Health Status Of Patients Research Articles

A human centered design approach to define and measure documentation quality using an EHR virtual simulation.

Electronic health record (EHR) documentation serves multiple functions, including recording patient health status, enabling interprofessional communication, supporting billing, and providing data to support the quality infrastructure of a Learning Healthcare System. There is no definition and standardized method to assess documentation quality in EHRs. Using a human-centered design (HCD) approach, we define and describe a method to measure documentation quality. Documentation quality was defined as timely, accurate, user-centered, and efficient. Measurement of quality used a virtual simulated standardized patient visit via an EHR vendor platform. By observing and recording documentation efforts, nurse practitioners (NPs) (N = 12) documented the delivery of an Age-Friendly Health System (AFHS) 4Ms (what Matters, Medication, Mentation, and Mobility) clinic visit using a standardized case. Results for timely documentation indicated considerable variability in completion times of documenting the 4Ms. Accuracy varied, as there were many types of episodes of erroneous documentation and extra time in seconds in documenting the 4Ms. The type and frequency of erroneous documentation efforts were related to navigation burden when navigating to different documentation tabs. The evaluated system demonstrated poor usability, with most participants scoring between 60 and 70 on the System Usability Scale (SUS). Efficiency, measured as click burden (the number of clicks used to navigate through a software system), revealed significant variability in the number of clicks required, with the NPs averaging approximately 13 clicks above the minimum requirement. The HCD methodology used in this study to assess the documentation quality proved feasible and provided valuable information on the quality of documentation. By assessing the quality of documentation, the gathered data can be leveraged to enhance documentation, optimize user experience, and elevate the quality of data within a Learning Healthcare System.

Differentially Expressed Blood ARLNC1 in Combination with PCA3/PSA have Reassuring Clinical Applications in the Early Diagnosis of Prostate Cancer in Iranians: A pilot study.

Prostate cancer (PCA) is the second most common malignancy in Western countries. Long non-coding RNAs are new markers in disease diagnosis. Our aim of this study was to investigate liquid biopsy biomarkers with high specificity and sensitivity for early diagnosis of PCA patients in Iran. Blood specimens were collected from 29 PCA, 32 benign prostate hyperplasia (BPH), and 29 control (CTRL) individuals. Real-time PCR analyzed expression amounts of PSA, ARLNC1, UCA1, and PCA3. The ROC curve (receiver operating characteristic curve) analysis evaluated the diagnostic power of the examined molecules for PCA. There was a significant upregulation of PCA3 in PCA and BPH groups compared to the controls (p values for PCA3=< 0.001 and BPH vs. CTRL = 0.0015) while there was no significant difference between PCA and BPH individuals. A significant upregulation of ARLNC1 was seen in BPH group compared to the controls (p value=0.0042). Also, PCA3 expression level showed a significant relationship with prostate volume. There was no significant difference in UCA1 and PSA expression levels among the three groups (>0.05). The PCA3/PSA ratio was significantly increased in PCA and BPH individuals vs. the CTRL group with high sensitivity and specificity. The gene expression of PCA3 and ARLNC1 in the BPH group showed a significant relationship with age. Our findings showed that in the diagnosis of prostate cancer, measuring the expression of PCA3, PSA, and ARLNC1 genes is necessary to determine the health, benign, or cancerous status of patients' prostate. Also, selecting the PCA3/PSA ratio provides a new approach for diagnosing this cancer if confirmed in a larger clinical sample size and functional studies.

COVID-19 Pandemic Increases in Nursing-Sensitive Quality Indicators.

The pandemic profoundly stressed practicing nurses and could have thereby affected trends in nursing-sensitive quality indicators (NSIs), measures that detect changes in patient health status directly affected by nursing care. The aim of the study was to determine if NSIs have worsened in response to the pandemic and then returned to prepandemic levels using data from 2019 through 2022. We conducted a cross-sectional descriptive study of annual trends, examining unit data from the National Database of Nursing Quality Indicators (NDNQI) from 2019 through 2022 for five indicators: rates of falls, central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), hospital-acquired pressure injuries (HAPI), and ventilator-associated events (VAE). The NDNQI is the largest repository of nursing quality indicators, which are derived from patient-level events, reported at the nursing unit level, and submitted quarterly by over 2,000 member hospitals. Adult medical-surgical or critical care inpatient nursing units with complete data for the 4 years were included, with samples ranging from 456 to 5,818 nursing units in 2,346 hospitals. Analysis of variance was conducted by comparing the 2019 rates to each subsequent year. In decreasing order of prevalence, the mean prepandemic rates were 6.58 VAE per 1,000 ventilator days (critical care only), 2.41 HAPI per 1,000 device days, 2.20 falls per 1,000 patient days, 0.96 CAUTI per 1,000 catheter days, and 0.68 CLABSI per 1,000 central line days for medical-surgical and critical care units combined. The rates for all five nurse-sensitive indicators increased significantly beginning in 2020 and have begun to decline but have not returned to baseline by 2022. The maximum rate was observed in 2020 for falls and 2021 for the remaining indicators. These increases to the maximum ranged from a 12% increase in CAUTI to 49% for CLABSI. NSIs increased during the pandemic and are now returning to baseline. The pandemic underscored the importance of nursing practice. The pandemic's enduring negative effects on the nursing workforce must be addressed to preserve patient safety.

Enhancing targeted tumor treatment: A novel fuzzy logic framework for precision drug delivery strategy selection

Objective:This study aims to address the challenge of selecting optimal drug delivery strategies for tumor patients by introducing a novel theoretical framework. Methods:We propose a fuzzy logic-based framework for quantitatively assessing Health States (HS) in tumor patients. This framework integrates quantified HS assessments with causality strength analyses, offering a comprehensive understanding of various drug delivery schemes’ effectiveness from pharmacokinetic and pharmacodynamic perspectives. Results:The efficacy of our approach is demonstrated through a series of real-world patient case studies, highlighting its potential to enhance the evaluation and selection of targeted drug delivery strategies. Conclusion:Our work contributes to the field by showcasing practical applications of fuzzy logic in targeted drug delivery systems (TDDs) and establishing a new benchmark for precision in drug delivery strategy selection. Significance:This study has significant implications for developing personalized medical treatments, potentially revolutionizing the field with a more nuanced and scientifically rigorous method for evaluating and selecting drug delivery protocols. Contributions:Development of a fuzzy logic framework for precise quantification of health states in tumor patients. Innovative integration of a causal system for comprehensively evaluating targeted drug delivery strategies.

The Impact of Radical Prostatectomy Versus Radiation Therapy on Cancer-Specific Mortality for Nonmetastatic Prostate Cancer: Analysis of an Other-Cause Mortality Matched Cohort

IntroductionStudies comparing radical prostatectomy (RP) to radiation therapy (RT) have consistently shown that patients undergoing RT have a higher risk of other-cause mortality (OCM) compared to RP, signifying poor health status of the former patients. We aimed to evaluate the impact of RP versus RT on cancer-specific mortality (CSM) over a cohort with equivalent OCM risk. Patients and MethodsThe SEER database was queried to identify patients with nonmetastatic PCa between 2004 and 2009. Patients were matched based on their calculated 10-year OCM risk and further stratified for D'Amico Risk Score and Gleason Grade. A Cox-regression model was used to calculate the 10-year OCM risk. Propensity-score based on the calculated OCM risk were used to match RP and RT patients. Cumulative incidence curves and Competing-risk regression analyses were used to examine the impact of treatment on CSM in the matched cohort. ResultsWe identified 55,106 PCa patients treated with RP and 36,674 treated with RT. After match, 6,506 patients were equally distributed for RT versus RP, with no difference in OCM rates (P = .2). The 10-year CSM rates were 8.8% versus 0.6% (P = .01) for RT versus RP in patients with unfavorable-intermediate-risk (Gleason Score 4 + 3) and 7.9% versus 3.9% (P = .003) for high-risk disease. There was no difference in CSM among RT and RP patients for favorable-intermediate-risk (Gleason Score 3 + 4) and low-risk disease. ConclusionsIn a matched cohort of PCa patients with comparable OCM between the 2 arms, RP yielded a more favorable CSM rate compared to RT only for unfavorable-intermediate- and high-risk groups.

Application of telemedicine technology for cardiovascular diseases management during the COVID-19 pandemic: a scoping review.

Recently, the use of telemedicine technology has increased due to the Covid-19 pandemic. Cardiovascular diseases management is one of the areas that has benefited from using this technology. This study aimed to identify the applications of telemedicine for cardiovascular diseases management during the Covid-19 pandemic. This scoping study was conducted in 2023. Various databases, including PubMed, Web of Science, Scopus, the Cochrane Library, Ovid, CINAHL, ProQuest, and IEEE Xplore along with Google Scholar were searched and all related quantitative, qualitative, and mixed-method studies published in English between 2020 and 2022 were included. Finally, the required data were extracted, and the findings were reported narratively. A total of 17 articles were included in this study. The results showed that teleconsultation via telephone and videoconferencing were the most common used technologies. Telemedicine helped to improve clinical impacts such as patient health status and quality of care, and reduced hospitalizations and re-admission rates compared to in-person visits. The non-clinical impact included reducing waiting time, in-person visits, and healthcare costs during the Covid-19 pandemic. The Covid-19 pandemic has led to an increased use of telemedicine technology, especially for patients with cardiovascular diseases. As teleconsultation and telemonitoring are useful for cardiovascular diseases management and regular examinations, future research should investigate how these technologies can be improved and used for a wider population.

Secure data communication in WSHN using EXP-MD5 and DHSK-ECC.

In the Healthcare (HC) sector, the usage of Wireless Sensor Healthcare Networks (WSHN) is attaining specific importance. The sensor device is implanted into the patient's body, and the sensed health information of patients is transformed via data aggregating devices like mobile devices, cameras, and so on, to the doctors. Thus, the early signs of diseases are identified, and remote monitoring of the patient's health is carried out by the physician on time. This aids in improving the health condition of the people and reduces the severity of disorders. But, the security gap in HC remains unresolved, despite various advantages. This work proposes secured data communication in WSHN using Exponential Message Digest5 (EXP-MD5) and Diffie Hellman Secret Key-based Elliptic Curve Cryptography (DHSK-ECC) techniques. Primarily, the patient registers their details in the Hospital Cloud Server (HCS). With hospital ID and patient ID, public and private keys are generated during registration. Afterward, by utilizing the Navie Shuffling (NS) technique, nCr combinations are created and shuffled. After shuffling, any of the randomly selected combinations are encoded utilizing the American Standard Code for Information Interchange (ASCII) code. For patient authentication, the ASCII code is further converted into a Quick Response(QR) code. Upon successful registration, the patient logs in to HCS. The patient can book for doctor's appointment if the login details are verified with those of the registered details. On consulting the doctor at the pre-informed time, the digital signature is created utilizing the Universal Unique Salt-based Digital Signature Algorithm (UUS-DSA) for authenticating the patient details. Further, for providing accessibility to all the authorized patients, the registered patients on HCS are considered as nodes. Then, an authorized path is created using the EXP-MD5 technique to protect each individual patient's details. The patient's IoT data is sensed, followed by authorized path creation. The data is encrypted via the DHSK-ECC algorithm for secure data transmission. Lastly, all the information is stored in HCS, so that the patient's health condition is regularly monitored by the doctor and the needy advice is suggested to the patients in the future. Also, hash matching is carried out when the doctor needs to access data. The proposed technique's efficacy is validated by the performance analysis in comparison with other conventional techniques. In this proposed research, the authentication is performed in multiple scenarios to enhance data security and user privacy. The patient details are authenticated during registration and verification to access the online consultation only by the authorized person. Further, the patient health information is encrypted in the proposed work after consultation so that the intrusion of medical records by malicious users and data tampering is prevented. Also, the sensed data gathered from patients are transferred to the HCS by creating the authorized path, which further enhances the security of patient data. Thus, the data communication of the WSHN is well-secured in this work through multi-level authentication and improved cryptography techniques.

DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

SummaryWireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.

Community-based analysis of stroke prevention and effect of public interventions in atrial fibrillation: results from the ARENA project.

Community-based interventions may promote awareness and adherence to atrial fibrillation (AF)-related therapies, potentially reducing adverse events. The ARENA project investigated the health status, therapies and events in AF patients in the Rhein-Neckar Region, Germany. The subproject "ARENA intervention" studied the effect of community-based interventions on AF-associated outcomes. From 2016 onward, patients with diagnosed AF were recruited for the observational ARENA registry. In 2018, an intervention period was initiated involving population-based information campaigns on AF diagnosis and therapies. The "control group" was recruited prior to initiation, and the "intervention group" afterward. Patients underwent standardized follow-up > 1year after recruitment. Clinical outcomes, therapy and quality of life were compared between the two groups. A total of 2769 patients were included. This real-world cohort showed high adherence to oral anticoagulation therapy (OAC) and an increased use of NOACs over vitamin K antagonists over time. In the intervention group (n = 1362), more patients continued OAC at follow-up (87.1% vs. 81.5%, P = 0.002). However, this difference was not significant in the patient subgroup with class I/IIa indications for OAC (90.1% vs. 87.5%, P = 0.11). AF-related re-hospitalization was lower in the intervention group (6.8% vs. 12.3%, P < 0.001). There was no significant difference in quality of life. AF-related anxiety was reduced at follow-up. Of note, nearly a quarter of all patients stated that ARENA had influenced their health perception. Tailored community-based campaigns may raise awareness for AF-related health issues, supporting therapy adherence. Future public strategies to improve quality of life in AF patients should be investigated, as the ARENA project hints at a potential benefit of population-based campaigns. ClinicalTrials.gov (Identifier: NCT02978248).

EVALUATING THE MSK HEALTH-STATUS (MSK-HQ) AND MSK CONDITIONS AFFECTING A MINORITIZED PRISONER POPULATION ACCESSING PHYSIOTHERAPY IN A UK REMAND PRISON

Purpose/BackgroundHealth inequalities are defined as unfair and avoidable differences in health across the population. People belonging to inclusion health groups are most at risk of experiencing these inequalities and tackling them is an NHS priority. Inclusion health groups include ‘people in contact with the justice system’ and BCHC NHS FT is commissioned to provide physiotherapy to prisoners at HMP Birmingham. The purpose of this study was to better understand the MSK health-status and MSK conditions affecting this minoritised patient population.Methods/ResultsThe MSK service at HMP Birmingham collects the MSK-HQ PROM for all prisoners attending physiotherapy. A retrospective analysis of the electronic patient record was performed using convenience sampling, with baseline MSK-HQ scores of prisoners seen between 01/04/2023 and 01/02/2024 captured alongside the primary presenting MSK condition. Simple descriptive statistics were used to evaluate the data. A total of 129 patients attended initial physiotherapy appointments within the time-period. Baseline MSK-HQ was collected for 62 patients (48%) with a mean (SD) score of 23.08(11.38). The most common MSK condition was Back pain with a count of 12(19.35%). The mean (SD) MSK-HQ score for prisoners with back pain was 22.08(9.98).ConclusionThis is the first known evaluation in the published literature of the baseline MSK health-status and primary MSK conditions affecting prisoners. A published evaluation of baseline MSK health-status for patients attending a community MSK service reported a mean (SD) MSK-HQ of 28.59(10.80) indicating that, a poorer MSK health-status is experienced by this minoritised group. Further work, co-produced with prisoners, is planned to explore further.Conflicts of interestNoneSources of fundingNone

Physical Activity and Epigenetic Aging in Breast Cancer Treatment.

Biological age, reflecting the cumulative damage in the body over a lifespan, is a dynamic measure more indicative of individual health than chronological age. Accelerated aging, when biological age surpasses chronological age, is implicated in poorer clinical outcomes, especially for breast cancer (BC) survivors undergoing treatments. This preliminary study investigates the impact of a 16-week online supervised physical activity (PA) intervention on biological age in post-surgery female BC patients. Telomere length was measured using qPCR, and the ELOVL2-based epigenetic clock was assessed via DNA methylation pyrosequencing of the ELOVL2 promoter region. Telomere length remained unchanged, but the ELOVL2 epigenetic clock indicated a significant decrease in biological age in the PA group, suggesting the potential of PA interventions to reverse accelerated aging processes in BC survivors. The exercise group showed improved cardiovascular fitness, highlighting PA's health impact. Finally, the reduction in biological age, as measured by the ELOVL2 epigenetic clock, was significantly associated with improvements in cardiovascular fitness and handgrip strength, supporting improved recovery. Epigenetic clocks can potentially assess health status and recovery progress in BC patients, identifying at-risk individuals in clinical practice. This study provides potential and valuable insights into how PA benefits BC survivors' health, supporting the immediate benefits of a 16-week exercise intervention in mitigating accelerated aging. The findings could suggest a holistic approach to improving the health and recovery of post-surgery BC patients.

The competitive dynamics of strategic risk‐taking, unethical behavior, and entry

AbstractResearch SummaryThis article argues that strategic risk‐taking in the form of unethical behavior can lead to successful entry and that this entry can drive incumbents to either adopt similar behavior or potentially be forced out of the market. Thus, entrepreneurs can introduce innovative, but socially destructive, behavior to a market and motivate its adoption by incumbents. Empirical support is found using a policy change in the liver transplant market to identify the use and adoption of unethical misrepresentation of patient health status. This article contributes to our understanding of entrepreneurial strategy as well as the competitive dynamics between entrants and incumbents in adopting new risky innovations and, in particular, unethical behaviors.Managerial SummaryThis article highlights the impact of strategic risk‐taking through unethical practices, showing how they can sometimes lead to a successful market entry. Successful entry can force existing market players to either follow suit or risk being forced out of the market. This study looks at the liver transplant market, where transplant centers took advantage of a previous policy by misrepresenting patient health status to move their patients to the top of the transplant list. The findings underscore the importance of understanding the competitive dynamics of adopting innovative yet ethically questionable strategies by both new entrants and established companies, offering valuable insights into how businesses navigate competitive pressures and ethical boundaries. It also highlights the importance of policymakers considering these competitive dynamics when setting policy.

Impact of the American Society of Anesthesiologists (ASA) classification on hip fracture surgery outcomes: insights from a retrospective analysis

BackgroundThe American Society of Anesthesiologists (ASA) classification is the most used system to assess patient health status before surgery, ranging from I to V levels. This study aims to explore the impact of different ASA risk classes (ASA II [mild risk] and ASA III [severe risk]) on clinical outcomes following hip fracture surgery, including all-cause mortality and postoperative complications.MethodsA retrospective analysis from 2019 to 2021 across three Jordanian centers was conducted. The study included patients aged 65 and above who underwent hip fracture repair surgeries. Preoperative measures, intraoperative management protocols, and postoperative care were collected. Clinical data were extracted from electronic medical records, including demographics, fracture type, intraoperative data, and postoperative outcomes.ResultsThe analysis included 1033 patients, with 501 (48.5%) in the mild anesthetic risk group (ASA I-II) and 532 (51.5%) in the severe anesthetic risk group (ASA III-V). The mean age was 73 years, with a higher prevalence of males in the severe risk group. Patients in the severe risk group had more comorbidities, higher ICU admissions (15.23% vs. 6.18%), longer hospital stays (median 7 vs. 6 days), and higher rates of postoperative thromboembolic complications (3.39% vs. 1.39%) compared to the mild risk group. Additionally, the severe risk group showed higher mortality rates both in-hospital mortality (3.38% vs. 1.39%) and all-cause mortality (16.92% vs. 10.36%). Multivariate analysis identified higher ASA score as independent risk factors for increased all-cause mortality (HR = 1.64 95%CI 1.51–2.34) and thromboembolic complications (OR = 2.85 95%CI 1.16-7). Length of hospital stay was significantly associated with higher ASA score (OR = 1.04 95%CI 0.96–1.11).ConclusionThe study underscores the significant impact of anesthetic risk on the outcomes of hip fracture surgeries. Patients with higher ASA scores associated with severe systemic diseases may have at increased risk of adverse outcomes.

Artificial mitochondrial transplantation (AMT) reverses aging of mesenchymal stromal cells and improves their immunomodulatory properties in LPS-induced synoviocytes inflammation

Nowadays, regenerative medicine techniques are usually based on the application of mesenchymal stromal cells (MSCs) for the repair or restoration of injured damaged tissues. However, the effectiveness of autologous therapy is limited as therapeutic potential of MSCs declines due to patient's age, health condition and prolonged in vitro cultivation as a result of decreased growth rate. For that reason, there is an urgent need to develop strategies enabling the in vitro rejuvenation of MSCs prior transplantation in order to enhance their in vivo therapeutic efficiency.In presented study, we attempted to mimic the naturally occurring mitochondrial transfer (MT) between neighbouring cells and verify whether artificial MT (AMT) could reverse MSCs aging and improve their biological properties. For that reason, mitochondria were isolated from healthy donor equine adipose-derived stromal cells (ASCs) and transferred into metabolically impaired recipient ASCs derived from equine metabolic syndrome (EMS) affected horses, which were subsequently subjected to various analytical methods in order to verify the cellular and molecular outcomes of the applied AMT.Mitochondria recipient cells were characterized by decreased apoptosis, senescence and endoplasmic reticulum stress while insulin sensitivity was enhanced. Furthermore, we observed increased mitochondrial fragmentation and associated PARKIN protein accumulation, which indicates on the elimination of dysfunctional organelles via mitophagy. AMT further promoted physioxia and regulated autophagy fluxes. Additionally, rejuvenated ASCs displayed an improved anti-inflammatory activity toward LPS-stimulated synoviocytes. The presented findings highlight AMT as a promising alternative and effective method for MSCs rejuvenation, for potential application in autologous therapies in which MSCs properties are being strongly deteriorated due to patients' condition.

The Effect of Health Literacy on Health Status in Patients with Heart Failure: A Path Analysis.

Managing heart failure (HF) is challenging because of its complexity and high rates of hospitalization, morbidity, and mortality. Effective management requires coordination between patients' abilities and healthcare services; however, low health literacy among patients with heart failure can adversely affect their health status. Therefore, the understanding relationship between health literacy and health status would provide the evidence for developing future intervention. This analytical study explored multilevel factors influencing health literacy and health status in patients with heart failure (HF) who underwent health service delivery at outpatient clinics. The sample consisted of 453 patients with HF from 12 hospitals located in five regional areas (north, south, east, northeast, central, and Bangkok) in Thailand. A hypothetical model was used to test the association between comorbidity, social support, patient-centered care, health service delivery, health literacy, and health status. Path analysis was used to analyze the data. The results indicated that 40.8% of patients with HF exhibited low or inadequate levels of health literacy, yet perceived their health status as good, with an average of 25.2±19.8 points. Health service delivery by a cardiologist and nurse case manager directly influences health literacy. Comorbidity had a direct impact on health status, whereas social support, patient-centered care, and the aforementioned health service delivery indirectly affected health status through health literacy. Importantly, health literacy status directly influences health status. Comorbidity, social support, patient-centered care, and health service delivery were related with health literacy and high level of health literacy could influence the better health status. To increase the quality of health care services, health care providers should promote health literacy and integrate the concept of patient-centered care for HF patients at outpatient clinics. Recommendations include the training of nurses as case managers.

Association of social support and health status outcomes in peripheral artery disease

ObjectiveA critical goal in the care of patients with peripheral artery disease (PAD) is to optimize their health status; that is, their symptoms, function, and quality of life. Social support has been proposed to be a predictor of disease-specific health status in patients with PAD. However, the prevalence of low perceived social support, the association with health status outcomes, and the interaction with other biopsychosocial variables, is unknown. Our aim was to assess the association of baseline perceived social support with health status at 12 months in patients with PAD. MethodsThe Patient-Centered Outcomes Related Treatment Practices in Peripheral Arterial Disease: Investigating Trajectories (PORTRAIT) registry, which enrolled patients with PAD in the United States, the Netherlands, and Australia from 2011 to 2015, was used. Perceived social support was assessed at baseline with the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Social Support Inventory (ESSI), and disease-specific (Peripheral Artery Disease Questionnaire [PAQ]) and generic health status (Euro-Quality of Life Visual Analog Scale [VAS] and EQ-5D-3L Index) questionnaires were assessed at baseline and 12 months. Low social support was defined as a score of ≤3 on two items and an ESSI score of ≤18. A hierarchical mixed level linear regression model adjusting for biopsychosocial variables was used to assess the association between low perceived social support and the ESSI score with health status at 12 months. ResultsA total of 949 patients were included (mean age, 67.64 ± 9.32 years; 37.9% female), with low social support being present in 18.2%. Patients with low social support were more likely to not be married or to be living alone (50.0% vs 77.5%; P < .001); have more financial constraints; have more depressive, stress, and anxiety symptoms; and have lower disease-specific and generic health status at baseline and at 12 months. In the unadjusted model, low social support was associated with a −7.02 (95% confidence interval [CI], −10.97 to −3.07) point reduction in the PAQ, −7.43 (95% CI, −10.33 to −4.54) in the VAS, and −0.06 (95% CI, −0.09 to −0.03) in the EQ-5D-3L Index. Adjusting for biopsychosocial factors minimally attenuated these associations (PAQ: −6.52; 95% CI, −10.55 to −2.49; P = .002; VAS: −5.39; 95% CI, 8.36 to −2.42; P < .001; EQ-5D-3L Index: −0.04; 95% CI, −0.07 to 0.01; P = .022). The ESSI per-point score was associated with a decrease of 0.51 (95% CI, 0.18-0.85; P = .003) in PAQ and 0.46 (95% CI, 0.12-0.61; P = .004) in the VAS. ConclusionsAmong patients with PAD, low social support was frequent and associated with a lower health status at 1 year independent of other biopsychosocial variables. Improving social support could improve health status and outcomes in PAD.

Are Patient-Reported Outcome Measures for Anterior Cruciate Ligament Injuries Validated for Spanish Language and Culture?

Patient-reported outcome measures (PROMs) have been adopted as a way to measure patient self-rated physical function and health status for patients with anterior cruciate ligament (ACL) injuries. Although multiple PROMs exist and have been translated into various languages, the cross-cultural adaptation and validity of these PROMs for Spanish-speaking patients is unknown. To evaluate the adaptation quality and psychometric properties of Spanish-language adaptations of PROMs for patients with ACL injuries. Scoping review; Level of evidence, 3. Under PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we reviewed published studies related to adaptation quality and psychometric properties of Spanish PROMs in patients with ACL injuries. The methodological quality of the included studies was assessed using the Guidelines for the Process of Cross-Cultural Adaptation of Self-Reported Measures, the Quality Criteria for Psychometric Properties of Health Status Questionnaires, and the Consensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. The level of evidence for each PROM was determined based on the number of studies, methodological quality, consistency of results, and sample size. The initial search strategy identified 5687 articles. After removal of duplicates, 1882 titles were screened, and 114 articles were assessed for eligibility. Six articles were selected for final review, comprising 4 PROMs: the Lysholm knee score, the Anterior Cruciate Ligament-Return to Sport After Injury (ACL-RSI), the Lower Extremity Functional Scale, and the Lower Limb Functional Index. Three studies followed all 6 processes for cross-cultural adaptation. None of the studies demonstrated all 14 domains required for cross-cultural validity (eg, description of translator expertise). The ACL-RSI achieved the highest level of evidence, with 3 of 9 domains demonstrating moderate evidence. This review identified 4 instruments that have been translated for Spanish-speaking patients with ACL injuries, none of which demonstrated appropriate adaptation or robust psychometric properties. The study highlights the need for improvement in PROMs for Spanish-speaking patients and the potential for mismeasurement and inappropriate application of PROM results in patients with ACL injuries.

Investigating dental health status in patients with psychiatric disorders

Investigating dental health status in patients with psychiatric disorders

Direct and Indirect Mapping of Assessment of Quality of Life - 6 Dimensions (AQoL-6D) Onto EQ-5D-5L Utilities Using Data From a Multicenter, Cross-Sectional Study of Malaysians With Chronic Heart Failure

ObjectivesThe Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population. MethodsCross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models’ predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified. ResultsAmong the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy. ConclusionsThis study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable.

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.

Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.