Optimal timing of delivery for pregnancies with prenatally diagnosed congenital diaphragmatic hernia: a propensity-score analysis using the inverse probability of treatment weighting

2021 ◽  
Author(s):  
Yoko Kawanishi ◽  
Masayuki Endo ◽  
Makoto Fujii ◽  
Tatsuo Masuda ◽  
Noriaki Usui ◽  
...  
Keyword(s):  
Propensity Score ◽  
Congenital Diaphragmatic Hernia ◽  
Diaphragmatic Hernia ◽  
Propensity Score Analysis ◽  
Optimal Timing ◽  
Inverse Probability ◽  
Score Analysis

scholarly journals Usefulness of β-lactam and Macrolide Combination Therapy for Treating Community-acquired Pneumonia Patients Hospitalized in the Intensive Care Unit: Propensity Score Analysis of a Prospective Cohort Study

2020 ◽  
Author(s):  
Akihiro Ito ◽  
Tadashi Ishida ◽  
Hiromasa Tachibana ◽  
Yosuke Nakanishi ◽  
Fumiaki Tokioka ◽  
...  
Keyword(s):  
Propensity Score ◽  
Combination Therapy ◽  
Odds Ratio ◽  
Propensity Score Analysis ◽  
Therapy Group ◽  
Community Acquired Pneumonia ◽  
Icu Mortality ◽  
Inverse Probability ◽  
Score Analysis ◽  
Severe Community Acquired Pneumonia

Abstract Background: Previous studies reported that β-lactam and macrolide combination therapy significantly improved outcomes for patients with severe community-acquired pneumonia hospitalized in the intensive care unit (ICU) compared with a non-macrolide regimen. However, whether β-lactam and macrolide therapy truly reduces mortality is controversial, because no randomized, controlled trials have been conducted. The aim of the present study was to evaluate the usefulness of β-lactam and macrolide combination therapy for severe community-acquired pneumonia patients hospitalized in the ICU compared with a non-macrolide β-lactam-containing regimen.Methods: A prospective, observational, cohort study of hospitalized pneumonia patients was performed. Hospitalized severe community-acquired pneumonia patients admitted to the ICU within 24 hours between October 2010 and October 2017 were included for analysis. The primary outcome was 30-day mortality, and secondary outcomes were 14-day mortality and ICU mortality. Inverse probability of treatment weighting analysis as a propensity score analysis was used to reduce biases, including six covariates: age, sex, C-reactive protein, albumin, Pneumonia Severity Index score, and APACHE II score.Results: A total of 78 patients were included. There were 48 patients in the non-macrolide-containing β-lactam therapy group, including β-lactam monotherapy and β-lactam and non-macrolide-containing combination therapy, and 30 patients in the macrolide combination therapy group. β-lactam and macrolide combination therapy significantly decreased 30-day mortality (16.7% vs. 43.8%; P=0.015) and 14-day mortality (6.7% vs. 31.3%; P=0.020), but not ICU mortality (10% vs 27.1%, P=0.08) compared with non-macrolide-containing β-lactam therapy. After adjusting by inverse probability of treatment weighting, macrolide combination therapy also decreased 30-day mortality (odds ratio, 0.29; 95%CI, 0.09-0.96; P=0.04) and 14-day mortality (odds ratio, 0.19; 95%CI, 0.04-0.92; P=0.04), but not ICU mortality (odds ratio, 0.34; 95%CI, 0.08-1.36; P=0.13).Conclusions: Combination therapy with β-lactam and macrolides significantly improved the prognosis of severe community-acquired pneumonia patients hospitalized in the ICU compared with a non-macrolide-containing β-lactam regimen on propensity score analysis.Trial registration: UMIN Clinical Trials Registry, UMIN000004353. Registered on 7 October 2010,

scholarly journals Propensity score analysis with partially observed covariates: How should multiple imputation be used?

2017 ◽  
Vol 28 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Clémence Leyrat ◽  
Shaun R Seaman ◽  
Ian R White ◽  
Ian Douglas ◽  
Liam Smeeth ◽  
...  
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Imputation Model ◽  
Complete Case ◽  
Inverse Probability ◽  
Score Analysis ◽  
Partially Observed

Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin’s rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin’s rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.

Efficacy of variceal embolisation in patients with TIPS implantation: a propensity score analysis

2018 ◽  
Vol 56 (01) ◽  
pp. E2-E89
Author(s):  
M Giesler ◽  
D Bettinger ◽  
M Rössle ◽  
R Thimme ◽  
M Schultheiss
Keyword(s):  
Propensity Score ◽  
Propensity Score Analysis ◽  
Score Analysis

scholarly journals Morbidity and mortality of lobectomy or pneumonectomy after neoadjuvant treatment: an analysis from the ESTS database

2019 ◽  
Author(s):  
Alessandro Brunelli ◽  
Gaetano Rocco ◽  
Zalan Szanto ◽  
Pascal Thomas ◽  
Pierre Emmanuel Falcoz
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Propensity Score ◽  
Propensity Score Analysis ◽  
Bronchopleural Fistula ◽  
Neoadjuvant Treatment ◽  
Mortality Rates ◽  
Induction Treatment ◽  
Score Analysis ◽  
Cardiopulmonary Complications ◽  
Lung Resections

Abstract OBJECTIVES To evaluate the postoperative complications and 30-day mortality rates associated with neoadjuvant chemotherapy before major anatomic lung resections registered in the European Society of Thoracic Surgeons (ESTS) database. METHODS Retrospective analysis on 52 982 anatomic lung resections registered in the ESTS database (July 2007–31 December 2017) (6587 pneumonectomies and 46 395 lobectomies); 5143 patients received neoadjuvant treatment (9.7%) (3993 chemotherapy alone and 1150 chemoradiotherapy). To adjust for possible confounders, a propensity case-matched analysis was performed. The postoperative outcomes (morbidity and 30-day mortality) of matched patients with and without induction treatment were compared. RESULTS 8.2% of all patients undergoing lobectomies and 20% of all patients undergoing pneumonectomies received induction treatment. Lobectomy analysis: propensity score analysis yielded 3824 pairs of patients with and without induction treatment. The incidence of cardiopulmonary complications was higher in the neoadjuvant group (626 patients, 16% vs 446 patients, 12%, P < 0.001), but 30-day mortality rates were similar (71 patients, 1.9% vs 75 patients, 2.0%, P = 0.73). The incidence of bronchopleural fistula and prolonged air leak >5 days were similar between the 2 groups (neoadjuvant: 0.5% vs 0.4%, P = 0.87; 9.2% vs 9.9%, P = 0.27). Pneumonectomy analysis: propensity score analysis yielded 1312 pairs of patients with and without induction treatment. The incidence of cardiopulmonary complications was higher in the treated patients compared to those without neoadjuvant treatment (neoadjuvant 275 cases, 21% vs 18%, P = 0.030). However, the 30-day mortality was similar between the matched groups (neoadjuvant 68 cases, 5.2% vs 5.3%, P = 0.86). Finally, the incidence of bronchopleural fistula was also similar between the 2 groups (neoadjuvant 1.8% vs 1.4%, P = 0.44). CONCLUSIONS Neoadjuvant chemotherapy is not associated with an increased perioperative risk after either lobectomy or pneumonectomy, warranting a more liberal use of this approach for patients with locally advanced operable lung cancer.

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