (TSST) procedures. After TSST, manometry showed a significant reduction of maximum transverse sinus pressures and trans-stenotic gradient pressures (p = 0.005 and p < 0.001, respectively). Chronic headaches, visual disturbance resolved and pulsatile tinnitus improved significantly. Visual fields remained similar, while papilledema Frisén scales and visual acuity significantly improved. The OCT calculated RNFL thickness significantly decreased in all patients. Stratification according to a minimal-low degree (Frisén 1-2) and moderate-marked degree (Frisén 3-4) papilledema demonstrated a significant reduction of RNFL thickness in both groups. Venous sinus stenting provides favorable clinical and neuro-ophthalmological outcomes. This study demonstrates that neuro-ophthalmologic testing augmented with OCT evaluation provides objective data that can be used as a biomarker for treatment success for managing patients with different extents of papilledema and may inform patient management.

Moving toward AI-assisted decision-making: Observation on clinicians' management of multimedia patient information in synchronous and asynchronous telehealth contexts.

Artificial intelligence (AI) intends to support clinicians' patient diagnosis decisions by processing and identifying insights from multimedia patient information. We explored clinicians' current decision-making patterns using multimedia patient information (MPI) provided by AI algorithms and identified areas where AI can support clinicians in diagnostic decision-making. We recruited 87 advanced practice nursing (APN) students who had experience making diagnostic decisions using AI algorithms under various care contexts, including telehealth and other healthcare modalities. The participants described their diagnostic decision-making experiences using videos, images, and audio-based MPI. Clinicians processed multimedia patient information differentially such that their focus, selection, and utilization of MPI influence diagnosis and satisfaction levels. To streamline collaboration between AI and clinicians across healthcare contexts, AI should understand clinicians' patterns of MPI processing under various care environments and provide them with interpretable analytic results for them. Furthermore, clinicians must be trained with the interface and contents of AI technology and analytic assistance.

Magnetic resonance spectroscopy outperforms perfusion in distinguishing between pseudoprogression and disease progression in patients with glioblastoma.

BackgroundThere is a need to establish biomarkers that distinguish between pseudoprogression (PsP) and true tumor progression in patients with glioblastoma (GBM) treated with chemoradiation.MethodsWe analyzed magnetic resonance spectroscopic imaging (MRSI) and dynamic susceptibility contrast (DSC) MR perfusion data in patients with GBM with PsP or disease progression after chemoradiation. MRSI metabolites of interest included intratumoral choline (Cho), myo-inositol (mI), glutamate + glutamine (Glx), lactate (Lac), and creatine on the contralateral hemisphere (c-Cr). Student T-tests and area under the ROC curve analyses were used to detect group differences in metabolic ratios and their ability to predict clinical status, respectively.Results28 subjects (63 ± 9 years, 19 men) were evaluated. Subjects with true progression (n = 20) had decreased enhancing region mI/c-Cr (P = .011), a marker for more aggressive tumors, compared to those with PsP, which predicted tumor progression (AUC: 0.84 [0.76, 0.92]). Those with true progression had elevated Lac/Glx (P = .0009), a proxy of the Warburg effect, compared to those with PsP which predicted tumor progression (AUC: 0.84 [0.75, 0.92]). Cho/c-Cr did not distinguish between PsP and true tumor progression. Despite rCBV (AUC: 0.70 [0.60, 0.80]) and rCBF (AUC: 0.75 [0.65, 0.84]) being individually predictive of tumor response, they added no additional predictive value when combined with MRSI metabolic markers.ConclusionsIncorporating enhancing lesion MRSI measures of mI/c-Cr and Lac/Glx into brain tumor imaging protocols can distinguish between PsP and true progression and inform patient management decisions.

Loperamide-Induced Cardiac Events: Case Reports and Review

Reports of cardiac arrhythmia secondary to loperamide toxicity have become increasingly common in the literature. We present two patients in their mid-20s, each having overdosed on loperamide and subsequently manifesting life-threatening cardiac arrhythmias not otherwise explained by known pathology. An analysis of the limited research available indicates that loperamide’s capacity to block ion channels may be responsible for these events. A better mechanistic understanding of loperamide’s effects can help inform clinical management of patients with these life-threatening symptoms as at this time no set guidelines for management have yet been established.

Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

Causes of Obstruction in the Outpatient Department of National Hospital, Sri Lanka: A Qualitative Study

Sri Lanka is a country in South Asia that has a high-level health care system. The National Hospital, Sri Lanka (NHSL) situated in Colombo, is the largest teaching hospital in Sri Lanka and the final referral centre in the country consisting of 3000 beds and the bed occupancy rate is 75 with 3.7 average lengths of stay. Outpatient care, sometimes called ambulatory care, is defined as medical care or treatment that does not require an overnight stay in a hospital or medical facility. The outpatient department (OPD) is considered as the window to hospital services and a patient's impression of the hospital begins at the OPD. The OPD of the National Hospital of Sri Lanka (NHSL) also faces the problem of congestion. This study aims to identify the causes of congestion at OPD of NHSL and provide solutions. Key informant interviews with relevant stakeholders, focus group discussions, desk review of secondary data, and direct observations of OPD processes were used as methodologies. Key informant interviews were conducted with the deputy director, OPD, medical officer-in-charge, and nursing sister in charge of the unit by the principal investigator. Two focus group discussions were conducted, with 12 randomly selected medical officers and 10 nursing officers with a minimum of one year of work experience at the OPD. Data retrieved from the desk review of the secondary data from the admission book, and OPD monthly statistics. Direct observation was used to triangulate the data. Around 690,000 patients are treated annually in OPD of NHSL. The average number of OPD patients per day is about 1900. Usually, a doctor at OPD of NHSL examines 36 patients per hour. Therefore, consultation time is limited to 1.6 minutes which is highly inadequate. Root causes for congestion were analyzed by using the Ishikawa diagram. Poor layout arrangements, delay of doctors and quota system of doctors, the non-availability of the patient information management system, absence of proper referral system in the country, the larger number of staff members were identified as root causes. Improvement of the layout of OPD in a unidirectional way, advised doctors to start duties on time abolish the quota system of doctors, establishment of the computer-based patients' registration system, establishing laboratory within OPD, establishing two more counters at OPD pharmacy were the major recommendation of this study to minimize the congestion at OPD, NHSL.

Machine Learning in Pituitary Surgery.

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

Cardiovascular events in patients treated with chimeric antigen receptor T-cell therapy for aggressive B-cell lymphoma.

Standard of care (SOC) chimeric antigen receptor (CAR) T-cell therapies such as axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are associated with multisystem toxicities. There is limited information available about cardiovascular (CV) events associated with SOC axi-cel or tisa-cel. Patients with CV comorbidities, organ dysfunction, or lower performance status were often excluded in the clinical trials leading to their Food and Drug Adminsitration approval. An improved understanding of CV toxicities in the real-world setting will better inform therapy selection and management of patients receiving these cellular therapies. Here, we retrospectively reviewed the characteristics and outcomes of adult patients with relapsed/refractory large B-cell lymphoma treated with SOC axi-cel or tisa-cel. Among the 165 patients evaluated, 27 (16%) developed at least one 30-day (30-d) major adverse CV event (MACE). Cumulatively, these patients experienced 21 arrhythmias, four exacerbations of heart failure/cardiomyopathy, four cerebrovascular accidents, three myocardial infarctions, and one patient died due to myocardial infaction. Factors significantly associated with an increased risk of 30-d MACE included age ≥60 years, an earlier start of cytokine release syndrome (CRS), CRS ≥ grade 3, long duration of CRS, and use of tocilizumab. After a median follow-up time of 16.2 months (range, 14.3-19.1), the occurrence of 30-d MACE was not significantly associated with progression-free survival or with overall survival. Our results suggest that the occurrence of 30-d MACE is more frequent among patients who are elderly, with early, severe, and prolonged CRS. However, with limited follow-up, larger prospective studies are needed, and multidisciplinary management of these patients is recommended.

Rapid genomic testing for critically ill children: time to become standard of care?

Rapid genomic testing in critically ill neonatal and paediatric patients has transformed the paradigm of rare disease diagnosis, delivering results in real time to inform patient management. More than 20 studies totalling over 1500 patients from diverse healthcare settings worldwide have now been published, forming a compelling evidence base for healthcare system implementation. We review the reported diagnostic and clinical outcomes, as well as broader evaluations of family and professional experiences, cost effectiveness, implementation challenges and bioethical issues arising from rapid testing. As rapid genomic testing transitions from the research to the healthcare setting to become a 'standard of care' test, there is a need to develop effective service delivery models to support scalability at both the laboratory and clinical level and promote equity of access, prompt test initiation, integrated multidisciplinary input and holistic family support. Harnessing the high level of professional engagement with rapid genomic testing programmes will continue to drive innovation and adoption, while close integration with emerging precision medicine approaches will be necessary to deliver on the promise of reduced infant and child mortality.

Combating Childhood Infections in LMICs: evaluating the contribution of Big Data Big data, biomarkers and proteomics: informing childhood diarrhoeal disease management in Low- and Middle-Income Countries

<h2>Abstract</h2> Despite efforts to reduce the global burden of childhood diarrhoea, 50% of all cases globally occur in children under five years in Low–Income and Middle- Income Countries (LMICs) and knowledge gaps remain regarding the aetiological diagnosis, introduction of diarrhoeal vaccines, and the role of environmental enteric dysfunction and severe acute malnutrition. Biomarkers may assist in understanding disease processes, from diagnostics, to management of childhood diarrhoea and the sequelae to vaccine development. Proteomics has the potential to assist in the identification of new biomarkers to understand the processes in the development of childhood diarrhoea and to aid in developing new vaccines. Centralised repositories that enable mining of large data sets to better characterise risk factors, the proteome of both the patient and the different diarrhoeal pathogens, and the environment, could inform patient management and vaccine development, providing a systems biological approach to address the burden of childhood diarrhoea in LMICs.

The effect of diabetes on abdominal aortic aneurysm growth over 2 years

ObjectiveAbdominal aortic aneurysm (AAA) is a common progressive disease and a significant cause of morbidity and mortality. Prior investigations have shown that diabetes mellitus (DM) may be relatively protective of AAA incidence and growth. The Non-invasive Treatment of Aortic Aneurysm Clinical Trial (N-TA3CT) is a contemporary study of small AAA growth that provides a unique opportunity to validate and explore the effect of DM on AAA. Confirming the effect of DM on AAA growth in this study may present opportunities to explore for clues to potential biologic mechanisms as well as inform current patient management. MethodsThis is a secondary analysis examining the association of diabetes and aneurysm growth within N-TA3CT: a placebo-controlled multicenter trial of doxycycline in 261 patients with AAA maximum transverse diameters (MTDs) between 3.5 and 5 cm. The primary outcome is the change in the MTD from baseline as determined by computed tomography (CT) scans obtained during the trial. Secondary outcome is the growth pattern of the AAA. Baseline characteristics and growth patterns were assessed with t tests (continuous) or χ2 tests (categorical). Unadjusted and adjusted longitudinal analyses were performed with a repeated measures linear mixed model to compare AAA growth rates between patients with and without diabetes. ResultsOf 261 patients, 250 subjects had sufficient imaging and were included in this study. There were 56 patients (22.4%) with diabetes and 194 (77.6%) without. Diabetes was associated with higher body mass index and increased rates of hypercholesterolemia and coronary artery disease (P < .05). Diabetes was also associated with increased frequency of treatment for atherosclerosis and hypertension including treatment with statin, angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, anti-platelet, and diuretic therapy (P < .05). Baseline MTD was not significantly different between those with (4.32 cm) and without DM (4.30 cm). Median growth rate for patients with diabetes was 0.12 cm/y (interquartile range, 0.07-0.22 cm/y) and 0.19 cm/y (interquartile range, 0.12-0.27 cm/y) in patients without DM, which was significantly different on unadjusted analysis (P < .0001). Diabetes remained significantly associated with AAA growth after adjustment for other relevant clinical factors (coef, −0.057; P < .0001). ConclusionsPatients with diabetes have more than a 35% reduction in the median growth rates of AAA despite more severe concomitant vascular comorbidities and similar initial sizes of aneurysms. This effect persists and remains robust after adjusted analysis; and slower growth rates may delay the time to reach repair threshold. Rapid growth (>0.5 cm/y) is infrequent in patients with DM.

Using AI to Predict Patient's Atypical Prescription Based on Individual's Feature-Set

<h3>Purpose/Objective(s)</h3> The goal of this project is to train unsupervised machine learning (ML) models to cluster patients into different groups based on their individual features (such as morphology, cancer stage, treatment intent, site location, etc.). Then, we use the distribution of the prescription - Rx (number of fractions times dose per fraction) within each cluster to establish typical Rx values as well as less frequent values. In clinical setting, a new patient's Rx can then be flagged if it does not fall into the typical range of Rx for the individual patient's feature-set. Additionally, the value of Rx is predicted with supervised ML models. This AI computed value can be compared against the clinical Rx to create an additional flag if there is a significant relative deviation. We aim to implement an automatic mechanism based on this ML model along with visualization tools to assist peer review during weekly chart rounds in order to provide extra safety for patients by flagging the infrequent used Rx. <h3>Materials/Methods</h3> We queried all radiation oncology patients from the electronic patient information management system treated between 01/01/2007 to 01/01/2021 (14 years of retrospective data) at our institution. Based on their diagnostic code (ICD9/ICD10 code), we categorize the patients into different disease groups (i.e., prostate, H&N, CNS patients and etc.) for various disease specific model training. Several clustering models (<b>k</b>-means, hierarchical) were applied to cluster patients into different groups based on their individual features, which includes morphology code, treatment intent, treatment techniques, treatment type (initial or cone down), TNM stage, treatment site and etc.). Then, supervised ML models (Random Forest Regression) were applied to predict each group's Rx distribution, which gives an estimate of the most frequent and least frequent Rx range within the group. A data pipeline was developed and then applied to prostate patients for initial testing. <h3>Results</h3> We applied agglomerative clustering to the prostate group and found natural partitions of the patients into <b>k</b> = 2-5 clusters. Observation of the Rx in different clusters showed that each cluster had distinct characteristic Rx's. We plotted the Rx distribution for each feature, the scatterplot of Rx against each feature, and Rx distribution for selected feature-sets for visualization. The supervised prediction results are pending. <h3>Conclusion</h3> This initial prostate ML model along with visualization tools can flag the Rx that are atypical for a particular cluster for further investigation in order to improve the efficiency of peer review process. This further investigation could result in detecting anomaly Rx therefore providing extra safety for patients.

Utilizing Service Recovery to Identify Bottlenecks in the Treatment Planning Process and Provides Sustainable Improvement in Operational Workflows

The Radiation Oncology treatment planning process necessitates many moving parts. Highly specialized teams work in concert with many aspects of planning, contingent on the completion of one item before the start of the next. A delay caused by unplanned barriers to completion can have significant impact on the final production time. Not all team members are aware of bottlenecks in the process. When bottlenecks occur, it is a fundamental practice to have a service recovery mechanism in place. Service recovery is the process of making things amenable for the patient after something in the process has gone erroneous with the healthcare experience. There is published data that indicates using a transparency dashboard provides benefits in time reduction in the treatment planning process. We contend that building a dashboard with service recovery identifiers could help improve overall time to start and further create a learning mechanism for teams to reduce barriers for a sustainable improvement.Using electronic patient information management system scripts and python language; we generated a dashboard with built in service recovery alerts before the failure occurs. Each step during the planning process were given three separate time labels. Standard streetlight colors with green indicating the activity are within the designed timeframe, yellow indicates that the due time is near, and red indicating the item has been delayed. Expected hours excluded hours before 8 am, after 5 pm, weekends, and holidays. Alert mechanisms were created to alert the yellow and red items to leaders who could act on determination of recovery mechanism. These mechanisms included a dashboard published on the local intranet that contained all patients and current stop light status and daily email updates of tasks that were in a yellow or red status.The results from this concerted effort were dramatic. From October 2019 to December 2020, we were able to reduce the time from CT SIM to start from 10.9 to 5.9 days, an astounding 46% reduction in overall time. The success can be attributed to significant reduction in yellow and red-light items. The total number of green lights demonstrated a 22% rise, 715 to 871. Yellow and red items were reduced by 41% (147 to 87) and 59% (298 to 123). More common bottlenecks included resident contours to MD approval; which demonstrated improvement in total time by 44% (2.7 to 1.5 days). Green light items were reduced by 28%, meanwhile reducing yellow and red-light items by 48 and 83% respectively. Dosimetry planning to MD Approved Plan demonstrated improvement total time of 44% (2.3 to 1.2 days). Green light items were reduced by 17%, meanwhile reduction in yellow and red by 41 and 88% respectively.A dashboard with built-in service recovery warnings proved to have sustained benefits in the treatment planning process. The treatment planning process benefited in identification of recurring bottlenecks in the treatment planning process which resulted sustained improvement throughout the 14-month process.

The patient experience of Wilson disease: a conceptual model based on qualitative research

BackgroundWilson disease (WD) is a rare disease wherein copper accumulates in tissues, leading to hepatic degeneration, neurological impairments, and psychiatric symptoms. This study aimed to characterize the patient experience of WD and develop a conceptual model containing key symptoms and impacts of the disease.ResultsA targeted literature review was conducted to develop a preliminary conceptual model of WD that was subsequently refined through one-on-one interviews with 3 WD clinicians and finalized following concept elicitation interviews with 11 patients and 1 caregiver. The literature review returned 30 articles, from which 45 concepts (35 signs/symptoms and 10 impacts) were selected for inclusion in the preliminary conceptual model. After interviews with clinicians, the model was expanded to include 45 signs/symptoms and 14 impacts. The final comprehensive conceptual model developed after interviews with patients included 54 symptoms in total (n = 22 hepatic, n = 19 neurological, n = 13 psychiatric), and 21 impacts. Across symptoms, patients reported a high level of bother, with approximately 49% of symptoms reported by patients having an average peak bother rating of ≥ 7 out of 10 (10 = most bothersome). Patient interviews identified 2 subgroups of patients: those who experience neurological, psychiatric, and hepatic symptoms and those who experience mostly hepatic and some psychiatric symptoms, but no neurological symptoms.ConclusionsThis research underscores the substantial multisystemic symptoms and impacts that patients with WD describe as highly bothersome in their lives. Hepatic symptoms emerged as especially common and important to patients with WD, possibly beyond what is commonly understood in research and clinical practice. Further, the description of 2 distinct patient groups may help to inform patient management and support more targeted drug development processes.

Acute Epstein‐Barr virus associated haemophagocytosis in an Asian female: What is the diagnosis?

Acute <scp>Epstein‐Barr</scp> virus associated haemophagocytosis in an Asian female: What is the diagnosis?

Assessment of Clinical Characteristics and Chest CT Findings of COVID-19 Prediagnosed Patients Admitted to the Emergency Department: A Retrospective Study

Introduction: This study aimed to define the clinical features and laboratory findings that may be associated with COVID-19 pneumonia in patients with suspected COVID-19 pneumonia who presented to the emergency department in order to be able to recognize the disease early until molecular tests are available, and thus to isolate sick people as soon as possible. Patients and Methods: We retrospectively examined patients who were evaluated with the suspicion of COVID-19 between March 2020 and August 2020. We used the patient information management system to record patient demographic and clinical features, laboratory findings, and CT results. In subgroups with and without pneumonia, we compared these characteristics in confirmed COVID-19 patients. Results: Our study included 1,277 patients who were evaluated for suspected COVID-19. COVID-19 pneumonia was detected in 71.2% (n=210) of a total of 295 (23.4%) patients with a diagnosis of COVID-19 who were confirmed by RT-PCR positivity. Although 56.2% (n=18) of those with pneumonia were men, no statistically significant relationship was detected between gender distribution and CT findings (p=0.449). When we compared the patients in 3 subgroups according to age groups, it was observed that 59.4% (n=38) of those with negative CT findings were in the 18-44 age group, and 44.8% (n=94) of those with positive CT findings were 65 and over age group, and this distribution was found to be statistically significant (p&lt;0.001). Pneumonia was detected in 58.3% (n=49) of COVID-19 cases (n=84) with a suspected exposure history (p=0.008). The most common symptoms in patients with positive CT findings (n=210) were fever (40.5% n=85) and fatigue (40.5% n=85), cough (32.9% n=69), and shortness of breath (28.6% n=60). The most common comorbidity in cases diagnosed with COVID-19 was HT (33.9%; n=100/295), followed by AF (19.3%; n=57/295), and the rates of HT and AF in those with COVID-19 pneumonia were statistically significantly higher compared to the CT-negative group (p&lt;0.001 and p=0.002, respectively).COVID-19 pneumonia was more common in hospitalized patients (n=124), and hospitalization lengths were longer in both emergency and ICU admissions (p&lt;0.001 and p=0.008). In our analysis in terms of the survival relationship, it was observed that 87.5% (n=35) of the COVID-19 cases who died had pneumonia (p&lt;0.042). Low saturation and high respiratory rate per minute at the time of admission were associated with COVID-19 pneumonia (p&lt;0.001). In the group with COVID-19 pneumonia, haemoglobin, GFR, and pCO2 values were recorded lower at the time of admission compared to patients with negative CT (p&lt;0.001, p&lt;0.001, p&lt;0.05, respectively), while leukocytes, neutrophils, CRP, D-dimer, LDH, hs-troponin, AST and bilirubin were recorded as higher (p&lt;0.05, p&lt;0.01, p&lt;0.001, p&lt;0.001, p&lt;0.001, p&lt;0.001, p=0.01, p=0.01, respectively). Conclusion: Identifying patient features linked with COVID-19 pneumonia at the time of admission may influence our decision to have CT scans in emergency departments, regardless of the RT-PCR result, and may result in early disease identification and treatment.