Atrial Fibrillation and Anterior Cerebral Artery Absence Reduce Cerebral Perfusion: A De Novo Hemodynamic Model^†

Round 1

Reviewer 1 Report

Dear Authors

Thank you very much for the opportunity to review this very good work. 

The work was written very reliable. Statistical method are adequate to the collected date. In my opinion the manuscript can be accepted for publication.

After re-analyzing the manuscript, I have a question: Has the presented model been tested in vivo ? If not, do the authors have such plans ? And one more question, important for me as a clinician: how will the presence of atherosclerotic lesions in the analyzed Willis circle arteries affect the presented model?

I congratulate the authors on their efforts to prepare this manuscript.

Best regards

Author Response

Author responses to reviewer comments for manuscript entitled “Atrial Fibrillation and Anterior Cerebral Artery Absence Reduce Cerebral Perfusion: A de Novo Hemodynamic Model” by Timothy J. Hunter, Jermiah J. Joseph, Udunna Anazodo, Sanjay R. Kharche, Christopher W. McIntyre and Daniel Goldman. (MS ID applsci-1561555).

January 28, 2022.

Reviewer 1 comments and authors’ responses.

Comment #1. Thank you very much for the opportunity to review this very good work. 

The work was written very reliable. Statistical method are adequate to the collected date. In my opinion the manuscript can be accepted for publication.

Response #1. We thank the reviewer for their generous comment.

Comment #2. After re-analyzing the manuscript, I have a question: Has the presented model been tested in vivo? If not, do the authors have such plans?

Response #2. The model is based on established biophysical principles and is indicative. However, a new Table 2 has been added to the revised manuscript at line 289. In addition, the extensible nature of the model has been discussed in section 4. Discussion at line 388.

Comment #3. And one more question, important for me as a clinician: how will the presence of atherosclerotic lesions in the analyzed Willis circle arteries affect the presented model?

Response #3. While the modelling of atherosclerotic lesions is outside the scope of this study, it is well within the capability of the presented model. Please see lines 460-490 for extended discussion of the capabilities of the model and future work.

Reviewer 2 Report

Overview:

Hunter et al. have developed a computational model investigating the anatomical variations in the cranial circulation and its effect on cranial perfusion during atrial fibrillation. This computational model further explores the role of ventricular rate during atrial fibrillation in the blood flow in the circle of Willis and cranial perfusion. This model could potentially risk-stratify the development of cognitive impairment in patients with atrial fibrillation based on the anatomical variation of the cerebral base arterial supply and ventricular rate during atrial fibrillation.

Major comments:

1. Line 68; the authors quote this sentence from Saglietto et al., indicating that the optimal ventricular rate during atrial fibrillation is 60 bpm. The RACE II trial (Lenient versus strict rate control in patients with atrial fibrillation, NEJM 2010) demonstrated that the lenient rate control (resting ventricular rate < 110 bpm) is as effective as strict rate control (resting ventricular rate of < 80 bpm). Strict rate control was associated with a higher rate of mortality and morbidity. The RACE II trial was a large-scale randomised control trial. Overwriting the findings of a randomised control trial based on the results of a computational model (Saglietto et al.) is not scientifically sound. I would recommend that the authors re-phrase their sentences.
2. Methods: cranial perfusion (CoW perfusion) is determined by systemic blood pressure, a product of cardiac output and peripheral vascular resistance (PVR). The cardiac output is the product of the stroke volume and heart rate, which seems to be accounted for in the computational model. How do the investigators model PVR in their model? Many variables are affecting PVR, including but not limited to sympathetic and parasympathetic output. Is it possible to factor in the PVR in a computational model? It would be worthwhile for the authors to elaborate on this in the methods section. Based on the first paragraph of the results (line 218), it seems that the authors found their model and pressure in the aorta on a constant PVR, and the only variable was the RR interval.
3. Has this computational model been validated? Computational models are precious if validated based on some in vivo studies.
4. In panel B of figure 5, the AF model (red line) demonstrates a reflective hyperperfusion following hypoperfusion. If a reflective sympathomimetic response is not accounted for in the computational model, what is causing the surge in CoW perfusion following hypoperfusion.
5. The third paragraph of discussion, line 297, contrasts sharply with the RACE II trial (Lenient versus strict rate control in patients with atrial fibrillation, NEJM 2010). The composite endpoint of the RACE II trial was mortality, stroke, and hospitalisation. If the cranial perfusion is the best with the lowest ventricular rate (50 bpm), why the composite outcome of lenient rate control in AF was better? I would strongly recommend that the authors discuss the contrast between their findings and RACE II in the discussion.

Author Response

Author responses to reviewer comments for manuscript entitled “Atrial Fibrillation and Anterior Cerebral Artery Absence Reduce Cerebral Perfusion: A de Novo Hemodynamic Model” by Timothy J. Hunter, Jermiah J. Joseph, Udunna Anazodo, Sanjay R. Kharche, Christopher W. McIntyre and Daniel Goldman. (MS ID applsci-1561555).

January 28, 2022.

Reviewer 2 comments and authors’ responses.

Comment #4. Hunter et al. have developed a computational model investigating the anatomical variations in the cranial circulation and its effect on cranial perfusion during atrial fibrillation. This computational model further explores the role of ventricular rate during atrial fibrillation in the blood flow in the circle of Willis and cranial perfusion. This model could potentially risk-stratify the development of cognitive impairment in patients with atrial fibrillation based on the anatomical variation of the cerebral base arterial supply and ventricular rate during atrial fibrillation.

Response #4. We appreciate the reviewer’s comments. Risk-stratification is definitely an application of the presented model. However, it was out of the scope of the manuscript and is part of the first author’s ongoing research. As such the following sentence has been added to the discussion on line 496. “The presented model is extensible and personalizable which will permit patient specific risk stratification [28].”

Comment #5. Line 68; the authors quote this sentence from Saglietto et al., indicating that the optimal ventricular rate during atrial fibrillation is 60 bpm. The RACE II trial (Lenient versus strict rate control in patients with atrial fibrillation, NEJM 2010) demonstrated that the lenient rate control (resting ventricular rate < 110 bpm) is as effective as strict rate control (resting ventricular rate of < 80 bpm). Strict rate control was associated with a higher rate of mortality and morbidity. The RACE II trial was a large-scale randomised control trial. Overwriting the findings of a randomised control trial based on the results of a computational model (Saglietto et al.) is not scientifically sound. I would recommend that the authors re-phrase their sentences.

Response #5. In line with the reviewers recommendation, the revised manuscript now has the new text in line 73-84:

“Saglietto et al. [12] have also used 0D modelling to predict that the optimal goal for heart rate control strategy should be around 60 bpm, considered a strict rate control.

The findings by Saglietto et al. [12] are in contrast to common practice of lenient rate control (< 110 bpm) which is based on findings from the RACE II trial, a large randomized control trial [13]. The RACE II trial was a consequential study which found that, compared to lenient rate control (< 110 bpm), strict rate control (< 80 bpm) was not more effective in reducing mortality in persistent AF patients. These findings have informed treatment strategies for persistent AF patients, however they do not consider increased risk for dementia, later confirmed by de Brujin et al. [2] in a longitudinal study. Modelling studies following de Brujin et al. [2] have aimed at elucidating the mechanism behind the in-creased risk and finding potential treatment strategies which mitigate it.”

Further, a discussion of the RACE II trial has been added on lines 431-439 as follows:

“It should be noted that this finding, along with previous modelling results [12] contradict the recommendation made based on the RACE II trial [13]. The study found that relative to strict rate control, lenient rate control was as effective in preventing mortality and other outcomes, and was easier to achieve. This finding has informed clinicians on rate control strategies in relation to preventing mortality in recent years. However, cognitive impairment/dementia were not considered as outcomes of this study, and heart rate had not yet been linked to hypoperfusion events associated with AF. Therefore, there is now growing evidence supporting strict rate control for preventing deleterious cognitive outcomes.”

Comment #6. Methods: cranial perfusion (CoW perfusion) is determined by systemic blood pressure, a product of cardiac output and peripheral vascular resistance (PVR). The cardiac output is the product of the stroke volume and heart rate, which seems to be accounted for in the computational model. How do the investigators model PVR in their model? Many variables are affecting PVR, including but not limited to sympathetic and parasympathetic output. Is it possible to factor in the PVR in a computational model? It would be worthwhile for the authors to elaborate on this in the methods section. Based on the first paragraph of the results (line 218), it seems that the authors found their model and pressure in the aorta on a constant PVR, and the only variable was the RR interval.

Response #6. The proposed model includes a representation of the baroreceptor mechanism which dynamically modulates PVR as well as the intrinsic heart rate. The following text has been added in the methods section at lines 149-157 for an elaboration on the implemented feedback mechanism: “The baroreflex is a feedback mechanism which works to maintain hemodynamic homeostasis. It modulates peripheral vascular resistance, heart rate, and heart contractility to maintain systemic blood pressure and flow at healthy levels. The baroreceptor mechanism is implemented according to the model proposed by Lin et al. (2012) [14]. The model dynamically calculates sympathetic nervous activity (SNA) and parasympathetic nervous activity (PNA) based on the mean arterial pressure, as well as arterial PCO2 which is assigned a constant value of 40 mmHg. Values for SNA and PNA are then used to dynamically modulate peripheral vascular resistance, intrinsic heart rate, as well as heart contractility via modulation terms [14].”

Comment #7. Has this computational model been validated? Computational models are precious if validated based on some in vivo studies.

Response #7. The present study is an investigation of hemodynamics based on well understood biophysical principles. While clinical data was not available for rigorous validation of the present model, components of the model have previously been validated for a variety of disease cases. Additionally, gross model outputs are compared to literature values to ensure that model function is representative of physiological phenomena. See text on lines 289-300: “ Model output statistics from a single simulation instance with CoW variant 1 (com-plete CoW) at 80 BPM. The statistics from the AF case are shown to be similar to those in the NSR case. Median systemic blood pressures of 117.44/77.81 mmHg (systolic/diastolic) for NSR and 119.51/78.95 mmHg for AF are shown to be similar to physiological levels. Additionally, total cerebral blood flow is 12.54 ml s-1 for the NSR case and 12.31 ml s-1 for the AF case.

Table 2. Model outputs under NSR conditions.

Model output statistics for a simulation run with CoW variant 1 (complete CoW), at a HR of 80 BPM under AF and NSR conditions. Systemic pressure and cerebral blood flow statistics are shown to be similar in both NSR and AF cases. Values are shown as median  standard deviation. Pa,sys: arterial systolic pressure; Pa,dias: arterial diastolic pressure; QACA: anterior cerebral artery flow rate; QMCA: middle cerebral artery flow rate; QPCA: posterior cerebral artery flow rate; CBF: cerebral blood flow.”

Discussion of these model outputs has been added on lines 388-400: “The present model is a composite of previously published models. It is based es-tablished biophysical modelling techniques, i.e. lumped parameter modelling using windkessel compartments. The components have been used previously to model a va-riety of disease cases, including AF. While direct model validation with in vivo data was not within the scope of the study, model outputs were presented for comparison with published values. Median arterial blood pressures (systolic/diastolic) were 117.44/77.81 mmHg and 119.51/78.95 mmHg for NSR and AF respectively, which are considered to be within healthy ranges. Additionally, blood flow in major cerebral arteries is presented for comparison with measured values published by Zarrinkoob et al. [28]. Zarrinkoob re-ports blood flow in the ACA, MCA and PCA to be 12%, 21% and 8% of total CBF re-spectively. The model shows corresponding values of 8%, 29% and 12% for the NSR case, and 8%, 30% and 12% for the AF case. Therefore the model reflects clinically measured blood flow distribution, with predominant blood flow occurring in the MCA.”

Comment #8. In panel B of figure 5, the AF model (red line) demonstrates a reflective hyperperfusion following hypoperfusion. If a reflective sympathomimetic response is not accounted for in the computational model, what is causing the surge in CoW perfusion following hypoperfusion.

Response #8. We thank the reviewer for their insightful comment. The description of the autoregulation mechanism has been expanded in the methods section at lines 162-170: “The model also implements cerebral autoregulation, which is a physiological mechanism that alters vascular resistance and compliance in order to maintain blood flow within healthy ranges in the case of widely varying cerebral perfusion pressure. Each down-stream region (Figure 2, RA, LA, RM, LM, RP, LP) is regulated by its own autoregulation function comprised of two integrated signals. The signals are blood flow rate in the region, which is calculated dynamically, and arterial PCO2 which is assigned at 40 mmHg. These two signals are applied to a first-order filter with time constants of 20 s for autoregulation and 40 s for CO2 control, and the resulting values are used to modulate compliance and resistance within the corresponding vascular region.”  

The following text has also been added to the discussion at lines 407-418: “Additionally in Figure 5 it can be observed that the initial hypoperfusion seen at 2-4 s is followed by hyperperfusion from 4-7 s. This is to be expected because of the reflexive nature of the autoregulation mechanism. The autoregulatory function modulates the resistance and compliance of the downstream cerebral vessels within which the blood flow is being observed. The autoregulation function acts on a time scale of approximately 20 s, therefore there is a small delay between the drop in blood flow and the response to de-crease resistance and increase compliance. This small delay in autoregulation function is thought to be the reason why spontaneous drops in arterial pressure due to irregular heart beats can cause transient hypoperfusion in the brain.”

Comment #9. The third paragraph of discussion, line 297, contrasts sharply with the RACE II trial (Lenient versus strict rate control in patients with atrial fibrillation, NEJM 2010). The composite endpoint of the RACE II trial was mortality, stroke, and hospitalisation. If the cranial perfusion is the best with the lowest ventricular rate (50 bpm), why the composite outcome of lenient rate control in AF was better? I would strongly recommend that the authors discuss the contrast between their findings and RACE II in the discussion.

Response #9. Please see author response #5 above.