Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.

Importance: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format. Objective: To determine whether an LLM can transform discharge summaries into a format that is more readable and understandable. Design, Setting, and Participants: This cross-sectional study evaluated a sample of the discharge summaries of adult patients discharged from the General Internal Medicine service at NYU (New York University) Langone Health from June 1 to 30, 2023. Patients discharged as deceased were excluded. All discharge summaries were processed by the LLM between July 26 and August 5, 2023. Interventions: A secure Health Insurance Portability and Accountability Act-compliant platform, Microsoft Azure OpenAI, was used to transform these discharge summaries into a patient-friendly format between July 26 and August 5, 2023. Main Outcomes and Measures: Outcomes included readability as measured by Flesch-Kincaid Grade Level and understandability using Patient Education Materials Assessment Tool (PEMAT) scores. Readability and understandability of the original discharge summaries were compared with the transformed, patient-friendly discharge summaries created through the LLM. As balancing metrics, accuracy and completeness of the patient-friendly version were measured. Results: Discharge summaries of 50 patients (31 female [62.0%] and 19 male [38.0%]) were included. The median patient age was 65.5 (IQR, 59.0-77.5) years. Mean (SD) Flesch-Kincaid Grade Level was significantly lower in the patient-friendly discharge summaries (6.2 [0.5] vs 11.0 [1.5]; P < .001). PEMAT understandability scores were significantly higher for patient-friendly discharge summaries (81% vs 13%; P < .001). Two physicians reviewed each patient-friendly discharge summary for accuracy on a 6-point scale, with 54 of 100 reviews (54.0%) giving the best possible rating of 6. Summaries were rated entirely complete in 56 reviews (56.0%). Eighteen reviews noted safety concerns, mostly involving omissions, but also several inaccurate statements (termed hallucinations). Conclusions and Relevance: The findings of this cross-sectional study of 50 discharge summaries suggest that LLMs can be used to translate discharge summaries into patient-friendly language and formats that are significantly more readable and understandable than discharge summaries as they appear in electronic health records. However, implementation will require improvements in accuracy, completeness, and safety. Given the safety concerns, initial implementation will require physician review.

Predictors of mortality among end-stage renal disease patients with COVID-19 admitted in a Philippine Tertiary Government Hospital: A retrospective cohort study

Background and Objective@#Several studies have examined the predictors of mortality among COVID-19-infected patients; however, to date, few published studies focused on end-stage renal disease patients. The present study,therefore, aims to determine the predictors of in-hospital mortality among end-stage renal disease patients with COVID-19 admitted to a Philippine tertiary hospital. @*Methods@#The researcher utilized a retrospective cohort design. A total of 449 adult end-stage renal disease patients on renal replacement therapy diagnosed with moderate-to-severe COVID-19 and were admitted at the National Kidney and Transplant Institute from June 2020 to 2021 were included. Logistic regression analysis was used to determine the factors associated with in-hospital mortality. @*Results@#In-hospital mortality among end-stage renal disease patients with COVID-19 was 31.18% (95% CI: 26.92- 35.69%). Older age (OR=1.03), male sex (OR=0.56), diabetes mellitus (OR=1.80), coronary artery disease (OR=1.71), encephalopathy (OR=7.58), and intubation (OR=30.78) were associated with in-hospital mortality. @*Conclusion@#Patients with ESRD and COVID-19 showed a high in-hospital mortality rate. Older age, diabetes mellitus, coronary artery disease, encephalopathy, and intubation increased the odds of mortality. Meanwhile, males had lower odds of mortality than females.

Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health.

BACKGROUND: Energy and dietary quality are known to differ between weekdays and weekends. Data-driven approaches that incorporate time, amount, and duration of dietary intake have previously been used to partition participants' daily weekday dietary intake time series into clusters representing weekday temporal dietary patterns (TDPs) linked to health indicators in United States adults. Yet, neither the relationship of weekend day TDPs to health indicators nor how the TDP membership may change from weekday to weekend is known. OBJECTIVES: This study was conducted to determine the association between TDPs on weekdays and weekend days and health indicators [diet quality, waist circumference (WC), body mass index (BMI), and obesity] and their overlap among participants. METHODS: A weekday and weekend day 24-hour dietary recall of 9494 nonpregnant United States adults aged 20-65 years from the cross-sectional National Health and Nutrition Examination Survey 2007-2018 was used to determine the timing and amount of energy intake. Modified dynamic time warping and kernel k-means algorithm clustered participants into 4 TDPs on weekdays and weekend days. Multivariate regression models determined the associations between TDPs and health indicators, controlling for potential confounders and adjusting for the survey design and multiple comparisons. The percentages of overlap in cluster membership between TDPs on weekdays and weekend days were also determined. RESULTS: United States adults with a TDP of evenly spaced, energy-balanced eating occasions, representing the TDP of more than one-third of all adults on weekdays and weekends, had significantly higher diet quality, lower BMI, WC, and odds of obesity when compared to those with other TDPs. Membership of most United States adults to TDPs varied from weekdays to weekends. CONCLUSIONS: Both weekday and weekend TDPs were significantly associated with health indicators. TDP membership of most United States adults was not consistent on weekdays and weekends.

Cohort profile: a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence.

PURPOSE: Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors. PARTICIPANTS: Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (<40%) since 2017, had at least one clinical encounter between 1 April 2021 and 31 October 2022 and active prescriptions for any of the four GDMTs (beta-blocker, ACEi/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), mineralocorticoid receptor antagonist (MRA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i)) during the study period. Patients with non-geocodable address or outside the continental USA were excluded. FINDINGS TO DATE: Among 39 963 patients in the cohort, the average age was 73±14 years old, 44% were female and 48% were current/former smokers. The common comorbid conditions were hypertension (77%), cardiac arrhythmias (56%), obesity (33%) and valvular disease (33%). During the study period, 33 606 (84%) patients had an active prescription of beta blocker, 32 626 (82%) had ACEi/ARB/ARNI, 11 611 (29%) MRA and 7472 (19%) SGLT2i. Ninety-nine per cent were from urban metropolitan areas. FUTURE PLANS: We will use the established cohort to develop a machine learning model to predict medication adherence, and to support ancillary studies assessing associates of adherence. For external validation, we will include data from an additional hospital system in NYC.

Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone.

BACKGROUND: Daily temporal patterns of energy intake (temporal dietary patterns [TDPs]) and physical activity (temporal physical activity patterns [TPAPs]) have been independently and jointly (temporal dietary and physical activity patterns [TDPAPs]) associated with health and disease status indicators. OBJECTIVE: The aim of this study was to compare the number and strength of association between clusters of daily TDPs, TPAPs, and TDPAPs and multiple health and disease status indicators. DESIGN: This cross-sectional study used 1 reliable weekday dietary recall and 1 random weekday of accelerometer data to partition to create clusters of participants representing the 3 temporal patterns. Four clusters were created via kernel-k means clustering algorithm of the same constrained dynamic time warping distance computed over the time series for each temporal pattern. PARTICIPANTS/SETTING: From the National Health and Nutrition Examination Survey (2003-2006), 1,836 US adults aged 20 through 65 years who were not pregnant and had valid diet, physical activity, sociodemographic, anthropometric, questionnaire, and health and disease status indicator data were included. MAIN OUTCOME MEASURES: Health status indicators used as outcome measures were body mass index, waist circumference, fasting plasma glucose, hemoglobin A1c, triglycerides, high-density lipoprotein cholesterol, total cholesterol, and systolic and diastolic blood pressure; disease status indicators included obesity, type 2 diabetes mellitus, and metabolic syndrome. STATISTICAL ANALYSES PERFORMED: Multivariate regression models determined associations between the clusters representing each pattern and health and disease status indicators, controlling for confounders and adjusting for multiple comparisons. The number of significant differences among clusters and adjusted R2 and Akaike information criterion compared the strength of associations between clusters of patterns and continuous and categorical health and disease status indicators. RESULTS: TDPAPs showed 21 significant associations with health and disease status indicators, including body mass index, waist circumference, obesity, and type 2 diabetes; TDPs showed 19 significant associations; and TPAPs showed 8 significant associations. CONCLUSIONS: TDPAPs and TDPs had stronger and more numerous associations with health and disease status indicators compared with TPAPs. Patterns representing the integration of daily dietary habits hold promise for early detection of obesity.

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults.

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Joint Temporal Patterns By Integrating Diet and Physical Activity.

Both diet and physical activity are associated with obesity and chronic diseases such as diabetes and metabolic syndrome. Early efforts in connecting dietary and physical activity behaviors to generate patterns rarely considered the use of time. In this paper, we propose a distance-based cluster analysis approach to find joint temporal diet and physical activity patterns among U.S. adults ages 20-65. Dynamic Time Warping (DTW) generalized to multi-dimensions is combined with commonly used clustering methods to generate unbiased partitioning of the National Health and Nutrition Examination Survey 2003-2006 (NHANES) dataset. The clustering results are evaluated using visualization of the clusters, the Silhouette Index, and the associations between clusters and health status indicators based on multivariate regression models. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint temporal patterns with association to health.

The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs.

Data-driven temporal dietary patterning (TDP) methods were previously developed. The objectives were to create data-driven temporal dietary patterns and assess concurrent validity of energy and time cut-offs describing the data-driven TDPs by determining their relationships to BMI and waist circumference (WC). The first day 24-h dietary recall timing and amounts of energy for 17,915 U.S. adults of the National Health and Nutrition Examination Survey 2007−2016 were used to create clusters representing four TDPs using dynamic time warping and the kernel k-means clustering algorithm. Energy and time cut-offs were extracted from visualization of the data-derived TDPs and then applied to the data to find cut-off-derived TDPs. The strength of TDP relationships with BMI and WC were assessed using adjusted multivariate regression and compared. Both methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events/day, associated with significantly lower BMI and WC compared to the other three clusters that had one energy intake peak/day at 13:00, 18:00, and 19:00 (all p < 0.0001). Participant clusters of the methods were highly overlapped (>83%) and showed similar relationships with obesity. Data-driven TDP was validated using descriptive cut-offs and hold promise for obesity interventions and translation to dietary guidance.

Guideline-Directed Medical Therapy in Newly Diagnosed Heart Failure With Reduced Ejection Fraction in the Community.

BACKGROUND: Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF. METHODS AND RESULTS: We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota, residents with newly diagnosed HFrEF (EF ≤ 40%) 2007-2017. We excluded patients with contraindications to medication initiation. We examined the use of beta-blockers, HF beta-blockers (metoprolol succinate, carvedilol, bisoprolol), angiotensin converting enzyme inhibitors (ACEis), angiotensin receptor blockers (ARBs), angiotensin receptor neprilysin inhibitors (ARNIS), and mineralocorticoid receptor antagonists (MRAs) in the first year after HFrEF diagnosis. We used Cox models to evaluate the association of being seen in an HF clinic with the initiation of GDMT. From 2007 to 2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta-blockers (92.6%) and ACEis/ARBs/ARNIs (87.0%) in the first year. However, only 63.8% of patients were treated with an HF beta-blocker, and few received MRAs (17.6%). In models accounting for the role of an HF clinic in initiation of these medications, being seen in an HF clinic was independently associated with initiation of new GDMT across all medication classes, with a hazard ratio (95% CI) of 1.54 (1.15-2.06) for any beta-blocker, 2.49 (1.95-3.20) for HF beta-blockers, 1.97 (1.46-2.65) for ACEis/ARBs/ARNIs, and 2.14 (1.49-3.08) for MRAs. CONCLUSIONS: In this population-based study, most patients with newly diagnosed HFrEF received beta-blockers and ACEis/ARBs/ARNIs. GDMT use was higher in patients seen in an HF clinic, suggesting the potential benefit of referral to an HF clinic for patients with newly diagnosed HFrEF.

Development of Advanced Heart Failure: A Population-Based Study.

BACKGROUND: Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. The goals of this study were to examine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes. METHODS: This was a population-based, retrospective cohort study of Olmsted County, Minnesota, residents with a new clinical diagnosis of HF between 2007 and 2017. Risk factors for the development of advanced HF (2018 European Society of Cardiology criteria) were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using the Andersen-Gill and Cox models, respectively. RESULTS: There were 4597 residents with incident HF from 2007 to 2017. The cumulative incidence of advanced HF was 11.5% (95% CI, 10.5%-12.5%) at 6 years after incident HF diagnosis overall and was 14.4% (95% CI, 12.3%-16.9%), 11.4% (95% CI, 8.9%-14.6%), and 11.7% (95% CI, 10.3%-13.2%) in patients with incident HF with reduced, mildly reduced, and preserved ejection fraction, respectively. Key demographics, comorbidities, and echocardiographic characteristics were independently associated with the development of advanced HF. Development of advanced HF was associated with increased risks of all-cause hospitalization (adjusted hazard ratio, 3.0 [95% CI, 2.7-3.4]; P<0.001), HF hospitalization (hazard ratio, 10.2 [95% CI, 8.7-12.1]), all-cause mortality (hazard ratio, 5.0 [95% CI, 4.5-5.6]; P<0.001), and cardiovascular mortality (hazard ratio, 7.8 [95% CI, 6.7-9.1]). CONCLUSIONS: In this population-based study, development of advanced HF was common and was associated with markedly increased morbidity and mortality.

Joint temporal dietary and physical activity patterns: associations with health status indicators and chronic diseases.

BACKGROUND: Diet and physical activity (PA) are independent risk factors for obesity and chronic diseases including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS). The temporal sequence of these exposures may be used to create patterns with relations to health status indicators. OBJECTIVES: The objectives were to create clusters of joint temporal dietary and PA patterns (JTDPAPs) and to determine their association with health status indicators including BMI, waist circumference (WC), fasting plasma glucose, glycated hemoglobin, triglycerides, HDL cholesterol, total cholesterol, blood pressure, and disease status including obesity, T2DM, and MetS in US adults. METHODS: A 24-h dietary recall and random day of accelerometer data of 1836 participants from the cross-sectional NHANES 2003-2006 data were used to create JTDPAP clusters by constrained dynamic time warping, coupled with a kernel k-means clustering algorithm. Multivariate regression models determined associations between the 4 JTDPAP clusters and health and disease status indicators, controlling for potential confounders and adjusting for multiple comparisons. RESULTS: A JTDPAP cluster with proportionally equivalent energy consumed at 2 main eating occasions reaching ≤1600 and ≤2200 kcal from 11:00 to 13:00 and from 17:00 to 20:00, respectively, and the highest PA counts among 4 clusters from 08:00 to 20:00, was associated with significantly lower BMI (P < 0.0001), WC (P = 0.0001), total cholesterol (P = 0.02), and odds of obesity (OR: 0.2; 95% CI: 0.1, 0.5) than a JTDPAP cluster with proportionally equivalent energy consumed reaching ≤1600 and ≤1800 kcal from 11:00 to 14:00 and from 17:00 to 21:00, respectively, and high PA counts from 09:00 to 12:00. CONCLUSIONS: The joint temporally patterned sequence of diet and PA can be used to cluster individuals with meaningful associations to BMI, WC, total cholesterol, and obesity. Temporal patterns hold promise for future development of lifestyle patterns that integrate additional temporal and contextual activities.

A Behavioral Economics-Electronic Health Record Module to Promote Appropriate Diabetes Management in Older Adults: Protocol for a Pragmatic Cluster Randomized Controlled Trial.

BACKGROUND: The integration of behavioral economics (BE) principles and electronic health records (EHRs) using clinical decision support (CDS) tools is a novel approach to improving health outcomes. Meanwhile, the American Geriatrics Society has created the Choosing Wisely (CW) initiative to promote less aggressive glycemic targets and reduction in pharmacologic therapy in older adults with type 2 diabetes mellitus. To date, few studies have shown the effectiveness of combined BE and EHR approaches for managing chronic conditions, and none have addressed guideline-driven deprescribing specifically in type 2 diabetes. We previously conducted a pilot study aimed at promoting appropriate CW guideline adherence using BE nudges and EHRs embedded within CDS tools at 5 clinics within the New York University Langone Health (NYULH) system. The BE-EHR module intervention was tested for usability, adoption, and early effectiveness. Preliminary results suggested a modest improvement of 5.1% in CW compliance. OBJECTIVE: This paper presents the protocol for a study that will investigate the effectiveness of a BE-EHR module intervention that leverages BE nudges with EHR technology and CDS tools to reduce overtreatment of type 2 diabetes in adults aged 76 years and older, per the CW guideline. METHODS: A pragmatic, investigator-blind, cluster randomized controlled trial was designed to evaluate the BE-EHR module. A total of 66 NYULH clinics will be randomized 1:1 to receive for 18 months either (1) a 6-component BE-EHR module intervention + standard care within the NYULH EHR, or (2) standard care only. The intervention will be administered to clinicians during any patient encounter (eg, in person, telemedicine, medication refill, etc). The primary outcome will be patient-level CW compliance. Secondary outcomes will measure the frequency of intervention component firings within the NYULH EHR, and provider utilization and interaction with the BE-EHR module components. RESULTS: Study recruitment commenced on December 7, 2020, with the activation of all 6 BE-EHR components in the NYULH EHR. CONCLUSIONS: This study will test the effectiveness of a previously developed, iteratively refined, user-tested, and pilot-tested BE-EHR module aimed at providing appropriate diabetes care to elderly adults, compared to usual care via a cluster randomized controlled trial. This innovative research will be the first pragmatic randomized controlled trial to use BE principles embedded within the EHR and delivered using CDS tools to specifically promote CW guideline adherence in type 2 diabetes. The study will also collect valuable information on clinician workflow and interaction with the BE-EHR module, guiding future research in optimizing the timely delivery of BE nudges within CDS tools. This work will address the effectiveness of BE-inspired interventions in diabetes and chronic disease management. TRIAL REGISTRATION: ClinicalTrials.gov NCT04181307; https://clinicaltrials.gov/ct2/show/NCT04181307. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28723.

Advanced Heart Failure Epidemiology and Outcomes: A Population-Based Study.

OBJECTIVES: The goal of this study was to evaluate the prevalence, characteristics, and outcomes of patients with advanced heart failure (HF) in a geographically defined population. BACKGROUND: Some patients with HF progress to advanced HF, characterized by debilitating HF symptoms refractory to therapy. Limited data are available on the epidemiology and outcomes of patients with advanced HF. METHODS: This was a population-based cohort study of all Olmsted County, Minnesota, adults with and without HF from 2007 to 2017. The 2018 European Society of Cardiology advanced HF diagnostic criteria were operationalized and applied to all patients with HF. Hospitalization and mortality in advanced HF, overall and according to ejection fraction (EF) type (reduced EF <40% [HFrEF], mid-range EF 40%-49% [HFmrEF], and preserved EF ≥50% [HFpEF]) were examined using Andersen-Gill and Cox models. RESULTS: Of 6,836 adults with HF, 936 (13.7%) met criteria for advanced HF. The prevalence of advanced HF increased with age and was higher in men. At advanced HF diagnosis, 396 (42.3%) patients had HFrEF, 134 (14.3%) had HFmrEF, and 406 (43.4%) had HFpEF. The median (interquartile range) time from advanced HF diagnosis to death was 12.2 months (3.7-29.9 months). The mean rate of hospitalization was 2.91 (95% CI: 2.78-3.06) per person-year in the first year after advanced HF diagnosis. There were no differences in risks of all-cause mortality or hospitalization by EF. Patients with advanced HFpEF were at lower risk for cardiovascular mortality compared with advanced HFrEF (HR: 0.79; 95% CI: 0.65-0.97). CONCLUSIONS: In this population-based study, more than one-half of patients with advanced HF had mid-range or preserved EF, and survival was poor regardless of EF.

Homelessness and Medicaid Churn.

Objectives: To identify ICD-10-CM diagnostic codes associated with the social determinants of health (SDOH), determine frequency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code. Design: Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indicator for homelessness. Main Outcome Measures: Any interruption in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identification numbers (CIN) for continuity. Methods: Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017. Results: ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrated linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016-2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experienced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn. Conclusions: Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness.

Temporal Dietary Patterns Are Associated with Obesity in US Adults.

BACKGROUND: The integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDPs), have been linked with dietary quality but relations to health are unknown. OBJECTIVE: The association between TDPs and selected health status indicators and obesity, type 2 diabetes (T2D), and metabolic syndrome (MetS) was determined. METHODS: The first-day 24-h dietary recall from 1627 nonpregnant US adult participants aged 20-65 y from the NHANES 2003-2006 was used to determine timing, amount of energy intake, and sequence of eating occasions (EOs). Modified dynamic time warping (MDTW) and kernel k-means algorithm clustered participants into 4 groups representing distinct TDPs. Multivariate regression models determined associations between TDPs and health status, controlling for potential confounders, and adjusting for the survey design and multiple comparisons (P <0.05/6). RESULTS: A cluster representing a TDP with evenly spaced, energy balanced EOs reaching ≤1200 kcal between 06:00 to 10:00, 12:00 to 15:00, and 18:00 to 22:00, had statistically significant and clinically meaningful lower mean BMI (P <0.0001), waist circumference (WC) (P <0.0001), and 75% lower odds of obesity compared with 3 other clusters representing patterns with much higher peaks of energy: 1000-2400 kcal between 15:00 and 18:00 (OR: 5.3; 95% CI: 2.8, 10.1), 800-2400 kcal between 11:00 and 15:00 (OR: 4.4; 95% CI: 2.5, 7.9), and 1000-2600 kcal between 18:00 and 23:00 (OR: 6.7; 95% CI: 3.9, 11.6). CONCLUSIONS: Individuals with a TDP characterized by evenly spaced, energy balanced EOs had significantly lower mean BMI, WC, and odds of obesity compared with the other patterns with higher energy intake peaks at different times throughout the day, providing evidence that incorporating time with other aspects of a dietary pattern may be important to health status.

Early Lessons on Bundled Payment at an Academic Medical Center.

INTRODUCTION: Orthopaedic care is shifting to alternative payment models. We examined whether New York University Langone Medical Center achieved savings under the Centers for Medicare and Medicaid Services Bundled Payments for Care Improvement initiative. METHODS: This study was a difference-in-differences study of Medicare fee-for-service patients hospitalized from April 2011 to June 2012 and October 2013 to December 2014 for lower extremity joint arthroplasty, cardiac valve procedures, or spine surgery (intervention groups), or for congestive heart failure, major bowel procedures, medical peripheral vascular disorders, medical noninfectious orthopaedic care, or stroke (control group). We examined total episode costs and costs by service category. RESULTS: We included 2,940 intervention episodes and 1,474 control episodes. Relative to the trend in the control group, lower extremity joint arthroplasty episodes achieved the greatest savings: adjusted average episode cost during the intervention period decreased by $3,017 (95% confidence interval [CI], -$6,066 to $31). For cardiac procedures, the adjusted average episode cost decreased by $2,999 (95% CI, -$8,103 to $2,105), and for spinal fusion, it increased by $8,291 (95% CI, $2,879 to $13,703). Savings were driven predominantly by shifting postdischarge care from inpatient rehabilitation facilities to home. Spinal fusion index admission costs increased because of changes in surgical technique. DISCUSSION: Under bundled payment, New York University Langone Medical Center decreased total episode costs in patients undergoing lower extremity joint arthroplasty. For patients undergoing cardiac valve procedures, evidence of savings was not as strong, and for patients undergoing spinal fusion, total episode costs increased. For all three conditions, the proportion of patients referred to inpatient rehabilitation facilities upon discharge decreased. These changes were not associated with an increase in index hospital length of stay or readmission rate. CONCLUSION: Opportunities for savings under bundled payment may be greater for lower extremity joint arthroplasty than for other conditions.

Twelve-lead ambulatory electrocardiographic monitoring in Brugada syndrome: Potential diagnostic and prognostic implications.

BACKGROUND: Patients with Brugada syndrome (BrS) are diagnosed and risk stratified on the basis of a spontaneous or drug-induced type 1 electrocardiographic (ECG) pattern, often at single time points not accounting for variation throughout the day. OBJECTIVES: The purpose of this study was to prospectively assess the overall burden of type 1 Brugada ECG changes using 12-lead 24-hour Holter monitoring and evaluate association with cardiac events. METHODS: From July 1, 2013 to December 31, 2015, patients with BrS were recruited from 3 Australian centers and the Australian Genetic Heart Disease Registry. All patients underwent clinical review, baseline ECG, and 12-lead 24-hour Holter assessment with precordial leads placed in the left and right second, third, and fourth intercostal spaces. The frequency, temporal, and spatial burden of type 1 BrS ECG pattern were analyzed and assessed for association with cardiac events. RESULTS: A total of 54 patients with BrS were recruited (n=44, 81% men; mean age 44 ± 13 years); the mean follow-up was 2.3 ± 2.5 years. Eleven of 32 patients (34%) initially classified as "drug-induced BrS" demonstrated a spontaneous type 1 pattern at least once over 24 hours. Patients with cardiac events had a significantly higher temporal burden of type 1 ST-segment elevation in the 24-hour monitoring period (total area under the curve 21% vs 15%; P = .008), being most pronounced between the hours of 1600 and 2400 (P = .027). CONCLUSION: Patients with BrS traditionally classified as drug-induced can exhibit spontaneous ECG changes with longer-term monitoring, particularly in the evening. Temporal burden on 12-lead Holter monitor was associated with cardiac events. Ambulatory 12-lead ECG monitoring may have potential utility in the diagnosis and risk stratification of patients with BrS.

[Diagnostic accuracy of outpatient polygraphy devices : A comparison with inpatient polysomnography in clinical routine]. / Diagnostische Genauigkeit ambulanter Polygraphiegeräte : Ein Vergleich zur stationären Polysomnographie im klinischen Alltag.

BACKGROUND: Cardiorespiratory polysomnography (PSG) is considered the reference method for diagnosis of obstructive sleep apnea (OSA). Due to waiting times and high costs, payers increasingly request outpatient polygraphy (PG) as an alternative to inpatient PSG. The aim of the present study was to evaluate the diagnostic accuracy of different outpatient PG devices compared to stationary PSG in clinical practice. MATERIALS AND METHODS: Externally collected outpatient PG findings of 406 patients were retrospectively compared with the corresponding PSG findings. RESULTS: Among the 406 patients were 343 men (85%) and 63 women (15%), with mean age 50 years. Mean body mass index (BMI) was 30 kg/m2. The rank correlation coefficient for PG- and PSG- derived apnea-hypopnea index (AHI) values was r = 0.574. On average, PG underestimated the AHI by 6.4 (±20.5) events/h. OSAS severity was determined correctly by PG in only 43% of cases. Sensitivity (90.7%) and specificity (45.2%) of ambulatory PG was calculated for the threshold value AHI ≥ 5/h. Based on the results of PG, an indicated therapy would have been omitted in 35 cases (9%) and unnecessary treatment initiated in 17 cases (4%). The PG devices used showed a comparable diagnostic accuracy (r = 0.513-0.657), with a sensitivity of 81.3-96.9% and a specificity of 33.3-50.0%. CONCLUSION: Outpatient PG cannot reliably assess OSA severity in clinical routine. Confirmation by PSG in a sleep lab in symptomatic patients is obligatory. Outpatient PG devices should only be used as an upstream screening method. The automatic evaluation of the PG should always be proofed.