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Moreover, when participants experienced an exacerbation of their HF symptoms, they were sick enough to be readmitted to the hospital.

Our findings confirm ongoing challenges with a complex group of sick patients with HF, with the majority on home inotropes with reduced ejection fraction, who developed an unavoidable progression of their illness and subsequent hospital readmission.

Our findings confirm ongoing challenges with a complex group of sick patients with HF, with the majority on home inotropes with reduced ejection fraction, who developed an unavoidable progression of their illness and subsequent hospital readmission.

Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly robust cross-fit estimators have been proposed to yield better statistical properties.

We conducted a simulation study to assess the performance of several different estimators for the average causal effect. The data generating mechanisms for the simulated treatment and outcome included log-transforms, polynomial terms, and discontinuities. We compared singly robust estimators (g-computation, inverse probability weighting) and doubly robust estimators (augmented inverse probability weighting, targeted maximum likelihood estimation). We estimated nuisance functions with parametric models and ensemble machine learning separately. We further assessed doubly robust cross-fit estimators.

With correctly specified parametric models, all of the estimators were unbiased and confidence intervals achieved nominal coverage. When used with machine learning, the doubly robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.

Due to the difficulty of properly specifying parametric models in high-dimensional data, doubly robust estimators with ensemble learning and cross-fitting may be the preferred approach for estimation of the average causal effect in most epidemiologic studies. However, these approaches may require larger sample sizes to avoid finite-sample issues.

Due to the difficulty of properly specifying parametric models in high-dimensional data, doubly robust estimators with ensemble learning and cross-fitting may be the preferred approach for estimation of the average causal effect in most epidemiologic studies. However, these approaches may require larger sample sizes to avoid finite-sample issues.

Anaphylaxis is a life-threatening allergic reaction that is difficult to identify accurately with administrative data. We conducted a population-based validation study to assess the accuracy of ICD-10 diagnosis codes for anaphylaxis in outpatient, emergency department, and inpatient settings.

In an integrated healthcare system in Washington State, we obtained medical records from healthcare encounters with anaphylaxis diagnosis codes (potential events) from October 2015 to December 2018. To capture events missed by anaphylaxis diagnosis codes, we also obtained records on a sample of serious allergic and drug reactions. Two physicians determined whether potential events met established clinical criteria for anaphylaxis (validated events).

Out of 239 potential events with anaphylaxis diagnosis codes, the overall positive predictive value (PPV) for validated events was 64% (95% CI = 58 to 70). The PPV decreased with increasing age. Common precipitants for anaphylaxis were food (39%), medications (35%), andelectronic health data.

Rates of stroke are higher in people living with HIV compared with age-matched uninfected individuals. Causes of elevated stroke risk, including the role of viremia, are poorly defined.

Between 1 January 2006 and 31 December 2014, we identified incident strokes among people living with HIV on antiretroviral therapy at five sites across the United States. We considered three parameterizations of viral load (VL) including (1) baseline (most recent VL before study entry), (2) time-updated, and (3) cumulative VL (copy-days/mL of virus). We used Cox proportional hazards models to estimate hazard ratios (HRs) for stroke risk comparing the 75th percentile ("high VL") to the 25th percentile ("low VL") of baseline and time-updated VL. We used marginal structural Cox models, with most models adjusted for traditional stroke risk factors, to estimate HRs for stroke associated with cumulative VL.

Among 15,974 people living with HIV, 139 experienced a stroke (113 ischemic; 18 hemorrhagic; eight were unknown type) over a median follow-up of 4.2 years. Median baseline VL was 38 copies/mL (interquartile interval 24, 3,420). High baseline VL was associated with increased risk of both ischemic (HR 1.3; 95% CI = 0.96-1.7) and hemorrhagic stroke (HR 3.1; 95% CI = 1.6-5.9). In time-updated models, high VL was also associated with an increased risk of any stroke (HR 1.8; 95% CI = 1.4-2.3). We observed no association between cumulative VL and stroke risk.

Our findings are consistent with the hypothesis that elevated HIV VL may increase stroke risk, regardless of previous VL levels.

Our findings are consistent with the hypothesis that elevated HIV VL may increase stroke risk, regardless of previous VL levels.

Collaborative research often combines findings across multiple, independent studies via meta-analysis. SBI0640756 Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG.

We compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG.

Our results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators.