The Association of Frailty and Neighborhood Disadvantage with Emergency Department Visits and Hospitalizations in Older Adults.

BACKGROUND: Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes. OBJECTIVE: To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR). DESIGN: In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up. KEY RESULTS: We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage. CONCLUSIONS: Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

Effect of Intensive Blood Pressure Control on Kidney Outcomes: Long-Term Electronic Health Record-Based Post-Trial Follow-Up of SPRINT.

BACKGROUND: Intensive BP lowering in the Systolic Blood Pressure Intervention Trial (SPRINT) produced acute decreases in kidney function and higher risk for AKI. We evaluated the effect of intensive BP lowering on long-term changes in kidney function using trial and outpatient electronic health record (EHR) creatinine values. METHODS: SPRINT data were linked with EHR data from 49 (of 102) study sites. The primary outcome was the total slope of decline in eGFR for the intervention phase and the post-trial slope of decline during the observation phase using trial and outpatient EHR values. Secondary outcomes included a ≥30% decline in eGFR to <60 ml/min per 1.73 m 2 and a ≥50% decline in eGFR or kidney failure among participants with baseline eGFR ≥60 and <60 ml/min per 1.73 m 2 , respectively. RESULTS: EHR creatinine values were available for a median of 8.3 years for 3041 participants. The total slope of decline in eGFR during the intervention phase was -0.67 ml/min per 1.73 m 2 per year (95% confidence interval [CI], -0.79 to -0.56) in the standard treatment group and -0.96 ml/min per 1.73 m 2 per year (95% CI, -1.08 to -0.85) in the intensive treatment group ( P < 0.001). The slopes were not significantly different during the observation phase: -1.02 ml/min per 1.73 m 2 per year (95% CI, -1.24 to -0.81) in the standard group and -0.85 ml/min per 1.73 m 2 per year (95% CI, -1.07 to -0.64) in the intensive group. Among participants without CKD at baseline, intensive treatment was associated with higher risk of a ≥30% decline in eGFR during the intervention (hazard ratio, 3.27; 95% CI, 2.43 to 4.40), but not during the postintervention observation phase. In those with CKD at baseline, intensive treatment was associated with a higher hazard of eGFR decline only during the intervention phase (hazard ratio, 1.95; 95% CI, 1.03 to 3.70). CONCLUSIONS: Intensive BP lowering was associated with a steeper total slope of decline in eGFR and higher risk for kidney events during the intervention phase of the trial, but not during the postintervention observation phase.

Clinical Phenotypes of Sepsis in a Cohort of Hospitalized Patients According to Infection Site.

OBJECTIVES: Clinical sepsis phenotypes may be defined by a wide range of characteristics such as site of infection, organ dysfunction patterns, laboratory values, and demographics. There is a paucity of literature regarding the impact of site of infection on the timing and pattern of clinical sepsis markers. This study hypothesizes that important phenotypic variation in clinical markers and outcomes of sepsis exists when stratified by infection site. DESIGN: Retrospective cohort study. SETTING: Five hospitals within the Wake Forest Health System from June 2019 to December 2019. PATIENTS: Six thousand seven hundred fifty-three hospitalized adults with a discharge International Classification of Diseases, 10th Revision code for acute infection who met systemic inflammatory response syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), or Sequential Organ Failure Assessment (SOFA) criteria during the index hospitalization. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome of interest was a composite of 30-day mortality or shock. Infection site was determined by a two-reviewer process. Significant demographic, vital sign, and laboratory result differences were seen across all infection sites. For the composite outcome of shock or 30-day mortality, unknown or unspecified infections had the highest proportion (21.34%) and CNS infections had the lowest proportion (8.11%). Respiratory, vascular, and unknown or unspecified infection sites showed a significantly increased adjusted and unadjusted odds of the composite outcome as compared with the other infection sites except CNS. Hospital time prior to SIRS positivity was shortest in unknown or unspecified infections at a median of 0.88 hours (interquartile range [IQR], 0.22-5.05 hr), and hospital time prior to qSOFA and SOFA positivity was shortest in respiratory infections at a median of 54.83 hours (IQR, 9.55-104.67 hr) and 1.88 hours (IQR, 0.47-17.40 hr), respectively. CONCLUSIONS: Phenotypic variation in illness severity and mortality exists when stratified by infection site. There is a significantly higher adjusted and unadjusted odds of the composite outcome of 30-day mortality or shock in respiratory, vascular, and unknown or unspecified infections as compared with other sites.

Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA.

Importance: The Sepsis Prediction Model (SPM) is a proprietary decision support tool created by Epic Systems; it generates a predicting sepsis score (PSS). The model has not undergone validation against existing sepsis prediction tools, such as Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), or quick Sepsis-Related Organ Failure Asessement (qSOFA). Objective: To assess the validity and timeliness of the SPM compared with SIRS, qSOFA, and SOFA. Design, Setting, and Participants: This retrospective cohort study included all adults admitted to 5 acute care hospitals in a single US health system between June 5, 2019, and December 31, 2020. Data analysis was conducted from March 2021 to February 2023. Main Outcomes and Measures: A sepsis event was defined as receipt of 4 or more days of antimicrobials, blood cultures collected within ±48 hours of initial antimicrobial, and at least 1 organ dysfunction as defined by the organ dysfunction criteria optimized for the electronic health record (eSOFA). Time zero was defined as 15 minutes prior to qualifying antimicrobial or blood culture order. Results: Of 60 507 total admissions, 1663 (2.7%) met sepsis criteria, with 1324 electronic health record-confirmed sepsis (699 [52.8%] male patients; 298 [22.5%] Black patients; 46 [3.5%] Hispanic/Latinx patients; 945 [71.4%] White patients), 339 COVID-19 sepsis (183 [54.0%] male patients; 98 [28.9%] Black patients; 36 [10.6%] Hispanic/Latinx patients; and 189 [55.8%] White patients), and 58 844 (97.3%; 26 632 [45.2%] male patients; 12 698 [21.6%] Black patients; 3367 [5.7%] Hispanic/Latinx patients; 40 491 White patients) did not meet sepsis criteria. The median (IQR) age was 63 (51 to 73) years for electronic health record-confirmed sepsis, 69 (60 to 77) years for COVID-19 sepsis, and 60 (42 to 72) years for nonsepsis admissions. Within the vendor recommended threshold PSS range of 5 to 8, PSS of 8 or greater had the highest balanced accuracy for classifying a sepsis admission at 0.79 (95% CI, 0.78 to 0.80). Change in SOFA score of 2 or more had the highest sensitivity, at 0.97 (95% CI, 0.97 to 0.98). At a PSS of 8 or greater, median (IQR) time to score positivity from time zero was 68.00 (6.75 to 605.75) minutes. For SIRS, qSOFA, and SOFA, median (IQR) time to score positivity was 7.00 (-105.00 to 08.00) minutes, 74.00 (-22.25 to 599.25) minutes, and 28.00 (-108.50 to 134.00) minutes, respectively. Conclusions and Relevance: In this cohort study of hospital admissions, balanced accuracy of the SPM outperformed other models at higher threshold PSS; however, application of the SPM in a clinical setting was limited by poor timeliness as a sepsis screening tool as compared to SIRS and SOFA.

Clinical Impact of a Sepsis Alert System Plus Electronic Sepsis Navigator Using the Epic Sepsis Prediction Model in the Emergency Department.

BACKGROUND: The Epic Sepsis Prediction Model (SPM) is a proprietary sepsis prediction algorithm that calculates a score correlating with the likelihood of an International Classification of Diseases, Ninth Revision code for sepsis. OBJECTIVE: This study aimed to assess the clinical impact of an electronic sepsis alert and navigator using the Epic SPM on time to initial antimicrobial delivery. METHODS: We performed a retrospective review of a nonrandomized intervention of an electronic sepsis alert system and navigator using the Epic SPM. Data from the SPM site (site A) was compared with contemporaneous data from hospitals within the same health care system (sites B-D) and historical data from site A. Nonintervention sites used a systemic inflammatory response syndrome (SIRS)-based alert without a sepsis navigator. RESULTS: A total of 5368 admissions met inclusion criteria. Time to initial antimicrobial delivery from emergency department arrival was 3.33 h (interquartile range [IQR] 2.10-5.37 h) at site A, 3.22 h (IQR 1.97-5.60; p = 0.437, reference site A) at sites B-D, and 6.20 h (IQR 3.49-11.61 h; p < 0.001, reference site A) at site A historical. After adjustment using matching weights, there was no difference in time from threshold SPM score to initial antimicrobial between contemporaneous sites. Adjusted time to initial antimicrobial improved by 2.87 h (p < 0.001) at site A compared with site A historical. CONCLUSIONS: Implementation of an electronic sepsis alert system plus navigator using the Epic SPM showed no difference in time to initial antimicrobial delivery between the contemporaneous SPM alert plus sepsis navigator site and the SIRS-based electronic alert sites within the same health care system.

Concordance between clinical outcomes in the Systolic Blood Pressure Intervention Trial and in the electronic health record.

BACKGROUND: Randomized trials are the gold standard for generating clinical practice evidence, but follow-up and outcome ascertainment are resource-intensive. Electronic health record (EHR) data from routine care can be a cost-effective means of follow-up, but concordance with trial-ascertained outcomes is less well-studied. METHODS: We linked EHR and trial data for participants of the Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial comparing intensive and standard blood pressure targets. Among participants with available EHR data concurrent to trial-ascertained outcomes, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for EHR-recorded cardiovascular disease (CVD) events, using the gold standard of SPRINT-adjudicated outcomes (myocardial infarction (MI)/acute coronary syndrome (ACS), heart failure, stroke, and composite CVD events). We additionally compared the incidence of non-CVD adverse events (hyponatremia, hypernatremia, hypokalemia, hyperkalemia, bradycardia, and hypotension) in trial versus EHR data. RESULTS: 2468 SPRINT participants were included (mean age 68 (SD 9) years; 26% female). EHR data demonstrated ≥80% sensitivity and specificity, and ≥ 99% negative predictive value for MI/ACS, heart failure, stroke, and composite CVD events. Positive predictive value ranged from 26% (95% CI; 16%, 38%) for heart failure to 52% (95% CI; 37%, 67%) for MI/ACS. EHR data uniformly identified more non-CVD adverse events and higher incidence rates compared with trial ascertainment. CONCLUSIONS: These results support a role for EHR data collection in clinical trials, particularly for capturing laboratory-based adverse events. EHR data may be an efficient source for CVD outcome ascertainment, though there is clear benefit from adjudication to avoid false positives.

Patient Perspectives on a Targeted Text Messaging Campaign to Encourage Screening for Diabetes: Qualitative Study.

BACKGROUND: A sizeable proportion of prediabetes and diabetes cases among adults in the United States remain undiagnosed. Patient-facing clinical decision support (CDS) tools that leverage electronic health records (EHRs) have the potential to increase diabetes screening. Given the widespread mobile phone ownership across diverse groups, text messages present a viable mode for delivering alerts directly to patients. The use of unsolicited text messages to offer hemoglobin A1c (HbA1c) screening has not yet been studied. It is imperative to gauge perceptions of "cold texts" to ensure that information and language are optimized to promote engagement with text messages that affect follow-through with health behaviors. OBJECTIVE: This study aims to gauge the perceptions of and receptiveness to text messages to inform content that would facilitate engagement with text messages intended to initiate a mobile health (mHealth) intervention for targeted screening. Messages were designed to invite those not already diagnosed with diabetes to make a decision to take part in HbA1c screening and walk them through the steps required to perform the behavior based solely on an automated text exchange. METHODS: In total, 6 focus groups were conducted at Wake Forest Baptist Health (WFBH) between September 2019 and February 2020. The participants were adult patients without diabetes who had completed an in-person visit at the Family and Community Medicine Clinic within the previous year. We displayed a series of text messages and asked the participants to react to the message content and suggest improvements. Content was deductively coded with respect to the Health Belief Model (HBM) and inductively coded to identify other emergent themes that could potentially impact engagement with text messages. RESULTS: Participants (N=36) were generally receptive to the idea of receiving a text-based alert for HbA1c screening. Plain language, personalization, and content, which highlighted perceived benefits over perceived susceptibility and perceived severity, were important to participants' understanding of and receptiveness to messages. The patient-physician relationship emerged as a recurring theme in which patients either had a desire or held an assumption that their provider would be working behind the scenes throughout each step of the process. Participants needed further clarification to understand the steps involved in following through with HbA1c screening and receiving results. CONCLUSIONS: Our findings suggest that patients may be receptive to text messages that alert them to a risk of having an elevated HbA1c in direct-to-patient alerts that use cold texting. Using plain and positive language, integrating elements of personalization, and defining new processes clearly were identified by participants as modifiable content elements that could act as facilitators that would help overcome barriers to engagement with these messages. A patient's relationship with their provider and the financial costs associated with texts and screening may affect receptiveness and engagement in this process.

Using an Electronic Health Record and Deficit Accumulation to Pragmatically Identify Candidates for Optimal Prescribing in Patients With Type 2 Diabetes.

Objective: Despite guidelines recommending less stringent glycemic goals for older adults with type 2 diabetes, overtreatment is prevalent. Pragmatic approaches for prioritizing patients for optimal prescribing are lacking. We describe glycemic control and medication patterns for older adults with type 2 diabetes in a contemporary cohort, exploring variability by frailty status. Research Design and Methods: This was a cross-sectional observational study based on electronic health record (EHR) data, within an accountable care organization (ACO) affiliated with an academic medical center/health system. Participants were ACO-enrolled adults with type 2 diabetes who were ≥65 years of age as of 1 November 2020. Frailty status was determined by an automated EHR-based frailty index (eFI). Diabetes management was described by the most recent A1C in the past 2 years and use of higher-risk medications (insulin and/or sulfonylurea). Results: Among 16,973 older adults with type 2 diabetes (mean age 75.2 years, 9,154 women [53.9%], 77.8% White), 9,134 (53.8%) and 6,218 (36.6%) were classified as pre-frail (0.10 < eFI ≤0.21) or frail (eFI >0.21), respectively. The median A1C level was 6.7% (50 mmol/mol) with an interquartile range of 6.2-7.5%, and 74.1 and 38.3% of patients had an A1C <7.5% (58 mmol/mol) and <6.5% (48 mmol/mol), respectively. Frailty status was not associated with level of glycemic control (P = 0.08). A majority of frail patients had an A1C <7.5% (58 mmol/mol) (n = 4,544, 73.1%), and among these patients, 1,755 (38.6%) were taking insulin and/or a sulfonylurea. Conclusion: Treatment with insulin and/or a sulfonylurea to an A1C levels <7.5% is common in frail older adults. Tools such as the eFI may offer a scalable approach to targeting optimal prescribing interventions.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.

BACKGROUND: Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. METHODS: A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. RESULTS: Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. CONCLUSIONS: Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Sex and race differences in safety and effectiveness of the HEART pathway accelerated diagnostic protocol for acute chest pain.

BACKGROUND: The HEART Pathway is an accelerated diagnostic protocol for Emergency Department patients with possible acute coronary syndrome. The objective was to compare the safety and effectiveness of the HEART Pathway among women vs men and whites vs non-whites. METHODS: A subgroup analysis of the HEART Pathway Implementation Study was conducted. Adults with chest pain were accrued from November 2013 to January 2016 from 3 Emergency Departments in North Carolina. The primary outcomes were death and myocardial infarction (MI) and hospitalization rates at 30 days. Logistic regression evaluated for interactions of accelerated diagnostic protocol implementation with sex or race and changes in outcomes within subgroups. RESULTS: A total of 8,474 patients were accrued, of which 53.6% were female and 34.0% were non-white. The HEART Pathway identified 32.6% of females as low-risk vs 28.5% of males (P = 002) and 35.6% of non-whites as low-risk vs 28.0% of whites (P < .0001). Among low-risk patients, death or MI at 30 days occurred in 0.4% of females vs 0.5% of males (P = .70) and 0.5% of non-whites vs 0.3% of whites (P = .69). Hospitalization at 30 days was reduced by 6.6% in females (aOR: 0.74, 95% CI: 0.64-0.85), 5.1% in males (aOR: 0.87, 95% CI: 0.75-1.02), 8.6% in non-whites (aOR: 0.72, 95% CI: 0.60-0.86), and 4.5% in whites (aOR: 0.83, 95% CI: 0.73-0.94). Interactions were not significant. CONCLUSION: Women and non-whites are more likely to be classified as low-risk by the HEART Pathway. HEART Pathway implementation is associated with decreased hospitalizations and a very low death and MI rate among low-risk patients regardless of sex or race.

D-CARE: The Dementia Care Study: Design of a Pragmatic Trial of the Effectiveness and Cost Effectiveness of Health System-Based Versus Community-Based Dementia Care Versus Usual Dementia Care.

BACKGROUND/OBJECTIVES: Although several approaches have been developed to provide comprehensive care for persons living with dementia (PWD) and their family or friend caregivers, the relative effectiveness and cost effectiveness of community-based dementia care (CBDC) versus health system-based dementia care (CBDC) and the effectiveness of both approaches compared with usual care (UC) are unknown. DESIGN: Pragmatic randomized three-arm superiority trial. The unit of randomization is the PWD/caregiver dyad. SETTING: Four clinical trial sites (CTSs) based in academic and clinical health systems. PARTICIPANTS: A total of 2,150 English- or Spanish-speaking PWD who are not receiving hospice or residing in a nursing home and their caregivers. INTERVENTIONS: Eighteen months of (1) HSDC provided by a nurse practitioner or physician's assistant dementia care specialist who works within the health system, or (2) CBDC provided by a social worker or nurse care consultant who works at a community-based organization, or (3) UC with as needed referral to the Alzheimer's Association Helpline. MEASUREMENTS: Primary outcomes: PWD behavioral symptoms and caregiver distress as measured by the Neuropsychiatric Inventory Questionnaire (NPI-Q) Severity and Modified Caregiver Strain Index scales. SECONDARY OUTCOMES: NPI-Q Distress, caregiver unmet needs and confidence, and caregiver depressive symptoms. Tertiary outcomes: PWD long-term nursing home placement rates, caregiver-reported PWD functional status, cognition, goal attainment, "time spent at home," Dementia Burden Scale-Caregiver, a composite measure of clinical benefit, Quality of Life of persons with dementia, Positive Aspects of Caregiving, and cost effectiveness using intervention costs and Medicare claims. RESULTS: The results will be reported in the spring of 2024. CONCLUSION: D-CARE will address whether emphasis on clinical support and tighter integration with other medical services has greater benefit than emphasis on social support that is tied more closely to community resources. It will also assess the effectiveness of both interventions compared with UC and will evaluate the cost effectiveness of each intervention.

Identification of very low-risk acute chest pain patients without troponin testing.

BACKGROUND: The HEART Pathway combines a History ECG Age Risk factor (HEAR) score and serial troponins to risk stratify patients with acute chest pain. However, it is unclear whether patients with HEAR scores of <1 require troponin testing. The objective of this study is to measure the major adverse cardiac event (MACE) rate among patients with <1 HEAR scores and determine whether serial troponin testing is needed to achieve a miss rate <1%. METHODS: A secondary analysis of the HEART Pathway Implementation Study was conducted. HEART Pathway risk assessments (HEAR scores and serial troponin testing at 0 and 3 hours) were completed by the providers on adult patients with chest pain from three US sites between November 2014 and January 2016. MACE (composite of death, myocardial infarction (MI) and coronary revascularisation) at 30 days was determined. The proportion of patients with HEAR scores of <1 diagnosed with MACE within 30 days was calculated. The impact of troponin testing on patients with HEAR scores of <1 was determined using Net Reclassification Improvement Index (NRI). RESULTS: Providers completed HEAR assessments on 4979 patients and HEAR scores<1 occurred in 9.0% (447/4979) of patients. Among these patients, MACE at 30 days occurred in 0.9% (4/447; 95% CI 0.2% to 2.3%) with two deaths, two MIs and 0 revascularisations. The sensitivity and negative predictive value for MACE in the HEAR <1 was 97.8% (95%CI 94.5% to 99.4%) and 99.1% (95% CI 97.7% to 99.8%), respectively, and were not improved by troponin testing. Troponin testing in patients with HEAR <1 correctly reclassified two patients diagnosed with MACE, and was elevated among seven patients without MACE yielding an NRI of 0.9% (95%CI -0.7 to 2.4%). CONCLUSION: These data suggest that patients with HEAR scores of 0 and 1 represent a very low-risk group that may not require troponin testing to achieve a missed MACE rate <1%. Trial registration number NCT02056964.

HEART Pathway Implementation Safely Reduces Hospitalizations at One Year in Patients With Acute Chest Pain.

STUDY OBJECTIVE: We determine whether implementation of the HEART (History, ECG, Age, Risk Factors, Troponin) Pathway is safe and effective in emergency department (ED) patients with possible acute coronary syndrome through 1 year of follow-up. METHODS: A preplanned analysis of 1-year follow-up data from a prospective pre-post study of 8,474 adult ED patients with possible acute coronary syndrome from 3 US sites was conducted. Patients included were aged 21 years or older, evaluated for possible acute coronary syndrome, and without ST-segment elevation myocardial infarction. Accrual occurred for 12 months before and after HEART Pathway implementation, from November 2013 to January 2016. The HEART Pathway was integrated into the electronic health record at each site as an interactive clinical decision support tool. After integration, ED providers prospectively used the HEART Pathway to identify patients with possible acute coronary syndrome as low risk (appropriate for early discharge without stress testing or angiography) or nonlow risk (appropriate for further inhospital evaluation). Safety (all-cause death and myocardial infarction) and effectiveness (hospitalization) at 1 year were determined from health records, insurance claims, and death index data. RESULTS: Preimplementation and postimplementation cohorts included 3,713 and 4,761 patients, respectively. The HEART Pathway identified 30.7% of patients as low risk; 97.5% of them were free of death and myocardial infarction within 1 year. Hospitalization at 1 year was reduced by 7.0% in the postimplementation versus preimplementation cohort (62.1% versus 69.1%; adjusted odds ratio 0.70; 95% confidence interval 0.63 to 0.78). Rates of death or myocardial infarction at 1 year were similar (11.6% versus 12.4%; adjusted odds ratio 1.00; 95% confidence interval 0.87 to 1.16). CONCLUSION: HEART Pathway implementation was associated with decreased hospitalizations and low adverse event rates among low-risk patients at 1-year follow-up.

Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. RESEARCH DESIGN AND METHODS: Youth (<20 years old) with potential evidence of diabetes (N = 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly. RESULTS: The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91. CONCLUSIONS: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patients with diabetes. We hypothesized that the odds of recording an "uncontrolled diabetes" DX increased after a hemoglobin A1c result above 9% and that this rate would vary across EHR segments. Our statistical models revealed that each DX indicating uncontrolled diabetes was 2.6% more likely to occur post-A1c>9% overall (adj-p=.0005) and 3.9% after controlling for EHR segment (adj-p<.0001). However, odds ratios varied across segments (1.021

Opportunistic Measurement of Skeletal Muscle Size and Muscle Attenuation on Computed Tomography Predicts 1-Year Mortality in Medicare Patients.

BACKGROUND: Opportunistic assessment of sarcopenia on CT examinations is becoming increasingly common. This study aimed to determine relationships between CT-measured skeletal muscle size and attenuation with 1-year risk of mortality in older adults enrolled in a Medicare Shared Savings Program (MSSP). METHODS: Relationships between skeletal muscle metrics and all-cause mortality were determined in 436 participants (52% women, mean age 75 years) who had abdominopelvic CT examinations. On CT images, skeletal muscles were segmented at the level of L3 using two methods: (a) all muscles with a threshold of -29 to +150 Hounsfield units (HU), using a dedicated segmentation software, (b) left psoas muscle using a free-hand region of interest tool on a clinical workstation. Muscle cross-sectional area (CSA) and muscle attenuation were measured. Cox regression models were fit to determine the associations between muscle metrics and mortality, adjusting for age, sex, race, smoking status, cancer diagnosis, and Charlson comorbidity index. RESULTS: Within 1 year of follow-up, 20.6% (90/436) participants died. In the fully-adjusted model, higher muscle index and muscle attenuation were associated with lower risk of mortality. A one-unit standard deviation (SD) increase was associated with a HR = 0.69 (95% CI = 0.49, 0.96; p = .03) for total muscle index, HR = 0.67 (95% CI = 0.49, 0.90; p < .01) for psoas muscle index, HR = 0.54 (95% CI = 0.40, 0.74; p < .01) for total muscle attenuation, and HR = 0.79 (95% CI = 0.66, 0.95; p = .01) for psoas muscle attenuation. CONCLUSION: In older adults, higher skeletal muscle index and muscle attenuation on abdominopelvic CT examinations were associated with better survival, after adjusting for multiple risk factors.

Safely Identifying Emergency Department Patients With Acute Chest Pain for Early Discharge.

BACKGROUND: The HEART Pathway (history, ECG, age, risk factors, and initial troponin) is an accelerated diagnostic protocol designed to identify low-risk emergency department patients with chest pain for early discharge without stress testing or angiography. The objective of this study was to determine whether implementation of the HEART Pathway is safe (30-day death and myocardial infarction rate <1% in low-risk patients) and effective (reduces 30-day hospitalizations) in emergency department patients with possible acute coronary syndrome. METHODS: A prospective pre-post study was conducted at 3 US sites among 8474 adult emergency department patients with possible acute coronary syndrome. Patients included were ≥21 years old, investigated for possible acute coronary syndrome, and had no evidence of ST-segment-elevation myocardial infarction on ECG. Accrual occurred for 12 months before and after HEART Pathway implementation from November 2013 to January 2016. The HEART Pathway accelerated diagnostic protocol was integrated into the electronic health record at each site as an interactive clinical decision support tool. After accelerated diagnostic protocol integration, ED providers prospectively used the HEART Pathway to identify patients with possible acute coronary syndrome as low risk (appropriate for early discharge without stress testing or angiography) or non-low risk (appropriate for further in-hospital evaluation). The primary safety and effectiveness outcomes, death, and myocardial infarction (MI) and hospitalization rates at 30 days were determined from health records, insurance claims, and death index data. RESULTS: Preimplementation and postimplementation cohorts included 3713 and 4761 patients, respectively. The HEART Pathway identified 30.7% as low risk; 0.4% of these patients experienced death or MI within 30 days. Hospitalization at 30 days was reduced by 6% in the postimplementation versus preimplementation cohort (55.6% versus 61.6%; adjusted odds ratio, 0.79; 95% CI, 0.71-0.87). During the index visit, more MIs were detected in the postimplementation cohort (6.6% versus 5.7%; adjusted odds ratio, 1.36; 95% CI, 1.12-1.65). Rates of death or MI during follow-up were similar (1.1% versus 1.3%; adjusted odds ratio, 0.88; 95% CI, 0.58-1.33). CONCLUSIONS: HEART Pathway implementation was associated with decreased hospitalizations, increased identification of index visit MIs, and a very low death and MI rate among low-risk patients. These findings support use of the HEART Pathway to identify low-risk patients who can be safely discharged without stress testing or angiography. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov . Unique identifier: NCT02056964.

Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record.

BACKGROUND: Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs). OBJECTIVE: The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems. METHODS: The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package. RESULTS: The final model to predict an elevated HbA1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non-high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities. CONCLUSIONS: The calculator created for predicting the probability of having an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.