Preoperative Fibrinogen Level and Bleeding in Liver Transplantation for End-stage Liver Disease: A Cohort Study.

BACKGROUND: Liver transplantation is a high-risk surgery associated with important perioperative bleeding and transfusion needs. Uncertainties remain on the association between preoperative fibrinogen level and bleeding in this population. METHODS: We conducted a cohort study that included all consecutive adult patients undergoing a liver transplantation for end-stage liver disease in 1 center. We analyzed the association between the preoperative fibrinogen level and bleeding-related outcomes. Our primary outcome was intraoperative blood loss, and our secondary outcomes were estimated perioperative blood loss, intraoperative and perioperative red blood cell transfusions, reinterventions for bleeding and 1-y graft and patient survival. We estimated linear regression models and marginal risk models adjusted for all important potential confounders. We used restricted cubic splines to explore potential nonlinear associations and reported dose-response curves. RESULTS: We included 613 patients. We observed that a lower fibrinogen level was associated with a higher intraoperative blood loss, a higher estimated perioperative blood loss and a higher risk of intraoperative and perioperative red blood cell transfusions (nonlinear effects). Based on an exploratory analysis of the dose-response curves, these effects were observed below a threshold value of 3 g/L for these outcomes. We did not observe any association between preoperative fibrinogen level and reinterventions, 1-y graft survival or 1-y patient survival. CONCLUSIONS: This study suggests that a lower fibrinogen level is associated with bleeding in liver transplantation. The present results may help improving the selection of patients for further studies on preoperative fibrinogen administration in liver transplant recipients with end-stage liver disease.

Adapting SIMEX to correct for bias due to interval-censored outcomes in survival analysis with time-varying exposure.

Many clinical and epidemiological applications of survival analysis focus on interval-censored events that can be ascertained only at discrete times of clinic visits. This implies that the values of time-varying covariates are not correctly aligned with the true, unknown event times, inducing a bias in the estimated associations. To address this issue, we adapted the simulation-extrapolation (SIMEX) methodology, based on assessing how the estimates change with the artificially increased time between clinic visits. We propose diagnostics to choose the extrapolating function. In simulations, the SIMEX-corrected estimates reduced considerably the bias to the null and generally yielded a better bias/variance trade-off than conventional estimates. In a real-life pharmacoepidemiological application, the proposed method increased by 27% the excess hazard of the estimated association between a time-varying exposure, representing the 2-year cumulative duration of past use of a hypertensive medication, and the hazard of nonmelanoma skin cancer (interval-censored events). These simulation-based and real-life results suggest that the proposed SIMEX-based correction may help improve the accuracy of estimated associations between time-varying exposures and the hazard of interval-censored events in large cohort studies where the events are recorded only at relatively sparse times of clinic visits/assessments. However, these advantages may be less certain for smaller studies and/or weak associations.

Intraoperative phlebotomies and bleeding in liver transplantation: a historical cohort study and causal analysis.

BACKGROUND: Liver transplantation is associated with major bleeding and red blood cell (RBC) transfusions. No well-designed causal analysis on interventions used to reduce transfusions, such as an intraoperative phlebotomy, has been conducted in this population. METHODS: We conducted a historical cohort study among liver transplantations performed from July 2008 to January 2021 in a Canadian centre. The exposure was intraoperative phlebotomy. The outcomes were blood loss, perioperative RBC transfusions (intraoperative and up to 48 hr after surgery), intraoperative RBC transfusions, and one-year survival. We estimated marginal multiplicative factors (MFs), risk differences (RDs), and hazard ratios by inverse probability of treatment weighting both among treated patients and the whole population. Estimates are reported with 95% confidence intervals (CIs). RESULTS: We included 679 patients undergoing liver transplantations of which 365 (54%) received an intraoperative phlebotomy. A phlebotomy did not reduce bleeding, transfusion risks, or mortality when estimated among the treated but reduced bleeding and transfusion risks when estimated among the whole population (MF, 0.85; 95% CI, 0.72 to 0.99; perioperative RD, -15.2%; 95% CI, -26.1 to -0.8; intraoperative RD, -14.7%; 95% CI, -23.2 to -2.8). In a subgroup analysis on 584 patients with end-stage liver disease, slightly larger effects were observed on both transfusion risks when estimated among the whole population while beneficial effects were observed on the intraoperative transfusion risk when estimated among the treated population. CONCLUSION: The use of intraoperative phlebotomy was not consistently associated with better outcomes in all targets of inference but may improve outcomes among the whole population. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT04826666); registered 1 April 2021.

RéSUMé: CONTEXTE: La transplantation hépatique est associée à des saignements importants et à de multiples transfusions de globules rouges (GR). Aucune analyse causale bien conçue sur l'effet d'interventions servant à réduire les transfusions, comme une phlébotomie peropératoire, n'a été menée dans cette population. MéTHODE: Nous avons mené une étude de cohorte historique incluant toutes les transplantations hépatiques réalisées dans un centre canadien de juillet 2008 à janvier 2021. L'exposition d'intérêt était une phlébotomie peropératoire. Les critères d'évaluation étaient le saignement peropératoire, les transfusions de GR périopératoires (peropératoires et jusqu'à 48 heures après la chirurgie), les transfusions de globules rouges peropératoires et la survie à un an. Des facteurs multiplicatifs (FM), des différences de risque (DR) et des rapports de risques instantanés marginaux ont été estimés en utilisant une pondération par l'inverse de la probabilité de traitement parmi les patients traités et parmi l'ensemble de la population. Les effets estimés ont été rapportés avec des intervalles de confiance (IC) à 95 %. RéSULTATS: Nous avons inclus 679 transplantations hépatiques dont 365 (54 %) ont bénéficié d'une phlébotomie peropératoire. La phlébotomie n'a pas réduit les saignements, le risque de transfusion ou la mortalité lorsque ses effets ont été estimés parmi les patients traités, mais a réduit les risques de saignement et de transfusion lorsque ses effets ont été estimés parmi l'ensemble de la population (FM = 0,85 (IC 95 %, 0,72 à 0,99); DR périopératoire = −15,2 % (IC 95 %, −26,1 % à −0,8 %); DR peropératoire = −14,7 % (IC 95 %, −23,2 % à −2,8 %)). Dans une analyse de sous-groupe portant sur 584 patients atteints d'une hépatopathie terminale, des effets légèrement plus importants ont été observés sur les deux risques transfusionnels lorsqu'estimés dans l'ensemble de la population, tandis que des effets bénéfiques ont été observés sur le risque transfusionnel peropératoire lorsqu'estimés parmi les patients traités. CONCLUSION: L'utilisation de la phlébotomie peropératoire n'a pas été systématiquement associée à de meilleurs résultats dans toutes les populations cibles, mais semble améliorer les résultats lorsque les effets sont estimés dans l'ensemble de la population. ENREGISTREMENT DE L'éTUDE: www.ClinicalTrials.gov (NCT04826666); enregistrée le 1er avril 2021.

Treatment Switch to Dolutegravir With 2 Nucleoside Reverse-Transcriptase Inhibitors (NRTI) in Comparison to Continuation With Protease Inhibitor/Ritonavir Among Patients With Human Immunodeficiency Virus at Risk for Prior NRTI Resistance: A Cohort Analysis of Real-World Data.

BACKGROUND: Switching antiretroviral regimens when human immunodeficiency virus (HIV) viremia is controlled for a new regimen is challenging when there is the potential for prior nucleoside reverse-transcriptase inhibitor (NRTI) resistance. The objective was to study virologic outcomes after switching to dolutegravir compared with remaining on a boosted protease inhibitor (protease inhibitor/ritonavir [PI/r]) regimen in people with HIV (PWH) with prior documented virologic failure and/or exposure to mono/dual NRTIs. METHODS: We used the Quebec HIV Cohort including 10 219 PWH whose data were collected at 4 sites in Montreal, Canada. We included all PWH with documented virologic failure or exposure to mono/dual NRTI therapy who were virologically suppressed on a PI/r-based regimen for at least 6 months on or after January 1, 2014 (n = 532). A marginal structural Cox model analysis was used to estimate the effect of the switch to dolutegravir on virologic outcome compared with remaining on PI/r. The outcome was defined as 2 consecutive viral loads (VLs) >50 copies/mL or 1 VL >50 copies/mL if it occurred at the last VL available. RESULTS: Among 532 eligible participants, 216 (40.6%) had their regimen switched to dolutegravir with 2 NRTIs, whereas 316 (59.4%) remained on the PI/r with 2 NRTIs. The weighted hazard ratio for the effect of dolutegravir switch on virologic failure compared with patients whose regimen remained on PI/r was 0.57 (95% confidence interval, 0.21-1.52). CONCLUSIONS: We did not find evidence of an increased risk for virologic failure after switching to dolutegravir from PI/r among patients with previous virologic failure or prior exposure to mono/dual NRTI.

Impact of discretization of the timeline for longitudinal causal inference methods.

In longitudinal settings, causal inference methods usually rely on a discretization of the patient timeline that may not reflect the underlying data generation process. This article investigates the estimation of causal parameters under discretized data. It presents the implicit assumptions practitioners make but do not acknowledge when discretizing data to assess longitudinal causal parameters. We illustrate that differences in point estimates under different discretizations are due to the data coarsening resulting in both a modified definition of the parameter of interest and loss of information about time-dependent confounders. We further investigate several tools to advise analysts in selecting a timeline discretization for use with pooled longitudinal targeted maximum likelihood estimation for the estimation of the parameters of a marginal structural model. We use a simulation study to empirically evaluate bias at different discretizations and assess the use of the cross-validated variance as a measure of data support to select a discretization under a chosen data coarsening mechanism. We then apply our approach to a study on the relative effect of alternative asthma treatments during pregnancy on pregnancy duration. The results of the simulation study illustrate how coarsening changes the target parameter of interest as well as how it may create bias due to a lack of appropriate control for time-dependent confounders. We also observe evidence that the cross-validated variance acts well as a measure of support in the data, by being minimized at finer discretizations as the sample size increases.

Data-adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss-based estimation.

Causal inference methods have been developed for longitudinal observational study designs where confounding is thought to occur over time. In particular, one may estimate and contrast the population mean counterfactual outcome under specific exposure patterns. In such contexts, confounders of the longitudinal treatment-outcome association are generally identified using domain-specific knowledge. However, this may leave an analyst with a large set of potential confounders that may hinder estimation. Previous approaches to data-adaptive model selection for this type of causal parameter were limited to the single time-point setting. We develop a longitudinal extension of a collaborative targeted minimum loss-based estimation (C-TMLE) algorithm that can be applied to perform variable selection in the models for the probability of treatment with the goal of improving the estimation of the population mean counterfactual outcome under a fixed exposure pattern. We investigate the properties of this method through a simulation study, comparing it to G-Computation and inverse probability of treatment weighting. We then apply the method in a real-data example to evaluate the safety of trimester-specific exposure to inhaled corticosteroids during pregnancy in women with mild asthma. The data for this study were obtained from the linkage of electronic health databases in the province of Quebec, Canada. The C-TMLE covariate selection approach allowed for a reduction of the set of potential confounders, which included baseline and longitudinal variables.