BACKGROUND/OBJECTIVES
To investigate potential mechanisms underlying the well‐established relationship of diabetes and obesity with cognitive decline, among older adults participating in a ...population‐based study.
DESIGN/SETTING
Ten‐year population‐based cohort study.
PARTICIPANTS
A total of 478 individuals aged 65 years and older.
MEASUREMENTS
We assayed fasting blood for markers of glycemia (glucose and hemoglobin A1c HbA1c), insulin resistance (IR) (insulin and homeostatic model assessment of IR), obesity (resistin, adiponectin, and glucagon‐like peptide‐1), and inflammation (C‐reactive protein). We modeled these indices as predictors of the slope of decline in global cognition, adjusting for age, sex, education, APOE*4 genotype, depressive symptoms, waist‐hip ratio (WHR), and systolic blood pressure, in multivariable regression analyses of the entire sample and stratified by sex‐specific median WHR. We then conducted WHR‐stratified machine‐learning (Classification and Regression Tree CART) analyses of the same variables.
RESULTS
In multivariable regression analyses, in the entire sample, HbA1c was significantly associated with cognitive decline. After stratifying by median WHR, HbA1c remained associated with cognitive decline in those with higher WHR. No metabolic indices were associated with cognitive decline in those with lower WHR. Cross‐validated WHR‐stratified CART analyses selected no predictors in participants older than 87 to 88 years. Faster cognitive decline was associated, in lower WHR participants younger than 87 years, with adiponectin of 11 or greater; and in higher WHR participants younger than 88 years, with HbA1c of 6.2% or greater.
CONCLUSIONS
Our population‐based data suggest that, in individuals younger than 88 years with central obesity, even modest degrees of hyperglycemia might independently predispose to faster cognitive decline. In contrast, among those younger than 87 years without central obesity, adiponectin may be a novel independent risk factor for cognitive decline. J Am Geriatr Soc 68:991–998, 2020
IMPORTANCE: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. OBJECTIVE: To derive sepsis phenotypes from clinical ...data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). DESIGN, SETTINGS, AND PARTICIPANTS: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). EXPOSURES: All clinical and laboratory variables in the electronic health record. MAIN OUTCOMES AND MEASURES: Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. RESULTS: The derivation cohort included 20 189 patients with sepsis (mean age, 64 SD, 17 years; 10 022 50% male; mean maximum 24-hour Sequential Organ Failure Assessment SOFA score, 3.9 SD, 2.4). The validation cohort included 43 086 patients (mean age, 67 SD, 17 years; 21 993 51% male; mean maximum 24-hour SOFA score, 3.6 SD, 2.0). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). CONCLUSIONS AND RELEVANCE: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.
Background/Objective
In population studies, most individuals with mild cognitive impairment (MCI) do not progress to dementia in the near term, but rather remain stable MCI or revert to normal ...cognition. Here, we characterized MCI subgroups with different outcomes over 5 years.
Setting/Participants
A population‐based cohort (N=1603).
Measurements
Clinical Dementia Rating (CDR); self‐reported medical conditions, subjective cognitive concerns, self‐rated health, depressive symptoms, blood pressure, medications, blood pressure, APOE genotype, cognitive domain composite scores.
Design
We compared 3 MCI subgroups who progressed to dementia (n=86), stabilized at MCI (n=384), or reverted to normal (n=252), to those who remained consistently normal (n=881), defining MCI as CDR = 0.5 and dementia as CDR≥1. Using multinomial logistic regression models adjusted for demographics, we examined the associations of each group with selected baseline characteristics.
Results
With the normal group for reference, worse subjective cognitive concerns, functional impairments, self‐rated health, and depressive symptoms were associated with being in any MCI group. Taking more prescription medications was associated with being in the stable MCI and reverter groups; diabetes and low diastolic blood pressure were associated with stable MCI. The APOE4 genotype was associated with stable and progressive MCI; stroke was associated with progressive MCI. All MCI subgroups were likely to have lower mean composite scores in all cognitive domains and more operationally defined impairments in attention, language, and executive function; reverters were more likely to lack memory and visuospatial impairments.
Conclusions
MCI subgroups with different 5‐year outcomes had some distinct characteristics suggesting different underlying causes. The progressors, unlike the reverters, had a profile broadly typical of Alzheimer's disease; the stable MCIs had other, including vascular, morbidity. These data shed light on the heterogeneity of MCI in the population. J Am Geriatr Soc 67:232–238, 2019.
Background and aims
Medication for opioid use disorder (MOUD) reduces harms associated with opioid use disorder (OUD), including risk of overdose. Understanding how variation in MOUD duration ...influences overdose risk is important as health‐care payers increasingly remove barriers to treatment continuation (e.g. prior authorization). This study measured the association between MOUD continuation, relative to discontinuation, and opioid‐related overdose among Medicaid beneficiaries.
Design
Retrospective cohort study using landmark survival analysis. We estimated the association between treatment continuation and overdose risk at 5 points after the index, or first, MOUD claim. Censoring events included death and disenrollment.
Setting and participants
Medicaid programs in 11 US states: Delaware, Kentucky, Maryland, Maine, Michigan, North Carolina, Ohio, Pennsylvania, Virginia, West Virginia and Wisconsin. A total of 293 180 Medicaid beneficiaries aged 18–64 years with a diagnosis of OUD and had a first MOUD claim between 2016 and 2017.
Measurements
MOUD formulations included methadone, buprenorphine and naltrexone. We measured medically treated opioid‐related overdose within claims within 12 months of the index MOUD claim.
Findings
Results were consistent across states. In pooled results, 5.1% of beneficiaries had an overdose, and 67% discontinued MOUD before an overdose or censoring event within 12 months. Beneficiaries who continued MOUD beyond 60 days had a lower relative overdose hazard ratio (HR) compared with those who discontinued by day 60 HR = 0.39; 95% confidence interval (CI) = 0.36–0.42; P < 0.0001. MOUD continuation was associated with lower overdose risk at 120 days (HR = 0.34; 95% CI = 0.31–0.37; P < 0.0001), 180 days (HR = 0.31; 95% CI = 0.29–0.34; P < 0.0001), 240 days (HR = 0.29; 95% CI = 0.26–0.31; P < 0.0001) and 300 days (HR = 0.28; 95% CI = 0.24–0.32; P < 0.0001). The hazard of overdose was 10% lower with each additional 60 days of MOUD (95% CI = 0.88–0.92; P < 0.0001).
Conclusions
Continuation of medication for opioid use disorder (MOUD) in US Medicaid beneficiaries was associated with a substantial reduction in overdose risk up to 12 months after the first claim for MOUD.
Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected ...number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response‐adaptive randomization (RAR) design targeting the endpoint of organ support‐free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post‐randomization time‐window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.
Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of ...follow‐up success is to identify a subpopulation among whom treatment effects are estimated to be beneficial, and enrolling future studies from this subpopulation. In this article we study confirmable responder class (CRC)
learning, where the objective is to learn in a random half of the dataset (training set) a subpopulation among whom the predicted conditional ATE (CATE) suggests clinically meaningful benefit, with maximum power of being confirmed via hypothesis test in the other half (test set). We studied a set of CRC learners across simulated datasets in which either all patients benefited, or only some did. Performance metrics included the rate of confirmation in the test set, and the classification accuracy of the CRC compared with the group with true CATE>0. In trials where all patients benefitted, only two learners were able to consistently identify most of the population as the CRC. One of these also performed especially well when only some patients benefitted, having relatively high confirmation rates, and showing robustness to the dimension of the covariate vector and population characteristics. This learner is based on cross‐validation, and is a possible avenue for further development of model selection criteria for CRC learning. We also show that the performance of all methods can be improved by using both halves of the original dataset as training and test sets in turn.
IMPORTANCE: Beginning in 2013, New York State implemented regulations mandating that hospitals implement evidence-based protocols for sepsis management, as well as report data on protocol adherence ...and clinical outcomes to the state government. The association between these mandates and sepsis outcomes is unknown. OBJECTIVE: To evaluate the association between New York State sepsis regulations and the outcomes of patients hospitalized with sepsis. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of adult patients hospitalized with sepsis in New York State and in 4 control states (Florida, Maryland, Massachusetts, and New Jersey) using all-payer hospital discharge data (January 1, 2011-September 30, 2015) and a comparative interrupted time series analytic approach. EXPOSURES: Hospitalization for sepsis before (January 1, 2011-March 31, 2013) vs after (April 1, 2013-September 30, 2015) implementation of the 2013 New York State sepsis regulations. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day in-hospital mortality. Secondary outcomes were intensive care unit admission rates, central venous catheter use, Clostridium difficile infection rates, and hospital length of stay. RESULTS: The final analysis included 1 012 410 sepsis admissions to 509 hospitals. The mean age was 69.5 years (SD, 16.4 years) and 47.9% were female. In New York State and in the control states, 139 019 and 289 225 patients, respectively, were admitted before implementation of the sepsis regulations and 186 767 and 397 399 patients, respectively, were admitted after implementation of the sepsis regulations. Unadjusted 30-day in-hospital mortality was 26.3% in New York State and 22.0% in the control states before the regulations, and was 22.0% in New York State and 19.1% in the control states after the regulations. Adjusting for patient and hospital characteristics as well as preregulation temporal trends and season, mortality after implementation of the regulations decreased significantly in New York State relative to the control states (P = .02 for the joint test of the comparative interrupted time series estimates). For example, by the 10th quarter after implementation of the regulations, adjusted absolute mortality was 3.2% (95% CI, 1.0% to 5.4%) lower than expected in New York State relative to the control states (P = .004). The regulations were associated with no significant differences in intensive care unit admission rates (P = .09) (10th quarter adjusted difference, 2.8% 95% CI, −1.7% to 7.2%, P = .22), a significant relative decrease in hospital length of stay (P = .04) (10th quarter adjusted difference, 0.50 days 95% CI, −0.47 to 1.47 days, P = .31), a significant relative decrease in the C difficile infection rate (P < .001) (10th quarter adjusted difference, −1.8% 95% CI, −2.6% to −1.0%, P < .001), and a significant relative increase in central venous catheter use (P = .02) (10th quarter adjusted difference, 4.8% 95% CI, 2.3% to 7.4%, P < .001). CONCLUSIONS AND RELEVANCE: In New York State, mandated protocolized sepsis care was associated with a greater decrease in sepsis mortality compared with sepsis mortality in control states that did not implement sepsis regulations. Because baseline mortality rates differed between New York and comparison states, it is uncertain whether these findings are generalizable to other states.
This Guide to Statistics and Methods discusses the various approaches to estimating variability in treatment effects, including heterogeneity of treatment effect, which was used to assess the ...association between surgery to close patent foramen ovale and risk of recurrent stroke in patients who presented with a stroke in a related JAMA article.
Abstract Background Over 4 billion people worldwide are exposed to dietary aflatoxins, which cause liver cancer (hepatocellular carcinoma, HCC) in humans. However, the population attributable risk ...(PAR) of aflatoxin-related HCC remains unclear. Methods In our systematic review and meta-analysis of epidemiological studies, summary odds ratios (ORs) of aflatoxin-related HCC with 95% confidence intervals were calculated in HBV+ and HBV− individuals, as well as the general population. We calculated the PAR of aflatoxin-related HCC for each study as well as the combined studies, accounting for HBV status. Results Seventeen studies with 1680 HCC cases and 3052 controls were identified from 479 articles. All eligible studies were conducted in China, Taiwan, or sub-Saharan Africa. The PAR of aflatoxin-related HCC was estimated at 17% (14–19%) overall, and higher in HBV+ (21%) than HBV− (8.8%) populations. If the one study that contributed most to heterogeneity in the analysis is excluded, the summarised OR of HCC with 95% CI is 73.0 (36.0–148.3) from the combined effects of aflatoxin and HBV, 11.3 (6.75–18.9) from HBV only and 6.37 (3.74–10.86) from aflatoxin only. The PAR of aflatoxin-related HCC increases to 23% (21–24%). The PAR has decreased over time in certain Taiwanese and Chinese populations. Conclusions In high exposure areas, aflatoxin multiplicatively interacts with HBV to induce HCC; reducing aflatoxin exposure to non-detectable levels could reduce HCC cases in high-risk areas by about 23%. The decreasing PAR of aflatoxin-related HCC reflects the benefits of public health interventions to reduce aflatoxin and HBV.
IMPORTANCE: The risk of cardiovascular disease (CVD) after infection is poorly understood. OBJECTIVE: To determine whether hospitalization for pneumonia is associated with an increased short-term and ...long-term risk of CVD. DESIGN, SETTINGS, AND PARTICIPANTS: We examined 2 community-based cohorts: the Cardiovascular Health Study (CHS, n = 5888; enrollment age, ≥65 years; enrollment period, 1989–1994) and the Atherosclerosis Risk in Communities study (ARIC, n = 15 792; enrollment age, 45-64 years; enrollment period, 1987–1989). Participants were followed up through December 31, 2010. We matched each participant hospitalized with pneumonia to 2 controls. Pneumonia cases and controls were followed for occurrence of CVD over 10 years after matching. We estimated hazard ratios (HRs) for CVD at different time intervals, adjusting for demographics, CVD risk factors, subclinical CVD, comorbidities, and functional status. EXPOSURES: Hospitalization for pneumonia. MAIN OUTCOMES AND MEASURES: Incident CVD (myocardial infarction, stroke, and fatal coronary heart disease). RESULTS: Of 591 pneumonia cases in CHS, 206 had CVD events over 10 years after pneumonia hospitalization. CVD risk after pneumonia was highest in the first year. CVD occurred in 54 cases and 6 controls in the first 30 days (HR, 4.07; 95% CI, 2.86-5.27); 11 cases and 9 controls between 31 and 90 days (HR, 2.94; 95% CI, 2.18-3.70); and 22 cases and 55 controls between 91 days and 1 year (HR, 2.10; 95% CI, 1.59-2.60). Additional CVD risk remained elevated into the tenth year, when 4 cases and 12 controls developed CVD (HR, 1.86; 95% CI, 1.18-2.55). In ARIC, of 680 pneumonia cases, 112 had CVD over 10 years after hospitalization. CVD occurred in 4 cases and 3 controls in the first 30 days (HR, 2.38; 95% CI, 1.12-3.63); 4 cases and 0 controls between 31 and 90 days (HR, 2.40; 95% CI, 1.23-3.47); 11 cases and 8 controls between 91 days and 1 year (HR, 2.19; 95% CI, 1.20-3.19); and 8 cases and 7 controls during the second year (HR, 1.88; 95% CI, 1.10-2.66). After the second year, the HRs were no longer statistically significant. CONCLUSIONS AND RELEVANCE: Hospitalization for pneumonia was associated with increased short-term and long-term risk of CVD, suggesting that pneumonia may be a risk factor for CVD.
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