Search results for: Priscilla Eng Lian Murphy

Authors: Yasmin A. M. Ferreira, Ana C. K. Pelissari, Sofia De C. F. Vicente, Raquel M. Da S. Campos, Deborah C. L. Masquio, Lian Tock, Lila M. Oyama, Flavia C. Corgosinho, Valter T. Boldarine, Ana R. Dâmaso

Abstract:

The prevalence of overweight and obesity is growing around the world and currently considered a global epidemic. Food and nutrition are essential requirements for promoting health and protecting non-communicable chronic diseases, such as obesity and cardiovascular disease. Specific dietary components may modulate the inflammation and oxidative stress in obese individuals. Few studies have investigated the dietary Inflammation Factor Rating (IFR) in obese adolescents. The IFR was developed to characterize an individual´s diet on anti- to pro-inflammatory score. This evaluation contributes to investigate the effects of inflammatory diet in metabolic profile in several individual conditions. Objectives: The present study aims to investigate the effects of a multidisciplinary weight loss therapy on inflammation factor rating and cardiometabolic risk in obese adolescents. Methods: A total of 26 volunteers (14-19 y.o) were recruited and submitted to 20 weeks interdisciplinary therapy allied to health education website- Ciclo do Emagrecimento®, including clinical, nutritional, psychological counseling and exercise training. The body weight was monitored weekly by self-report and photo. The adolescents answered a test to evaluate the knowledge of the topics covered in the videos. A 24h dietary record was applied at the baseline and after 20 weeks to assess the food intake and to calculate IFR. A negative IFR suggests that diet may have inflammatory effects and a positive IFR indicates an anti-inflammatory effect. Statistical analysis was performed using the program STATISTICA version 12.5 for Windows. The adopted significant value was α ≤ 5 %. Data normality was verified with the Kolmogorov Smirnov test. Data were expressed as mean±SD values. To analyze the effects of intervention it was applied test t. Pearson´s correlations test was performed. Results: After 20 weeks of treatment, body mass index (BMI), body weight, body fat (kg and %), abdominal and waist circumferences decreased significantly. The mean of high-density lipoprotein cholesterol (HDL-c) increased after the therapy. Moreover, it was found an improvement of inflammation factor rating from -427,27±322,47 to -297,15±240,01, suggesting beneficial effects of nutritional counselling. Considering the correlations analysis, it was found that pro-inflammatory diet is associated with increase in the BMI, very low-density lipoprotein cholesterol (VLDL), triglycerides, insulin and insulin resistance index (HOMA-IR); while an anti-inflammatory diet is associated with improvement of HDL-c and insulin sensitivity Check index (QUICKI). Conclusion: The 20-week blended multidisciplinary therapy was effective to reduce body weight, anthropometric circumferences and improve inflammatory markers in obese adolescents. In addition, our results showed that an increase in inflammatory profile diet is associated with cardiometabolic parameters, suggesting the relevance to stimulate anti-inflammatory diet habits as an effective strategy to treat and control of obesity and related comorbidities. Financial Support: FAPESP (2017/07372-1) and CNPq (409943/2016-9)

Keywords: cardiometabolic risk, inflammatory diet, multidisciplinary therapy, obesity

Procedia PDF Downloads 167

Authors: Ali Baker, Russel Krawitz

Abstract:

This paper describes a rare occurrence where a potentially fatal complication of COVID-19 infection (MIS-A) was misdiagnosed as an acute abdomen. As most patients with this syndrome present with fever and gastrointestinal symptoms, they may inadvertently fall under the care of the surgical unit. However, unusual imaging findings and a poor response to anti-microbial therapy should prompt clinicians to suspect a non-surgical etiology. More than half of MIS-A patients require ICU admission and vasopressor support. Prompt referral to a physician is key, as the cornerstone of treatment is IVIG and corticosteroid therapy. A 32 year old woman presented with right sided abdominal pain and fevers. She had also contracted COVID-19 two months earlier. Abdominal examination revealed generalised right sided tenderness. The patient had raised inflammatory markers, but other blood tests were unremarkable. CT scan revealed extensive lymphadenopathy along the ileocolic chain. The patient proved to be a diagnostic dilemma. She was reviewed by several surgical consultants and discussed with several inpatient teams. Although IV antibiotics were commenced, the right sided abdominal pain, and fevers persisted. Pan-culture returned negative. A mild cholestatic derangement developed. On day 5, the patient underwent preparation for colonoscopy to assess for a potential intraluminal etiology. The following day, the patient developed sinus tachycardia and hypotension that was refractory to fluid resuscitation. That patient was transferred to ICU and required vasopressor support. Repeat CT showed peri-portal edema and a thickened gallbladder wall. On re-examination, the patient was Murphy’s sign positive. Biliary ultrasound was equivocal for cholecystitis. The patient was planned for diagnostic laparoscopy. The following morning, a marked rise in cardiac troponin was discovered, and a follow-up echocardiogram revealed moderate to severe global systolic dysfunction. The impression was post-COVID MIS with myocardial involvement. IVIG and Methylprednisolone infusions were commenced. The patient had a great response. Vasopressor support was weaned, and the patient was discharged from ICU. The patient continued to improve clinically with oral prednisolone, and was discharged on day 17. Although MIS following COVID-19 infection is well-described syndrome in children, only recently has it come to light that it can occur in adults. The exact incidence is unknown, but it is thought to be rare. A recent systematic review found only 221 cases of MIS-A, which could be included for analysis. Symptoms vary, but the most frequent include fever, gastrointestinal, and mucocutaneous. Many patients progress to multi-organ failure and require vasopressor support. 7% succumb to the illness. The pathophysiology of MIS is only partly understood. It shares similarities with Kawasaki disease, macrophage activation syndrome, and cytokine release syndrome. Importantly, by definition, the patient must have an absence of severe respiratory symptoms. It is thought to be due to a dysregulated immune response to the virus. Potential mechanisms include reduced levels of neutralising antibodies and autoreactive antibodies that promote inflammation. Further research into MIS-A is needed. Although rare, this potentially fatal syndrome should be considered in the unwell surgical patient who has recently contracted COVID-19 and poses a diagnostic dilemma.

Keywords: acute-abdomen, MIS, COVID-19, ICU

Procedia PDF Downloads 95

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

Abstract:

Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

Procedia PDF Downloads 62