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Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. In addition, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this article, we derive bias formulas for proximal inference estimators under a linear structural equation model. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators.

Background: Women with previous pre-eclampsia are at an increased risk of developing recurrent pre-eclampsia. Intervention with low dose aspirin had been recommended to reduce the incidence of recurrent pre-eclampsia. However, the association between interventions and maternal and neonatal outcomes in subsequent pregnancies in women with previous pre-eclampsia has not been fully studied.Methods: In this prospective study, a total of 41 patients with previous pre-eclampsia received low dose aspirin and active management (including psychological and physiological intervention), between 10 to 28 weeks until 32 to 34 weeks in our regional referral hospital. The recurrence of pre-eclampsia, and maternal and neonatal outcomes in this pregnancy were analyzed and compared to our previous study which reported a 60% recurrence of pre-eclampsia in our regional referral hospital.Results: Thirteen women with previous pre-eclampsia developed recurrent pre-eclampsia. The time of onset or severity of pre-eclampsia in the previous pregnancy was not associated with the incidence of recurrent pre-eclampsia. The time of onset of previous pre-eclampsia was also not associated with the time of onset in subsequent pre-eclampsia. However, the number of severe recurrent pre-eclampsia was significantly reduced, compared to their first pregnancies. The number of SGA and stillbirth/neonatal death was also significantly reduced in recurrent pre-eclampsia that was actively managed, compared to their first pregnancies.Conclusion: Despite the small sample size included in this study, our study demonstrates that active obstetric management reduces the incidence of recurrent pre-eclampsia, compared to our previous study, and reduces the severity of recurrent pre-eclampsia. It also improves neonatal outcomes in recurrent pre-eclampsia. However, because of no controls in this study, our findings need to confirmed by a case-control or randomized clinical trial study.

Introduction: Neuromuscular blockade (NMB) in ventilated patients may cause benefit or harm. We applied "incremental interventions" to determine the impact of altering NMB initiation aggressiveness., Methods: Retrospective cohort study of ventilated patients with PaO2/FiO2 ratio < 150 mmHg and PEEP≥ 8cmH2O from the Medical Information Mart of Intensive Care IV database (MIMIC-IV version 1.0) estimating the effect of incremental interventions on in-hospital mortality and ventilator-free days, modifying hourly propensity for NMB initiation to be aggressive or conservative relative to usual care, adjusting for confounding with inverse probability weighting., Results: 5221 patients were included (13.3% initiated on NMB). Incremental interventions estimated a strong effect on NMB usage: 5-fold higher hourly odds of initiation increased usage to 36.5% (CI = [34.3%,38.7%]) and 5-fold lower odds decreased usage to 3.8% (CI = [3.3%,4.3%]). Aggressive and conservative strategies demonstrated a U-shaped mortality relationship. 5-fold higher or lower propensity increased in-hospital mortality by 2.6% (0.95 CI = [1.5%,3.7%]) or 1.3% (0.95 CI = [0.1%,2.5%]) respectively. In secondary analysis of a healthier patient cohort, results were similar, however conservative strategies also improved ventilator-free days., Interpretation: Aggressive or conservative initiation of NMB may worsen mortality. In healthier populations, marginally conservative NMB initiation strategies may lead to increased ventilator free days with minimal impact on mortality., Competing Interests: Declaration of competing interest Funding was also provided by the MIT-IBM Watson AI Lab. LL was in part funded by NIH grants R01 EB017205 and 1U01TR003528-01A1. EBK and DT having received lecturing fees for educational talks from Hamilton Medical Inc. ZS was primarily employed by IBM Research during some of the work., (Copyright © 2024 Elsevier Inc. All rights reserved.)

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Most studies examining factors associated with pediatric influenza (flu) and coronavirus disease (COVID-19) vaccination uptake focus on parental demographics. We examined whether the childhood cultural health environment (CHE) of parents (measured by self-reported regular attendance at doctor and dentist visits during childhood) was associated with flu and COVID-19 vaccination of their children. Using 2023 survey data from 397 US parents and causal inference methods, we estimated the average causal effect of parental CHE on flu vaccination rates (0.16 [95 % confidence interval: 0.06,0.27]) and COVID-19 (0.14 [95 % confidence interval: 0.04,0.24]), indicating that if all parents had attended regular doctor/dentist visits as children, flu and COVID-19 vaccination rates in children would be 16 % and 14 % higher, respectively, than if none had. Our findings suggest that early life exposure to medical and dental care has significant and lasting effects on the health of individuals and families., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier India Pvt Ltd. Published by Elsevier Ltd.. All rights reserved.)

The occupational health burden and mechanisms that link gig work to health are understudied. We described injury and assault prevalence among food delivery gig workers in New York City (NYC) and assessed the effect of job dependence on injury and assault through work-related mechanisms and across transportation modes (electric bike and moped versus car). Data were collected through a 2022 survey commissioned by the NYC Department of Consumer and Worker Protection among delivery gig workers between October and December 2021 in NYC. We used modified Poisson regression models to estimate the adjusted prevalence rate ratio associations between job dependence and injury and assault. Of 1650 respondents, 66.9% reported that food delivery gig work was their main or only job (i.e., fully dependent). About 21.9% and 20.8% of respondents reported being injured and assaulted, respectively. Injury and assault were more than twice as prevalent among two-wheeled drivers, in comparison to car users. Fully dependent respondents had a 1.61 (95% confidence interval (CI) 1.20, 2.16) and a 1.36 (95% CI 1.03, 1.80) times greater prevalence of injury and assault, respectively, than partially dependent respondents after adjusting for age, sex, race and ethnicity, language, employment length, transportation mode, and weekly work hours. These findings suggest that fully dependent food delivery gig workers, especially two-wheeled riders, are highly vulnerable to the negative consequences of working conditions under algorithmic management by the platforms. Improvements to food delivery gig worker health and safety are urgently needed, and company narratives surrounding worker autonomy and flexibility need to be revisited., (© 2024. The Author(s).)

Background: The optimal approach for transitioning from strict lung protective ventilation to support modes of ventilation when patients determine their own respiratory rate and tidal volume remains unclear. While aggressive liberation from lung protective settings could expedite extubation and prevent harm from prolonged ventilation and sedation, conservative liberation could prevent lung injury from spontaneous breathing., Research Question: Should physicians take a more aggressive or conservative approach to liberation?, Methods: Retrospective cohort study of mechanically ventilated patients from the Medical Information Mart for Intensive Care IV database (MIMIC-IV version 1.0) estimating effects of incremental interventions modifying the propensity for liberation to be more aggressive or conservative relative to usual care, with adjustment for confounding via inverse probability weighting. Outcomes included in-hospital mortality, ventilator free days, and ICU free days. Analysis was performed on the entire cohort as well as subgroups differentiated by PaO2/FiO2 ratio, and SOFA., Results: 7433 patients were included. Strategies multiplying the odds of a first liberation relative to usual care at each hour had a large impact on time to first liberation attempt (43 h under usual care, 24 h (0.95 CI = [23,25]) with an aggressive strategy doubling liberation odds, and 74 h (0.95 CI = [69,78]) under a conservative strategy halving liberation odds). In the full cohort, we estimated aggressive liberation increased ICU-free days by 0.9 days (0.95 CI = [0.8,1.0]) and ventilator free days by 0.82 days (0.95 CI = [0.67,0.97]), but had minimal effect on mortality (only a 0.3% (0.95 CI = [-0.2%,0.8%]) difference between minimum and maximum rates). With baseline SOFA≥ 12 (n = 1355), aggressive liberation moderately increased mortality (58.5% [0.95 CI = (55.7%,61.2%)]) compared with conservative liberation (55.1% [0.95 CI = (51.6%,58.6%)])., Interpretation: Aggressive liberation may improve ventilator free and ICU free days with little impact on mortality in patients with SOFA score < 12. Trials are needed., Competing Interests: Declaration of Competing Interest This work was conducted by EBK with the support of a KL2 award (an appointed KL2 award) from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award KL2 TR002542). Funding was also provided by the MIT-IBM Watson AI Lab and NIH grant 5R01 EB017205. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centers, or the National Institutes of Health. EBK and DT having received lecturing fees for educational talks from Hamilton Medical Inc. ZS was primarily employed by IBM Research during much of the work., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Adverse effects of excessive sedation in critically ill mechanically ventilated patients are well described. Although guidelines strongly recommend minimizing sedative use, additional agents are added as infusions, often empirically. The tradeoffs associated with such decisions remain unclear., Methods: To test the hypothesis that a pragmatic propofol-based sedation regimen with restricted polypharmacy (RP; ie, prohibits additional infusions unless a predefined propofol dosage threshold is exceeded) would increase coma-and ventilator-free days compared with usual care (UC), we performed a retrospective cohort study of adults admitted to intensive care units (ICUs) of a tertiary-level medical center who were mechanically ventilated, initiated on propofol infusion, and had >50% probability of need for continued ventilation for the next 24 hours. We compared RP to UC, adjusting for baseline and time-varying confounding (demographics, care unit, calendar time of admission, vitals, laboratories, other interventions such as vasopressors and fluids, and more) through inverse probability weighting in a target trial framework. Ventilator-free days and coma-free days within 30 days of intubation and in-hospital mortality were the outcomes of interest., Results: A total of 7974 patients were included in the analysis, of which 3765 followed the RP strategy until extubation. In the full cohort under UC, mean coma-free days were 23.5 (95% confidence interval [CI], [23.3-23.7]), mean ventilator-free days were 20.6 (95% CI, [20.4-20.8]), and the in-hospital mortality rate was 22.0% (95% CI, [21.2-22.8]). We estimated that an RP strategy would increase mean coma-free days by 1.0 days (95% CI, [0.7-1.3]) and ventilator-free days by 1.0 days (95% CI, [0.7-1.3]) relative to UC in our cohort. Our estimate of the confounding-adjusted association between RP and in-hospital mortality was uninformative (-0.5%; 95% CI, [-3.0 to 1.9])., Conclusions: Compared with UC, RP was associated with more coma- and ventilator-free days. Restricting addition of adjunct infusions to propofol may represent a viable strategy to reduce duration of coma and mechanical ventilation. These hypothesis-generating findings should be confirmed in a randomized control trial., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 International Anesthesia Research Society.)

The case-crossover design of Maclure is widely used in epidemiology and other fields to study causal effects of transient treatments on acute outcomes. However, its validity and causal interpretation have only been justified under informal conditions. Here, we place the design in a formal counterfactual framework for the first time. Doing so helps to clarify its assumptions and interpretation. In particular, when the treatment effect is nonnull, we identify a previously unnoticed bias arising from strong common causes of the outcome at different person-times. We analyze this bias and demonstrate its potential importance with simulations. We also use our derivation of the limit of the case-crossover estimator to analyze its sensitivity to treatment effect heterogeneity, a violation of one of the informal criteria for validity. The upshot of this work for practitioners is that, while the case-crossover design can be useful for testing the causal null hypothesis in the presence of baseline confounders, extra caution is warranted when using the case-crossover design for point estimation of causal effects., (© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

We thank the discussants for their insightful commentary and questions. In our rejoinder, we extend our analysis to additional settings, control strategies, and sources of bias relevant to how case-crossover is often used in practice, as suggested by multiple discussants. In particular, we consider: control exposures that follow occurrence of events, settings with shared exposure trajectories (which are common in case-crossover studies of effects of air pollution), bias due to non-transient treatment effects, removing bias due to time trends in treatment using control subjects, and extending our results to the continuous time setting. We also take the opportunity to clarify an easily misinterpreted comment we made about collapsibility, which we thank Andersen and Martinussen for highlighting. Throughout, in the spirit of the discussants. contributions, we iterate between general bias formulas and simulations and numerical studies from illustrative simplified scenarios to build insight., (© 2022 The International Biometric Society.)

Background: Polypharmacy of sedatives (PP) is a potentially modifiable, iatrogenic risk factor for ICU delirium. The extent to which sedative PP influenced development of high rates of delirium among critically ill COVID-19 patients is unknown. We tested the hypothesis that PP, defined as the use of four or more classes of intravenous agents, is a mediator in the causal pathway of mechanical ventilation and delirium., Methods: Retrospective cohort study of adults admitted with a primary diagnosis of RT-PCR+ for SARS-CoV2 to ICUs of a tertiary-level academic medical center between February 2020 and April 2021. Mediation analysis was conducted with bootstrap estimation to assess whether an association between mechanical ventilation and delirium was mediated by PP. Analyses were adjusted for potential confounders related to mechanical ventilation, mediator, and outcome, including age, gender, vasopressor use, median RASS scores, SOFA score within 24 h of admission, and maximum CRP levels., Results: A total of 212 patients were included in the analysis. Of total patients, 72.6%(154/212) of patients had delirium (CAM-ICU+) during ICU stay. 54.7%(116/212) patients received PP. Mechanical ventilation (OR 3.81 [1.16-12.52]) and PP (OR 7.38 [2.4-22.68]) were identified as risk factors for development of ICU delirium after adjusting for prespecified confounders. PP acts as a mediator in the causal pathway between mechanical ventilation and delirium. 39% (95% CI: 17%-94%) of the effect of mechanical ventilation on delirium was mediated through PP., Conclusion: PP mediates approximately 39% of the effect of mechanical ventilation on delirium, which is clinically and statistically significant. Studies should assess whether mitigating PP could lead to reduction in ICU delirium., Implication Statement: PP of sedatives (defined as use of four or more intravenous agents) mediates approximately 39% of the effect of mechanical ventilation on development of ICU delirium. Avoidance of sedative PP may represent a viable strategy for reduction of ICU delirium., (© 2022 Acta Anaesthesiologica Scandinavica Foundation.)

With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context., (© 2022 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Purpose: Driving pressure (ΔP) and mechanical power (MP) may be important mediators of lung injury in acute respiratory distress syndrome (ARDS) however there is little evidence for strategies directed at lowering these parameters. We applied predictive modeling to estimate the effects of modifying ventilator parameters on ΔP and MP., Methods: 2,622 ARDS patients (Berlin criteria) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV version1.0) admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center between 2008 and 2019 were included. Flexible confounding-adjusted regression models for time varying data were fit to estimate the effects of adjusting PEEP and tidal volume (V T ) on ΔP, and adjusting V T and respiratory rate (f) on MP., Results: Reduction in V T reduced ΔP and MP, with more pronounced effect on MP with lower compliance. Strategies reducing f, consistently increased MP (when V T was adjusted to maintain consistent minute ventilation). Adjustment of PEEP yielded a U-shaped effect on ΔP., Conclusions: This novel conditional modeling confirmed expected response patterns for ΔP, with the response to adjustments depending on patients' lung mechanics. Furthermore a V T -driven approach should be favored over a f -driven approach when aiming to reduce MP., (Copyright © 2022 by Daedalus Enterprises.)

The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into two steps. In the first step, the researcher selects some set of variables to adjust for. In the second step, the researcher implements a causal inference algorithm to adjust for the selected variables and estimate the average treatment effect. In this paper, we use a simulation study to explore the operating characteristics and robustness of state-of-the-art methods for step two (statistical adjustment for selected variables) when step one (variable selection) is performed in a realistically sub-optimal manner. More specifically, we study the robustness of a cross-fit machine learning based causal effect estimator to the presence of extraneous variables in the adjustment set. The take-away for practitioners is that there is value to, if possible, identifying a small sufficient adjustment set using subject matter knowledge even when using machine learning methods for adjustment., (©2021 AMIA - All rights reserved.)

Methods for estimating optimal treatment strategies typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must account for competition between individuals in resource usage. The problem of incorporating resource constraints into optimal treatment strategies has been solved for point exposures (1), that is, treatment strategies entailing a decision at just one time point. However, attempts to directly generalize the point exposure solution to dynamic time-varying treatment strategies run into complications. We sidestep these complications by targeting the optimal strategy within a clinically defined subclass. Our approach is to employ dynamic marginal structural models to estimate (counterfactual) resource usage under the class of candidate treatment strategies and solve a constrained optimization problem to choose the optimal strategy for which expected resource usage is within acceptable limits. We apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration., (© 2021 John Wiley & Sons Ltd.)

Background: Fluid overload is associated with poor outcomes. Clinicians might be reluctant to initiate diuretic therapy for patients with recent vasopressor use. We estimated the effect on 30-day mortality of withholding or delaying diuretics after vasopressor use in patients with probable fluid overload., Methods: This was a retrospective cohort study of adults admitted to ICUs of an academic medical centre between 2008 and 2012. Using a database of time-stamped patient records, we followed individuals from the time they first required vasopressor support and had >5 L cumulative positive fluid balance (plus additional inclusion/exclusion criteria). We compared mortality under usual care (the mix of care actually delivered in the cohort) and treatment strategies restricting diuretic initiation during and for various durations after vasopressor use. We adjusted for baseline and time-varying confounding via inverse probability weighting., Results: The study included 1501 patients, and the observed 30-day mortality rate was 11%. After adjusting for observed confounders, withholding diuretics for at least 24 h after stopping most recent vasopressor use was estimated to increase 30-day mortality rate by 2.2% (95% confidence interval [CI], 0.9-3.6%) compared with usual care. Data were consistent with moderate harm or slight benefit from withholding diuretic initiation only during concomitant vasopressor use; the estimated mortality rate increased by 0.5% (95% CI, -0.2% to 1.1%)., Conclusions: Withholding diuretic initiation after vasopressor use in patients with high cumulative positive balance (>5 L) was estimated to increase 30-day mortality. These findings are hypothesis generating and should be tested in a clinical trial., (Copyright © 2021 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches., (©2021 AMIA - All rights reserved.)

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks) applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output., (©2020 AMIA - All rights reserved.)

Many adverse drug reactions (ADRs) are caused by drug-drug interactions (DDIs), meaning they arise from concurrent use of multiple medications. Detecting DDIs using observational data has at least three major challenges: (1) The number of potential DDIs is astronomical; (2) Associations between drugs and ADRs may not be causal due to observed or unobserved confounding; and (3) Frequently co-prescribed drug pairs that each independently cause an ADR do not necessarily causally interact, where causal interaction means that at least some patients would only experience the ADR if they take both drugs. We address (1) through data mining algorithms pre-filtering potential interactions, and (2) and (3) by fitting causal interaction models adjusting for observed confounders and conducting sensitivity analyses for unobserved confounding. We rank candidate DDIs robust to unobserved confounding more likely to be real. Our rigorous approach produces far fewer false positives than past applications that ignored (2) and (3)., (©2020 AMIA - All rights reserved.)

Objective: In septic patients, multiple retrospective studies show an association between large volumes of fluids administered in the first 24 h and mortality, suggesting a benefit to fluid restrictive strategies. However, these studies do not directly estimate the causal effects of fluid-restrictive strategies, nor do their analyses properly adjust for time-varying confounding by indication. In this study, we used causal inference techniques to estimate mortality outcomes that would result from imposing a range of arbitrary limits ("caps") on fluid volume administration during the first 24 h of intensive care unit (ICU) care., Design: Retrospective cohort study SETTING: ICUs at the Beth Israel Deaconess Medical Center, 2008-2012 PATIENTS: One thousand six hundred thirty-nine septic patients (defined by Sepsis-3 criteria) 18 years and older, admitted to the ICU from the emergency department (ED), who received less than 4 L fluids administered prior to ICU admission MEASUREMENTS AND MAIN RESULTS: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III). We employed a dynamic Marginal Structural Model fit by inverse probability of treatment weighting to obtain confounding adjusted estimates of mortality rates that would have been observed had fluid resuscitation volume caps between 4 L-12 L been imposed on the population. The 30-day mortality in our cohort was 17%. We estimated that caps between 6 and 10 L on 24 h fluid volume would have reduced 30-day mortality by - 0.6 to - 1.0%, with the greatest reduction at 8 L (- 1.0% mortality, 95% CI [- 1.6%, - 0.3%])., Conclusions: We found that 30-day mortality would have likely decreased relative to observed mortality under current practice if these patients had been subject to "caps" on the total volume of fluid administered between 6 and 10 L, with the greatest reduction in mortality rate at 8 L.

Huntington's Disease (HD) is a neurodegenerative disorder with serious motor, cognitive, and behavioral symptoms. Chorea, a motor symptom of HD characterized by abrupt involuntary movements, is typically treated with tetrabenazine or certain off-label antipsychotics. Clinical trial evidence about the impact of these drugs in the HD population is scant. However, multiple observational HD registries have recently been used with success to model HD progression 1,2 and provide an opportunity to obtain effect estimates in the absence of clinical trials. We use a dataset integrated from four large-scale HD registries to generate evidence on the efficacy of chorea treatments for chorea as well as their impact on other aspects of HD progression. Clinical conclusions are meant only to illustrate our methodological approach. We employ parametric G-computation for causal inference to adjust for confounding and accommodate irregular visits and treatment patterns. We fit Bayesian hierarchical models to the results of multiple related analyses to share strength across studies and handle multiple comparisons concerns.

Background and Purpose: Thrombolysis rates among minor stroke (MS) patients are increasing because of increased recognition of disability in this group and guideline changes regarding treatment indications. We examined the association of delays in door-to-needle (DTN) time with stroke severity., Methods: We performed a retrospective analysis of all stroke patients who received intravenous tissue-type plasminogen activator in our emergency department between July 1, 2011, and February 29, 2016. Baseline characteristics and DTN were compared between MS (National Institutes of Health Stroke Scale score ≤5) and nonminor strokes (National Institutes of Health Stroke Scale score >5). We applied causal inference methodology to estimate the magnitude and mechanisms of the causal effect of stroke severity on DTN., Results: Of 315 patients, 133 patients (42.2%) had National Institutes of Health Stroke Scale score ≤5. Median DTN was longer in MS than nonminor strokes (58 versus 53 minutes; P =0.01); fewer MS patients had DTN ≤45 minutes (19.5% versus 32.4%; P =0.01). MS patients were less likely to use emergency medical services (EMS; 62.6% versus 89.6%, P <0.01) and to receive EMS prenotification (43.9% versus 72.4%; P <0.01). Causal analyses estimated MS increased average DTN by 6 minutes, partly through mode of arrival. EMS prenotification decreased average DTN by 10 minutes in MS patients., Conclusions: MS had longer DTN times, an effect partly explained by patterns of EMS prenotification. Interventions to improve EMS recognition of MS may accelerate care., (© 2017 American Heart Association, Inc.)

Objective: An individual's birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions., Methods: We developed a hypothesis-free method that minimizes publication and disease selection biases by systematically investigating disease-birth month patterns across all conditions. Our dataset includes 1 749 400 individuals with records at New York-Presbyterian/Columbia University Medical Center born between 1900 and 2000 inclusive. We modeled associations between birth month and 1688 diseases using logistic regression. Significance was tested using a chi-squared test with multiplicity correction., Results: We found 55 diseases that were significantly dependent on birth month. Of these 19 were previously reported in the literature (P < .001), 20 were for conditions with close relationships to those reported, and 16 were previously unreported. We found distinct incidence patterns across disease categories., Conclusions: Lifetime disease risk is affected by birth month. Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)