Association of Preoperative Geriatric Assessment With Length of Stay After Combined Cardiac Surgery.

BACKGROUND: For older patients undergoing cardiac surgery, geriatric factors are known to increase postoperative complications and prolong length of stay (LOS). Comprehensive geriatric assessment (CGA) is an evidence-based method for geriatric evaluation to develop an individualized-care plan to optimize physical, functional, and social issues. This study analyzed the association between preoperative CGA and hospital LOS after combined cardiac surgery. METHODS: This retrospective monocentric study included all patients aged 75 years and greater who underwent combined cardiac surgery between 2014 and 2017. Hospital LOS, intensive care unit LOS, and postoperative complications were compared between patients with or without preoperative CGA before and after propensity-score matching. RESULTS: Mean age of the 407 patients was 79.6 years; 114 underwent a preoperative CGA (28%). For 305 patients (74.9%), coronary artery bypass was associated with aortic valve replacement. After propensity-score matching, a significant difference was found between the 2 groups (preoperative CGA versus none) for in-hospital LOS (12 versus 13 days; P = .04) and intensive care unit LOS (3 versus 4 days; P = .01). In multivariable analysis, a significant association remained between hospital LOS and CGA (P = .02), renal function (P = .02), mitral replacement (P = .001), and complications (P = .001). CONCLUSIONS: Our results favor the use of systematic preoperative CGA. These encouraging results need to be validated by prospective studies that assess the impact of individualized-care plan established after CGA on postoperative outcomes. With an aging population, efforts are required to determine how to implement preoperative individualized-care plans to improve postoperative outcomes for vulnerable patients undergoing cardiac surgery.

Inverse probability weighting to control confounding in an illness-death model for interval-censored data.

Multistate models with interval-censored data, such as the illness-death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi-Markov model with interval-censored data. Using simulated data, we validated the performances of the proposed approach. We also illustrated the usefulness of the method by an application aiming to evaluate the relationship between the inadequate size of an aortic bioprosthesis and its degeneration or/and patient death. We have updated the R package multistate to facilitate the future use of this method.