Changes In Health Status Research Articles

Changes in Secondary Health Conditions Among Individuals With Spinal Cord Injury After Transition From Inpatient Rehabilitation to Community Living.

The aim of the study is to examine the changes in the burden of secondary health conditions and their risk factors among newly injured individuals with spinal cord injury after discharge from inpatient rehabilitation to community living. This is a prospective cohort study. Among 280 included persons, the majority were men with traumatic spinal cord injury and paraplegia. The most common secondary health conditions at discharge from rehabilitation and in the community setting were pain (60.8% vs. 75.9%), urinary tract infections (27.8% vs. 47.6%), and pressure sores (9.9% vs. 27.5%). There was also a significant increase in reported heart disease (9.3% vs. 14.8%) and diabetes (0% vs. 4.2%). The odds of experiencing pain, pressure sores, depression and urinary tract infections increased after discharge from rehabilitation, whereas the odds of respiratory problems decreased significantly during the observation period. Factors such as sex, injury severity, activity limitations, and duration of follow-up were identified as potential risk factors influencing secondary health condition occurrence. Our study emphasizes a significant rise in secondary health conditions after the transition to community living. Future research should prioritize development of targeted intervention programs tailored to address the unique needs of affected individuals at this pivotal stage in the continuum of spinal cord injury care.

Methodological considerations in calculating the minimal clinically important change score for the core outcome measures index (COMI): insights from a large single-centre spine surgery registry.

The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods. Data from a spine centre in Switzerland were used to evaluate variations in MCIC for the COMI score. Distribution-based and anchor-based methods (predictive and nonpredictive) were applied. Bayesian bootstrap estimated confidence intervals. From 27,003 cases, 9821 met the inclusion criteria. Distribution-based methods yielded MCIC values from 0.4 to 1.4. Anchor-based methods showed more variability, with MCIC values from 1.5 to 4.9. Predictive anchor-based methods also provided variable MCIC values for improvement (0.3-2.4), with high sensitivity and specificity. MCIC calculation methods produce varying values, emphasizing careful method selection. Distribution-based methods likely measure minimal detectable change, while non-predictive anchor-based methods can yield high MCIC values due to group averaging. Predictive anchor-based methods offer more stable and clinically relevant MCIC values for improvement but are affected by prevalence and reliability corrections.

Automatic telephonic follow up in heart failure by a natural language processing artificial intelligence: pilot program

Abstract Introduction Heart failure (HF) burden on public health is substantial and efficient patient management strategies are needed. Traditional methods of HF monitoring face limitations in scalability and effectiveness. Artificial Intelligence (AI) might offer novel solutions for enhanced resource management and alleviation of healthcare burdens. Purpose To study the feasibility of implementing automatic remote monitoring via phone calls operated by AI in a HF unit. Methods We conducted a non-randomized, open, pilot trial employing an AI technology based on natural language processing for autonomous telephone outreach for the follow up of HF patients. 86 patients from a tertiary hospital HF unit undergoing pharmacological titration and education were enrolled. The AI conducted weekly calls to gather symptomatology, vital signs, and health status changes, which were then displayed on a virtual platform featuring alerts preconfigured by the research team. These alerts guided patient communication and intervention as deemed necessary by a specialized nurse. Results Characteristics of patients are shown in Table 1. The AI followed these patients for a median of 251.5 days. 2773 telephonic calls were performed. We estimate an equivalent to 1445h of work (17.2 days/month). Adherence was high during the study, with 98% of the calls completed. 1427 alerts were generated, being the most frequent weight change (251; 17.6%), orthopnea (220; 15.4%) and fatigue (187; 13.1%). It led to 487 remote interventions, most of them consisting in calling the patient for assessment (409; 84.0%), but also in adjusting medication remotely (34; 7.0%). See Figure 2. A HF telemedicine specialized nurse required 205min per week for alert review, 2.38min/patient-week. 2 patients died, one from cardiovascular causes, and 7 patients had a HF hospitalization, 42.9% with previous alarms. 20 events of worsening HF were recorded, comprising hospitalization, emergency visit or IV diuretic administration. Oral diuretic dose was increased in 22 occasions, 15 of them remotely in response to an alert raised by the AI operator. At 6 months functional class globally improved (p = 0.028), but NTproBNP increased (p=0.0472). Quality of life was evaluated at baseline and at 6 months, with no change in EQ5D5L (median 0.717, change -0.005, p 0.71) or PHQ4 (median 2.88, change -0.29, p 0.7792), but KCCQ12 total score worsened (median 74.49, change -2.32, p 0.048). Experience was rated with 8.97/10 in a satisfaction questionnaire. All patients recommended the program, remarking a closer follow up, better communication and a safety feeling. Conclusions AI enables closer monitoring of patients with HF, maintaining contact with stable patients while also allowing for resource allocation and tracking of higher-risk patients or those experiencing changes in symptoms. A randomized clinical trial comparing to standard follow-up is needed to evaluate this promising technology.Table 1Figure 2

Gender and Education Differences in Mental Health in Belgium: a Bayesian Age-Period-Cohort Analysis

Abstract Background Mental illnesses (MI) constitute a growing public health problem, with reports of a higher prevalence among middle-aged adults (age effect), during specific periods such as economic crisis (period effect) and in recent birth cohorts (cohort effect). However, few studies have assessed whether these age-period-cohort (APC) effects were related to general population trends or if they are driven by changes in the mental health status of more vulnerable groups. Therefore, this study aimed to assess gender and educational differences underlying observed APC effects in the prevalence of MI in the Belgian adult population. Methods This study used data from six cross-sectional waves of the Belgian Health Interview Survey (1997-2018, ∼ 10,000 respondents per wave). Mental health was assessed using the 12-item General Health Questionnaire. Bayesian hierarchical APC models were applied on the prevalence of MI, taking into account gender and educational differences, as well as some potential confounding factors. Results Overall, this study confirmed the higher prevalence of MI in middle-aged adult, in 2013, and in more recent birth cohorts (1980-2000). Accounting for the interaction between gender and education, we found that the MI educational gradient was more pronounced for women than men. Finally, we found that APC trends were different between gender. The odds of having MI tended to be stable across age groups for women, while men showed an increased odds between 50-60 years and a decrease between 60-65 years. Regarding period and cohort effects, the increase odds in 2013 was mainly present in men, while the increase in recent birth cohorts was mainly present in women. Conclusions This study emphasizes that the age-period-cohort effects on mental health are different depending on gender and education, thus enriching our understanding on the underlying mechanisms to better guide future public (mental) health policies. Key messages • This study shows the importance of studying not only the overall trends in mental illness, but also the underlying differences in its distribution. • Insights into gender and educational differences in Age-Period-Cohort effects on mental health is crucial for the development of timely and efficient public health interventions.

Changes in mental health and illness perception in patients with hypertrophic cardiomyopathy referred to hybrid cardiac telerehabilitation in comparison to usual care - a randomised-control trial

Abstract Purpose Hypertrophic cardiomyopathy (HCM) is a complex cardiac disorder, that influences patients' physical capacity and quality of life, as well as the risk of sudden cardiac death (SCD). Patients with HCM often experience emotional distress due to the uncertainty of their condition and have a threatening illness perception (the way individuals make sense of their illness, which can have a significant impact on adherence to treatment). Current studies suggest that some HCM patients could benefit from hybrid cardiac telerehabilitation (HCTR) in terms of somatic and psychological status. This study is part of a research project investigating the safety and effectiveness of hybrid cardiac telerehabilitation for HCM patients. The specific focus of this part is to evaluate the changes in mental health status and illness perception of HCM patients who have been referred for cardiac telerehabilitation compared to those receiving usual care. Methods An RCT study was conducted, with the inclusion criteria for the study of being above 18 y.o., HCM without LVOT obstruction, NYHA Class II or III, LVEF ≥ 50%, and having CIED. The study involved telerehabilitation (HCTR) and control (CG) groups. Both groups were completing questionnaires assessing illness perception (Brief IPQ) and overall psychosocial distress (GHQ-28) with its 4 domains: somatic symptoms, anxiety, social impairment, and depression. Psychological assessment was performed at the beginning and at the end of telerehabilitation/outpatient care, and at 3, 6 and 12 months after the end of care (T1/T2/T3/T4/T5). Results There was a significant improvement in the overall distress level in the HCTR group (p=0.003) but not in the CG (p=0.559). It remained significant in the HCTR group for the T2 (p<0.001), T3 (p=0.008), and T4 (p<0.001). The positive change was observed in somatic symptoms only in the HCTR group: for T2 (p=0.025) and T3 (p=0.032) compared to the T1. The gradual decrease in anxiety level was observed in the HCTR group, but not in the CG, with a significant difference for T4 (p=0.013) compared to T1. Although there was an improvement in social impairment in both groups (p=0.049 for the CG, and p=0<0.001 for the HCTR group), only in the HCTR group it held a stable pattern after the intervention (p<0.001 for T2; p=0.013 for T3; p=0.032 for T4). There were no changes in the level of depressive symptoms in either of the compared groups. There were also no significant differences in the illness perception (p=0.200 for the HCTR group and p=0.090 for the CG), although the HCTR group showed short-term improvement after three months of HCTR (T2) (p=0.032). Conclusions There are significant benefits of hybrid cardiac telerehabilitation for mental health, level of distress, and illness perception in HCM patients. It is worth noticing that this improvement is short-term, and prolonged effects might require providing additional support during extended care.overall distress in HCTR vs CGsubscales of GHQ_28 in HCTR vs CG

Telemedicine as a tool for bridging geographical inequity: insights in geospatial interactions from a study on chronic heart failure patients

IntroductionChronic heart failure patients experience large disparities in quality of and access to treatment, with rural populations receiving lower levels of care. Telemonitoring of patients is increasingly being used as an important tool for improving patient management and care and might reduce geographical inequities in healthcare.MethodsWe investigate the presence and magnitude of a geospatial interaction effect on the health benefit of a supplementary telemedicine intervention, by analyzing the relationship between distance to regular place of treatment and the benefit of telemedicine in a secondary analysis of data from a previously conducted RCT. We use change in EQ5D health status, SF-36 Physical component score and SF-36 Mental component score as the outcomes. In the unadjusted analysis, intervention group and distance group and the interaction term are included as the independent variables, in the adjusted analysis, multiple socioeconomic and health related variables are included to account for potential confounders.ResultsWe find evidence of a significant interaction between the effects of telemedicine and long distance to treatment for change in EQ5D health status (unadjusted: p = 0.016, adjusted p = 0.009) and unadjusted but not adjusted mental component score (unadjusted: p = 0.013, adjusted p = 0.0728), for the change in physical component score the interaction term was not significant (unadjusted: p = 0.118, adjusted p = 0.092).ConclusionIn our study we find that supplementary telemedicine is likely to reduce the health access inequities associated with geographical distance for chronic heart failure patients. However, our sample size was modest and further research is needed to confirm these findings.

Inside CKD: a microsimulation modelling study projects the clinical and economic burden of chronic kidney disease in Hungary

ObjectivesThe Inside CKD programme implemented a microsimulation modelling approach to project the clinical and economic burden of chronic kidney disease (CKD) between 2024 and 2027 in Hungary.MethodsUsing the peer-reviewed Inside CKD microsimulation, a virtual Hungarian population was generated that was derived from national records, local demographic data and published epidemiological data. These inputs defined the likelihood of a change in health state for each individual as they progressed through the model in annual increments. Individual CKD status, including disease progression, cardiorenal complications and associated costs, was tracked annually to generate the population-level projections of the clinical and economic burden of CKD.ResultsBy 2027, people with CKD were projected to constitute 13.3% of the Hungarian national population. The prevalence of heart failure, myocardial infarction and stroke in people with CKD were projected to remain consistently high, reaching 323 447, 69 188 and 120 118 by 2027, respectively. Kidney replacement therapy cases were predicted to remain high at 20 515 in 2024 and 22 325 in 2027, with associated costs increasing from 71.4 billion HUF in 2024 to 79.6 billion HUF in 2027. Total annual healthcare costs associated with treating CKD were projected to constitute 5.4% of the overall national healthcare budget in 2027.ConclusionsInside CKD demonstrates that the future burden of CKD in Hungary will be substantial unless current management strategies change. The high prevalence of undiagnosed CKD and associated cardiorenal complications highlight the urgent need for policy interventions focused on early diagnosis and timely intervention to mitigate the future burden of CKD.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making.

Characterizing the progression from mild cognitive impairment to dementia: a network analysis of longitudinal clinical visits

BackgroundWith the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.ObjectiveWe aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, of the participants in Mayo Clinic Study of Aging (MCSA) from initial mild cognitive impairment (MCI) diagnosis to dementia onset through network analysis.MethodsWe used the data from the MCSA, a prospective population-based cohort study of cognitive aging, and examined the changing association among variables (i.e., participants’ health conditions) from the first visit of MCI diagnosis to the visit of dementia onset using network analysis. The number of participants for this study are 97 with the number of visits ranging from 2 visits (30 months) to 7 visits (105 months). We identified the network communities among variables from three-fold collection of instances: (i) the first MCI diagnosis, (ii) progression to dementia, and (iii) dementia diagnosis. We determine the variables that play a significant role in the dementia onset, aiming to identify and prioritize specific variables that prominently contribute towards developing dementia. In addition, we explore the sex-specific impact of variables in relation to dementia, aiming to investigate potential differences in the influence of certain variables on dementia onset between males and females.ResultsWe found correlation among certain variables, such as neuropsychiatric symptoms and chronic conditions, throughout the progression from MCI to dementia. Our findings, based on patterns and changing variables within specific communities, reveal notable insights about the time-lapse before dementia sets in, and the significance of progression of correlated variables contributing towards dementia onset. We also observed more changes due to certain variables, such as cognitive and functional scores, in the network communities for the people who progressed to dementia compared to those who does not. Most changes for sex-specific analysis are observed in clinical dementia rating and functional activities questionnaire during MCI onset are followed by chronic diseases, and then by NPI-Q scores.ConclusionsNetwork analysis has shown promising potential to capture significant longitudinal changes in health conditions, spanning from the MCI diagnosis to dementia progression. It can serve as a valuable analytic approach for monitoring the health status of individuals in cognitive impairment assessment. Furthermore, our findings indicate a notable sex difference in the impact of specific health conditions on the progression of dementia.

The impact of Scotland's minimum unit pricing for alcohol policy on people accessing services for alcohol dependence: A difference-in-difference structured interview study.

A minimum unit price (MUP) for alcohol of £0.50 per unit (1 UK unit = 10 mL/8 g alcohol) was introduced in Scotland in May 2018. Few previous studies have examined the impact of alcohol pricing policies on people who are alcohol dependent. This study aimed to evaluate the effect of MUP on people who are alcohol dependent including changes in alcohol consumption and health status, as well as potential unintended consequences. Three waves of cross-sectional data were collected in Scotland (intervention) and Northern England (control) at 0-6 months pre-implementation then 3-9 months and 18-22 months post-implementation. The sample was N = 706 people receiving treatment related to their alcohol use. We collected structured interview data including recent drinking information via a 7-day timeline-follow-back drinking diary. Difference-in-difference analyses estimated change in indicators in Scotland compared to England at both post-implementation timepoints. The proportion of participants consuming alcohol costing on average <£0.50 per unit in Scotland decreased from 60.6% at 0-6 months prior to MUP implementation to 6.3% at 3-9 months post-implementation (p < 0.0004). There was no significant change in the indicators for alcohol consumption, severity of dependence, health status, other substance use, deprivation level or parenting. The introduction of MUP in Scotland was associated with increases in the prices paid for alcohol by people with dependence and presenting to treatment services. There was no evidence of changes in their alcohol consumption or health status. There was also no evidence of harmful unintended consequences for this population.

Psychiatric Comorbidities in Women with Complete Androgen Insensitivity Syndrome or Müllerian Aplasia/Agenesis.

Understanding mental health issues facing individuals with disorders/differences of sex development (DSD) is crucial for optimizing evidence-based practices in this population. To compare the prevalence of psychiatric diagnoses among patients diagnosed with complete androgen insensitivity syndrome (CAIS) or Müllerian duct aplasia/agenesis (MA) to male and female reference groups. Retrospective cohort study. Three large integrated health systems. All individuals with confirmed CAIS or MA enrolled in one of three Kaiser Permanente healthcare systems between January 1, 1988, and January 31, 2017. For each DSD patient, age-, race/ethnicity- and health system-matched male and female referents with typical sex development were randomly selected. Mental health diagnoses and use of psychiatric medications. The prevalence of anxiety and depressive disorders in the CAIS and MA cohorts was approximately twice as high as in male referents without DSD, but the corresponding differences relative to female referents were less evident. A subgroup of MA patients with uterine agenesis had higher prevalence of bipolar disorder than either reference group, but these results were accompanied by wide confidence intervals. Women with CAIS and MA more frequently filled psychiatric medications compared to male but not female referents. On balance, these findings are reassuring, albeit requiring confirmation in other settings. Future studies using longitudinal designs and patient-reported outcomes are needed to evaluate changes in mental health status of CAIS and MA patients at different ages and different intervals following initial diagnosis.

Health status of individuals with polyhandicap across a 5-year follow-up period

The present longitudinal study examined a large sample of individuals with PIMD/Polyhandicap to: (i) describe the evolution over time of the health status in terms of severity, (ii) identify the potential predictors of health status change. This study used the data of the French national EVAL-PLH cohort. Inclusion criteria were: individuals with PIMD/Polyhandicap; age > 3 years at the time of inclusion; age at onset of cerebral lesion younger than 3 years old. The definition of the change in health status over the 5-year period was derived from the Polyhandicap Severity Scale. Among the 875 individuals included in 2015–2016, 492 (56.2%) individuals were assessed in 2020–2021. According to the definition, 68.8% (n = 309), 64.7% (n = 310), and 80.2% (n = 368) of the individuals presented with a worsened or stable severity status based on the global score, the abilities score, and the comorbidities score, respectively. Compared with the individuals with a non-worsened health status (global score), the individuals with a stable or worsened health status were more likely to receive care in specialized rehabilitation centres, more likely to have an antenatal or progressive aetiology, and more likely to have a higher dependency level. The current study provides a robust perspective of the worsened health among persons with PIMD/Polyhandicap over time.

How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences.

This study was designed to test hypotheses regarding the path dependence of health-outcome values in the form of linear additivity of health-state utilities and diminishing marginal utility of health outcomes. We employed a discrete-choice experiment to quantify patient treatment preferences for major depressive disorder. In a series of choice questions, participants evaluated seven symptom-improvement sequences and out-of-pocket costs over 6-week durations. Money-equivalent values were derived from a deductive latent-class mixed-logit analysis. The discrete-choice experiment was completed by 751 respondents with self-reported major depressive disorder recruited from an online commercial panel. The class-membership probability was 0.83 for latent-class preferences consistent with supporting relative importance weights for all symptom-improvement sequences in the study design. First, we found strong support for diminishing marginal utility in symptom-improvement sequences. The money-equivalent value of an initial week of normal mood was $147 (95% confidence interval: $128, $166) and a second week of normal mood was $70 ($49, $91). Furthermore, for short treatment durations where conventional discounting was not a factor, equivalent changes in health status were valued more highly for an earlier onset of effect: holding subsequent symptom patterns constant, $338 (211, 454) versus $70 (49, 91) for improvements starting in week 2 versus week 3 and $147 ($128, $166) versus $29 (-$4, $64) for improvements starting in week 3 versus week 4. Our findings imply that conventional quality-adjusted life-year calculations in which health values are assumed to be path independent can understate the value of health improvements that appear earlier in a sequence.

Endovascular Ablation of the Greater Splanchnic Nerve in Heart Failure With Preserved Ejection Fraction

Greater splanchnic nerve ablation may improve hemodynamics in patients with heart failure and preserved ejection fraction (HFpEF). To explore the feasibility and safety of endovascular right-sided splanchnic nerve ablation for volume management (SAVM). This was a phase 2, double-blind, 1:1, sham-controlled, multicenter, randomized clinical trial conducted at 14 centers in the US and 1 center in the Republic of Georgia. Patients with HFpEF, left ventricular ejection fraction of 40% or greater, and invasively measured peak exercise pulmonary capillary wedge pressure (PCWP) of 25 mm Hg or greater were included. Study data were analyzed from May 2023 to June 2024. SAVM vs sham control procedure. The primary efficacy end point was a reduction in legs-up and exercise PCWP at 1 month. The primary safety end point was serious device- or procedure-related adverse events at 1 month. Secondary efficacy end points included HF hospitalizations, changes in exercise function and health status through 12 months, and baseline to 1-month change in resting, legs-up, and 20-W exercise PCWP. A total of 90 patients (median [range] age, 71 [47-90] years; 58 female [64.4%]) were randomized at 15 centers (44 SAVM vs 46 sham). There were no differences in adverse events between groups. The primary efficacy end point did not differ between SAVM or sham (mean between-group difference in PCWP, -0.03 mm Hg; 95% CI, -2.5 to 2.5 mm Hg; P = .95). There were also no differences in the secondary efficacy end points. There was no difference in the primary safety end point between the treatment (6.8% [3 of 44]) and sham (2.2% [1 of 46]) groups (difference, 4.6%; 95% CI, -6.1% to 15.4%; P = .36). There was no difference in the incidence of orthostatic hypotension between the treatment (11.4% [5 of 44]) and sham (6.5% [3 of 46]) groups (difference, 4.9%; 95% CI, -9.2% to 18.8%; P = .48). Results show that SAVM was safe and technically feasible, but it did not reduce exercise PCWP at 1 month or improve clinical outcomes at 12 months in a broad population of patients with HFpEF. ClinicalTrials.gov Identifier: NCT04592445.

B-140 Use of the metabolic health index for correlation with comorbidity reduction for patients undergoing bariatric surgery

Abstract Background Bariatric surgery is a commonly performed procedure aimed at addressing obesity and its associated health issues. However, while weight loss is often a primary focus, the broader improvements in metabolic health, including reductions in conditions such as type 2 diabetes mellitus (T2DM) and dyslipidemia, are not consistently quantified. The recent development of the Metabolic Health Index (MHI) by van Loon et al. provides a potential laboratory index that encompasses various metabolic parameters to predict improvements in comorbidities post-surgery. Our study aimed to assess the utility of MHI in correlating with changes in metabolic health status among patients undergoing bariatric surgery at a U.S. medical center. Methods In our current study, we conducted a retrospective analysis of 300 patients who had undergone bariatric surgery between 2012 and 2019 at the University of Minnesota. We selected patients who had both baseline and one-year post-surgery laboratory values available, including age, hemoglobin A1C (HbA1c), triglycerides, potassium, and creatinine for estimated glomerular filtration rate (eGFR), which were necessary for calculating MHI using the equation of van Loon et al. Thirty patients met the inclusion criteria and their eGFRs were calculated using the 2021 CKD-EPI equation. Using the published formulae, we calculated the MHI for each patient before and after surgery. Patients were stratified based on improvements in diabetic (T2DM) and dyslipidemic status, with lower stratification numbers indicating better outcomes. We then conducted correlation analyses to examine the relationship between changes in MHI, individual components of MHI, BMI, and the reduction in the two comorbidities. We also examined if any of the initial values of MHI, its components and BMI correlated with the magnitude of reduction in the two comorbidities. Results In the patient cohort examined, initially 46.7% of the population were non-diabetic and this changed to 53.3% one year after surgery. The number of patients with diabetes decreased from 33.3% to 13.3%, indicating a benefit of bariatric surgery in alleviating type 2 diabetes mellitus (T2DM). Dyslipidemia decreased from 36.7% to 13.3% post-surgery. Most of the patients had reductions in both the BMI and MHI scales and a linear relationship between the two values was observed. A Spearman correlation analysis revealed that the values of HbA1c (rho= 0.56, p = 0.0014)) and MHI (rho = 0.5, p = 0.005) had a correlation with the reduction in T2DM and the reduction in triglycerides (rho= 0.73, p= 0.0) correlated with reduction in dyslipidemia. On the other hand, the reduction in BMI did not correlate with the improvement in the comorbidities examined. It was determined that the initial values of MHI, its components, and BMI had no correlation to the degree of reduction in comorbidities. Conclusions Our findings suggest that MHI and HbA1C may serve as valuable indicators of metabolic health improvements following bariatric surgery, while BMI changes alone may not fully capture these improvements. Laboratory-based measurements such as MHI offer a promising approach for assessing and monitoring metabolic health outcomes in bariatric surgery patients, providing insights beyond traditional weight-based metrics.

Prevention of postpartum methamphetamine use with micronized progesterone trial (PROMPT): A pilot randomized controlled trial protocol

While most pregnant individuals with methamphetamine use disorder (MUD) achieve abstinence, the postpartum period remains a vulnerable time for return to methamphetamine use (MU). Promising data from human and animal models, including three randomized controlled trials, suggest that micronized progesterone may prevent postpartum return to cocaine and nicotine use by reducing cravings. The primary objective of this study is to assess feasibility of enrollment and randomization of postpartum individuals with MUD to micronized progesterone to prevent return to MU. The secondary objectives are to evaluate safety, establish a preliminary estimate of efficacy, and characterize the association between allopregnanolone levels and methamphetamine cravings. This is a pilot double-blind placebo randomized controlled trial. We plan to enroll 40 postpartum individuals with MUD over 24-months. Individuals, stratified by opioid use disorder (OUD), are randomized 1:1-400mg oral micronized progesterone daily or placebo and attend weekly study sessions for 12 weeks. Feasibility is measured by achieving 80% of enrollment goal. Safety is evaluated by side effect frequency, mental health status changes, lactation and medical complications. Efficacy is assessed by comparing proportion of participants with return to MU and time to return to MU based on self-report or urine testing between treatment and control groups. Salivary allopregnanolone levels and methamphetamine cravings are compared between the groups. Study results will provide a first critical step towards potential intervention for prevention of return to MU among postpartum individuals. Completion of this trial will set the stage for a large-scale efficacy trial.

Socioeconomic deprivation modifies green space and mental health associations: A within person study

Living in an area with good availability and accessibility of residential green spaces such as parks, woodlands, and residential gardens can improve mental health and reduce the global disease burden. Unlike for physical health, it is not well understood if mental health and green space associations might be modified by local area deprivation. Existing evidence for this association comes from cross-sectional studies, widely considered vulnerable to confounding and bias. Individual time-invariant mental health status, personality, and other factors may result in positive effect modification on green space and mental health associations in more deprived areas. We use fixed-effects models that remove time-invariant confounding by calculating differences within-persons to eliminate this bias and add to the existing evidence. We modelled changes in mental health status, green space, and deprivation (relative to the within-person mean) within 54,666 individuals with a combined total of 300,710 mental health scores from one of the world’s largest panel surveys: “Understanding Society” in the UK. We found a positive effect of increasing residential green space on mental health and this was positively modified and intensified by area deprivation before and after adjusting for confounding. Our results support providing green space to protect against the negative impact of socioeconomic deprivation on health, particularly for those moving from a less deprived to a more deprived area.

Research on the Psychological Impact of the Policy Practice of Preventing Minors from Indulging in Online Games in China

With the expansion of online games, there is a relatively more severe issue involving minors excessive use of games. To this end, the Chinese government has issued several policy initiatives that are intended to stop minors from getting hooked on online games. This study aims to examine the psychological consequences of these policy practices on children. Through literature review, and case study, the changes in minors mental health status before and after the implementation of the policy were assessed, and the role of family, school and social environment in the implementation of the policy was also examined. This research identified that these policies have successfully limited the overuse of online games among minors to a certain extent but at the same time, it has introduced new psychological issues like anxiety and social isolation among the young people. Also, the issues that parents and educators encounter in the process of policy execution are also worth considering. Based on the findings of this study, the following recommendations, such as introducing flexible time limits and regular review of the policy, are made in order to enhance policy implementation towards the improvement of the mental health of minors.

Perspectives of Nursing Home Staff in Advance Care Planning Conversations: Experiences from the APPROACHES Project

ObjectivesAdvance care planning (ACP) is considered a best practice in the nursing home setting; however, there is a lack of consistency in the training of nursing home staff and implementation of structured ACP programs. A qualitative study interviewing ACP specialists in nursing homes was conducted to understand the experience of staff engaged in Aligning Patient Preferences – a Role offering Alzheimer's patients, Caregivers, and Healthcare providers Education and Support (APPROACHES), an embedded pragmatic clinical trial to improve ACP. DesignQualitative interviews regarding ACP specialists’ experiences and perceived intervention impact. Setting and ParticipantsStaff of intervention-assigned nursing home facilities who completed a minimum of 10 ACP conversations with residents during APPROACHES program implementation. MethodsFourteen staff were interviewed. Interviews were transcribed and coded by the research team. ResultsThere were 21 codes identified that were then distilled into the following 5 themes: (1) experiences with the ACP specialist program, (2) engaging in ACP conversations, (3) considerations related to dementia, (4) benefits and challenges of ACP, and (5) involvement of multiple people in the ACP process. Participant responses suggested variability in experiences with the ACP specialist program and highlighted many aspects relevant to engaging in conversations with families and residents, particularly those living with dementia. Benefits of ACP, including relationship building and increased preparedness for changes in health status, were balanced with challenges related to sensitive conversation topics and logistical difficulties in scheduling ACP discussions. ACP specialists discussed the multiple roles that others played in the ACP process. Conclusions and ImplicationsFindings from this analysis provide insights into tailoring APPROACHES and other ACP programs for full-scale implementation in the nursing home setting. Nursing home staff experiences tailoring the program to fit their environments were reflective of the pragmatic nature of the ACP specialist program.

Efficacy of Remote Health Monitoring in Reducing Hospital Readmissions Among High-Risk Postdischarge Patients: Prospective Cohort Study.

Patients with respiratory or cardiovascular diseases often experience higher rates of hospital readmission due to compromised heart-lung function and significant clinical symptoms. Effective measures such as discharge planning, case management, home telemonitoring follow-up, and patient education can significantly mitigate hospital readmissions. This study aimed to determine the efficacy of home telemonitoring follow-up in reducing hospital readmissions, emergency department (ED) visits, and total hospital days for high-risk postdischarge patients. This prospective cohort study was conducted between July and October 2021. High-risk patients were screened for eligibility and enrolled in the study. The intervention involved implementing home digital monitoring to track patient health metrics after discharge, with the aim of reducing hospital readmissions and ED visits. High-risk patients or their primary caregivers received education on using communication measurement tools and recording and uploading data. Before discharge, patients were familiarized with these tools, which they continued to use for 4 weeks after discharge. A project manager monitored the daily uploaded health data, while a weekly video appointment with the program coordinator monitored the heart and breathing sounds of the patients, tracked health status changes, and gathered relevant data. Care guidance and medical advice were provided based on symptoms and physiological signals. The primary outcomes of this study were the number of hospital readmissions and ED visits within 3 and 6 months after intervention. The secondary outcomes included the total number of hospital days and patient adherence to the home monitoring protocol. Among 41 eligible patients, 93% (n=38) were male, and 46% (n=19) were aged 41-60 years, while 46% (n=19) were aged 60 years or older. The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, and total hospital stay days at 3 and 6 months after intervention. At 3 months after intervention, average hospitalizations decreased from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03), and average ED visits decreased from 0.48 (SD 0.09) to 0.06 (SD 0.04; P<.001). Average hospital days decreased from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08). At 6 months after intervention, average hospitalizations decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), and average ED visits decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.02). Average hospital days decreased from 7.48 (SD 2.32) to 6.03 (SD 3.12; P=.73). By integrating home telemonitoring with regular follow-up, our research demonstrates a viable approach to reducing hospital readmissions and ED visits, ultimately improving patient outcomes and reducing health care costs. The practical application of telemonitoring in a real-world setting showcases its potential as a scalable solution for chronic disease management.