<i>Background:</i> The clinical diagnosis of stroke subtype is often inaccurate during the first 24 h after stroke onset. Several candidate diagnostic tests might be useful for early determination of subtype, but there is no consensus on what level of accuracy is required to be useful in the selection of patients for subtype-specific studies or therapies. <i>Methods:</i> A decision analysis was developed to explore the treatment options and estimate the requisite threshold of diagnostic accuracy. Four management strategies were considered: treat all (TA), treat based on new test (TBNT), treat based on clinical diagnosis (TBCD) and treat none (TN). Sensitivity analyses were performed over a wide range of the assumptions in the model. <i>Results:</i> The preferred treatment strategy was dependent on the probability and severity of adverse effects and the positive predictive value (PPV) of the proposed diagnostic test. For a potential therapy with infrequent (7.5%) but severe side effects, TN was preferable, but TBNT dominated if the PPV of the new test was at least 81%. For a therapy with frequent (25%) but mild adverse effects, TBNT was preferable. TA was favored for a therapy with infrequent and mild side effects and TN for a therapy with severe and frequent adverse events. TBCD was never the preferred option unless the PPV of the new test was less accurate than clinical diagnosis alone. <i>Conclusions:</i> Clinical diagnosis of stroke subtype is insufficient for patient selection, but a new diagnostic test with PPV ≥81% may be useful for early subtype diagnosis and patient selection for stroke subtype-specific clinical trials.
ABSTRACT Coronavirus disease of 2019 (COVID-19) has impacted the world in unprecedented ways since first emerging in December 2019. In the last two years, the scientific community has made an enormous effort to understand COVID-19 and potential interventions. As of June 15, 2021, there were more than 140,000 COVID-19 focused manuscripts on PubMed and preprint servers, such as medRxiv and BioRxiv . Preprints, which constitute more than 15% of all manuscripts, may contain more up-to-date research findings compared to published papers, due to the sometimes lengthy timeline between manuscript submission and publication. Including preprints in systematic reviews and meta-analyses thus has the potential to improve the timeliness of reviews. However, there is no clear guideline on whether preprints should be included in systematic reviews and meta-analyses. Using a prototypical example of a rapid systematic review examining the comparative effectiveness of COVID-19 therapeutics, we propose including all preprints in the systematic review by assigning them a weight we term the “confidence score”. Motivated by our observation that, unlike the traditional journal submission process which is unobserved, the timeline from submission to publication for a preprint can be observed and can be modeled as a time-to-event outcome. This observation provides a unique opportunity to model and quantify the probability that a preprint will be published, which can be used as a confidence score to weight preprints in systematic reviews and meta-analyses. To obtain the confidence score, we propose a novel survival cure model, which incorporates both the time from posting to publication for a preprint, and key characteristics of the study described in the content of the preprint. Using meta data from 158 preprints on evaluating therapeutic options for COVID-19 posted through 09/03/2020, we demonstrate the utility of the confidence score in weighting of preprints in a systematic review. Our proposed method has the potential to advance timely systematic reviews of the evidence examining COVID-19 and other clinical conditions with rapidly evolving evidence bases by providing an approach for inclusion of unpublished manuscripts.
Primary graft dysfunction is a severe acute lung injury syndrome after lung transplantation. Long-term outcomes of subjects with primary graft dysfunction have not been studied.We sought to test the relationship of primary graft dysfunction with both short- and long-term mortality using a large registry.We used data collected on 5,262 patients in the United Network for Organ Sharing/International Society of Heart and Lung Transplantation registry between 1994 and 2000. We assessed outcomes in all subjects; to assess potential bias from the effects of early mortality, we also evaluated subjects who survived at least 1 year, using Cox proportional hazards models with time-varying covariates.The overall incidence of primary graft dysfunction was 10.2% (95% confidence intervals [CI], 9.2, 10.9). The incidence did not vary by year over the period of observation (p = 0.22). All-cause mortality at 30 days was 42.1% for primary graft dysfunction versus 6.1% in patients without graft dysfunction (relative risk = 6.95; 95% CI, 5.98, 8.08; p < 0.001); among subjects who died by 30 days, 43.6% had primary graft dysfunction. Among patients surviving at least 1 year, those who had primary graft dysfunction had significantly worse survival over ensuing years (hazard ratio, 1.35; 95% CI, 1.07, 1.70; p = 0.011). Adjustment for clinical variables including bronchiolitis obliterans syndrome did not change this relationship.Primary graft dysfunction contributes to nearly half of the short-term mortality after lung transplantation. Survivors of primary graft dysfunction have increased risk of death extending beyond the first post-transplant year.
Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed.We simulated data, informed by post-lung transplant mortality data and tested the following two approaches for detecting model change: (1) the "Direct Approach," it compares coefficients of the model refit on recent data to those at baseline; and (2) "Calibration Regression," it fits a logistic regression model of the log-odds of the observed outcomes versus the linear predictor from the baseline model (i.e., the log-odds of the predicted probabilities obtained from the baseline model) and tests whether the intercept and slope differ from 0 and 1, respectively. Four scenarios were simulated using logistic regression for binary outcomes as follows: (1) we fixed all model parameters, (2) we varied the outcome prevalence between 0.1 and 0.2, (3) we varied the coefficient of one of the ten predictors between 0.2 and 0.4, and (4) we varied the outcome prevalence and coefficient of one predictor simultaneously.Calibration regression tended to detect changes sooner than the Direct Approach, with better performance (e.g., larger proportion of true claims). When the sample size was large, both methods performed well. When two parameters changed simultaneously, neither method performed well.Neither change detection method examined here proved optimal under all circumstances. However, our results suggest that if one is interested in detecting a change in overall incidence of an outcome (e.g., intercept), the Calibration Regression method may be superior to the Direct Approach. Conversely, if one is interested in detecting a change in other model covariates (e.g., slope), the Direct Approach may be superior.
BACKGROUND: Oral anticoagulants (OAC) is indicated for stroke prevention in patients with atrial fibrillation (AF) with a moderate or high risk of stroke. Despite the benefits of stroke prevention, only 50%-60% of Americans with nonvalvular AF and a moderate or high risk of stroke receive OAC medication. OBJECTIVE: To understand the extent to which low OAC use by patients with AF is attributed to underprescribing or underfilling once the medication is prescribed. METHODS: This is a retrospective cohort study that used linked claims data and electronic health records from Optum Integrated data. Participants were adults (aged ≥ 18 years) with first AF between January 2013 and June 2017. The outcomes included (1) being prescribed OACs within 180 days of AF diagnosis or not and (2) filling an OAC prescription or not among patients with AF who were prescribed an OAC within 150 days of AF diagnosis. Multivariable logistic regression models were constructed to determine factors associated with underprescribing and underfilling. RESULTS: Of the 6,141 individuals in the study cohort, 51% were not prescribed OACs within 6 months of their AF diagnosis. Of the 2,956 patients who were prescribed, 19% did not fill it at the pharmacy. In the final adjusted model, younger age, location (Northeast and South), a low CHA2DS2-VASc score, and a high HAS-BLED score were associated with a lower likelihood of being prescribed OACs. Among patients who were prescribed, Medicare enrollment (odds ratio [OR] [95% CI] = 2.2 [1.3-3.7]) and having a direct oral anticoagulant prescription (1.5 [1.2-1.9]) were associated with a lower likelihood of filling the prescription. CONCLUSIONS: Both underprescribing and underfilling are major drivers of low OAC use among patients with AF, and solutions to increase OAC use must address both prescribing and filling. DISCLOSURES: Research reported in this study was supported by the National Heart, Lung and Blood Institute (K01HL142847 and R01HL157051). Dr Guo is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465), PhMRA Foundation Research Starter Award, and the University of Florida Research Opportunity Seed Fund. Dr Hernandez reports scientific advisory board fees from Pfizer and Bristol Myers Squibb, outside of the submitted work.
Background Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. Objective This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. Methods In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium’s data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Results Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. Conclusions The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.
