Optimising Ventilatory Strategies by Using Positive Respiratory Integer Measurements

• Introduction and rationale

Intermittent Positive Pressure Ventilation (IPPV) is required for almost every patient under general anesthesia. It is recognized that IPPV is an unphysiological way of breathing and it can have serious complications when instituted incorrectly. 1 Known complications of IPPV include (but are not limited to) desaturation, atelectasis and baro-/volutrauma.

Many studies have tried to determine the optimal settings for ventilatory parameters during IPPV. 2 Currently, however, it is unknown whether the use of specific ventilatory parameter sets is related to improved outcomes for patients undergoing procedures under general anesthesia.

IPPV is commonly instituted using a set of parameters which determine respiratory characteristics among which are Peak Inspiratory Pressure (PIP), Positive End Expiratory Pressure (PEEP), Plateau Pressure (PlatP) and the Fraction of Inspired Oxygen(FiO2). 3 Adjustment of these parameters allows the clinician to optimize the mechanical characteristics of the artificial respiration of the patient.

IPPV changes intrathoracic pressure in a cyclic way. Pressure increases during inspiration and decreases during expiration. The influence of increased intrathoracic pressure, caused by IPPV, on hemodynamic parameters is well known and is one of the factors used by anesthesiologists when instituting IPPV. IPPV-pressures may both increase or decrease hemodynamic parameters such as cardiac preload and left ventricular wall tension, thus influencing not only oxygenation and decarbonization, but also cardiac output, myocardial oxygen consumption and direct cerebral oxygenation. The relationship between respiration and circulation exhibits complex dynamics and understanding this may help to promote more natural and physiologic breathing patterns, leading to improved oxygenation and reduced respiratory distress.

In addition to direct influence of positive ventilatory pressures on hemodynamic factors the cyclic nature of both IPPV and hemodynamic factors needs to be taken into account when investigating the relationship between IPPV and patient outcomes. When changes in respiratory pressures coincide with related physiological phenomena, each cycle may pose an additive influence on both parameters. Furthermore, coinciding physiological cycles may lead to "respiratory entrainment," where the ventilator-induced breaths becomes asynchronizous with the patient's intrinsic respiratory rhythm, leading to ineffective ventilation and increased risk of lung injury. Additionally, reducing respiratory entrainment may help to reduce the likelihood of mechanical fatigue and stress on the lung tissue, as it avoids repetitive or periodic stress on the respiratory system. Using mathematically identified integer sets as ventilatory parameter settings may reduce the coinciding of respiratory cycles with recurring physiological phenomena. Furthermore, the role of mathematical identification of optimal integer sets for use in ventilator parameters can help find optimal settings to reduce complications from Intermittent Positive Pressure Ventilation

Research question To determine the relationship between the use of mathematically identifiable integer ventilator parameter sets and patient outcomes

METHOD Description study design

This is a retrospective database study. A database search of all cases of adult patients undergoing procedures in the UMCG under general anesthesia with IPPV between 01-01-2018 and 01-04-2023 will be conducted. Researchers will use anonymized data only from the existing patient data records.

This study will investigate the relationship of mathematically identifiable integer sets of ventilator setting parameters during surgery performed under general anesthesia with IPPV. Data will be extracted from the Electronic Patient Database and all data will be handled anonymously.

Parameter integer sets will be identified using probabilistic (or Monte Carlo) algorithms and sieve methods. Non-multifactorial and multifactorial sets will be compared and only non-negative integers will be considered for analysis

Univariate, multivariate regression methods and mixed-model analyses, where appropriate, will be used to determine which integer sets in ventilatory parameter settings during surgery performed under general anesthesia and IPPV are related to patient outcomes such as SpO2 and FiO2/ SpO2-ratio