Patient Health History Research Articles

Ranking and Contextual Selection

Context-Sensitive Simulation-Based Decisions When There Is No Time to Simulate Stochastic simulation is a powerful tool for discovering system design decisions that are the best possible (optimal) when averaged over real-world uncertainty. However, in applications such as personalized medicine and web content optimization, even better decisions can be made if they are tailored to specific, contemporaneous covariate information, such as patient health history and user reading habits. Unfortunately, in these and similar applications, there is no time to perform a refined simulation optimization. In “Ranking and Contextual Selection,” Keslin, Nelson, Pagnoncelli, Plumlee, and Rahimian use off-the-shelf simulation optimization methods to create a database of covariates and associated decisions that form a covariate-to-decision classifier and an upper confidence bound on its optimality gap when applied to covariates not in the database. A realistic example of web page assortment optimization is presented using a data set from Yahoo!.

Importance of health history analysis in Parkinson's disease

The objective of this research article is to investigate the impact of various health history factors on the risk of developing Parkinson's disease (PD). From the medical history we can identify PD Symptoms and this also help to detect the progression of PD symptoms. By conducting statistical analyses, the study seeks to identify independent risk and protective factors for Parkinson's disease (PD), considering variations in impact across genders and BMI categories. IntroductionIn the diagnosis of PD the analysis of previous health history is very rare in practice while the main diagnosis have been done through the different motor and non-motor symptoms taking in consideration besides the cardinal symptoms of PD for identification and determination the stages of PD. Here we have analyzed the impact of 56 different diseases, symptoms, and surgeries which a subject may have experienced in their life before PD, considered as a health history. MethodsThe behavioral impact for each types of health history have been analyzed statistically with 31,265 subjects including PD, and Control. In this analysis we have calculated the variation of impact for both the Male, and Female, as well as subjects BMI. Results98.12 % PD patients, where 97.63 % Male PD, and 98.71 % Female PD were found with at least one health history record. Coronary heart disease odds ratio (OR) 2.15 (1.85–2.51), Colon Cancer OR 2.11 (1.45–3.05), Cranial brain surgery OR 6.21 (5.11–7.56) have the higher risks to PD. Having the history of Asthma OR 0.66 (0.6–0.72), Anemia OR 0.56 (0.51–0.63), Cirrhosis in Liver OR 0.7 (0.57–0.86), Cosmetic surgery OR 0.7 (0.64–0.77), and Gastritis OR 0.78 (0.71–0.87) have been found to be protective to PD. The risk of developing PD varies between male, and female including subjects BMI for each individual health history types. The diseases which reduce the oxygen saturation in blood like, anemia, asthma, and thalassemia act as protective to PD. ConclusionsIn this study we have analyzed fifty six diseases which include surgeries as a health history of PD patients. Study suggests that a thorough health history could greatly aid in understanding the onset and progression of Parkinson's disease (PD).

Assessment of cardiovascular risk in women with premature ovarian failure

BACKGROUND: Premature ovarian failure is a common disease that affects quality of life and cardiovascular risk in young women. AIM: To assess the relative risk of cardiovascular events in patients with primary and secondary premature ovarian failure. MATERIAL AND METHODS: The study included three groups of patients comparable in age: the first — women with primary premature ovarian failure at the age of 39.8±4.3 years; the second — women with secondary premature ovarian failure, age 40.1±4.1 years; the third was the control group at the age of 40.3±3.9 years, there were 133 people in each group. The cardiovascular profile (smoking status, metabolic health, lipid profile, glucose level, blood pressure, pulse and cardiovascular history) of patients with premature ovarian failure was analyzed. Identification of relationships between the studied signs (increase in waist circumference, increase in systolic blood pressure 140 mm Hg, increase in diastolic blood pressure 90 mm Hg, dyslipidemia, arterial hypertension, arrhythmia, coronary heart disease, diabetes mellitus, metabolic syndrome) carried out by calculating the odds ratio and relative risk. The 10-year risk of developing cardiovascular diseases was also calculated using the SCORE2 scale. RESULTS: Patients with both primary and secondary premature ovarian failure had a higher relative risk of developing cardiovascular events compared with healthy women. The 10-year risk of developing cardiovascular events according to the SCORE2 scale was statistically significantly increased in women with premature ovarian failure compared to the control group. In patients with primary failure it was 9.4% (confidence interval 6–14%; p=0.018), in patients with secondary failure — 7.6% (confidence interval 4–14%; p=0.023), and in the control group — 5.1% (confidence interval 3–14%; p=0.039). CONCLUSION: Patients with premature ovarian failure have a higher cardiovascular risk compared to a group of healthy women.

A Transformer Approach for Cognitive Impairment Classification and Prediction.

Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. We analyzed 3 target feature-sets from the National Alzheimer Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)-dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants' future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

Healthcare 5.0: A secure and distributed network for system informatics in medical surgery

IntroductionHealth records serve not only as a database of a patient's health history and treatment process but also as a crucial tool for doctors to diagnose and treat patients. However, the storage and sharing of these records are sensitive issues as they involve maintaining patient privacy and ensuring data transparency, security, and interoperability between different parties. Challenges to achieving these goals in the current surgical process can impact the allocation of medical resources and surgical outcomes. MethodsThis article proposes a healthcare 5.0 framework for medical surgery that deploys a secure and distributed network using Blockchain to demonstrate transactions between different parties in the orthopedic surgery process. The proposed network uses the Hyperledger Composer platform for deployment, and a patient-doctor-supplier orthopedic surgery network is designed and implemented to enable the safe sharing of medical records. ResultsA benchmarking tool was implemented for analyzing different scenarios of applying blockchain technology to orthopedic surgery. The application of blockchain technology to orthopedic surgery presents a promising solution for data sharing and supply chain management in the field. The integration of blockchain with cloud storage and hybrid encryption ensures secure and efficient storage of Electronic Health Record (EHR) and Personal Health Record (PHR) data. By leveraging the tamper-proof nature of blockchain and addressing concerns regarding centralized data storage, this scenario demonstrates enhanced security, improved access efficiency, and privacy protection in medical data sharing. ConclusionsThe article demonstrates the feasibility of using an IoT-based blockchain network in orthopedic surgery, which can reduce medical errors and improve data interoperability among different parties. This unique application of blockchain enables secure sharing of medical records, ensuring transparency, security, and interoperability. The network design may also be applicable to other surgeries and medical applications in the future.

An interpretable predictive deep learning platform for pediatric metabolic diseases.

Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. The cohort included 49517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry.

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

Are there sex-based disparities in cataract surgery?

To investigate sex-based differences in the occurrence of intra-operative and post-operative complications and associated visual outcomes following cataract surgery. This was a retrospective study of patients who had phacoemulsification cataract surgery at the University of Colorado School of Medicine. Data collected included the patient's health history, ocular comorbidities, operative and post-operative complications, and the post-operative best corrected visual acuity (BCVA). The data were analyzed using univariate and multivariable logistic regression with generalized estimating equations to account for the correlation of some patients having two eyes included in the study. A total of 11 977 eyes from 7253 patients were included in the study. Ocular comorbidities differed by sex, with males having significantly higher percentages of traumatic cataracts (males 0.7% vs females 0.1%), prior ocular surgery (6.7% vs 5.5%), and mature cataracts (2.8% vs 1.9%). Conversely, females had significantly higher rates of pseudoexfoliation (2.0% vs 3.2%). In unadjusted analysis, males had higher rates of posterior capsular rupture (0.8% vs 0.4%) and vitreous loss (1.0% vs 0.6%), but this difference was not significant after adjustment for confounders. Males had a significantly increased risk of post-operative retinal detachment, but in multivariable analysis this was no longer significant. Males were significantly less likely to undergo post-operative neodymium-doped yttrium aluminum garnet (Nd:YAG) laser capsulotomy for posterior capsule opacification (OR=0.8, 95%CI=0.7-0.9, P=0.0005). The BCVA was slightly worse for males pre-operatively; but post-operatively, both sexes exhibited similar visual acuity of Snellen equivalent 20/25. The study finds that in a cohort of patients presenting for cataract surgery, sex differences exist in pre-operative comorbidities and surgical characteristics that contribute to higher rates of some complications for males. However, observed surgical complication rates exhibit almost no difference by sex after adjusting for pre-operative differences and post-operative BCVA is similar between sexes.

Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study’s results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

Corneal Specialists’ Confidence in Identifying Causal Organisms of Microbial Keratitis

Purpose Microbial keratitis (MK) is a potentially blinding corneal disease caused by an array of microbial etiologies. However, the lack of early organism identification is a barrier to optimal care. We investigated clinician confidence in their diagnosis of organism type on initial presentation and the relationship between confidence and presenting features. Methods This research presents secondary data analysis of 72 patients from the Automated Quantitative Ulcer Analysis (AQUA) study. Cornea specialists reported their confidence in organism identification. Presenting sample characteristics were recorded including patient demographics, health history, infection morphology, symptoms, and circumstances of infection. The association between confidence and presenting characteristics was investigated with 2-sample t-tests, Wilcoxon tests, and Chi-square or Fisher’s exact tests. Results Clinicians reported being “confident or very confident” in their diagnosis of the causal organism in MK infections for 39 patients (54%) and “not confident” for 33 patients (46%). Confidence was not significantly associated with patient demographics, morphologic features, or symptoms related to MK. MK cases where clinicians reported they were confident, versus not confident in their diagnosis, showed significantly smaller percentages of previous corneal disease (0% versus 15%, p = 0.017), were not seen by an outside provider first (69% versus 94%, p = 0.015), or had no prior labs drawn (8% versus 33%, p = 0.046), and a significantly larger percentage of cases wore contact lenses (54% versus 28%, p = 0.029). Conclusion In almost half of MK cases, cornea specialists reported lack of confidence in identifying the infection type. Confidence was related to ocular history and circumstances of infection but not by observable signs and symptoms or patient demographics. Tools are needed to assist clinicians with early diagnosis of MK infection type to expedite care and healing.

Applying story theory as a systematic approach for obtaining a patient health history.

Applying story theory as a systematic approach for obtaining a patient health history.

Concordance between patient-reported and physician-documented comorbidities and symptoms among Stage 4 breast cancer patients.

Comorbidities and symptoms in metastatic breast cancer patients impact treatment decisions and influence prognosis and quality of life. The objective of this study is to examine the concordance between physician documentation and patient reports of comorbidities and symptoms to understand their comparative effectiveness. New patients with metastatic breast cancer completed an electronic intake survey assessing patient health history and symptoms. Physician documentation across 54 comorbidities and 42 symptoms was abstracted from notes for the corresponding clinic visits between November 2016 and March 2020. Concordance between patient reports and medical records for each condition and hazards ratios for each patient versus physician reported comorbidity and symptom were assessed. A total of 168 patients were included in the analysis (age, median = 56 years, range = 29-86 years; 131 white [78.9%]). Twenty-three of 54 comorbidities had a moderate to high level of agreement between patients and physicians (κ ≥ 0.40). Physicians documented higher numbers of comorbidities that can be objectively measured which also had higher concordance (e.g., diabetes [κ = 0.83] and hypertension [κ = 0.79]) while patients reported higher numbers of comorbidities that are more subjective which also had lower concordance (anxiety [κ = 0.30], GERD [κ = 0.36]). One physician-documented and two patient-reported comorbidities were significantly associated with survival (p < 0.05). Only 2 of 42 symptoms had a moderate to high level of agreement between patients and physicians. One physician-documented and nine patient-reported symptoms were significantly associated with decreased survival (p < 0.05). Agreement between patients' and physicians' reporting of comorbidities varies substantially, and patient reports can complement physician documentation. Physicians significantly underreported symptoms versus patients; thus, concordance was also low. Multiple patient-reported symptoms were predictive of survival; thus, incorporating them can provide more informative estimates of predicted survival.

Outcomes of Cranioplasty Reconstructions: Review of Cranioplasty Implants and Free Flap Coverage Variables that Affect Implant Exposure.

Complex scalp wounds with cranial/dural involvement are challenging to reconstruct. Successful reconstruction can be achieved with cranial implants/hardware and free flap coverage. Wounds can breakdown and require revision procedures. We addressed reconstructive outcomes of different implants requiring free flaps. To determine the factors associated with implant exposure. Multi-institutional retrospective review of 82 patients, 2000-2020, repaired with cranial implants and free flap coverage. Implant exposure occurred in 13/82 (16%) reconstructions. Flap atrophy or thinning leading to implant exposure occurred in 11/82 (13%) reconstructions, including partial flap atrophy OR 0.05 (95% CI 0.0-0.35) and total flap atrophy OR 0.34 (95% CI 0.02-19.66). Revision surgeries that occurred subsequent to flap reconstruction were also associated with implant exposure (OR 0.02 (95% CI 0.0-0.19)). Implant exposure was not associated with radiation therapy, patient health history, implant type, flap type, or postoperative complications. Implant exposure is associated with free flap atrophy, leading to inadequate implant coverage and the need for revision surgeries. Completing reconstruction with adequate soft tissue bulk and coverage and avoiding revision surgery may decrease the risk for implant exposure over time. 4 Laryngoscope, 133:2954-2958, 2023.

Characteristics of Patients With Pulmonary Arterial Hypertension in a Pulmonary Hypertension Association-Accredited Comprehensive Care Center: A Contrast in Features When Compared With US National Registry Data.

Background Since the initial description in the 1980s, our understanding of the diversity of pulmonary arterial hypertension (PAH) has continued to evolve. In this study, we report the characteristics of patients seen in an academic medical center for PAH from August 2020 through November 2021 and contrast those with nationally reported data from the United States Pulmonary Hypertension Scientific Registry (USPHSR). Study Design Investigators at the University of Utah Pulmonary Hypertension Program prospectively enrolled adult patients diagnosed with WHO Group 1 PAH, who were evaluated between August 2020 and November 2021 in a program-specific registry. Patient exposure and health histories were collected through structured interviews and questionnaires, along with clinical data and medication use. A total of 242 patients were enrolled in the University of Utah Pulmonary Hypertension Registry (UUPHR). Results Of the 242 enrolled patients, the most common etiology was associated PAH (APAH), accounting for 71.1% of the population. The second largest etiology was idiopathic PAH (IPAH) at 26.4%. The remaining patients were distributed between familial PAH (FPAH), pulmonary veno-occlusive disease (PVOD), and others. Of the total population classified as APAH, 39% of cases were noted as secondary to connective tissue disease (CTD) and 33% as toxin-induced. These represented 28% and 24% of the total population, respectively. Conclusions In this US-based accredited academic medical center, the etiology of PAH in our patient population contrasts with national registry data. In the UUPHR, APAH, specifically CTD-PAH and toxin-associated PAH, accounts for the majority of patients with PAH. This contrasts with IPAH, which nationally is the most reported cause of PAH. Differences in our population may reflect the regional variation of the referral site, but it is noteworthy for its contrast with historically reported phenotypes.

Application of DeLone and McLean Methods to Determine Supporting Factors for the Successful Implementation of Electronic Medical Records at Bali Mandara Eye Hospital

Information technology in the health sector is currently an important factor in providing health services, especially hospitals. Bali Mandara Eye Hospital has implemented information technology in the registration process and bill issuance from 2015. Electronic Medical Record is a new application that will be implemented in 2021 at Bali Mandara Eye Hospital. Electronic Medical Record is a computerized system for recording patient health history. The implementation of electronic medical records at the Bali Mandara Eye Hospital for 1 year has many problems both in terms of regulations, systems and users. Therefore, the DeLone and McLean methods are used to determine the success rate of the implementation of electronic medical records. In this method, research is conducted on 6 variables, namely System Quality, Information Quality, Service Quality, Intention of Use, User Satisfaction and Net Benefits. From each variable, the researcher combines indicators from other researchers that are more appropriate for measuring electronic medical records. The results obtained that all statements submitted are valid. The success rate of electronic medical records at the Bali Mandara Eye Hospital was obtained at 3.22 or 80.50%. To be able to increase this value, it is recommended to improve the Information Quality, System Quality and User Satisfaction

Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care.

Physicians establish diagnosis by assessing a patient's signs, symptoms, age, sex, laboratory test findings and the disease history. All this must be done in limited time and against the backdrop of an increasing overall workload. In the era of evidence-based medicine it is utmost important for a clinician to be abreast of the latest guidelines and treatment protocols which are changing rapidly. In resource limited settings, the updated knowledge often does not reach the point-of-care. This paper presents an artificial intelligence (AI)-based approach for integrating comprehensive disease knowledge, to support physicians and healthcare workers in arriving at accurate diagnoses at the point-of-care. We integrated different disease-related knowledge bodies to construct a comprehensive, machine interpretable diseasomics knowledge-graph that includes the Disease Ontology, disease symptoms, SNOMED CT, DisGeNET, and PharmGKB data. The resulting disease-symptom network comprises knowledge from the Symptom Ontology, electronic health records (EHR), human symptom disease network, Disease Ontology, Wikipedia, PubMed, textbooks, and symptomology knowledge sources with 84.56% accuracy. We also integrated spatial and temporal comorbidity knowledge obtained from EHR for two population data sets from Spain and Sweden respectively. The knowledge graph is stored in a graph database as a digital twin of the disease knowledge. We use node2vec (node embedding) as digital triplet for link prediction in disease-symptom networks to identify missing associations. This diseasomics knowledge graph is expected to democratize the medical knowledge and empower non-specialist health workers to make evidence based informed decisions and help achieve the goal of universal health coverage (UHC). The machine interpretable knowledge graphs presented in this paper are associations between various entities and do not imply causation. Our differential diagnostic tool focusses on signs and symptoms and does not include a complete assessment of patient's lifestyle and health history which would typically be necessary to rule out conditions and to arrive at a final diagnosis. The predicted diseases are ordered according to the specific disease burden in South Asia. The knowledge graphs and the tools presented here can be used as a guide.

Relation of coronary calcium scoring with cardiovascular events in patients with diabetes: The CLARIFY Registry

IntroductionCoronary artery calcium (CAC) scoring is not routinely performed in patients with diabetes based on an existing class I indication for statin therapy in these patients. However, CAC scoring may improve risk classification and prediction of atherosclerotic cardiovascular disease (ASCVD) events beyond risk scores in asymptomatic individuals with prediabetes and diabetes, warranting CAC assessment in this population. The routine availability through provision of no-charge CAC as an alternative to routine probabilistic risk scores may improve utilization of preventive therapies especially in traditionally underserved populations. MethodsProspective observational study in a large health system offering no-charge CAC scoring for primary prevention risk prediction with available glycosylated hemoglobin (HbA1c) measurements between June 2015 and March 2019 were divided according to no diabetes (HbA1c <5.7 %), prediabetes (HbA1c 5.7 %–6.4 %), or diabetes (HbA1c ≥ 6.5 % or charted history) and followed for major adverse cardiovascular events [myocardial infarction, stroke, death (MACE) or coronary revascularization]. Patient characteristics, health history, laboratory data, and statin prescription rates were measured at baseline and at one year after CAC scoring. ResultsA total of 12,194 subjects with available HbA1c underwent CAC scoring during the study period (6462 diabetes, 2062 prediabetes, and 3670 without diabetes). At a median follow-up of 1.2 years, there were 458 MACE events (71 patients without diabetes, 66 patients with prediabetes, and 321 patients with diabetes). Among patients with diabetes or prediabetes, increased CAC was associated with MACE (HR 1.38 [1.26–1.51], p < 0.001) and MACE or revascularization (HR 1.70 [1.57–1.85], p < 0.001). In patients with diabetes, CAC category was associated with greater statin initiation (89.6 % for CAC≥400 vs 60.1 % for CAC = 0, p < 0.001) and high intensity statin initiation (42.2 % for CAC≥400 vs 16.8 % for CAC = 0, p < 0.001) at one year post CAC scoring. Patients with diabetes had greater reductions in systolic blood pressure, LDL-C, total cholesterol, and triglycerides from baseline with a CAC ≥400 compared to a lower CAC category (p = 0.007). ConclusionsCAC burden is associated with ASCVD risk in patients with diabetes. CAC scoring increases statin prescriptions and reduces ASCVD risk in patients with diabetes, potentially warranting routine CAC assessment in this population.

Searching for the Relationship between the Concentration of Heavy Metals in the Blood and the Clinical Course of Multiple Sclerosis: A Cross-Sectional Study in Poland.

(1) Background: Of all environmental pollutants, heavy metals have the most detrimental effect on public health because they remain in the ecosystem and are not biodegradable. The neurotoxicity of heavy metals, including cadmium and lead, has been well documented, and blood levels of heavy metals serve as a biomarker of exposure, reflecting their content in soft tissues. Multiple sclerosis (MS) is one of the most common chronic neurodegenerative diseases. The pathogenesis of MS is complex and relies on the interaction between genetic and environmental factors. The aim of this study was to search for the relationship between the values of cadmium and lead concentration in the blood and the health history and functional status of patients with MS. (2) Methods: The study group consisted of 151 patients with a clinical diagnosis of MS. Determination of the presence of tested elements in serum was performed using an ELAN DRC-e ICP mass spectrometer. (3) Results: Statistical analysis demonstrated that an increase in the level of cadmium was accompanied by an increase in the level of lead. There was no significant correlation between the blood lead concentration and the functional status assessed by EDSS among patients with MS in Poland. However, a tendency towards worse functional status of patients with higher blood lead concentration (p = 0.07) was demonstrated. Patients who experienced first MS symptoms at older age had higher blood levels of the tested metals. (4) Conclusions: The concentrations of cadmium and lead in the blood of MS patients in Poland were not factors differentiating their functional status and the course of the disease.

Older Adult Patients and Care Partners as Knowledge Brokers in Fragmented Health Care.

To describe older adult patients' and care partners' knowledge broker roles during emergency department (ED) visits. Older adult patients are vulnerable to communication and coordination challenges during an ED visit, which can be exacerbated by the time and resource constrained ED environment. Yet, as a constant throughout the patient journey, patients and care partners can act as an information conduit, or knowledge broker, between fragmented care systems to attain high-quality, safe care. Participants included 14 older adult patients ( 65years old) and their care partners (e.g., spouse, adult child) who presented to the ED after having experienced a fall. Human factors researchers collected observation data from patients, care partners and clinician interactions during the patient's ED visit. We used an inductive content analysis to determine the role of patients and care partners as knowledge brokers. We found that patients and care partners act as knowledge brokers by providing information about diagnostic testing, medications, the patient's health history, and care accommodations at the disposition location. Patients and care partners filled the role of knowledge broker proactively (i.e. offer information) and reactively (i.e. are asked to provide information by clinicians or staff), within-ED work system and across work systems (e.g., between the ED and hospital), and in anticipation of future knowledge brokering. Patients and care partners, acting as knowledge brokers, often fill gaps in communication and participate in care coordination that assists in mitigating health care fragmentation.

Oral Health of COVID-19 Patients Using Extracorporeal Membrane Oxygenation: A Clinical Study of 19 Cases.

The oral health of coronavirus disease 2019 (COVID-19) patients in the intensive care unit (ICU) is an important issue in treatment of respiratory failure. We retrospectively investigated the oral health history of severe COVID-19 patients who received extracorporeal membrane oxygenation (ECMO) from April 2020 to December 2020 using the oral assessment guide from Fukuoka University (OAG-F). Nineteen consecutive patients (median age: 62 years) were divided into two groups according to survival (survivors, n = 12; non-survivors, n = 7). A univariate analysis revealed no significant differences between the groups in sex, age, body mass index (BMI), or the number of remaining teeth, whereas the ECMO assistance of non-survivors (median: 34 days) was prolonged in comparison to survivors (median: 8 days; p < 0.05). Among the factors of OAG-F, significant differences were observed between the groups in the conditions of the saliva, mucous membrane, and gingiva. The total scores in non-survivors (median: 19) were significantly higher in comparison to survivors (Median: 15.5), suggesting that the frequency of oral health deterioration was higher in non-survivors (p < 0.05). Taken together, these findings suggest that poor oral health is associated with mortality in COVID-19 patients receiving ECMO in the ICU.