Correction to: Impact on mortality of prompt admission to critical care for deteriorating ward patients: an instrumental variable analysis using critical care bed strain.
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SUBMITTER: Harris S
PROVIDER: S-EPMC6006216 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
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Correction to: Impact on mortality of prompt admission to critical care for deteriorating ward patients: an instrumental variable analysis using critical care bed strain.
Harris Steve S Singer Mervyn M Sanderson Colin C Grieve Richard R Harrison David D Rowan Kathryn K
Intensive care medicine 20180501 5
This article was originally published under a CC BY-NC 4.0 license, but has now been made available under a CC BY 4.0 license. The PDF and HTML versions of the paper have been modified accordingly. ...[more]
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Project description:PURPOSE:To estimate the effect of prompt admission to critical care on mortality for deteriorating ward patients. METHODS:We performed a prospective cohort study of consecutive ward patients assessed for critical care. Prompt admissions (within 4 h of assessment) were compared to a 'watchful waiting' cohort. We used critical care strain (bed occupancy) as a natural randomisation event that would predict prompt transfer to critical care. Strain was classified as low, medium or high (2+, 1 or 0 empty beds). This instrumental variable (IV) analysis was repeated for the subgroup of referrals with a recommendation for critical care once assessed. Risk-adjusted 90-day survival models were also constructed. RESULTS:A total of 12,380 patients from 48 hospitals were available for analysis. There were 2411 (19%) prompt admissions (median delay 1 h, IQR 1-2) and 9969 (81%) controls; 1990 (20%) controls were admitted later (median delay 11 h, IQR 6-26). Prompt admissions were less frequent (p < 0.0001) as strain increased from low (22%), to medium (15%) to high (9%); the median delay to admission was 3, 4 and 5 h respectively. In the IV analysis, prompt admission reduced 90-day mortality by 7.4% (95% CI 1.7-18.5%, p = 0.117) overall, and 16.2% (95% CI 1.1-31.3%, p = 0.036) for those recommended for critical care. In the risk-adjust survival model, 90-day mortality was similar. CONCLUSION:After allowing for unobserved prognostic differences between the groups, we find that prompt admission to critical care leads to lower 90-day mortality for patients assessed and recommended to critical care.
| S-EPMC6006241 | biostudies-literature
Project description:ObjectiveExamine the endogenous relationship between uncompensated care and hospital patient experience scores.Data sources/study settingThe Hospital Consumer Assessment of Healthcare Providers and Systems Survey, CMS Healthcare Cost Report Information System, and the US Census Bureau.Study designThe exogenous change in uncompensated care caused by the 2014 Medicaid expansion was exploited to measure the effect of uncompensated care on patient experience scores using a 2SLS regression with instrumental variables approach.Data collection/extraction methodsU.S. general, short-term hospitals whose DSH status remained constant and had nonmissing data for 2011-2015, which totaled 969 unique hospitals per year.Principal findingsThe effect of uncompensated care on patient experience was in the predicted direction, with three of the 10 measures being statistically significant. A one percentage point increase in uncompensated care costs resulted in a 0.25-0.50 percentage point decrease in select patient experience scores.ConclusionsResults indicate a weak relationship between uncompensated care and patient experience scores, as a reduction in uncompensated care is related to quality improvement for some hospitals. These findings have implications for hospitals as they navigate changing reimbursement structures and policy makers considering changes to Obama-era health care reforms.
| S-EPMC6505421 | biostudies-literature
Project description:ObjectiveTo examine the effects of potentially inappropriate medication (PIM) use on health care outcomes in elderly individuals using an instrumental variable (IV) approach.Data sources/study settingRepresentative claim data from the universal health insurance program in Taiwan from 2007 to 2010.Study designWe employed a panel study design to examine the relationship between PIM and hospitalization. We applied both the naive generalized estimating equation (GEE) model, which controlled for the observed patient and hospital characteristics, and the two-stage residual inclusion (2SRI) GEE model, which further accounted for the unobserved confounding factors. The PIM prescription rate of the physician most frequently visited by each patient was used as the IV.Principal findingsThe naive GEE models indicated that patient PIM use was associated with a higher likelihood of hospitalization (odds ratio [OR], 1.399; 95 percent confidence interval [CI], 1.363-1.435). Using the physician PIM prescribing rate as an IV, we identified a stronger significant association between PIM and hospitalization (OR, 1.990; 95 percent CI, 1.647-2.403).ConclusionsPIM use is associated with increased hospitalization in elderly individuals. Adjusting for unobserved confounders is needed to obtain unbiased estimates of the relationship between PIM and health care outcomes.
| S-EPMC4946035 | biostudies-literature
Project description:ImportanceIt is unknown which deteriorating ward patients benefit from intensive care unit (ICU) transfer.ObjectivesTo use an instrumental variable (IV) method that assesses heterogeneity and to evaluate estimates of person-centered treatment effects of ICU transfer and 28-day hospital mortality by age and illness severity.Design, setting, and participantsAn analysis of a prospective cohort study from November 1, 2010, to December 31, 2011. The dates of this analysis were June 1, 2017, to June 30, 2018. The setting was a multicenter study of 49 UK National Health Service hospitals. Participants were 9192 deteriorating ward patients assessed for ICU transfer (4596 matched pairs). The study matched on baseline characteristics to strengthen the IV and to balance observed confounders between the comparison groups.ExposuresTransfer to the ICU or continued care on general wards.Main outcomes and measuresMortality at 28 days (primary outcome) and 90 days. To address unobserved confounding, ICU bed availability was the IV for whether or not a patient was transferred. The study used the IV approach to evaluate estimates of treatment effect of ICU transfer and mortality according to age and physiological severity alone and in combination.ResultsBoth comparison groups included 4596 patients. In the group assessed with "many" ICU beds available (median, 7), 52.8% were male, and the mean (SD) age was 65.2 (17.7) years; in the group assessed with "few" ICU beds available (median, 2), 53.3% were male, and the mean (SD) age was 65.0 (17.3) years. The overall 28-day mortality estimates were 23.2% (2090 predicted deaths) if all of the matched patients were transferred vs 28.1% (2534 predicted deaths) if none of the matched patients were transferred, an estimated risk difference of -4.9% (95% CI, -26.4% to 16.6%). The estimated effects of ICU transfer differed by age and by physiological severity according to the National Early Warning Score (NEWS): the absolute risk differences in 28-day mortality after ICU transfer ranged from 7.7% (95% CI, -5.5% to 21.0%) for ages 18 to 23 years to -5.0% (95% CI -26.5% to 16.6%) for age 78 to 83 years and ranged from 3.7% (95% CI, -12.1% to 19.5%) for NEWS of 0 to -25.4% (95% CI, -50.6% to -0.2%) for NEWS of 19. The absolute risk differences for elderly patients (≥75 years) were -11.6% (95% CI, -39.0% to 15.8%) for those with high NEWS (>6), -4.8% (95% CI, -30.5% to 20.9%) for those with moderate NEWS (5-6), and -1.0% (95% CI, -24.8% to 22.8%) for those with low NEWS (<5). The corresponding estimates for subgroups of younger patients (<75 years) were -8.4% (95% CI, -31.0% to 14.1%), -2.1% (95% CI, -21.1% to 16.9%), and 1.4% (95% CI, -14.5% to 17.4%).Conclusions and relevanceThis study using a this person-centered IV approach found that the benefits of ICU care may increase among patients at high levels of baseline physiological severity across different age groups, especially among elderly patients.
| S-EPMC6484590 | biostudies-literature
Project description:BackgroundDelayed response to clinical deterioration of ward patients is common.MethodsWe performed a prospective before-and-after study in all patients admitted to two clinical ward areas in a district general hospital in the UK. We examined the effect on clinical outcomes of deploying an electronic automated advisory vital signs monitoring and notification system, which relayed abnormal vital signs to a rapid response team (RRT).ResultsWe studied 2139 patients before (control) and 2263 after the intervention. During the intervention the number of RRT notifications increased from 405 to 524 (p = 0.001) with more notifications triggering fluid therapy, bronchodilators and antibiotics. Moreover, despite an increase in the number of patients with "do not attempt resuscitation" orders (from 99 to 135; p = 0.047), mortality decreased from 173 to 147 (p = 0.042) patients and cardiac arrests decreased from 14 to 2 events (p = 0.002). Finally, the severity of illness in patients admitted to the ICU was reduced (mean Acute Physiology and Chronic Health Evaluation II score: 26 (SD 9) vs. 18 (SD 8)), as was their mortality (from 45% to 24%; p = 0.04).ConclusionsDeployment of an electronic automated advisory vital signs monitoring and notification system to signal clinical deterioration in ward patients was associated with significant improvements in key patient-centered clinical outcomes.Trial registrationClinicalTrials.gov, NCT01692847 . Registered on 21 September 2012.
| S-EPMC5348741 | biostudies-literature
Project description:ObjectivesPatients undergoing lung resection are at risk of perioperative complications, many of which necessitate unplanned critical care unit admission in the postoperative period. We sought to characterize this population, providing an up-to-date estimate of the incidence of unplanned critical care admission, and to assess critical care and hospital stay, resource use, mortality, and outcomes.MethodsA multicenter retrospective cohort study of patients undergoing lung resection in participating UK hospitals over 2 years. A comprehensive dataset was recorded for each critical care admission (defined as the need for intubation and mechanical ventilation and/or renal replacement therapy), in addition to a simplified dataset in all patients undergoing lung resection during the study period. Multivariable regression analysis was used to identify factors independently associated with critical care outcome.ResultsA total of 11,208 patients underwent lung resection in 16 collaborating centers during the study period, and 253 patients (2.3%) required unplanned critical care admission with a median duration of stay of 13 (4-28) days. The predominant indication for admission was respiratory failure (68.1%), with 77.8% of patients admitted during the first 7 days following surgery. Eighty-seven (34.4%) died in critical care. On multivariable regression, only the diagnosis of right ventricular dysfunction and the need for both mechanical ventilation and renal-replacement therapy were independently associated with critical care survival; this model, however, had poor predictive value.ConclusionsAlthough resource-intensive and subject to prolonged stay, following unplanned admission to critical care after lung resection outcomes are good for many patients; 65.6% of patients survived to hospital discharge, and 62.7% were discharged to their own home.
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Project description:In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.
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Project description:Instrumental variables (IV) are used to draw causal conclusions about the effect of exposure E on outcome Y in the presence of unmeasured confounders. IV assumptions have been well described: (1) IV affects E; (2) IV affects Y only through E; (3) IV shares no common cause with Y. Even when these assumptions are met, biased effect estimates can result if selection bias allows a noncausal path from E to Y. We demonstrate the presence of bias in IV analyses on a sample from a simulated dataset, where selection into the sample was a collider on a noncausal path from E to Y. By applying inverse probability of selection weights, we were able to eliminate the selection bias. IV approaches may protect against unmeasured confounding but are not immune from selection bias. Inverse probability of selection weights used with IV approaches can minimize bias.
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Project description:Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.
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Project description:During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.
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