Risk Factors and Predictors of Prolonged Mechanical Ventilation Following Cardiac Surgery: A Narrative Review

TL;DR

This paper reviews factors that predict prolonged mechanical ventilation after cardiac surgery and highlights the importance of identifying these predictors to improve patient care and healthcare efficiency.

Contribution

The paper synthesizes existing literature to identify consistent risk factors and potential predictors for prolonged ventilation after cardiac surgery.

Findings

Patients with higher heart failure classifications (NYHA or CCS) are at elevated risk for prolonged ventilation.

Prolonged ventilation is associated with increased mortality and administrative challenges in critical care units.

Prediction models can help tailor treatment and increase ventilator-free days for patients.

Abstract

The subset of patients requiring prolonged mechanical ventilation is significantly high worldwide, making it an important topic of continuous and ongoing research. Over the years, various articles have shown that there may be predictors of prolonged ventilation that could be applied in healthcare to make it more patient-centered. The available literature suggests that authors have different definitions of “prolonged” ventilation. However, most critical care units embrace caution if a patient needs mechanical ventilation for more than 48 to 72 hours. The major benefits of mechanical ventilation are an overall decrease in the work of breathing and the facilitation of relatively easier pumping from an ailing heart. An elevated risk of prolonged ventilation after cardiac surgery exists in patients with higher classes of heart failure (as classified by the New York Heart Association (NYHA) or Canadian Cardiovascular Society (CCS)), a pre-existing congenital or acquired cardiac abnormality, and patients with renal failure, to name a few. The impact on quality of life has also been widely studied; as mortality rates increase with factors like age and days dependent on ventilation. Patients undergoing prolonged ventilation constitute an administrative challenge for critical care units, highlighting how multiple patients in this bracket can overwhelm the healthcare system. The use of prediction models in this context can aid healthcare delivery tremendously. Using different predictors, we can craft tailor-made treatment options and achieve the goal of more ventilator-free days per patient.

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