Discussion
This study showed the reliability of Simulresp when predicting blood gas
values of healthy child over 8 years old, confirming O. Flechelles et
al. data (2,4). The inconsistencies of Simulresp’s prediction regarding
under 8 years old and/or ventilator supported subjects might be due to
the fact that 1977 CJ Dickinson’s model didn’t considered physiologic
and physio-pathologic specificities of these conditions (8). Children
respiratory system differs from that of the adult, anatomically and
mechanically, mainly during the first 6 to 8 years. Less than 20% of
“adult” cells are present at birth. In the first years of life, lung
growth occurs by adding or creating new alveoli (18). This
alveolarization is accompanied by an increase in the capacity of the
lung to perform gas exchanges (18). Alveolarization is considered
complete around the age of 6 to 8 years. Thereafter, the alveolar
surface will grow due to the growth of the child. This growth of the
alveolar surface is related to lung growth but is not associated with an
increase in the number of alveoli (18). In addition, from birth to the
age of 2 to 3 years, the shape of the rib cage will evolve from a
circular shape to a more oval or rectangular shape (19). This shape
causes a lower mechanical efficiency of the thoracic cavity compared to
adults (20), especially since it is associated with a flattened less
efficient diaphragm. Besides, the discrepancies between a high
compliance rib cage and a low compliance lung will altered the residual
functional capacity. Thus, the child must develop several dynamic
compensation mechanisms to maintain its reserves in oxygen and avoid
atelectasis. Mechanical ventilation substitutes positive pressure
insufflation by an external machine induced to negative pressure
insufflation by diaphragmatic contraction. The installation of a
ventilatory support requires four elements: a suitable interface between
the respirator and the patient, an energy source that operates the
machine, an insufflation whose magnitude and rhythm will be regulated or
controlled and a system to monitor the performance of the respirator and
the condition of the patient. These four elements are associated with
mechanic, physic and physiologic constraints that can be difficult to
predict, record or standardize in the form of mathematical equations. In
addition, if these four elements are mandatory, they are not sufficient
to ensure the effectiveness of the respiratory support and other
elements such as the adaptation of the parameters, the synchrony between
the patient and the machine, the need for sedation or the type of
respirator are to be considered and added as constraints and limits to
the success of the modeling.
The goal of validation is giving confidence to users on simulator
results because the future use of a simulator depends on it. The quality
assessment procedures of a simulator are essential to ensure its
validity and usability (5). The validity of a simulator will depend
directly on the objective for which it was developed (2,13,21,22).
Targets, safety ranges, gap between simulated values and expected values
can differ depending on simulator goals. For example, when the simulator
is used for teaching, an inaccurate output can likely be tolerated if
the evolution or prediction approaches a physiologically normal value,
and both repeatability and reproducibility are satisfactory. On the
other hand, use in care cannot be conceived with a simulator that would
not be accurate.
Several cardio-respiratory simulators are described in the literature
(2,22,23), but there are few descriptions of the validation process.
Most of the publications on the subject focus on the description of the
simulator’s performance rather than on the actual technical report of
the process applied to guarantee the quality of the simulator and its
prediction (24). Some teams took the trouble to evaluate the performance
of their simulator by comparing the simulated data with data observed in
real patients in mechanical ventilation (25,26). However, the content of
these articles remains focused on the description of the purpose of the
simulator and how it is developed. The idea of carrying out and
presenting a complete validation process, aimed at judging the ability
of the simulator to provide a fair and reliable prediction in time and
situations, only rarely seems to be part of the research protocol
(5,13,27).
The strength of this work lies in the large number of tests performed.
In addition, we evaluated each quality component of the simulator:
accuracy, repeatability, reproducibility and robustness. We have thus
been able to highlight the limitations of the simulator, particularly as
regards the prediction of the patient under 8 years of age, the patient
who is ill and the patient in mechanical ventilation. This work has
major limitations. The use of graphical representations to judge the
accuracy of the simulator, although described in the literature (5,13)
may seems trivial and in all cases particularly subjective and
unreliable. In addition, the question arises as to the choice of
statistical tests carried out. While it is certain that the measurement
of Pearson or Spearman correlation coefficients is insufficient to judge
the accuracy and concordance between simulated and reference values, the
relevance of the other statistical methods applied remains equally
questionable (15,28). Nonetheless, the performed tests were based on
previously described simulator evaluations (25,29). Finally, the
question of external applicability arises. The results we obtained apply
only to SimulResp and cannot be extrapolated to other simulators, even
if they were developed using the Dickinson model.
This work allowed us to better define the next steps in the development
of SimulResp. In this actual form, SimulResp is limited by the limits of
the model it is built on. For this reason, we are currently working on
the content of the algorithm in order to improve SimulResp. we need to
study and modified the formula within the model in order to make it
accurate when simulating blood gas value of under 8 years old patients.
To do so, we have gathered a high-resolution database (30,31), on which
we intend to apply several machine learning approaches (32,33) Besides,
we have begun to develop the method that will allow us to calibrate the
simulator to be reliable in several respiratory physio-pathological
situations (normal compliance (> 2
ml/cmH2O/kg), abnormal compliance, increased resistance)
and hemodynamic (shock states) in both spontaneous and invasive
ventilation (2,30). SimulResp, once completely validated, will be
integrated in future clinical decision support systems and will collect
data from real patient and then simulate breathing pattern with
accelerated time. Resulting simulated blood gases will be presented to
physician whom could test different ventilator settings to determine the
best one and adjust the patient’s therapy in an individualized but still
protocolized care.