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:URLhttp://www.clinicaltrials.gov. Unique identifierNCT02056964.
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).
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.
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.
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.
Background: Guidelines recommend less stringent glycemic goals for older adults with type 2 diabetes mellitus (T2DM) and frailty or comorbidity. However, pragmatic, scalable approaches to identify ...candidates for de-intensification of T2DM regimens are lacking.
Methods: Analysis of electronic health record (EHR) data for patients ≥65 years with T2DM from an accountable care organization as of 11/1/2020. Frailty was determined based on a 54-item electronic Frailty Index (eFI) derived from the EHR. Other data included the level of glycemic control, use of higher-risk medications regimens (active prescription of insulin, sulfonylurea, or combinations of the two), the incidence of emergency department (ED) visits and hospitalizations, and all-cause mortality.
Results: Amongst 16973 patients, 53.9% were female, 77.8% white, with a mean age of 75.5 (SD=6.9) years. Based on the eFI, 6218 (36.6%) patients were classified as frail (eFI>0.21). During short-term follow-up (median=116 days), compared to fit patients (eFI≤0.10), patients classified as frail exhibited a higher incidence of ED visits and hospitalizations (hazard ratio = 3.05, 95% CI: 2.35 to 3.95) and all-cause mortality (hazard ratio = 7.33, 95% CI: 3.61 to 14.88). A large number of patients classified as frail based on the eFI had HbA1c levels <7.5% based on their most recent measure (N=4544, 73.1%). In this population, 1408 (31%) were prescribed no T2DM medication, 1013 (22.3%) were prescribed metformin alone, and 1755 (38.6%) were on a higher-risk T2DM medication (sulfonylurea or insulin). In frail patients with HbA1c<7.5%, patients taking metformin only exhibited the lowest rate of ED visits and hospitalizations (hazard ratio = 0.58, 95% CI: 0.44 to 0.77 compared to all other groups).
Conclusions: The eFI is a pragmatic and scalable tool to identify vulnerable older adults with T2DM that may benefit from de-prescribing consistent with guideline recommendations.
Disclosure
C. Usoh: None. K. M. Lenoir: None. N. M. Pajewski: None. K. E. Callahan: None.
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.
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.
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.
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.
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.
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.
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.
Youth (<20 years old) with potential evidence of diabetes (
= 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.
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 (
) (>0.95), specificity (
) (>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 (
= 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
,
, and PPV for type 2 diabetes using the combined method were ≥0.91.
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.
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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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.
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.
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 A
(HbA
) 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.
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 HbA
screening and walk them through the steps required to perform the behavior based solely on an automated text exchange.
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.
Participants (N=36) were generally receptive to the idea of receiving a text-based alert for HbA
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 HbA
screening and receiving results.
Our findings suggest that patients may be receptive to text messages that alert them to a risk of having an elevated HbA
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.
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