Abstract
Background
The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines ...for frailty management. However, there have been limited applications of EHR-based approaches in the United States.
Methods
We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls.
Results
The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI.
Conclusions
Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.
Abstract
Background
Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction ...models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability.
Method
We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study.
Results
Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest).
Conclusions
Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
In the July 2016 issue of this journal, Dr. Mary Tinetti proposed that geriatric medicine abandon attempts to increase the numbers of board‐certified geriatricians and change focus to the development ...of a “small elite workforce.” What would be gained and what sacrificed by accepting this challenge? We agree that the best clinical use of a scarce resource, specialty trained geriatricians, is to care for frail, complex, severely ill elderly adults and to help design and study novel interventions in research, education, and care models to improve the care of all older adults, but for this to happen, all other providers must attain specific competency in the care of older adults. This article responds and discusses alternative pathways for teaching geriatrics care, training specialists, and geriatrics fellows.
Accounting for continual evolution of deleterious L1 retrotransposon families, which can contain hundreds to thousands of members remains a major issue in mammalian biology. L1 activity generated ...upwards of 40% of some mammalian genomes, including humans where they remain active, causing genetic defects and rearrangements. L1 encodes a coiled coil-containing protein that is essential for retrotransposition, and the emergence of novel primate L1 families has been correlated with episodes of extensive amino acid substitutions in the coiled coil. These results were interpreted as an adaptive response to maintain L1 activity, however its mechanism remained unknown. Although an adventitious mutation can inactivate coiled coil function, its effect could be buffered by epistatic interactions within the coiled coil, made more likely if the family contains a diverse set of coiled coil sequences-collectively referred to as the coiled coil sequence space. Amino acid substitutions that do not affect coiled coil function (i.e., its phenotype) could be "hidden" from (not subject to) purifying selection. The accumulation of such substitutions, often referred to as cryptic genetic variation, has been documented in various proteins. Here we report that this phenomenon was in effect during the latest episode of primate coiled coil evolution, which occurred 30-10 MYA during the emergence of primate L1Pa7-L1Pa3 families. First, we experimentally demonstrated that while coiled coil function (measured by retrotransposition) can be eliminated by single epistatic mutations, it nonetheless can also withstand extensive amino acid substitutions. Second, principal component and cluster analysis showed that the coiled coil sequence space of each of the L1Pa7-3 families was notably increased by the presence of distinct, coexisting coiled coil sequences. Thus, sampling related networks of functional sequences rather than traversing discrete adaptive states characterized the persistence L1 activity during this evolutionary event.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
IMPORTANCE: Advance care planning (ACP), especially among vulnerable older adults, remains underused in primary care. Additionally, many ACP initiatives fail to integrate directly into the electronic ...health record (EHR), resulting in infrequent and disorganized documentation. OBJECTIVE: To determine whether a nurse navigator–led ACP pathway combined with a health care professional–facing EHR interface improves the occurrence of ACP discussions and their documentation within the EHR. DESIGN, SETTING, AND PARTICIPANTS: This was a randomized effectiveness trial using the Zelen design, in which patients are randomized prior to informed consent, with only those randomized to the intervention subsequently approached to provide informed consent. Randomization began November 1, 2018, and follow-up concluded November 1, 2019. The study population included patients 65 years or older with multimorbidity combined with either cognitive or physical impairments, and/or frailty, assessed from 8 primary care practices in North Carolina. INTERVENTIONS: Participants were randomized to either a nurse navigator–led ACP pathway (n = 379) or usual care (n = 380). MAIN OUTCOMES AND MEASURES: The primary outcome was documentation of a new ACP discussion within the EHR. Secondary outcomes included the usage of ACP billing codes, designation of a surrogate decision maker, and ACP legal form documentation. Exploratory outcomes included incident health care use. RESULTS: Among 759 randomized patients (mean age 77.7 years, 455 women 59.9%), the nurse navigator–led ACP pathway resulted in a higher rate of ACP documentation (42.2% vs 3.7%, P < .001) as compared with usual care. The ACP billing codes were used more frequently for patients randomized to the nurse navigator–led ACP pathway (25.3% vs 1.3%, P < .001). Patients randomized to the nurse navigator–led ACP pathway more frequently designated a surrogate decision maker (64% vs 35%, P < .001) and completed ACP legal forms (24.3% vs 10.0%, P < .001). During follow-up, the incidence of emergency department visits and inpatient hospitalizations was similar between the randomized groups (hazard ratio, 1.17; 95% CI, 0.92-1.50). CONCLUSIONS AND RELEVANCE: A nurse navigator–led ACP pathway integrated with a health care professional–facing EHR interface increased the frequency of ACP discussions and their documentation. Additional research will be required to evaluate whether increased EHR documentation leads to improvements in goal-concordant care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03609658
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.
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