In this study of more than 20,000 nonsurgical patients receiving mechanical ventilation at 37 acute care hospitals from 2002 to 2003, higher hospital volume was associated with improved survival. ...After adjustment for the severity of illness and the characteristics of the hospitals, mortality in the hospital was 34 percent in hospitals in the lowest quartile in terms of the number of patients receiving mechanical ventilation per year and 26 percent in the highest quartile.
In more than 20,000 nonsurgical patients receiving mechanical ventilation at 37 acute care hospitals from 2002 to 2003, higher hospital volume was associated with improved survival.
The association between the number of patients treated in a hospital (hospital volume) and patient outcome is well established for numerous medical and surgical conditions.
1
This relationship has been extensively documented in the surgical literature, where higher patient volume is associated with improved survival in situations involving trauma care, cardiac surgery, ruptured aortic aneurysms, and several types of cancer surgery.
2
–
6
Select medical conditions, including acute myocardial infarction
7
and the acquired immunodeficiency syndrome,
8
are also thought to have outcomes related to the volume of patients seen. Reasons for the relationship between volume and outcome in health care are unknown but . . .
Group‐randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only ...a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group‐level covariates between arms. Therefore, covariate‐based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model‐based and randomization‐based tests under both simple and constrained randomization designs, with varying degrees of analysis‐based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F‐test, the KC‐corrected GEE t‐test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387‐1396), and two permutation tests when the prognostic group‐level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis‐based adjustment for non‐prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.
Abstract The use of stepped wedge designs in cluster-randomized trials and implementation studies has increased rapidly in recent years but there remains considerable debate regarding the merits of ...the design. We discuss three key issues in the design and analysis of stepped wedge trials — time-on-treatment effects, treatment effect heterogeneity and cohort studies.
This study showed that inmates released from prison in Washington State between 1999 and 2003 were at high risk for death, especially during the first 2 weeks after release. Most of the deaths among ...the recently released inmates were due to drug overdose. Inmates were 129 times more likely to die from drug overdose during the first 2 weeks after release than were other residents of Washington State.
This study showed that inmates released from prison were at high risk for death, especially during the first 2 weeks after release. Inmates were 129 times more likely to die from drug overdose during the first 2 weeks after release than were other residents of Washington State.
At the end of 2004, more than 3% of adults in the United States were in jail, in prison, or on probation or parole.
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At the end of 2001, there were approximately 5.6 million adults who had ever been incarcerated in a state or federal prison,
2
not including stays in local jails.
Prisoners' reentry — their return to the community from prison — can be stressful as former inmates try to obtain housing, reintegrate into their families and communities, find employment,
3
,
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and gain access to health care. Studies outside the United States have suggested a high mortality rate after . . .
IntroductionDigital mental health tools have become popular alternatives to traditional psychotherapy. One emerging form of digital mental health is message-based care, the use of text messages or ...asynchronous voice or video messaging to provide psychotherapy. There has been no research into whether this is an effective method of psychotherapy as a stand-alone treatment or in combination with traditional psychotherapy.Methods and analysisThis is a sequential, multiple assignment randomised trial to compare message-based care, videoconference-psychotherapy and a combination of the two treatments in 1000 depressed adults. Participants will be recruited through Talkspace, a digital mental health company, and randomised to receive 6 weeks of either message-based care only or videoconference-psychotherapy only. At 6 weeks, participants will be evaluated for their response to treatment. Those with a 50% or more response to treatment will continue with their assigned condition. Those who do not respond will be randomised to either monthly videoconference-psychotherapy or weekly videoconference-psychotherapy plus message-based care. Primary outcomes will be depression and social functioning. We will also explore moderators of treatment outcome.Ethics and disseminationThe study received ethics approval from the University of Washington Institutional Review Board. Results of this study will be presented in peer-reviewed journals and at professional conferences.Trial registration numberNCT04513080; Pre-results.
Summary
Scalable and accurate identification of specific clinical outcomes has been enabled by machine-learning applied to electronic medical record systems. The development of classification models ...requires the collection of a complete labeled data set, where true clinical outcomes are obtained by human expert manual review. For example, the development of natural language processing algorithms requires the abstraction of clinical text data to obtain outcome information necessary for training models. However, if the outcome is rare then simple random sampling results in very few cases and insufficient information to develop accurate classifiers. Since large scale detailed abstraction is often expensive, time-consuming, and not feasible, more efficient strategies are needed. Under such resource constrained settings, we propose a class of enrichment sampling designs, where selection for abstraction is stratified by auxiliary variables related to the true outcome of interest. Stratified sampling on highly specific variables results in targeted samples that are more enriched with cases, which we show translates to increased model discrimination and better statistical learning performance. We provide mathematical details and simulation evidence that links sampling designs to their resulting prediction model performance. We discuss the impact of our proposed sampling on both model training and validation. Finally, we illustrate the proposed designs for outcome label collection and subsequent machine-learning, using radiology report text data from the Lumbar Imaging with Reporting of Epidemiology study.
The PTSD Checklist for DSM‐5 (PCL‐5) is a measure of posttraumatic stress disorder (PTSD) symptom severity that is widely used for clinical and research purposes. Although previous work has examined ...metrics of minimal important difference (MID) of the PCL‐5 in veteran samples, no work has identified PCL‐5 MID metrics among adults in primary care in the United States. In this secondary analysis, data were evaluated from primary care patients (N = 971) who screened positive for PTSD and participated in a large clinical trial in federally qualified health centers in three U.S. states. Participants primarily self‐identified as women (70.2%) and White (70.3%). We calculated test–retest reliability using clinic registry data and multiple distribution‐ and anchor‐based metrics of MID using baseline and follow‐up survey data. Test–retest reliability (Pearson's r, Spearman's ρ, intraclass correlation coefficient) ranged from adequate to excellent (.79–.94), with the shortest time lag demonstrating the highest reliability estimate. The MID for the PCL‐5 was estimated using multiple approaches. Distribution‐based approaches indicated an MID range of 8.5–12.5, and anchor‐based approaches indicated an MID range of 9.8–11.7. Taken together, the MID metrics indicate that PCL‐5 change scores of 9–12 likely reflect real change in PTSD symptoms and indicate at least an MID for patients, whereas PCL‐5 change scores of 5 or less likely are not reliable. These findings can help inform clinicians using the PCL‐5 in similar populations to track patient responses to treatment and help researchers interpret PCL‐5 score changes in clinical trials.
Abstract
Background
Stepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems. However, to generate definitive evidence, a ...correct sample size calculation is crucial to ensure such studies are properly powered. The seminal work of Hussey and Hughes (Contemp Clin Trials 28(2):182–91, 2004) provides an analytical formula for power calculations with normal outcomes using a linear model and simple random effects. However, minimal development and evaluation have been done for power calculation with non-normal outcomes on their natural scale (e.g., logit, log). For example, binary endpoints are common, and logistic regression is the natural multilevel model for such clustered data.
Methods
We propose a power calculation formula for SWD with either normal or non-normal outcomes in the context of generalized linear mixed models by adopting the Laplace approximation detailed in Breslow and Clayton (J Am Stat Assoc 88(421):9–25, 1993) to obtain the covariance matrix of the estimated parameters.
Results
We compare the performance of our proposed method with simulation-based sample size calculation and demonstrate its use on a study of patient-delivered partner therapy for STI treatment and a study that assesses the impact of providing additional benchmark prevalence information in a radiologic imaging report. To facilitate adoption of our methods we also provide a function embedded in the R package “swCRTdesign” for sample size and power calculation for multilevel stepped-wedge designs.
Conclusions
Our method requires minimal computational power. Therefore, the proposed procedure facilitates rapid dynamic updates of sample size calculations and can be used to explore a wide range of design options or assumptions.
Background
To determine the prevalence and severity of acute kidney injury (AKI) at different time frames in relation to gestational age (GA) and birthweight (BW) in extremely low gestational age ...neonates (ELGAN). Our hypothesis is that ELGAN with lower GA and lower BW have higher AKI rates.
Methods
A total of 923 ELGAN enrolled in the Preterm Erythropoietin Neuroprotection Trial were evaluated from birth until death or hospital discharge. AKI was defined according to kidney disease: improving global outcomes (KDIGO) definition from clinically-derived serum creatinine (SCr) measurements. Severe AKI was defined as stage 2 or higher.
Results
For the entire cohort, 351/923 (38.0%, CI = 34.8–41.3%) had at least one episode of stage 1 or higher AKI and 168/923 (18.2%, CI = 15.7–20.7%) had at least one episode of severe (stage 2 or higher) AKI. The prevalence of AKI stage 1 or higher for the entire cohort during the early (days 3–7), middle (days 8–14), and late follow-up period (after day 14) was 112/923 (12.1%, CI = 10.0–14.3%), 142/891 (15.9%, CI = 13.5–18.4%), and 249/875 (28.5%, CI = 25.4–31.5%), respectively. The rates of severe AKI during the hospital course were 27.8%, 21.9%, 13.6%, and 9.4% for the 24-, 25-, 26-, and 27-week GA groups, respectively. AKI rates were significantly higher with decreasing GA and decreasing BW for stated time trends (all
p
< 0.01 using tests for trend).
Conclusions
AKI is relatively common in ELGAN during their initial hospital course and is associated with lower GA and BW.
Mixed models are commonly used to analyze stepped wedge trials (SWTs) to account for clustering and repeated measures on clusters. One critical issue researchers face is whether to include a random ...time effect or a random treatment effect. When the wrong model is chosen, inference on the treatment effect may be invalid. We explore asymptotic and finite‐sample convergence of variance component estimates when the model is misspecified and how misspecification affects the estimated variance of the treatment effect. For asymptotic results, we rely on analytical solutions rather than simulation studies, which allow us to succinctly describe the convergence of misspecified estimates, even though there are multiple roots for each misspecified model. We found that both direction and magnitude of the bias associated with model‐based standard errors depends on the study design and magnitude of the true variance components. We identify some scenarios in which choosing the wrong random effect has a large impact on model‐based inference. However, many trends depend on trial design and assumptions about the true correlation structure, so we provide tools for researchers to investigate specific scenarios of interest. We use data from an SWT on disinvesting from weekend services in hospital wards to demonstrate how these results can be applied as a sensitivity analysis, which quantifies the impact of misspecification under a variety of settings and directly compares the potential consequences of different modeling choices. Our results will provide guidance for prespecified model choices and supplement sensitivity analyses to inform confidence in the validity of results.
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