Categories Of Medical Conditions Research Articles

Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering.

With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies-pulse-coupled neural network based on the structure tensor and maximum based-are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method's outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.

Medical profile of patients referred to an Australian postgraduate oral surgery clinic.

Thorough knowledge of a patient's medical history and medications is necessary for providing safe oral surgery care, and may be considered a form of risk management. This study investigated the prevalence of medical conditions and medication types in patients referred to an Australian postgraduate oral surgery clinic over 2 years. A retrospective cross-sectional study of the clinical records of 233 randomized patients referred to the Griffith University (Queensland, Australia) postgraduate oral surgery clinic in 2018 and 2019 was performed. Medical conditions and medications were counted and categorized, and descriptive statistics were generated. In all, 133 patients (57%) had at least one medical condition. 58% of them (77) had two or more categories of medical conditions, representing nearly a third (33.0%) of all sampled patients. The most prevalent category of medical conditions was psychiatric (25.3%), followed closely by cardiovascular (24.5%) diseases. Cardiovascular medications were the most prevalent, comprising 23.6% of all medications recorded, followed by psychotropics (18.3%). Over half of patients referred to the postgraduate oral surgery clinic had at least one systemic medical condition. Nearly a third of patients referred had at least two distinct systemic medical conditions. With an ageing population and the accompanying rise in multimorbidity globally, dental school curricula must adapt to prepare students to meet these challenges in their careers.

Clinical Epidemiology and Risk Factors for Critical Outcomes Among Vaccinated and Unvaccinated Adults Hospitalized With COVID-19-VISION Network, 10 States, June 2021-March 2023.

The epidemiology of coronavirus disease 2019 (COVID-19) continues to develop with emerging variants, expanding population-level immunity, and advances in clinical care. We describe changes in the clinical epidemiology of COVID-19 hospitalizations and risk factors for critical outcomes over time. We included adults aged ≥18 years from 10 states hospitalized with COVID-19 June 2021-March 2023. We evaluated changes in demographics, clinical characteristics, and critical outcomes (intensive care unit admission and/or death) and evaluated critical outcomes risk factors (risk ratios [RRs]), stratified by COVID-19 vaccination status. A total of 60 488 COVID-19-associated hospitalizations were included in the analysis. Among those hospitalized, median age increased from 60 to 75 years, proportion vaccinated increased from 18.2% to 70.1%, and critical outcomes declined from 24.8% to 19.4% (all P < .001) between the Delta (June-December, 2021) and post-BA.4/BA.5 (September 2022-March 2023) periods. Hospitalization events with critical outcomes had a higher proportion of ≥4 categories of medical condition categories assessed (32.8%) compared to all hospitalizations (23.0%). Critical outcome risk factors were similar for unvaccinated and vaccinated populations; presence of ≥4 medical condition categories was most strongly associated with risk of critical outcomes regardless of vaccine status (unvaccinated: adjusted RR, 2.27 [95% confidence interval {CI}, 2.14-2.41]; vaccinated: adjusted RR, 1.73 [95% CI, 1.56-1.92]) across periods. The proportion of adults hospitalized with COVID-19 who experienced critical outcomes decreased with time, and median patient age increased with time. Multimorbidity was most strongly associated with critical outcomes.

Short sleep duration is associated with a wide variety of medical conditions among United States military service members

ObjectivesThis cross-sectional study investigated self-reported sleep duration and its association with a comprehensive range of clinically-diagnosed medical condition categories (CDMCs), as well as the relationship between short sleep duration (≤6 h) and demographic/lifestyle factors, among United States military service members (SMs). MethodsA stratified random sample of SMs (n = 20,819) completed an online questionnaire on usual daily hours of sleep and demographic/lifestyle characteristics. CDMCs for a six-month period prior to questionnaire completion were obtained from a comprehensive military electronic medical surveillance system and grouped into 33 CDMCs covering both broad and specific medical conditions. Prevalence of CDMCs was compared among three sleep duration categories (≤4, 5–6 and ≥7 h). ResultsSMs reported a mean ± standard deviation of 6.3 ± 1.4 h of sleep per day. After adjustment for demographic/lifestyle characteristics, shorter sleep duration was associated with higher odds of a medical condition in 25 of 33 CDMCs, with most (n = 20) demonstrating a dose-response relationship. The five CDMCs with the largest differences between ≤4 vs ≥ 7 h sleep were: diseases of the nervous system (odds ratio [OR] = 2.9, 95% confidence interval [95%CI] = 2.4–3.4), mental/behavioral diseases (OR = 2.7, 95%CI = 2.3–3.2), diseases of the musculoskeletal system (OR = 1.9, 95%CI = 1.6–2.1), diseases of the circulatory system (OR = 1.7, 95%CI = 1.3–2.2), and diseases of the digestive system (OR = 1.6, 95%CI = 1.2–2.0). Six hours of sleep or less was independently associated with older age, less formal education, race, Hispanic ethnicity, higher body mass index, smoking, and military service branch. ConclusionsIn this young, physically active population, reporting shorter sleep duration was associated with a higher risk of multiple CDMCs.

Applying Linear and Nonlinear Regression Analysis Models to Study the Possible Relationships between Cancer’s Risk and CVD/Stroke Risk over a 12-Year Period for a Type 2 Diabetes Patient Based on GH-Method: MathPhysical Medicine (No. 551)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and regression analyses, to study the predicted Cancer risk probability as the output (dependent variable) by using his MI-based CVD/Stroke risk as the input (independent variable). Since 1/1/2012, the author has been collecting various data related to his health (~3 million data) which includes 4 categories of medical conditions, obesity, diabetes, hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). Due to his limited knowledge in earlier years, the datasets from 2010 to 2012 are incomplete; therefore, the data used in this study for the initial period of 2010-2012 are his best-guessed data. Previously, he researched and published a few articles regarding the risks of having CVD/Stroke and Cancers based on his enhanced metabolism index (MI) model. In this particular paper, adopting a regression analysis model, he is able to compare the previously calculated Cancer risks based on the enhanced MI model versus the newly regression predicted Cancer risks using his MI-based CVD risks as input. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be &gt; 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be &lt; 0.05 to be considered as statistically significant. In summary, there are two specific conclusions worth mentioning: 1. The time-domain analysis results have revealed that the correlation between his Cancer risk and CVD risk are very high (89%). In addition, his enhanced MI-based model Cancer Risk and Regression Predicted Cancer Risk are highly correlated as well. 2. The space-domain linear regression analysis has shown that there is a variance of 1.0 existing between his Cancer risk and CVD risk. When using the nonlinear polynomial model, his variance still reaches 99.6%. This finding has further proven his Cancer risk and CVD risk are highly correlated together. Furthermore, he has conducted two more linear regression analyses of Cancer risk vs. Lifestyle and CVD risk vs. Lifestyle which reveal two extremely high linear variances at 1.0. These findings offer additional indication that Lifestyle, the common root-cause, contributes highly on both CVD risk and Cancer risk.

Applying Multiple Regression Analyses to Compare the Regression Predicted and Originally Calculated CVD/Stroke Risk Probabilities Using Medical Condition and Lifestyle Detail Scores as Inputs over a 10-year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 546)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted CVD/Stroke risk probability as the output (dependent variable) by using his medical condition and lifestyle detail scores as inputs (independent variables). Since 1/1/2012, the author has been collecting various data related to his health (~3 million data) which includes 4 categories of medical conditions, obesity, diabetes, hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). However, due to the limitation of his learned knowledge in earlier years, the data from 2012-2013 is incomplete; therefore, the scores in this study for the initial period of 2012-2013 are his best-guessed data based on an incomplete dataset. Previously, he has researched and published a few articles regarding the risks of having CVD/Stroke based on his metabolism index (MI). In this paper, he will compare his previously calculated CVD risks based on the MI method versus the newly calculated CVD risks based on multiple regression analysis methods. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be &gt; 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be &lt;0.05 to be considered as statistically significant.

Applying Linear Regression Analysis Model to Predict HbA1C Values Using the Daily Estimated Average Glucose (eAG) Values from a 14-Month Period for a type 2 Diabetes Patient based on GH-Method: Math-Physical Medicine (No. 559)

Since 1/1/2012, the author has been collecting various biomedical and lifestyle data related to his health conditions (~3 million data) which includes 4 categories of medical conditions, 4 chronic diseases consisting of obesity, diabetes (via finger-piercing glucose and HbA1C), hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). Starting on 1/1/2013, he accumulates 4 glucose data per day, one for fasting plasma glucose (FPG) and 3 for postprandial plasma glucose (PPG). In addition, beginning on 5/8/2018, his glucoses are automatically measured using a continuous glucose monitoring (CGM) sensor device to collect 96 glucose data per day. Over the past 12 years, he has tested for his HbA1C value each quarter since 2010. Through research work on type 2 diabetes (T2D) from 2015 to 2017, he developed several math-physical models to predict his HbA1C values prior to labtests for HbA1C at a clinic or hospital. He compares his predicted HbA1C values against the lab-tested HbA1C values and has achieved a 99% to 100% prediction accuracy. In this article, he investigates his predicted HbA1C values via the daily estimated average glucose (eAG) values using a linear regression model from the past 14-month period from 10/1/2020 to 11/24/2021. This report is part of a series in his research work based on statistical regression model for the month of November 2021. Several written regression articles were initiated from his body temperature (BT) which he started to measure on 10/1/2020. In order to compare some of his research results from this period, he selected the same 14-month window. In conclusion, the regression predicted HbA1C versus measured HbA1C, based on the daily glucose value, can be described by the following statistically derived equation: Regression predicted HbA1C (Y) = 0.0167*measured eAG (X) + 4.2327 This linear regression equation has the following conclusive key data: Correlation = 61% (not very high, but enough) Variance = 37% (sufficient) Significance F &amp; p-value = 2% (&lt;5%) Furthermore, when we put the 3 datasets (measure A1C, predicted A1C, and lab-tested A1C) together, we observe that all three average HbA1C values during this 14-month period are equal to 6.1, which achieved 100% prediction accuracy.

The Healthy States of America: Creating a Health Taxonomy with Social Media

Since the uptake of social media, researchers have mined online discussions to track the outbreak and evolution of specific diseases or chronic conditions such as influenza or depression. To broaden the set of diseases under study, we developed a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition or disease from unstructured social media text. With that tool at hand, we processed Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of the first comprehensive taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy's sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores. As opposed to counts of disease mentions or counts with no knowledge of our taxonomy's structure, we found that our disease-specific health scores are causally linked with the officially reported prevalences of 18 conditions.

The global incidence of in-flight medical emergencies: A systematic review and meta-analysis of approximately 1.5 billion airline passengers.

Medical emergencies during short- or long-haul commercial airline flights have become more commonplace due to the aviation industry's contemporary growth, the popularization of commercial flights, and an increased aging of air travelers with significant comorbidities. However, the precise incidence of onboard medical events on commercial airlines and the most common medical conditions is unclear. In this systematic review and meta-analysis, we explored the incidence of in-flight medical emergencies among airline passengers and estimated the incidence rate by physiological body system, or organ class/syndrome for emergencies that may be associated with different body systems. We limited our search to cohort studies published between 1945 to October 31, 2020 in MEDLINE, Embase, Cochrane Library and official reports from the Federal Aviation Administration/International Air Transport Association, regardless of the language of publication. Only studies that evaluated the overall frequency of onboard medical events on commercial air carriers (in which they also presented the total number of annual revenue passengers) and the frequency of events by physiological body systems or organ class/syndrome were included. We excluded case reports and case series, systematic or narrative reviews, and studies addressing specific health-related conditions. Two independent investigators performed first- and second-phase study screening, abstracted data, and appraised risk of bias. We rated the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Using a quality effect model, we meta-analyzed data associated with the incidence of in-flight medical emergencies, all-cause fatality, incidence of medical events by medical condition category, frequency of en-route diversion, presence of medical personnel on board, and the use of an automatic external defibrillator. We also extracted data regarding the cost of flight diversion. Of 18 individual studies with approximately 1.5 billion passengers, 11 reported the overall incidence of in-flight medical emergencies. Low certainty of evidence suggested that the global incidence of in-flight medical emergencies was 18.2 events per million passengers (95% CI 0.5 to 53.4 per million; I2 = 100%, P < 0.001, very low certainty), and an all-cause mortality rate was 0.21 per million passengers (95% CI 0 to 0.76 per million; I2 = 99%, P < 0.001, low certainty). The four most common categories of medical conditions or syndromes during flight were syncope, gastrointestinal events, respiratory and neurological diagnostic groups. The diversion rate was 11.1 per 100,000 flights (95% CI 5.9 to17.6 per 100,000 flights; I2 = 97%, P < 0.001), with an average cost ranging from $15,000 to $893,000 per unplanned emergency landing across studies which examined this outcome. In-flight medical events on commercial travels are extremely low with a corresponding very low in-flight mortality rate. Associated costs derived from en-route diversion might significantly influence airlines' budgetary equilibrium. Novel and modern standardized reporting systems or platforms should be internationally provided and enforced by health and aviation authorities to obtain higher quality patient-passengers datasets. Onboard volunteer medical providers must be aware of everyday life-threatening events during commercial flights and should consider the establishment of a connection between the aircraft and ground-based medical advisory services while assisting in-flight medical events.

Association between Mental Disorders and Subsequent Medical Conditions

BackgroundPersons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.MethodsWe used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016, for a total of 83.9 million person-years. We assessed 10 broad types of mental disorders and 9 broad categories of medical conditions (which encompassed 31 specific conditions). We used Cox regression models to calculate overall hazard ratios and time-dependent hazard ratios for pairs of mental disorders and medical conditions, after adjustment for age, sex, calendar time, and previous mental disorders. Absolute risks were estimated with the use of competing-risks survival analyses.ResultsA total of 698,874 of 5,940,299 persons (11.8%) were identified as having a mental disorder. The median age of the total population was 32.1 years at entry into the cohort and 48.7 years at the time of the last follow-up. Persons with a mental disorder had a higher risk than those without such disorders with respect to 76 of 90 pairs of mental disorders and medical conditions. The median hazard ratio for an association between a mental disorder and a medical condition was 1.37. The lowest hazard ratio was 0.82 for organic mental disorders and the broad category of cancer (95% confidence interval [CI], 0.80 to 0.84), and the highest was 3.62 for eating disorders and urogenital conditions (95% CI, 3.11 to 4.22). Several specific pairs showed a reduced risk (e.g., schizophrenia and musculoskeletal conditions). Risks varied according to the time since the diagnosis of a mental disorder. The absolute risk of a medical condition within 15 years after a mental disorder was diagnosed varied from 0.6% for a urogenital condition among persons with a developmental disorder to 54.1% for a circulatory disorder among those with an organic mental disorder.ConclusionsMost mental disorders were associated with an increased risk of a subsequent medical condition; hazard ratios ranged from 0.82 to 3.62 and varied according to the time since the diagnosis of the mental disorder. (Funded by the Danish National Research Foundation and others; COMO-GMC ClinicalTrials.gov number, NCT03847753.)

Diagnosis Prevalence and Comorbidity in a Population of Mobile Integrated Community Health Care Patients.

Frequent calls to 911 and requests for emergency services by individuals place a costly burden on emergency response systems and emergency departments (EDs) in the United States. Many of the calls by these individuals are non-emergent exacerbations of chronic conditions and could be treated more effectively and cost efficiently through another health care service. Mobile integrated community health (MICH) programs present a possible partial solution to the over-utilization of emergency services by addressing factors which contribute to a patient's likelihood of frequent Emergency Medical Services (EMS) use. To provide effective care to eligible individuals, MICH providers must have a working understanding of the common conditions they will encounter. The purpose of this descriptive study was to evaluate the diagnosis prevalence and comorbidity among participants in the Queen Anne's County (Maryland USA) MICH Program. This fundamental knowledge of the most common medical conditions within the MICH Program will inform future mobile integrated health programs and providers. This study examined preliminary data from the MICH Program, as well as 2017 Maryland census data. It involved secondary analysis of de-identified patient records and descriptive statistical analysis of the disease prevalence, degree of comorbidity, insurance coverage, and demographic characteristics among 97 program participants. Diagnoses were grouped by their ICD-9 classification codes to determine the most common categories of medical conditions. Multiple linear regression models and chi-squared tests were used to assess the association between age, sex, race, ICD-9 diagnosis groups, and comorbidity among program enrollees. Results indicated the most prevalent diagnoses included hypertension, high cholesterol, esophageal reflux, and diabetes mellitus. Additionally, 94.85% of MICH patients were comorbid; the number of comorbidities per patient ranged from one to 13 conditions, with a mean of 5.88 diagnoses per patient (SD=2.74). Overall, patients in the MICH Program are decidedly medically complex and may be well-suited to additional community intervention to better manage their many conditions. The potential for MICH programs to simultaneously improve patient outcomes and reduce health care costs by expanding into larger public health and addressing the needs of the most vulnerable citizens warrants further study.ScharfBM, BissellRA, TrevittJL, JenkinsJL.Diagnosis prevalence and comorbidity in a population of mobile integrated community health care patients.Prehosp Disaster Med. 2019;34(1):46-55.

Relationship Quality among Chronically Ill Children and their Parents

The present study examined the quality of parent–child relationships as reported by 383 parents of chronically ill children. The medical condition category, child’s mobility, education type and childcare assistance were examined. Parents answered the Parent–Child Relationship Inventory (PCRI). In addition, 45 parents of chronically ill children were interviewed. The results indicated a significant difference in most aspects of the PCRI (limit setting, promotion of autonomy, communication, satisfaction and involvement) across medical condition categories. Further child variables (mobility, assistance and education type) had an effect on the PCRI subscales as well. Interview analysis revealed a difference in the reported quality of parent–child relationships between medical conditions categories. Parents of mentally ill children primarily felt anger and guilt in their relationship with their ill children, whereas parents of children with cancer and autoimmune disease primarily felt depressed. Our results suggest the need for further study of parent–child relationships in and between different chronic childhood medical conditions in an attempt to address parents’ and children’s needs in an optimal way.

Impact of regional socioeconomic variation on coordination and cost of ambulatory care: investigation of claims data from Bavaria, Germany

ObjectivesA considerable proportion of regional variation in healthcare use and health expenditures is to date still unexplained. The aim was to investigate regional differences in the gatekeeping role of general...

Comparison of Content on the American Board of Internal Medicine Maintenance of Certification Examination With Conditions Seen in Practice by General Internists

Success on the internal medicine (IM) examination is a central requirement of the American Board of Internal Medicine's (ABIM's) Maintenance of Certification program (MOC). Therefore, it is important to understand the degree to which this examination reflects conditions seen in practice, one dimension of content validity, which focuses on the match between content in the discipline and the topics on the examination questions. To assess whether the frequency of questions on IM-MOC examinations were concordant with the frequency of conditions seen in practice. The 2010-2013 IM-MOC examinations were used to calculate the percentage of questions for 186 medical condition categories from the examination blueprint, which balances examination content by considering importance and frequency of conditions seen in practice. Nationally representative estimates of conditions seen in practice by general internists were estimated from the primary diagnosis for 13 832 office visits (2010-2013 National Ambulatory Medical Care Surveys) and 108 472 hospital stays (2010 National Hospital Discharge Survey). Prevalence of conditions included on the IM-MOC examination questions. The outcome measure was the concordance between the percentages of IM-MOC examination questions and the percentages of conditions seen in practice during either office visits or hospital stays for each of 186 condition categories (eg, diabetes mellitus, ischemic heart disease, liver disease). The concordance thresholds were 0.5 SD of the weighted mean percentages of the applicable 186 conditions seen in practice (0.74% for office visits; 0.51% for hospital stays). If the absolute differences between the percentages of examination questions and the percentages of office visit conditions or hospital stay conditions seen were less than the applicable concordance threshold, then the condition category was judged to be concordant. During the 2010-2013 IM-MOC examination periods, 3600 questions (180 questions per examination form) were administered and 3461 questions (96.1%) were mapped into the 186 study conditions (mean, 18.6 questions per condition). Comparison of the percentages of 186 categories of medical conditions seen in 13 832 office visits and 108 472 hospital stays with the percentages of 3461 questions on IM-MOC examinations revealed that 2389 examination questions (69.0%; 95% CI, 67.5%-70.6% involving 158 conditions) were categorized as concordant. For concordance between questions and office visits only, 2010 questions (58.08%; 95% CI, 56.43%-59.72% of all examination questions) involving 145 conditions were categorized as concordant. For concordance between questions and hospital stays only, 1456 questions (42.07%; 95% CI, 40.42%-43.71% of all examination questions) involving 122 conditions were categorized as concordant. Among questions on IM-MOC examinations from 2010-2013, 69% were concordant with conditions seen in general internal medicine practices, although some areas of discordance were identified.

P064: Coastal family practice residency: simulation curriculum needs assessment survey

Introduction/Innovation Concept: Medical simulation is becoming increasingly useful for healthcare education. Simulation-based crisis resource management (CRM) has been shown to produce improvements in skill acquisition, communication and team behaviors. Simulation has become a key component of most Family Practice (FP) residency programs and many programs are moving towards developing formal simulation curriculums. The Coastal FP Residency is a relatively new and unique program with a large emphasis on rural medicine. Graduates have gone on to practice in remote areas with less access to supports for critically ill patients. Therefore, an effective simulation curriculum, focused on Emergency Medicine, is of great importance to this program. Methods: To develop our curriculum, Kern’s framework for medical education was selected given its prior success in similar endeavors. The first step of this approach involves a needs assessment, which we accomplished in the form of an online survey. The questionnaire included pre-defined topics pertaining to the training needs of FP Residents destined for Rural Practice with respect to technical skills, CRM skills, specific medical conditions and categories of medical conditions. Classification of answers included multiple choice, 5-point Likert scales as well as an option for free-text answers. The survey was distributed to pre-identified participants including stakeholders/educators within the Coastal FP residency program as well as simulation education leads for FP residencies throughout British Columbia (BC). Current residents, as well as program graduates were also asked to complete the survey. Curriculum, Tool, or Material: The results of this survey were used to develop formal goals and objectives which were in turn used to write or adapt 24 cases for the curriculum. Cases from categories (e.g. Pediatrics) rated as “Extremely Important” on the Likert scale were included proportionally more in the curriculum. The cases were also designed to assess/practice a higher proportion of CRM elements considered important and to address commonly identified difficulties in resuscitation. Cases were developed, where possible, using local or national guidelines and are currently in the stage of peer review (by a minimum of two peers). Conclusion: The curriculum will be implemented in July 2017 and we will transition towards the evaluation phase. Our goal is to develop and distribute formalized needs assessments to rural FP residencies across BC so that they may develop dynamic, formal curriculums of their own.

Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

BackgroundDepression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study.MethodsA multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009–2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary.ResultsFive clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters.ConclusionThis methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.

A comparison of pain assessment by physicians, parents and children in an outpatient setting

IntroductionOur objective was to compare pain assessments by patients, parents and physicians in children with different medical conditions, and analyse how this affected the physicians' administration of pain relief.Patients and...

Assessing psychological factors affecting medical conditions: comparison between different proposals

ObjectiveWe compared the provisional Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for Somatic Symptom Disorders (SSD) and an alternative classification based on the Diagnostic Criteria for Psychosomatic Research (DCPR) as to prevalence and associations with dimensional measures of psychological distress and functioning in a population of medical patients. MethodSeventy consecutive outpatients with congestive heart failure were administered an ad hoc structured clinical interview for the identification of DSM-5 SSD, the section concerning hypochondriasis of the Structured Clinical Interview for DSM-IV, the Structured Interview for DCPR and Paykel's Clinical Interview for Depression. Subjects also completed the Symptom Questionnaire and the Psychosocial Index. Global assessment of functioning was performed with the DSM-IV Axis V. ResultsA diagnosis within DSM-5 SSD was found in 13 patients (18.5%): 61.5% of them were diagnosed with the Psychological Factors Affecting Medical Condition category. Twenty-nine patients (41.4%) were classified according to the DCPR-based proposal: illness denial, demoralization and irritable mood were the most frequent specifiers. The DCPR-based classification showed a greater number of significant associations with dimensional measures of psychological distress, global functioning and stress. ConclusionCompared to DSM-5 SSD, the DCPR-based proposal was more sensitive in detecting psychological factors relevant to illness course and provided a better characterization of such factors. The DCPR-based proposal was also superior in identifying patients with increased psychological distress and poor psychosocial functioning.

Posttraumatic stress disorder, substance use disorders, and medical comorbidity among returning U.S. veterans

Evidence suggests that posttraumatic stress disorder (PTSD) and substance use disorders (SUD) are associated with poorer physical health among U.S. veterans who served in Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF). No research of which we are aware has examined the independent and interactive effects of PTSD and SUD on medical comorbidity among OEF/OIF veterans. This cross-sectional study examined medical record data of female and male OEF/OIF veterans with ≥ 2 Veterans Affairs primary care visits (N = 73,720). Gender-stratified logistic regression analyses, adjusted for sociodemographic factors, were used to examine the association of PTSD, SUD, and their interaction on the odds of medical diagnoses. PTSD was associated with increased odds of medical diagnoses in 9 of the 11 medical categories among both women and men, range of odds ratios (ORs) ranged from 1.07 to 2.29. Substance use disorders were associated with increased odds of 2 of the 11 medical categories among women and 3 of the 11 medical categories among men; ORs ranged from 1.20 to 1.74. No significant interactions between PTSD and SUD were detected for women or men. Overall, findings suggest that PTSD had a stronger association with medical comorbidity (in total and across various medical condition categories) than SUD among female and male OEF/OIF veterans.

Napping, Nighttime Sleep, and Cardiovascular Risk Factors in Mid-Life Adults

To evaluate the relations between sleep characteristics and cardiovascular risk factors and napping behavior, and to assess whether daytime napping leads to subsequent better or worse sleep. The sample consisted of 224 (African American, Caucasian, and Asian) middle-aged men and women. Sleep measures included nine nights of actigraphy and sleep diaries, sleep questionnaires, and one night of polysomnography to measure sleep disordered breathing. More frequent napping was associated with shorter nighttime sleep duration averaged across the nine nights of actigraphy (especially among African Americans), more daytime sleepiness, more pain and fatigue by diary, and increased body mass index and waist circumference. Shorter nighttime sleep duration was associated with taking a nap during the next day and taking a nap was associated with less efficient sleep the next night. Napping in middle-aged men and women is associated with overall less nighttime sleep in African Americans and lower sleep efficiency as measured by actigraphy, and increased BMI and central adiposity. These findings point to the importance of measuring of napping in understanding associations of sleep with cardiovascular risk.