Social Frailty Index: Development and validation of an index of social attributes predictive of mortality in older adults.

While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.

Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

BACKGROUND: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. OBJECTIVE: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. DESIGN: This was a cohort study. SETTING: Veterans Affairs (VA) EHR data. PARTICIPANTS: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). MEASUREMENTS AND ANALYTIC METHODS: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. RESULTS: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. LIMITATION: Our results should be confirmed in non-VA EHRs. CONCLUSION: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering.

BACKGROUND: To obtain phase-contrast X-ray images, single-grid imaging systems are effective, but Moire artifacts remain a significant issue. The solution for removing Moire artifacts from an image is grid rotation, which can distinguish between these artifacts and sample information within the Fourier space. However, the mechanical movement of grid rotation is slower than the real-time change in Moire artifacts. Thus, Moire artifacts generated during real-time imaging cannot be removed using grid rotation. To overcome this problem, we propose an effective method to obtain phase-contrast X-ray images using instantaneous frequency and noise filtering. RESULT: The proposed phase-contrast X-ray image using instantaneous frequency and noise filtering effectively suppressed noise with Moire patterns. The proposed method also preserved the clear edge of the inner and outer boundaries and internal anatomical information from the biological sample, outperforming conventional Fourier analysis-based methods, including absorption, scattering, and phase-contrast X-ray images. In particular, when comparing the phase information for the proposed method with the x-axis gradient image from the absorption image, the proposed method correctly distinguished two different types of soft tissue and the detailed information, while the latter method did not. CONCLUSION: This study successfully achieved a significant improvement in image quality for phase-contrast X-ray images using instantaneous frequency and noise filtering. This study can provide a foundation for real-time bio-imaging research using three-dimensional computed tomography.

Rates and timing of subsequent amputation after initial minor amputation.

OBJECTIVE: Studies evaluating major amputation after initial minor amputation are few with rates of subsequent major amputation ranging from 14% to 35% with limited understanding of associated comorbidities and time to limb loss. The aim of this study is to determine the major amputation rates for patients who had already undergone an initial minor amputation and determine which factors are associated with the need for subsequent major amputation. METHODS: Using statewide data between 2005 and 2013, patients with peripheral artery disease (PAD), diabetes mellitus (DM), and combined PAD/DM who had a lower extremity ulcer and who had also undergone a minor amputation were identified. These patients were evaluated for the rate of subsequent major amputation and competing risk Cox proportional hazards modeling was used to study which factors were associated with the risk of subsequent limb loss. RESULTS: The cohort consisted of 11,597 patients (DM, n = 4254; PAD, n = 2142; PAD/DM, n = 5201) with lower extremity ulcers who underwent an initial minor amputation. The rate of any subsequent amputation was highest in patients with PAD/DM (23% vs DM = 17%, PAD = 17%; P = not statistically significant). The rate of subsequent minor amputation was 16% in the PAD/DM versus 15.2% in PAD and 12.2% in patients with DM (P < .001). Patients with PAD/DM had the highest rate of subsequent major amputation (6.3% vs DM = 5.2%, PAD = 2.1%; P < .001). There was no statistically significant difference in the median time to major amputation among the three groups (PAD/DM, 13 months; DM, 14 months; PAD, 8.6 months; P = NS). Patients who were revascularized before a repeat minor amputation had a decreased risk of a major amputation compared with those who were intervened on after a repeat minor amputation (hazard ratio, 0.002; 95% confidence interval, 0-0.22). Patients treated completely in the outpatient setting were also less likely to undergo subsequent major amputation (hazard ratio, 0.7; 95% confidence interval, 0.5-0.98) compared with those who required hospitalization or presented to the emergency room. CONCLUSIONS: Patients with ulcers and combined PAD and DM have a higher risk for secondary major and minor amputation than patients with either disease alone with half of the limb loss occurring at approximately 1 year after the initial minor amputation. Additionally, early diagnosis and appropriate referral may result in decreased limb loss for these patients.

Resistive versus active assisted robotic training for the upper limb after a stroke: A randomized controlled study.

BACKGROUND: Selection of a suitable training modality according to the status of upper limb function can maximize the effects of robotic rehabilitation; therefore, it is necessary to identify the optimal training modality. OBJECTIVES: This study aimed to compare robotic rehabilitation approaches incorporating either resistance training (RET) or active-assisted training (AAT) using the same rehabilitation robot in people with stroke and moderate impairment. METHODS: In this randomized controlled trial, we randomly allocated 34 people with stroke who had moderate impairment to either the experimental group (RET, n = 18) or the control group (AAT, n = 16). Both groups performed robot-assisted therapy for 30 min, 5 days per week, for 4 weeks. The same rehabilitation robot provided resistance to the RET group and assistance to the AAT group. Body function and structure, activity, and participation outcomes were evaluated before, during, and after the intervention. RESULTS: RET led to greater improvements than AAT in terms of smoothness (p = 0.006). The Fugl-Meyer Assessment (FMA)-upper extremity (p < 0.001), FMA-proximal (p < 0.001), Action Research Arm Test-gross movement (p = 0.011), and kinematic variables of joint independence (p = 0.017) and displacement (p = 0.011) also improved at the end of intervention more in the RET group. CONCLUSIONS: Robotic RET was more effective than AAT in improving upper limb function, structure, and activity among participants with stroke who had moderate impairment.

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.

BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases. RESEARCH QUESTION: Can we identify implicit bias in clinical notes, and are biases stable across time and geography? STUDY DESIGN AND METHODS: To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence). RESULTS: In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors. INTERPRETATION: Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

Estimating Vitamin K Antagonist Anticoagulation Benefit in People With Atrial Fibrillation Accounting for Competing Risks: Evidence From 12 Randomized Trials.

BACKGROUND: Patients with atrial fibrillation have a high mortality rate that is only partially attributable to vascular outcomes. The competing risk of death may affect the expected anticoagulant benefit. We determined if competing risks materially affect the guideline-endorsed estimate of anticoagulant benefit. METHODS: We conducted a secondary analysis of 12 randomized controlled trials that randomized patients with atrial fibrillation to vitamin K antagonists (VKAs) or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of VKAs to prevent stroke or systemic embolism using 2 methods-first using a guideline-endorsed model (CHA2DS2-VASc) and then again using a competing risk model that uses the same inputs as CHA2DS2-VASc but accounts for the competing risk of death and allows for nonlinear growth in benefit. We compared the absolute and relative differences in estimated benefit and whether the differences varied by life expectancy. RESULTS: A total of 7933 participants (median age, 73 years, 36% women) had a median life expectancy of 8 years (interquartile range, 6-12), determined by comorbidity-adjusted life tables and 43% were randomized to VKAs. The CHA2DS2-VASc model estimated a larger ARR than the competing risk model (median ARR at 3 years, 6.9% [interquartile range, 4.7%-10.0%] versus 5.2% [interquartile range, 3.5%-7.4%]; P<0.001). ARR differences varied by life expectancies: for those with life expectancies in the highest decile, 3-year ARR difference (CHA2DS2-VASc model - competing risk model 3-year risk) was -1.3% (95% CI, -1.3% to -1.2%); for those with life expectancies in the lowest decile, 3-year ARR difference was 4.7% (95% CI, 4.5%-5.0%). CONCLUSIONS: VKA anticoagulants were exceptionally effective at reducing stroke risk. However, VKA benefits were misestimated with CHA2DS2-VASc, which does not account for the competing risk of death nor decelerating treatment benefit over time. Overestimation was most pronounced when life expectancy was low and when the benefit was estimated over a multiyear horizon.

Overestimation of anticoagulant benefit in patients with atrial fibrillation and low life expectancy: evidence from 12 randomized trials.

Background: Patients with atrial fibrillation (AF) have a high rate of all-cause mortality that is only partially attributable to vascular outcomes. While the competing risk of death may affect expected anticoagulant benefit, guidelines do not account for it. We sought to determine if using a competing risks framework materially affects the guideline-endorsed estimate of absolute risk reduction attributable to anticoagulants. Methods: We conducted a secondary analysis of 12 RCTs that randomized patients with AF to oral anticoagulants or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of anticoagulants to prevent stroke or systemic embolism using two methods. First, we estimated the ARR using a guideline-endorsed model (CHA 2 DS 2 -VASc) and then again using a Competing Risk Model that uses the same inputs as CHA 2 DS 2 -VASc but accounts for the competing risk of death and allows for non-linear growth in benefit over time. We compared the absolute and relative differences in estimated benefit and whether the differences in estimated benefit varied by life expectancy. Results: 7933 participants had a median life expectancy of 8 years (IQR 6, 12), determined by comorbidity-adjusted life tables. 43% were randomized to oral anticoagulation (median age 73 years, 36% women). The guideline-endorsed CHA 2 DS 2 -VASc model estimated a larger ARR than the Competing Risk Model (median ARR at 3 years, 6.9% vs. 5.2%). ARR differences varied by life expectancies: for those with life expectancies in the highest decile, 3-year ARR difference (CHA 2 DS 2 -VASc model - Competing Risk Model 3-year risk) was -1.2% (42% relative underestimation); for those with life expectancies in the lowest decile, 3-year ARR difference was 5.9% (91% relative overestimation). Conclusion: Anticoagulants were exceptionally effective at reduced stroke risk. However, anticoagulant benefits were misestimated with CHA 2 DS 2 -VASc, which does not account for the competing risk of death nor decelerating treatment benefit over time. Overestimation was most pronounced in patients with the lowest life expectancy and when benefit was estimated over a multi-year horizon.

Trends in Geriatric Conditions Among Older Adults Admitted to US ICUs Between 1998 and 2015.

BACKGROUND: Older adults are increasingly admitted to the ICU, and those with disabilities, dementia, frailty, and multimorbidity are vulnerable to adverse outcomes. Little is known about how pre-existing geriatric conditions have changed over time. RESEARCH QUESTION: How have changes in disability, dementia, frailty, and multimorbidity in older adults admitted to the ICU changed from 1998 through 2015? STUDY DESIGN AND METHODS: Medicare-linked Health and Retirement Survey (HRS) data identifying patients 65 years of age and older admitted to an ICU between 1998 and 2015. ICU admission was the unit of analysis. Year of ICU admission was the exposure. Disability, dementia, frailty, and multimorbidity were identified based on responses to HRS surveys before ICU admission. Disability represented the need for assistance with ≥ 1 activity of daily living. Dementia used cognitive and functional measures. Frailty included deficits in ≥ 2 domains (physical, nutritive, cognitive, or sensory function). Multimorbidity represented ≥ 3 self-reported chronic diseases. Time trends in geriatric conditions were modeled as a function of year of ICU admission and were adjusted for age, sex, race or ethnicity, and proxy interview status. RESULTS: Across 6,084 ICU patients, age at admission increased from 77.6 years (95% CI, 76.7-78.4 years) in 1998 to 78.7 years (95% CI, 77.5-79.8 years) in 2015 (P < .001 for trend). The adjusted proportion of ICU admissions with pre-existing disability rose from 15.5% (95% CI, 12.1%-18.8%) in 1998 to 24.0% (95% CI, 18.5%-29.6%) in 2015 (P = .001). Rates of dementia did not change significantly (P = .21). Frailty increased from 36.6% (95% CI, 30.9%-42.3%) in 1998 to 45.0% (95% CI, 39.7%-50.2%) in 2015 (P = .04); multimorbidity rose from 54.4% (95% CI, 49.2%-59.7%) in 1998 to 71.8% (95% CI, 66.3%-77.2%) in 2015 (P < .001). INTERPRETATION: Rates of pre-existing disability, frailty, and multimorbidity in older adults admitted to ICUs increased over time. Geriatric principles need to be deeply integrated into the ICU setting.

THE USES AND ABUSES OF AN AGE-PERIOD-COHORT METHOD: ON THE LINEAR ALGEBRA AND STATISTICAL PROPERTIES OF INTRINSIC AND RELATED ESTIMATORS.

As a sophisticated and popular age-period-cohort method, the Intrinsic Estimator (IE) and related estimators have evoked intense debate in demography, sociology, epidemiology and statistics. This study aims to provide a more holistic review and critical assessment of the overall methodological significance of the IE and related estimators in age-period-cohort analysis. We derive the statistical properties of the IE from a linear algebraic perspective, provide more precise mathematical proofs relevant to the current debate, and demonstrate the essential, yet overlooked, link between the IE and classical statistical tools that have been employed by scholars for decades. This study offers guidelines for the future use of the IE and related estimators in demographic research. The exposition of the IE and related estimators may help redirect, if not settle, the logic of the debate.

The unrealized potential: cohort effects and age-period-cohort analysis.

This study aims to provide a systematical introduction of age-period-cohort (APC) analysis to South Korean readers who are unfamiliar with this method (we provide an extended version of this study in Korean). As health data in South Korea has substantially accumulated, population-level studies that explore long-term trends of health status and health inequalities and identify macrosocial determinants of the trends are needed. Analyzing long-term trends requires to discern independent effects of age, period, and cohort using APC analysis. Most existing health and aging literature have used cross-sectional or short-term available panel data to identify age or period effects ignoring cohort effects. This under-use of APC analysis may be attributed to the identification (ID) problem caused by the perfect linear dependency across age, period, and cohort. This study explores recently developed three APC models to address the ID problem and adequately estimate the effects of A-P-C: intrinsic estimator-APC models for tabular age by period data; hierarchical cross-classified random effects models for repeated cross-sectional data; and hierarchical APC-growth curve models for accelerated longitudinal panel data. An analytic exemplar for each model was provided. APC analysis may contribute to identifying biological, historical, and socioeconomic determinants in long-term trends of health status and health inequalities as well as examining Korean's aging trajectories and temporal trends of period and cohort effects. For designing effective health policies that improve Korean population's health and reduce health inequalities, it is essential to understand independent effects of the three temporal factors by using the innovative APC models.

Deregulation of catalase, not MnSOD, is associated with necrotic death of p53-defective DF-1 cells under antimycin A-induced oxidative stress.

One of distinct genetic alterations in spontaneously immortalized DF-1 cells was found to be dysfunction of p53 and E2F-1 as well as altered antioxidant gene expression (upregulation of MnSOD and downregulation of catalase). We have characterized the cellular responses of primary and immortal DF-1 cells to oxidative stress and found that DF-1 cells were more sensitive to oxidative stress than their primary counterparts when treated with antimycin A. The increased DF-1 cell death by oxidative stress was accompanied by an increase in the levels of intracellular superoxide anions and hydrogen peroxide. The cell death in DF-1 cells by antimycin A showed none of the hallmarks of apoptosis, but displayed a significantly increased necrotic cell population. Anti-apoptotic Bcl-2 failed to inhibit oxidative-induced necrotic cell death in the DF-1 cells. However, this necrotic cell death was significantly decreased by treatment with hydrogen peroxide scavengers such as sodium pyruvate and N-acetyl-cysteine. Interestingly, overexpression of human catalase in DF-1 cells endowed cells resistant to the oxidative stress by antimycin A treatment, although the downregulation of MnSOD by an antisense strategy showed no evident change in the cytotoxic effect caused by antimycin A. Taken together, the present study might provide new therapeutic approach for tumor cells having the loss of p53 function and the altered antioxidant functions.

Development of Purtscher-like retinopathy after pre-eclampsia combined with acute pancreatitis.

Visual disturbances are common among women with pre-eclampsia. The obstetricians should have an understanding of the various conditions associated with visual impairments. We report a case of Purtscher-like retinopathy developed after pre-eclampsia combined with acute pancreatitis. A 33-year-old primigravida with twin pregnancy was admitted to the department of obstetrics and gynecology for preterm labor and pre-eclampsia at 36+4 weeks gestation. After a cesarean section, she complained of abdominal pain and visual acuity loss. She was diagnosed with Purtscher-like retinopathy combined with acute pancreatitis after ophthalmologic examination and an abdominal computed tomography scan. Purtscher-like retinopathy, most often observed after trauma, is very rare in pre-eclampsia. In addition, while visual disturbances from other conditions are transient, it can result in persistent visual impairments. Thus, when a patient with pre-eclampsia complains of visual problems for a long period of time, obstetricians should consider an ophthalmologic evaluation and treatments during the earliest stage of the disease.

A case of lymphangioleiomyomatosis originated in the pelvic cavity.

Lymphangioleiomyomatosis is a rare disease that is characterized by proliferation of abnormal smooth muscle-like cells, especially that which occurs in the pulmonary parenchyme. It primarily affects women of child-bearing age. The majority of primary lymphangioleiomyomatosis occurs in the lung, but there are a few reports of extrapulmonary cases. We experienced a rare case of lymphangioleiomyomatosis which originated in the pelvic cavity (in the posterior portion of the uterus), and report with brief review of literatures.