The Association of Fever with Total Mechanical Ventilation Time in Critically Ill Patients
Dong Won ParkMoritoki EgiMasaji NishimuraYoujin ChangGee Young SuhChae‐Man LimJae Yeol KimKeiichi TadaKoichi MatsuoShinhiro TakedaRyosuke TsurutaTakeshi YokoyamaSeon‐Ok KimYounsuck Koh
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This research aims to investigate the impact of fever on total mechanical ventilation time (TVT) in critically ill patients. Subgroup analysis was conducted using a previous prospective, multicenter observational study. We included mechanically ventilated patients for more than 24 hours from 10 Korean and 15 Japanese intensive care units (ICU), and recorded maximal body temperature under the support of mechanical ventilation (MAX(MV)). To assess the independent association of MAX(MV) with TVT, we used propensity-matched analysis in a total of 769 survived patients with medical or surgical admission, separately. Together with multiple linear regression analysis to evaluate the association between the severity of fever and TVT, the effect of MAX(MV) on ventilator-free days was also observed by quantile regression analysis in all subjects including non-survivors. After propensity score matching, a MAX(MV) ≥ 37.5°C was significantly associated with longer mean TVT by 5.4 days in medical admission, and by 1.2 days in surgical admission, compared to those with MAX(MV) of 36.5°C to 37.4°C. In multivariate linear regression analysis, patients with three categories of fever (MAX(MV) of 37.5°C to 38.4°C, 38.5°C to 39.4°C, and ≥ 39.5°C) sustained a significantly longer duration of TVT than those with normal range of MAX(MV) in both categories of ICU admission. A significant association between MAX(MV) and mechanical ventilator-free days was also observed in all enrolled subjects. Fever may be a detrimental factor to prolong TVT in mechanically ventilated patients. These findings suggest that fever in mechanically ventilated patients might be associated with worse mechanical ventilation outcome.Keywords:
Subgroup analysis
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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KEY POINT Propensity score matching can reduce confounding in observational research by creating two groups that are well balanced with respect to baseline characteristics.Related Article, see p 1618 In this issue of Anesthesia & Analgesia, Miyao and Kotake1 report results of an observational study on the association between hydroxyethyl starch (HES) and renal morbidity in surgical patients. The authors used propensity scores to match 8823 patients who received HES to 8823 controls who had not received HES. In observational research, the treatment groups are not randomly assigned. Rather, treatment assignment is typically affected by individual patient characteristics and provider preference or choice. Therefore, patients with a certain exposure (eg, HES administration) usually systemically differ from patients without the exposure, and such differences may confound the relationship with the outcome (eg, renal morbidity).2 Any direct comparison between treatment groups is therefore likely biased, and statistical methods to reduce confounding are thus required when analyzing observational outcome data. Traditionally, multivariable regression has been used for this purpose.3 More recently, methods based on propensity scores have become popular alternatives.4 This Statistical Minute focuses on propensity score matching (PSM) as applied by Miyao and Kotake.1 Alternatively, and at least equally useful, propensity scores can also be used to weight, rather than to match, observations for subsequent analyses (inverse probability of treatment weighting [IPTW]). Other propensity score–based approaches, such as using propensity scores for stratification or as covariates in regression analyses, are inferior in reducing confounding compared to PSM and IPTW.4 The propensity score is the probability of exposure to a particular treatment given baseline covariates.4 As treatment is usually a binary variable (eg, patient received HES versus did not receive HES), the propensity score is commonly estimated using logistic regression, in which the treatment assignment is the outcome variable, and in which baseline covariates are the independent variables. In PSM, patients who received the treatment are matched to one or several control patients with "similar" propensity scores. Technically, matching is often not performed on the propensity score itself, but on the logit of the propensity score (natural logarithm of the odds of treatment), and a maximum allowable distance (caliper) of 0.2 standard deviations is commonly recommended.4 This results—on average—in groups with comparable covariate patterns; in other words, there is no systematic difference and so these covariates can no longer confound the between-group comparison. This is akin to the situation in a randomized trial. However, randomization controls for both observed and unobserved confounders, while propensity scores can only balance observed confounders. Thus, residual bias is still possible. After matching, researchers should calculate standardized differences (differences in means or proportions divided by the pooled standard deviation) between the matched groups to assess whether the matching was successful in balancing baseline covariates. Generally speaking, absolute standardized differences of <0.1 indicate adequate balance. When baseline covariates are well balanced, the outcome variable(s) can be compared between the 2 groups using standard statistical techniques, including simple hypothesis tests, regression techniques, or survival analysis.3,5 However, there is a considerable debate in statistical literature about whether the matched design must be accounted for in the analysis (eg, whether to use a paired or unpaired t test to compare a continuous outcome).4Figure.: Excerpt from Table 3 and Table 4 in Miyao and Kotake.1 Table 3 shows the improvement in balance among covariates between the groups (only 2 of 36 covariates shown in this excerpt), with a marked reduction of the standardized differences (shown as percent; 1.6% ≙ 0.016) after matching. Table 4 shows the estimated risk of AKI after HES administration before and after PS matching. Note that the unadjusted analysis (crude odds ratio) substantially overestimated the relationship between HES and AKI compared to the more valid PSM analysis (adjusted odds ratio). AKI indicates acute kidney injury; CI, confidence interval; HES, hydroxyethyl starch; IQR, interquartile range; PS, propensity score.
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Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being hospitalized with a diagnosis of acute myocardial infarction. The exposure of interest was receipt of smoking cessation counseling prior to hospital discharge and the outcome was mortality with 3 years of hospital discharge. We illustrate the following concepts: first, how to specify the propensity score model; second, how to match treated and untreated participants on the propensity score; third, how to compare the similarity of baseline characteristics between treated and untreated participants after stratifying on the propensity score, in a sample matched on the propensity score, or in a sample weighted by the inverse probability of treatment; fourth, how to estimate the effect of treatment on outcomes when using propensity score matching, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, or covariate adjustment using the propensity score. Finally, we compare the results of the propensity score analyses with those obtained using conventional regression adjustment.
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Propensity score–based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We describe 4 different ways of using the propensity score: matching on the propensity score, inverse probability of treatment weighting using the propensity score, stratification on the propensity score, and covariate adjustment on the propensity score (with a focus on the first 2). We provide recommendations for the use and reporting of propensity score methods for the conduct of observational studies in neurologic research.
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TO THE EDITOR: An article by Kim et al 1 was recently published comparingthesurgicalandoncologicoutcomesbetweenlaparoscopic andopengastrectomyforgastriccancer.Theauthorsusedpropensity scorematchingtominimizetheselectionbiasinaretrospectivecohort of patients from multiple centers. Kim et al tried to adjust for confounding factors by using propensity score matching, and they concluded that laparoscopic gastrectomy for gastric cancer seems comparable with open surgery. However, we have several significant concernsaboutthecase-matchingmethodthatwasusedinthisstudy. The propensity score analysis was developed to minimize the differences in patients’ covariates, which could become confounding factors in the examination of treatment effects in observational stud
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