To provide key pharmacological concepts underlying drug-drug interactions (DDIs), a decision-making framework, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19.DDIs are frequently encountered in the acutely ill. The implications of DDIs include either increased risk of drug toxicity or decreased effectiveness, which may have severe consequences in the acutely ill due to lower physiological and neurocognitive reserves in these patients. In addition, an array of additional therapies and drug classes have been used for COVID-19 that were not typically used in the acute care setting. In this update on DDIs in the acutely ill, we provide key pharmacological concepts underlying DDIs, including a discussion of the gastric environment, the cytochrome P-450 (CYP) isozyme system, transporters, and pharmacodynamics in relation to DDIs. We also provide a decision-making framework that elucidates the identification of DDIs, risk assessment, selection of alternative therapies, and monitoring. Finally, important DDIs pertaining to contemporary acute care clinical practice related to COVID-19 are discussed.Interpreting and managing DDIs should follow a pharmacologically based approach and a systematic decision-making process to optimize patient outcomes. Previously uncommon drug classes used in the acutely ill have introduced new drug-drug interactions (DDIs) in patients with COVID-19.Pharmacological concepts to consider include the gastric environment, cytochrome P-450 isoenzyme system, transporters, and pharmacodynamics in relation to DDIs.The decision-making for DDIs is complex in the acutely ill and should include a decision-making framework.1-3 Most DDIs in the literature are considered to be potential unless a clinically relevant outcome occurs.1 For the purpose of this article, we have referred to these simply as DDIs. More recently, introduction of new therapies for the management of coronavirus disease 2019 (COVID-19) has had implications for practice due to DDIs that were not previously commonly encountered in acutely ill patients. The implications of a DDI include either increased risk of drug toxicity or decreased effectiveness. This may have severe consequences in more severely ill patients with COVID-19 due to the lower physiological and neurocognitive reserves of such patients.4 In a recent systematic review of patients with COVID-19, numerous DDIs were associated with harm, most of which would have been preventable if identified with drug interaction tools.5 Thus, an updated review of DDIs to guide clinicians who manage acutely ill patients with COVID-19 is needed.Drug-drug interactions (DDIs) are frequently encountered in the acutely ill. Among the most severely ill patients, such as those in the intensive care unit (ICU), 58% encounter at least one potential DDI, with some studies identifying up to 5 potential DDIs per patient.In this clinical consultation, we provide key pharmacological concepts underlying DDIs, a decision-making framework for clinicians, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19.6 The COVID-19 drug interactions webpage is another resource to help clinicians navigate DDIs with emerging COVID-19 therapies.7 Identification of a DDI is followed by an assessment of changes in expected area under the curve (AUC) and/or maximum concentration, the time course of these changes, and pharmacodynamic (PD) effects. These concepts are further elaborated in the next section. Finally, a determination of risk is needed for either continuation of the interacting drugs with monitoring or use of alternative therapy.The decision-making framework for DDIs in patients with COVID-19  is conce8 For example, continuation of a statin may be acceptable if serum creatinine kinase levels and renal function are monitored. Similarly, QTc-prolonging agents may be continued in patients with a shorter baseline QTc interval and ongoing electrocardiogram monitoring. In an analysis of management of potential DDIs in the ICU, monitoring was the advised management strategy in 81% of cases.8 Thus, it is common to continue certain therapies with DDIs in the ICU, more so than in other environments. A decision to continue drugs in the presence of DDIs requires consideration of the expected effects of the DDI, risk evaluation, the availability of alternative therapies, monitoring, and the harm mitigation strategies available.9Circumstances for severely ill patients such as those in the ICU may allow for monitoring and treatment of adverse effects to mitigate potential harm from DDIs. Monitoring of serum drug concentrations, physical status, or biomarkers indicative of harm may allow for the use of a drug-drug combination even when an interaction is present.10 The perpetrator (inducer or inhibitor) affects the pharmacokinetic (PK) and PD behavior of the victim (substrate).Key pharmacological concepts in the acutely ill related to COVID-19 DDIs pertain to the gastric environment, the cytochrome P-450 (CYP) isoenzyme system, transporters, and PD effects. These are briefly discussed below and are depicted in 11 This can result in highly variable and often reduced bioavailability of orally or enterally administered COVID-19 therapies.11 One known DDI relevant to COVID-19 therapies that pertains to the gastric environment involves the combination of oral magnesium antacids and corticosteroids. The absorption of oral dexamethasone was evaluated via measurement of urinary excretion of 11-hydroxycorticosteroids in 6 healthy individuals.12 This analysis showed a reduction of approximately 75% in the bioavailability of dexamethasone when it was administered concurrently with magnesium trisilicate. The probable mechanism involves adsorption of dexamethasone on the surface of the antacid, and this interaction becomes relevant when oral or enteral corticosteroids are used. In these circumstances, the dose of dexamethasone should be separated from that for the antacid by more than 2 hours.Severely ill patients such as those in the ICU may have poor gastrointestinal perfusion, changes in gastric pH, and altered gastrointestinal motility.13 Perpetrator drugs can be either an inhibitor or an inducer of CYP enzymes. An inhibitor slows down the metabolism of the victim drug, while an inducer increases its metabolism. The Food and Drug Administration (FDA) maintains a useful resource for clinicians that describes substrates, inhibitors, and inducers of CYP enzymes.14 When evaluating DDIs in acutely ill patients with COVID-19, the extent of the change in exposure  and the time course of this change are important considerations. The strength of the inhibitor  and the sensitivity of the substrate  both determine the extent of change in exposure. Strong, moderate, and weak inhibitors increase the AUC of sensitive substrate by ≥5-fold, ≥2-fold to <5-fold, and ≥1.25-fold to <2-fold, respectively.14 For example, ritonavir (used with nirmatrelvir for COVID-19) is a strong inhibitor of CYP3A4. Thus, it can increase the AUC of some victim drugs by 5-fold or more, which can have severe consequences. The time course of enzyme inhibition is also important to consider. Onset of inhibition with ritonavir occurs upon first administration, while extinction of inhibition is related to the half-life of ritonavir and the time required for the activity of the CYP enzyme to return.15 This means that a DDI should be expected for several days, even when nirmatrelvir-boosted ritonavir is discontinued upon hospital admission.Most DDIs with COVID-19 therapies involve the CYP isoenzyme system. A detailed description of the CYP isoenzyme system and its role in drug variability has previously been published.16 If there is decreased function of CYP2C19, metabolism by CYP3A4 becomes the dominant pathway. This was highlighted by a DDI study of voriconazole and ritonavir (a strong CYP3A4 inhibitor) in healthy individuals.17 In this study, the voriconazole AUC increased 1.5-fold in ultrarapid metabolizers and 9.1-fold in poor metabolizers. In the severely ill, the CYP isoenzyme system and enzyme expression are subject to a substantial degree of variability that is further affected by the presence of inflammation. Evidence has indicated that increased inflammatory status results in isoform-specific changes to the metabolic activity of CYP enzymes.18 The activity of some CYP enzymes increases while that of others decreases in such patients. For example, in patients with moderate to severe COVID-19, the activity of CYP2C19 and CYP3A4 decreased by 75% and 23%, respectively, whereas the activity of CYP2C9 increased by 56%.19 The extent of the change in the AUC of a victim drug in the presence of inflammation and a DDI can be difficult to predict.DDIs may also be genotype dependent. For instance, voriconazole is predominantly metabolized by CYP2C19 and to a lesser extent by CYP3A4 and CYP2C9.20 FDA has provided guidance that clinical DDI studies should be conducted for new drugs that are substrates of the following types of transporters: P-glycoprotein (P-gp), anion-transporting polypeptide (OATP), breast cancer resistance protein (BCRP), organic anion transporter (OAT), organic cation transporter, and multidrug and toxin extrusion (MATE) proteins.21 These transporters can play an important part in DDIs because they regulate the access of a substrate to metabolizing enzymes, control the influx and efflux of drugs from enterocytes and hepatocytes, or influence renal secretion. Examples of inducers, inhibitors, and substrates for these transporters are listed by FDA.14 The OAT and P-gp transporters are most pertinent with regard to DDIs involving COVID-19 therapies and are discussed later in this article in regard to baricitinib, ritonavir-boosted nirmatrelvir, and remdesivir.The human body has hundreds of transporters, but clinical relevance is currently known for only a minority of these.22 These PD interactions are typically additive but can also be synergistic  and in some cases antagonistic . Examples of PD DDIs in COVID-19 relate to the use of multiple medications such as interleukin-6 (IL-6) inhibitors, Janus kinase (JAK) inhibitors, and corticosteroids that can all cause immunosuppression. Generally, PD DDIs will result in increased toxicity rather than decreased effectiveness.PD interactions are characterized by the ability of a DDI pair to have an altered combined effect that is not related to a change in drug concentration.23 will be discussed as these are most likely to be seen in contemporary clinical practice. We also acknowledge that recommendations can change and new therapies are being investigated. The mechanisms for DDIs for each COVID-19 drug and the implications are shown in Targeted therapies for COVID-19 have challenged acute care providers with new DDIs due to the use of some drug classes that were previously not routinely used in the hospital setting. As a starting point, therapies for COVID-19 currently recommended by the National Institutes of Health (NIH)25 Biologics with a molecular weight of more than 69 kDa are not renally eliminated and do not require PK analysis in the context of renal impairment by FDA.25 In addition, these monoclonal antibodies are not metabolized by CYP enzymes. As these agents are indicated in the outpatient setting for those at high risk of progression to severe disease, it is possible that these agents have been administered to some patients before hospital admission. However, there are no known DDIs with these drugs, and their prior use would not affect selection of therapies during hospitalization.Monoclonal antibodies target spike protein components of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These drugs include bebtelovimab and the combination product tixagevimab/cilgavimab, which have a molecular weight of approximately 144 kDa and 149 kDa, respectively.26 In the RECOVERY trial, a dose of 6 mg was administered intravenously (IV) or orally daily for 10 days.26 However, hydrocortisone was also shown to be beneficial in a severely ill cohort in the REMAP-CAP trial, with a dose of 50 mg IV every 6 hours or 100 mg IV every 8 hours, suggesting a class effect.27 The equivalent corticosteroid dose based on glucocorticoid effects was higher in REMAP-CAP than in the RECOVERY trial . This has led to interest in using higher doses of dexamethasone (12-20 mg daily).28 An ongoing investigation from the RECOVERY trial using higher doses of dexamethasone in the first 5 days (20 vs 6 mg daily) has shown an increased risk of mortality in the high-dose group, with contributing factors of hyperglycemia, non–COVID-19 infection, and gastrointestinal bleeding.29 Although definitive evidence of the optimal corticosteroid regimen in COVID-19 is emerging, we can presume that DDIs that can increase the dexamethasone AUC by more than 3-fold relative to the standard regimen of 6 mg daily could be harmful. Use of corticosteroids in hospitalized patients, especially in the critically ill, is not new. However, it is important not to overlook the fact that dexamethasone is a CYP3A4 substrate (major) and an inducer of CYP3A4 (weak). For example, itraconazole, which is a strong inhibitor of CYP3A4, has been shown to increase the dexamethasone AUC by 3.3- to 3.7-fold.30 It is possible that other strong CYP3A4 inhibitors may cause similar increases in dexamethasone exposure. We suggest that, when a standard dose is used in the presence of a strong CYP3A4 inhibitor, there should be strict monitoring  and correction of blood glucose levels . A temporary dose reduction to 4 mg per day in the presence of a strong inhibitor may be considered in rare circumstances , but this requires a careful risk-benefit assessment for a given patient to ensure that such a dose reduction does not impede their recovery from COVID-19.The best evidence for use of corticosteroids to treat COVID-19 comes from dexamethasone.31 Corticosteroids are also known to enhance potassium elimination. Patients using diuretics should have their potassium concentration monitored daily during hospitalization as part of their metabolic panels.32 A low potassium level can be harmful particularly in patients with cardiac dysrhythmias or those managed with digoxin. Finally, dosing for magnesium antacids should be separated by at least 2 hours from that for corticosteroids if an oral corticosteroid is used, as adsorption can decrease the AUC of the corticosteroid by more than 75%.12Dexamethasone is a mild inducer of CYP3A4, which is unlikely to have important clinical implications. However, drug concentrations for CYP3A4 substrates with a narrow therapeutic index  should be monitored at least once during hospitalization after dexamethasone initiation.34 which have been administered IV as single doses of 8 mg/kg (up to 800 mg) and 400 mg, respectively. In the RECOVERY trial, 29% of patients were administered a second dose of tocilizumab based on clinician discretion.33 While these agents are FDA approved for chronic use in rheumatoid arthritis, clinicians in the acute care setting may be more familiar with tocilizumab, which is used for cytokine release syndrome at doses similarly high to those used for COVID-19. The terminal half-life of tocilizumab is 21 days,35 and, from a DDI perspective, it is expected that the effects of single doses used for COVID-19 would last at least for the duration of hospitalization. Blockade of IL-6 helps normalize previously upregulated CYP enzyme activity due to chronic inflammation,35 and DDIs involving CYP substrates and IL-6 inhibitors may therefore be flagged by databases in the context of chronic inflammatory states such as rheumatoid arthritis. However, this is not relevant to COVID-19 as any change in CYP activity due to treatment would just return the patient to their baseline state. Similar to other monoclonal antibodies, these drugs have a high molecular weight and are not eliminated renally.35 Additionally, they are not eliminated via hepatic pathways but should be used with caution or avoided in patients with elevated levels of liver enzymes  due to their potential to cause liver injury.36 The mechanism for this is unknown but may be related to IL-6 inhibition or effects on the immune system. No DDIs are expected due to renal changes or CYP enzymes.The IL-6 inhibitors investigated for COVID-19 include tocilizumab and sarilumab,3 cells/µL.36 For example, one expected interaction is between corticosteroids and IL-6 inhibitors, as these are both indicated in severely ill patients with COVID-19 requiring oxygen or ventilatory support.23 This is a PD DDI that would likely be flagged in databases. However, even though there is increased risk for superinfection, dexamethasone in combination with either an IL-6 inhibitor or JAK inhibitor has been well tolerated and led to improved outcomes in large trials.34 Thus, our decision-making framework suggests that this combination is appropriate.IL-6 inhibitors have immunosuppressive and hematological effects. Caution is advised in patients who are already immunosuppressed as a consequence of other therapies, with an absolute neutrophil count of less than 500 cells/µL or a platelet count of less than 50 × 1038 These are administered orally or enterally for hospitalized patients and were used in the aforementioned trials for 10 to 14 days. It is important to recognize that these agents have different interaction profiles. Baricitinib is a CYP3A4 substrate; however, studies in healthy individuals have shown that strong inhibitors  or inducers  do not result in a meaningful change in baricitinib AUC.39 Baricitinib is also a substrate for several transporters, including OAT3, P-gp, BCRP, and MATE2-K. However, only changes to OAT3 activity have been shown to result in an important change in its elimination, with the OAT3 inhibitor probenecid increasing the AUC of baricitinib by 2-fold.39 FDA has dose reduction recommendations  for baricitinib when it is used with strong transporter (OAT3) inhibitors.39 Another strong OAT3 inhibitor is teriflunomide, which is used for multiple sclerosis. Fortunately, use of strong OAT3 inhibitors is uncommon.Bariticinib and tofacitinib are the JAK inhibitors with the most evidence for use based on COVID-19 clinical trials.40 Fluconazole is a moderate CYP3A4 inhibitor and a strong CYP2C19 inhibitor. Co-administration of fluconazole increased the AUC of tofacitinib by 79%.40 FDA has a dose reduction recommendation  for tofacitinib when it is used with a strong CYP3A4 inhibitor or the combination of a moderate CYP3A4 inhibitor and a strong CYP2C19 inhibitor.41 Numerous drugs inhibit these CYP enzymes, requiring regular checking for interactions via a database based on a patient’s medication profile, with subsequent reduction in the dose of tofacitinib if indicated.Tofacitinib is a substrate for CYP3A4 (major) and CYP2C19 (minor). Administration of a strong inhibitor of CYP3A4 (ketoconazole) increased the AUC by 103%, and a strong CYP3A4 inducer reduced the AUC by 84%.42 Use of JAK inhibitors with IL-6 inhibitors is emerging in small case series of patients with COVID-19, with no evidence of serious harm,43 but this combination has not been thoroughly investigated and cannot be recommended by our decision-making framework due to an increased risk of secondary infections and lack of evidence.41JAK inhibitors also have immunosuppressive effects. Thus, there is a PD interaction when using them with dexamethasone. However, similar to the IL-6 and dexamethasone combination, use of a JAK inhibitor and dexamethasone is appropriate according to our decision-making framework. Clinical trials have shown beneficial effects when these medications are used in combination for COVID-19, although a risk of superinfections is present.23 Ritonavir, a well-known perpetrator, is a P-gp inhibitor and a strong CYP3A4 inhibitor. A DDI in this context is built into the formulation by the incorporation of ritonavir, resulting in a boosted effect of nirmatrelvir. In the case of nirmatrelvir, drug exposure is significantly higher when this is co-administered with ritonavir due to CYP3A4 inhibition, enhancing the effect against COVID-19. NIH guidelines recommend ritonavir-boosted nirmatrelvir for nonhospitalized patients with COVID-19 who are at high risk of progressing to severe disease.23 If the admission is unrelated to COVID-19, completion of the ritonavir-boosted nirmatrelvir course would be reasonable. In either scenario, DDIs are likely to be apparent as inhibition of CYP3A4 can be sustained for 3 to 7 days following discontinuation, with the delay mostly due to the time required for liver enzyme activity to return to normal after suppression.44-46 Furthermore, ritonavir can induce other CYP enzymes , with the effect potentially lasting for a few weeks. This lagging effect on CYP enzymes increases the probability of unrecognized DDIs because discontinued therapies do not appear on the patient’s active medication profile. There are numerous drugs that are major substrates for CYP3A4 and P-gp; while it is impractical for us to provide a list of these medications that may be used in the hospital and their associated risks, it is important to recognize that sensitive substrates may have a 5-fold increase or greater in AUC. In most circumstances, we would recommend withholding the substrate temporarily if clinically possible and/or using an alternative agent. For example, the AUC of simvastatin increased 31-fold in the presence of ritonavir.47 The risk for rhabdomyolysis with such an increase in AUC is high, and withholding the statin in this situation is clearly required.When patients treated with ritonavir-boosted nirmatrelvir in the community are admitted to the ICU, providers must decide whether to continue this therapy. In some circumstances, hospital admission may be related to a DDI involving ritonavir-boosted nirmatrelvir. If the admission is due to worsening COVID-19 associated with oxygen/ventilatory support, ritonavir-boosted nirmatrelvir is likely to be discontinued. Although transition to dexamethasone plus an IL-6 or JAK inhibitor is warranted, this does not prevent the occurrence of DDIs due to ongoing CYP inhibition.48 and there are therefore no known drug interactions with this agent. While this is appealing from a DDI perspective, use of molnupiravir is no longer favored relative to ritonavir-boosted nirmatrelvir due to the results of the PANOMAMIC trial, which did not show a reduction in the frequency of hospitalization or death with molnupiravir.49The other oral antiviral used for COVID-19 is molnupiravir. It is not a substrate for CYP enzymes or common human drug transporters,50 Remdesivir is a minor substrate for several metabolic and transport pathways, including plasma and tissue esterases, CYP enzymes , and transporters (OATP1B1 and P-gp). DDI studies with remdesivir are lacking. Thus, the potential for interactions is theoretical and no dose adjustments are recommended. Although hydroxychloroquine and chloroquine are not recommended and are potentially harmful in COVID-19, these agents may also antagonize the antiviral effect of remdesivir.51 A published case report highlights that remdesivir’s P-gp–mediated efflux from hepatocytes may be impaired by P-gp inhibitors, potentially contributing to an enhanced hepatotoxic effect of remdesivir.52 There is also a possible risk for increased QTc interval with remdesivir.53 Thus, QTc interval monitoring is recommended when there is a DDI with another agent that can prolong QTc.Remdesivir is an IV administered antiviral and one of the first agents to show a benefit by reducing the time to clinical recovery in COVID-19.When new drugs are introduced for clinical use, such as those for COVID-19, the new drug application is supplemented with preclinical information that is submitted to regulatory authorities. This provides the basic information required to assess the potential for DDIs based on affinity for metabolizing enzymes and transporters, which we have utilized in this article. In addition, many of the therapies recommended for COVID-19 were repurposed from other indications, which has allowed for a body of postmarketing studies that provides additional information to guide DDI decisions. Decisions can be guided by the extent of expected change in PK and PD parameters, anticipated clinical effects, and a risk evaluation. Among the various COVID-19 therapies, ritonavir-boosted nirmatrelvir has been one of the most challenging for clinicians from a DDI perspective. Clinicians in the acute care environment should carefully consider that, even after ritonavir-boosted nirmatrelvir is ceased upon hospitalization, clinically relevant DDIs may be present. A systematic and pharmacologically based decision-making process should be applied to improve outcomes and avoid harm.


Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super-resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse AR hemodynamics in a non-invasive manner.The online version of this article (doi:10.1007/s10554-023-02815-z) contains supplementary material, which is available to authorized users. Aortic valve regurgitation, or aortic regurgitation (AR), is a common type of valvular heart disease where the aortic valve does not close properly, causing reflux of blood from the aorta into the left ventricle . This reCardiovascular four-dimensional (4D) flow magnetic resonance imaging (MRI) is a novel imaging technique to quantify full-field blood flow velocities, providing a three-dimensional (3D) velocity field across a region of interest throughout the cardiac cycle. Currently, due to the small width of the regurgitant jet and the limited spatiotemporal resolution of 4D flow MRI, it fails to accurately capture the complex hemodynamics of AR. The combination of computational fluid dynamics (CFD) and deep learning with 4D flow MRI will help to generate higher resolution images and recover hemodynamic parameters lost in current MRI images, with a target upsample factor of 4. This work will extend what has already been completed in 4DFlowNet  by usingAR occurs when the aortic valve does not close properly, causing blood to flow back into the left ventricle from the aorta. This forces the heart to work harder and pump more blood to the aorta, which can cause further heart problems in the future. AR also has varying levels of intensity, from trace or mild through moderate to severe . Acute A4D flow MRI is an established imaging technique that captures the temporal changes of 3D blood flow patterns within individual vascular structures , 6 and hDeep learning  has had The combination of 4D flow MRI with deep learning has been explored in multiple ways to increase resolution and provide more accurate estimates of physical quantities , 21, 22.Recent development in object detection has proven significant in advancing ANN architecture , and appModelling of the aortic valve was done using ANSYS 2021 R1, with CFX chosen for CFD simulations, and was an iterative process to determine the best parameters and options that would increase efficiency and accuracy.The design of the problem geometry focused on simplicity, over replicating a real-life aortic valve during regurgitation, to capture the flow features of the regurgitant jet, which are not captured in the 4D flow data at typical resolution, and not handled well by 4DFlowNet. The jet characteristics are modelled using a variety of eccentricities and angulations to capture the variation in jet properties, giving the additional flow information required for training of the flow super-resolution network. The basic geometry is radially symmetrical and shaped like a 3D cylinder with a constricted section part way along the length, resembling a Venturi tube. The constricted section represents the gap in the aortic valve present for AR to occur. Figure In total, 20 different simulations were generated using 20 different geometries with a time step size of 0.001 s. The first set of 10 geometries  to be used as HR images  between the paired HR images and the SR images generated from the corresponding input LR ones. The voxel-wise loss was calculated as the mean sum of squared differences between each velocity component:The MSE of fluid and non-fluid regions were calculated as separate terms due to the imbalance and irregularity of these regions within a specific patch. This gives the total loss to be:The original loss function in 4DFlowNet contained a weighted velocity gradient term to smoothen the gradient between neighbouring vectors . This waN is the total number of voxels in the fluid domain, x.The relative speed error (RE), the relative difference between the SR velocity magnitude (speed) compared to the actual HR speed on the validation set, was used to measure network performance and save model checkpoints. This was only calculated in fluid regions to avoid zero division error, as well as adding a small number  and the structural similarity (SSIM) metric  in all tTraining was performed using a Tesla V100 GPU with 32 GB memory with networks being trained for 200 epochs. Improvements in relative speed error (RE) plateaued around the 100 epoch mark for 4DFlowNet-Res while still improving for 4DFlowNet-CSP and 4DFlowNet-Dense up till the very last epoch. This can be seen in Fig. Networks were tested on one complete geometry consisting of 71 timeframes with no (phase/velocity) aliasing, and the same complete geometry with full aliasing. These predictions were required to be patch-based since patches were used as the input and output for each network. The complete geometry was reconstructed by stitching together multiple SR velocity field patches, which was done with a stride of  license at SR images were analysed visually and quantitatively to better understand how each model was performing. Figure The values for each evaluation metric were collected and compared in “Appendices 1 and 2” to quantify the performance of every model. Again, these values were taken from the time frame with peak flow, with peak velocities of 2.186, 0.355, and 0.349 m sThe metrics were plotted in “Appendix 3”. Briefly, 4DFlowNet-Dense-A and 4DFlowNet-CSP-A seem to perform the best with the lowest RMSE error and largest SSIM in the principle flow direction , and written informed consent was obtained from each participant. Two sets of in vivo 4D flow MRI data (CH34 and CH37) were acquired and used exclusively to show how the network would perform, seen in Fig. , and wriThese datasets were LR images only, with no HR images available to compare the predicted SR images against. However, the SR image produced was compared against the original 4D flow MRI image to help better understand the prediction. The SR image seems to have effectively removed noise from the LR image and strengthened the AR signal. Other networks performed similarly, with predicted SR images almost identical to the one shown. The top set of data (CH34) was also segmented and visualised with ParaView , shown ix. Since the peak velocities in the other flow directions, y and z, were almost 10 times smaller than that in the principle direction, networks would have difficulty differentiating between velocity and noise. This is evident in the y and z RMSE values, which were over half of their corresponding peak velocities. The y and z SSIM values were also much worse, potentially exhibiting strange behaviour in these low velocity fields too [A noteworthy consideration when analysing the results is that the focus should be more towards metrics and values obtained in the principle flow direction elds too .The main limitation of previous work has been related to insufficient data and flow characteristics , 20, 22.In terms of the synthetic LR and HR image pairs, the downsampling process and patch-based approach seemed to work effectively. The noisy LR patches enabled the network to learn noise removal while also enabling greater generalisation to unknown flow characteristics or geometries . HoweverIncorporating aliased patches into the training data seemed to work effectively as well, improving the RMSE across networks trained with all geometries. This was done by choosing VENC values lower than the maximum velocity, within a particular time frame, with a 10% probability. Note that this probability was chosen arbitrarily. For the VENC, if it is set too high, visualization of the jet may not be obtained and be inaccurate, as well as having poorer SNR. On the other hand, if it is set too low, flow characteristics may be lost and a mosaic pattern will be shown . This meA major drawback of residual blocks is that they suffer from limited learning ability . This waWith sufficient or abundant data, cross stage partial or dense networks may be preferred. The learning ability in these types of networks have a significantly higher ceiling than residual networks , 31, witDespite the network architecture seeming to work quite effectively, there are still improvements that could be made on top of modifying the residual blocks. Presently, the network is not taking full advantage of the temporal aspect in 4D flow MRI. Modifying it to incorporate characteristics of recurrent neural networks  may helppeak velocities through the vena contracta are used to symbolize disease severity. In fact, derivation of regional pressure drops are routinely derived from such peak velocity measures using the so called simplified Bernoulli equation [The clinical motivation for the current study was the assessment of regurgitant or highly stenotic valvular flow; instances where quantification of regional velocities and changes in pressure act as effective biomarkers to describe the severity of disease. In these instances, and in clinical practice, assessment of equation  Below is the link to the electronic supplementary material.


The global prevalence of heart failure (HF) is rising and carries a heavy social and economic burden. Type 2 diabetes mellitus (T2DM) patients are at an increased risk of incident HF even in the absence of cardiovascular risk factors. Patients with established HF are at an increased risk of death following a worsening HF event. Various trials on sodium-glucose cotransporter-2 (SGLT2) inhibitors have shown that these novel drugs prevent incident HF and reduce the risk of worsening HF in both patients with T2DM and those without diabetes. This literature review analyzed the data from 13 randomized controlled trials that met the pre-specified inclusion criteria. The aim was to compare the clinical outcomes of SGLT2 inhibitors for primary and secondary prevention of HF in patients with T2DM and those without diabetes. In addition, this study collated and summarized the patients’ clinical characteristics with respect to the clinical outcome, and finally, it evaluated the safety considerations when using SGLT2 inhibitors. The data showed that SGLT2 inhibitors are effective and safe in the primary and secondary prevention of HF across a broad spectrum of patient populations and care settings. Therefore, wider eligibility for their use should be considered. Heart failure (HF) is the final common pathway for most heart diseases. Global prevalence and incidence have been on the rise, more so in developing nations . HF carrThe hypoglycemic drugs, sodium-glucose cotransporter-2 (SGLT2) inhibitors, have emerged as formidable drugs demonstrating primary and secondary prevention of HF events in both T2DM and non-diabetes mellitus (DM) patients ,10. SomeThis study will synthesize the available evidence on the efficacy of SGLT2 inhibitors for the primary and secondary prevention of HF in both T2DM and non-DM patients. In addition, it will help come up with an algorithm that will aid clinicians in determining which patients would benefit from and can be safely initiated on an SGLT2 inhibitor for purposes of HF prevention based on the patient’s clinical characteristics and clinical parameters, as evidenced in clinical trials.MethodologyA systematic literature search using Google Scholar, PubMed, Cochrane Library, MEDLINE, Embase, Findit, and ScienceDirect databases was done for studies that were relevant to this narrative literature review. The keywords used for the literature search were related to SGLT2 inhibitors , “heart failure” (“HFrEF” and “HFpEF), “type 2 diabetes,” and “cardiovascular and renal outcome with SGLT2 inhibitors.” The inclusion criteria for this review were limited to randomized control trials (RCTs) published between 2012 and 2022 and in the English language.A total of 13 studies were included in this literature review, as shown in the PRISMA chart  of SGLT2 inhibitors, these drugs were serendipitously shown to confer a reduction of HF events among T2DM patients, a finding that was independent of their glucose-lowering effect. Data show that SGLT2 inhibitors result in a moderate reduction in the incidence of major adverse cardiac events (MACE); however, their strongest and most consistent benefit is in the reduction of incident HF. Notably, the overwhelming majority of the patients had no history of HF; hence evidence on primary prevention of HF was apparent. These findings were further confirmed in a meta-analysis by Kaze et al., where the pooled effect size of SGLT2 inhibitors from data of the CVOTs and the renal outcome trials found that using an SGLT2 inhibitor lowered the risk of MACE by 17%  and substantially lowered the risk of hospitalization for heart failure (HHF) by 38%  .The magnitude of the effect in the reduction of HHF was an unexpected but welcome discovery, as no previous CVOT of a hypoglycemic drug had demonstrated a reduction in the incidence of HF.SGLT2 inhibitors also demonstrated a compelling and significant role in the primary prevention of HF in patients without pre-existing CVD. This was first evident from the DECLARE TIMI 58 trial, which recruited the largest number of individuals free from CVD but exhibiting multiple CV risk factors. However, this study did not demonstrate a significant reduction in MACE; a finding that could suggest that SGLT2 inhibitors are more efficacious in the prevention of MACE in individuals with a greater CVD risk or prior history of CV events. The data from CREDENCE, DAPA-CKD, and SCORED trials further supported this evidence of HF prevention in patients without CVD as these trials also enrolled a significant percentage of people without CVD. This is an important finding because DM patients are at increased risk of incident HF even without established CVD .In a systematic review and meta-analysis of data from EMPAREG OUTCOME, CANVAS program, and DECLARE TIMI 58 trials, SGLT2 inhibitors lowered the risk of HHF by 31%, and this risk reduction was maintained regardless of the presence of atherosclerotic cardiovascular disease (ASCVD) . SGLT2 i2, SGLT2 inhibitors still conferred the benefit of a reduction in HHF without worsening renal function. The data from these trials show a relationship between baseline renal function and a decrease in HHF. SGLT2 inhibitors resulted in a greater risk reduction of HHF in patients with a worse baseline renal function without renal function deterioration. Instead, SGLT2 inhibitors reduced the composite of renal endpoints . This is an important finding because the risk of HF increases with a declining eGFR [Renal outcomes trials with SGLT2 inhibitors -18 also ing eGFR ; therefoSGLT2 inhibitors for the primary prevention of HF in patients without diabetesThe only tangible evidence of a possible benefit for the primary prevention of HF with SGLT2 inhibitors in non-diabetic patients comes from the DAPA-CKD study . In the SGLT2 inhibitors for the secondary prevention of HF in both T2DM and non-diabetic patients with HFrEFThe DAPA-HF study  and the There was an almost similar reduction in the incidence of HHF in both trials. Interestingly, the EMPEROR-Reduced study did not show a significant reduction in the incidence of CV mortality. This finding could be explained by a much sicker population in the EMPEROR-Reduced study compared to the DAPA-HF study.The SOLOIST-WHF study had a majority of the patients with an LVEF < 50% . This stSGLT2 inhibitors for the secondary prevention of HF in both T2DM and non-diabetic patients with HFpEF and HFmrEFThe two landmark trials on SGLT2 inhibitors on HFpEF, the EMPEROR-Preserved  and the Despite this study being described as the “first positive trial in HFpEF,” it did not seem to confer any significant reduction in CV mortality and in overall mortality. This study was also not strictly restricted to patients with HFpEF. It recruited patients with an LVEF > 40%, which, according to the 2021 ESC definition, encompasses both HF with mildly reduced ejection fraction (HFmrEF) (LVEF = 40-49%) and HFpEF (LVEF ≥ 50%) . HoweverThe DELIVER study is the largest and most recent trial of SGLT2 inhibitors in patients with HFpEF and HFmrEF. Dapagliflozin led to an 18% reduction in the composite outcome of CV death or worsening HF with no interaction of diabetes status. The NNT to prevent an event was 32 after a median duration of treatment of 2.3 years. Although the DELIVER study was not powered to assess dapagliflozin’s effect on CV mortality, the results indicated that dapagliflozin did not reduce CV death or overall mortality, but did show benefit in the reduction of HHF by 23%. Similar to the EMPEROR-Preserved study, this study recruited patients with an LVEF > 40% and therefore included patients with HFmrEF (LVEF = 40-49%) as well as those with HFpEF (≥50%). However, unlike its predecessor, the EMPEROR-Preserved study, the DELIVER study did not show any attenuation in benefits with dapagliflozin with increasing LVEF .In the SOLOIST-WHF study, sotagliflozin was also reported to be significant in the reduction of HHF in patients with HFpEF . But because of the small number of patients with HFpEF, there was no definitive recommendation. Just like in the EMPEROR-Preserved study, the SOLOIST-WHF study did not show a reduction in CV mortality or overall mortality. Disappointingly, these three SGLT2 inhibitors did not result in a mortality reduction in patients with HFpEF. Therefore, there is currently no therapeutic drug that confers a reduction in mortality in patients with HFpEF. This uncertainty is conveyed in clinical guidelines where diuretics are the only therapy with a class 1 recommendation for the management of patients with HFpEF. Currently, SGLT2 inhibitors are not recommended as guideline-directed medical therapy for HFpEF [A meta-analysis of the EMPEROR-Preserved and the DELIVER trial done by Vaduganathan et al.  showed tClinical characteristics and patient demographics in the studies of primary prevention of HFMost of the participants’ clinical characteristics were fairly similar, with only a few exceptions  in most studies was about 8% to 8.3%, reflecting poor glycemic control. Only a small percentage of patients had a prior history of HF.2, and only 30% had evidence of micro or macroalbuminuria.The majority of patients in the EMPEREG OUTCOME, CANVAS, and VERTIS-CV trials had established CVD. The DECLARE-TIMI 58 study had the lowest number of patients with existing CVD but the highest number of patients with multiple CV risk factors. Regarding the overall CV and renal risk, patients in the DECLARE-TIMI 58 study harbored the lowest risk, with only 41% of patients having established CVD. Additionally, the patients in the DECLARE-TIMI 58 study had fairly good renal function compared to the other studies. Only 7.4% of the patients had an eGFR below 60 mL/min/1.73 m2. The mean eGFR ranged from 37 to 56 mL/min/1.73 m2, which was lower than in the CVOTs, and more than half of these patients showed evidence of either micro or macroalbuminuria. Despite differences in the study design and baseline patient characteristics, the findings for a reduction in risk for HHF were remarkably significant and consistent. However, patients' baseline characteristics seemed to influence the SGLT2 inhibitors’ benefit regarding the reduction in MACE. Trials that significantly reduced MACE were those with patients with a higher baseline CV risk and/or advanced kidney disease.The baseline clinical characteristics in the studies focusing primarily on renal outcome -19 were Clinical characteristics and patient demographics in the studies of secondary prevention of HFThe baseline characteristics are shown in Table The trials on HFrEF had more men and a relatively younger population compared to the trials on HFpEF. This was consistent with the epidemiology of HFpEF where advancing age and female sex are at increased risk for developing HFpEF . All theInclusion into any trial required obtaining a prespecified baseline natriuretic peptide level and measuring the LVEF by an echocardiogram. The minimum N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels ranged from as low as 300 pg/ml in individuals in sinus rhythm in the EMPEROR-Preserved and the DELIVER trials to 5,000 pg/ml in individuals with atrial fibrillation or atrial flutter in the EMPEROR-Reduced trial. However, the median NT-proBNP across the trials ranged from as low as 974 pg/ml in the EMPEROR-Preserved trial to as high as 1,910 pg/ml in the EMPEROR-Reduced trial. The mean LVEF was as low as 27% in trials of HFrEF, with both trials on HFpEF having a mean LVEF of 54%. The EMPEROR trials included patients with an eGFR as low as 20 mL/min/1.73 m². The SOLOIST-WHF trial, however, had the lowest mean eGFR of approximately 50 mL/min/1.73 m², with all the other trials having a mean eGFR of about 60 mL/min/1.73 m² or slightly higher.The patients in all the trials were on background guideline-directed medical therapy for HF. In particular, trials on HFrEF had more background use of angiotensin receptor-neprilysin inhibitors (ARNI) and mineralocorticoid receptor antagonists (MRA) than trials on HFpEF. Comparing the two main trials on HFrEF, it is notable that patients in the EMPEROR-Reduced trial were sicker than those in the DAPA-HF trial judging from their higher levels of NT-proBNP and much more reduced LVEF. Regarding the background use of HF therapy, more patients in the EMPEROR-Reduced were on an ARNI compared to those in the DAPA-HF trial. However, this did not affect the efficacy of the SGLT2 inhibitor in reducing the risk for HHF as both trials had very similar results. To further support these findings, Packer et al.  showed tThe DELIVER trial had more patients in NYHA functional class III-IV and with higher NT-proBNP than in the EMPEROR-Preserved trial. This was perhaps because more patients in the DELIVER trial had a history of HHF (40%) compared to the EMPEROR-Preserved trial (23%). Another notable difference is that more patients in the EMPEROR-Preserved trial were on an angiotensin-converting enzyme inhibitor (ACE-I) or an angiotensin receptor blocker (ARB) compared to patients in the DELIVER trial.The SOLOIST-WHF trial was in a unique care setting compared to all the other trials. It recruited only T2DM patients with worsening HF. Therefore, patients had a much higher median NT-proBNP  and a lower mean LVEF (35%). Additionally, the patients had the lowest mean eGFR out of all the trials (approximate eGFR of 50 mL/min/1.73 m²).SafetyData from studies on SGLT2 inhibitors show that these drugs are generally safe, well tolerated, and with an acceptable risk-benefit profile. The commonest adverse event reported in all trials was the small but increased risk for genitourinary infections. This includes urinary tract infections (UTIs) and genital mycotic infections. This risk is attributed to the increased amount of glucose in the urinary tract, which makes it a fertile environment for the growth of commensal microorganisms . RegardiAnother serious but rare adverse event seen with SGLT2 inhibitors, particularly in DM patients, is diabetic ketoacidosis (DKA). Therefore, clinical conditions predisposing to an increase in risk should be evaluated for and managed ,40. SGLTVolume depletion resulting in hypotension is also a concern with SGLT2 inhibitor use because of their diuretic effect. However, no overt volume reduction was reported that resulted in a significantly increased risk for hypotension. This risk can, however, be reduced by dose adjustment of diuretic therapy. Acute kidney injury is a potential adverse event with SGLT2 inhibitors because of the hemodynamic changes that occur at the glomerular level . ContrarLimitations and recommendationsMost of the data on the prevention of HF and the comparison between T2DM and non-DM patients came from a subgroup analysis, and this may have affected the power of the studies. It was also difficult to determine which phenotype of HF was prevented in the CVOTs and renal outcome trials because the definition of HF was not clearly defined. Currently, there is a paucity of data on the primary prevention of HF in patients with myocardial infarction, despite it being the leading cause of HF. Two major trials are ongoing in these cohorts, i.e., “Dapagliflozin Effects on Cardiovascular Events in Patients With an Acute Heart Attack (DAPA MI)” and “Empagliflozin in patients post myocardial infarction (EMPACT MI)” trials, which should give us more insights ,42.In conclusion, SGLT2 inhibitors are effective for the primary and secondary prevention of HF across a broad spectrum of patient populations and care settings. The data have demonstrated that SGLT2 inhibitors reduce the risk of HF irrespective of diabetes or HF status and are generally safe with no clear evidence of any major safety concerns. We found that these medications are useful in both HFrEF and HFpEF irrespective of the diabetes status of a patient. These medications are generally well tolerated and are safe to use although there is a slightly increased risk of genitourinary infections and euglycemic ketoacidosis. Further randomized controlled trials would be useful to explore the efficacy and safety of these medications.


Chronic heart failure (CHF), as the final stage of the progression of many cardiovascular disorders, is one of the main causes of hospitalization and death in the elderly and has a substantial impact on patients' quality of life (QOL). Exercise-based cardiac rehabilitation (CR) has been shown to considerably enhance QOL and prognosis. Given the barriers to center-based CR faced by most developing countries in the form of expensive instruments, the development of home-based CR is necessary. Tai Chi, as an instrument-free exercise, has been shown to be successful in treating elderly CHF individuals. Fu Yang, as one of the academic concept of Traditional Chinese Medicine (TCM), believes that the fundamental pathogenesis of CHF is the gradual decline of Yang, and emphasizes the restoration of Yang physiological function in the treatment process. Therefore, we develope a home-based Tai Chi exercise rehabilitation program called Fu Yang Tai Chi (FYTC) for elderly CHF patients by combining the Fu Yang Theory of TCM with the CR theory. The objective of this study is to evaluate the effectiveness, acceptability, and safety of the program.We suggest conducting a parallel randomized controlled clinical trial with open label. Eighty CHF elderly participants will be randomly assigned in a 1:1 ratio to the FYTC rehabilitation program group or the moderate-intensity aerobic walking control group. Eligible participants will engage in either three sessions weekly of FYTC or walking exercise for 12 weeks. The primary outcome is the relative change in 6 min walk distance (6MWD). The secondary outcomes are the plasma levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP), QOL, left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), self-rating anxiety scale (SAS) and depression scale (SDS), exercise skills, and noninvasive hemodynamic monitoring. Throughout the trial, adverse events will be recorded for safety evaluation. Researchers who are blinded to the treatment allocation will analyze the data.This research was authorized by the Guang'anmen Hospital Ethics Committee of the Chinese Academy of Medical Sciences (2022-141-KY). Our findings will be shared online and in academic conferences as well as in peer-reviewed journals.ChiCTR2200063511. According to epidemiological surveys, the mortality rate was 7.2% and readmission rate was 31.9% for patients with chronic heart failure (CHF) . Lower QCardiac rehabilitation (CR) is an essential component of chronic heart failure management . IncreasTai Chi is a mind-body exercise with medium-intensity aerobic exercise and safe for the elderly. Tai Chi is commonly considered as a type of exercise that involves an active mindful element, which means it focuses on training profound inwardly mental attention in addition to the habitual bodily movements involved in traditional forms of exercise . Many clHowever, there are no clinical trials to confirm the effectiveness and safety of the exercises; therefore, we designed a single-center, open label, randomized controlled trial (RCT) to study the effects of “Fu Yang Tai Chi” as an adjunctive therapy on the quality of life of elderly patients with CHF. If the results are positive, this therapy could become a good way for older people with CHF to exercise at home that is also affordable.2.2.1.The Guang'anmen Hospital of the Chinese Academy of Traditional Chinese Medicine serves as the recruitment site for the participants and the single center for this open, randomized controlled trial. A total of 80 volunteers will be enlisted after providing written informed consent. All of the prospective patients will be given comprehensive information regarding the trial in detail, covering topics such as its objectives, procedures, and intervention approaches as well as its potential benefits and drawbacks. Throughout the 12-week testing period, we will randomly assign participants in a 1:1 ratio to either the Fu Yang Tai Chi exercise group or the walking control group. The outcomes will be assessed at 12 weeks (immediately after the intervention). The study design process is shown in the study flow chart in 2.2.2.2.1.(1)Aged between 60 and 75 years old;(2)Fulfilling the diagnostic criteria for heart failure with reduced ejection fraction (HFrEF) included in the 2021 ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure;(3)New York Heart Association functional classification (NYHA class) II and III;(4)Currently taking part in less than 60 min of physical exercise per week;(5)Learned about the clinical trial programs and voluntary participants and provided written informed consent.2.2.2.(1)Limited range of motion in the extremities;(2)Inadequately managed hypertension (systolic blood pressure ≥180 mmHg or diastolic blood pressure ≥100 mmHg);(3)Resting heart rate over 120 beats per minute, or complicated by malignant arrhythmia ;(4)Patients who suffer from a serious infection, chronic obstructive pulmonary disease, respiratory failure, or pulmonary cardiac disease;(5)Patients with severe primary illnesses, including tumors and hematopoietic system disorders;(6)Severe mental illness or cognitive dysfunction;(7)Combined severe metabolic and hormonal disorders (such as thyrotoxicosis and diabetic ketosis);(8)Recent treatments by modern or traditional Chinese medicine rehabilitation methods in the past 3 months.2.3.Following the completion of the baseline evaluation, qualified participants will be assigned to either the FYTC exercise group or the walking control group in a ratio of 1:1 using a table of random numbers that is created by the statistical program SPSS 25.0. The grouping information will be maintained by an independent statistician who is unaware of the participant recruiting, evaluation, and intervention processes. The statistician will give the list to the research coordinator after placing it in opaque, sealed, sequentially numbered envelopes. The research coordinator will open one envelope at a time after screening to receive a random number and information about participant grouping.2.4.Both patients and personnel were unaware of the group the patient would be allocated to at the time of performing the baseline 6 min walk test. It is impossible to maintain blinding of trial participants and exercise instructors due to the nature of the intervention. However, researchers assessing primary outcome assessment will be masked for group assignment.2.5.Participants in both groups will receive home-based cardiac rehabilitation. Eligible participants will be randomly assigned to the FYTC exercise group or the walking control group, and an individualized exercise prescription will be formulated. Patients in both groups will be asked to exercise at least 3 times per week for 12 weeks. Exercise intensity will be monitored using Borg's Rating of Perceived Exertion (RPE) scale or adjusted using the heart rate reserve method based on the HR max determined during the initial 6 min walk test. The 6 min walk test will be conducted at the hospital. During the process of home-based CR we will equip each subject with a heart rate detector, and the monitoring will be conducted remotely through WeChat video and the heart rate detector. Exercise sessions in both groups will consist of warm-up, exercise, and relaxation periods. In addition, an exercise diary will be provided to all participants to record their daily physical activities over the trial period.2.5.1.The FYTC exercise group will practice FYTC at home with remote guidance training by two professional Tai Chi instructors. Each exercise routine will last at least 50 min  three times a week for a 12-week period. The FYTC exercise core workout (30 min) will consist of the following eight movements : stake-sIn the continuous training, the Borg RPE range of 11–13 will be used to regulate exercise intensity and energy expenditure, while the patient's heart rate will be monitored by a heart rate monitor. All participants will be instructed to maintain their usual activities and refrain from new strength training. Video review of the WeChat software and individual feedback from subjects will be checked frequently for accuracy and compliance in an effort to ensure that participants perform each movement. We will ask all patients to go about their daily activities and refrain from attempting any novel forms of strength training.2.5.2.Participants in the control group will walk at a moderate intensity three times weekly at consistent for a total of twelve weeks. Each routine will consist of three components: a 10 min warm-up, a 30 min session of moderate-intensity walking exercise, and a 10 min rest period. Participants will be guided and supervised by a professional cardiac rehabilitation coach throughout the intervention. The Borg RPE range of 11–13 or 40%–80% of reserve heart rate will be used to regulate exercise intensity and energy expenditure. Our cardiac rehabilitation program will be customized for each participant and closely supervised. Each participant who take part in the study will receive careful monitoring in a cardiac rehabilitation program that will be specifically designed for them. To achieve moderate intensity exercise, exercise will be progressively increased in duration and intensity.2.6.Each participant will receive the standard of care treatment as determined by their doctor in accordance with clinical recommendations , 27, suc2.7.2.7.1.The items to be measured and the window of time for data collection are listed in (1)Variations in N-terminal pro-B-type natriuretic peptide (NT-proBNP) plasma levels;(2)Improvement in quality of life (QOL). Improvement in QOL will be evaluated using the Minnesota Living with Heart Failure Questionnaire (MLHFQ) and the Pittsburgh Sleep Quality Index (PSQI). The MLHFQ which is the most commonly used scale for measuring quality of life in CHF patients, will be utilized to evaluate how these two exercise regimens affected patient quality of life from a physical, emotional, social, and psychological standpoint. A self-assessment questionnaire called the PSQI  rates sl(3)Alterations in self-rating anxiety scale (SAS) and self-rating depression scale (SDS) scores;(4)Improvement in left ventricular end-diastolic internal diameter (LVEDd) and left ventricular ejection fraction (LVEF) echocardiographic measures;(5)Enhancement of exercise abilities: Exercise abilities include dexterity, balance, and grip strength. An instrument made by CAMRY (product model EH101) called a grip strength dynamometer will be used to test grip strength; it measures the maximal isometric muscular strength of the hand and forearm muscles. We will independently measure the time of one-foot standing with eyes closed and one-foot standing with eyes open for the balancing test , confirm(6)Changes in noninvasive cardiac output monitoring (NICOM): NICOM is a noninvasive hemodynamic monitoring technique based on bioelectrical impedance , 31. TheThe secondary outcomes will be as follows:2.7.2.All study participants will receive safety monitoring during the intervention, focusing on vital signs , laboratory testing , and adverse events (AEs), which will be recorded throughout the trial. Exercise injury, falls, and drug withdrawal symptoms  will be included in the AEs . The rel2.8.β = 0.1). Considering a possible withdrawal rate of 20%, the total number of patients required will be 76. To facilitate statistical and clinical collection, the sample size will be expanded to 80.The size of the sample is determined by comparing the projected improvement in 6MWD between the walking control group and the FYTC exercise group. In the current study , the meaκ stands for the ratio between the two sample cases (κ = 1), δ for the expected improvement in the six-minute walk trial, and σ for the SD.In this equation, 2.9.t test with two sample will be used to characterize continuous variables with normal distributions. A modified t test will be employed when the variance between the groups with a cutoff of 0.05. In cases where the data do not have a normal distribution,the Mann-Whitney U test will be used to examine intragroup or intergroup variations before and after therapy.Third-party statisticians who will be blinded to the group assignment and intervention method carried out the statistical analysis. For missing data, we use regression modeling or average interpolation for missing data prediction, avoiding as much as possible the impact of missing values on the analysis results. The intention-to-treat (ITT) concept will be used to analyze primary or secondary outcomes. Data analysis will be performed using the statistical program SPSS 25.0 . For non-normal distributions, the median and IQR will be used to represent continuous variables, while counts or percentage will be used to describe categorical variables. Fisher's exact probability approach or the chi-square test will be used to define categorical variables. Continuous variables that conform to a normal distribution will be described by mean and SD. Student's P values under 0.05 will be regarded as indicative of statistical significance for all tests, which are all run in both directions.If necessary, We will perform multivariate analyses using multiple linear regression to account for adjustments for possible confounders covariates selected on the basis of clinical relevance and univariate outcomes. All 2.10.p-CRF and the electrical CRF, and any changes to the data will be carefully tracked and documented.Two cardiologists will constitute the independent Data and Safety Monitoring Committee (DSMC), which is responsible for monitoring trial data  and, if needed, suspending the experiment. Before the trial began, all of the researchers, including research assistants, investigators, doctors, and statisticians, participated in unified preclinical trial training to ensure that they had a comprehensive understanding of the trial protocol, standard study protocols, and their respective responsibilities. For instance, research assistants will encourage volunteers to exercise at home and complete activity diaries. Additionally, all researchers will receive training in outcome evaluation, data management, and consistent case report form (CRF) completion. Regular gatherings will be set up to discuss trial-related issues and choose the best remedies. The DSMC statistician will also compare the data between the 2.11.The trial is still ongoing. Enrollment began in October 2022 and is expected to end in December 2023.3.This study explore the impact of home-based Tai Chi exercise on clinical outcomes in individuals who suffered from chronic heart failure. Safety concerns will be also taken into consideration. Having a high morbidity and mortality rate, CHF is a severe issue everywhere, particularly in societies with aging populations, and it places a heavy physical and psychological burden on patients , 36. TheAs a set of Tai Chi exercises designed specifically for elderly patients with chronic heart failure, Fu Yang Tai Chi exercise retains the essence of traditional Tai Chi and starts from a holistic model of tuning the body, mind and breath. It also discards the drawbacks of aerobic exercise and emphasizes the importance of spiritual care in moderate exercise, realizing the combination of “exercise prescription” and “psychological prescription.” The whole set of movements is even and slow, combining movement and stillness, and is a unique connotation of aerobic physical and mental exercise.The trial will use a rigorous random allocation method and blind to evaluators and statistical analysts to reduce bias. Therefore, the study is expected to produce reliable results. Four limitations of the current research should be acknowledged. In comparison to previous multicenter comprehensive investigations, the current research's sample size is modest, and it is a single-center study. It is unknown whether other locations and ethnic groups may show comparable results. Second, it is challenging to keep track of the research participants' increased physical activity. Although all participants will be requested to keep a record of their regular physical activity or exercise, this is insufficient to adequately gauge their daily activity levels. Third, this experiment is only followed up for 12 weeks. However, follow-up time should be suitably increased to clarify long-term clinical results and the possible effects of home-based Tai Chi training.


Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public’s health. A couple of recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided recent annual estimates of the cessation rate by age group.We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009–2018 period using data from the National Health Interview Survey. We focused on cessation rates in the 24–44, 45–64 and 65 + age groups.The findings show that cessation rates follow a consistent u-shaped curve over time with respect to age . Over the course of the study, the cessation rates in the 25–44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45–64 age group exhibited a substantial increase of 70%, from 2.5% in 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time.The Kalman filter approach offers a real-time estimation of cessation rates that would be helpful for monitoring smoking cessation behavior, of interest in general but also for tobacco control policymakers. Adult smoking prevalence in the United States decreased markedly from 2009 (20.6%) to 2018 (13.7%), according to the National Health Interview Survey (NHIS) [Previous studies have estimated the US smoking cessation rate using different surveys and approaches –5. In 19In a 2012 article, Zhu et al.  analyzedKalman filters have the ability to generate efficient estimates of hidden states of a dynamic system based on noisy and indirect measurements from single or multiple data sources . FurtherAs such, here we use a Kalman filter-based approach to estimate annual cessation rates among adults, unknown parameters of a discrete mathematical model of smoking prevalence, by age groups  over 2009–2018, using annual NHIS smoking prevalence data. Since smoking prevalence among 18–24-year-old smokers depends on both the smoking initiation and cessation rates for that age group, we chose to exclude it from our analysis. We stopped the analysis in 2018 due to survey design changes in 2019 that rendered smoking prevalence not directly comparable before and after that year. This is the first study to employ a Kalman filter to estimate annual age-group-specific smoking cessation rates. Our findings show how age-group-specific smoking cessation rates changed absolutely and relatively during the years studied. This approach provides tobacco regulators and researchers with a new tool to monitor changes in tobacco use trends in a timely manner.In this section, we first introduce a discrete mathematical model of smoking prevalence utilized to quantify annual smoking cessation rates for the three specified age groups  , 20.To estimate the cessation rates by age group for each year from 2009 to 2018, we employed the following dynamic mathematical model adapted from [Where A Kalman filter can be applied to estimate the unknown cessation rate ap. 7 in . Eq. (5)The age-specific population for each year during the 2009–2018 period was extracted from the US Census Bureau . The 200r age 25 . The effr age 25 , 29. EveThe weighted average cessation rate for the population aged 25 and over in year Kalman filter-based methods utilize recursive Bayesian updates to estimate an unknown variable in a dynamical system while accounting for the uncertainty of observations . These aTo maintain a desirable balance between accuracy and computational tractability of a filtering method, we utilized the Central Difference Kalman filter (CDKF) , 31 to eThe estimated cessation rates with their standard deviations for the three age groups, as well as the weighted average rate with its standard deviation from 2009 to 2017, are shown in The cessation rate in a specific year, say 2009, reflects the rate of quitting that occurs between 2009 and 2010, and its calculation requires both 2009 and 2010 NHIS-observed prevalence. Therefore, to estimate 2009–2017 cessation rates, we need 2009–2018 NHIS data.Readers can find all the numeric values used to produce We have demonstrated the use of a Kalman filter approach to estimate annual smoking cessation rates, unknown parameters of a mathematical model of smoking prevalence, by age group from 2009 through 2017. Our study represents the first of its kind to employ this methodology in tobacco control. This approach can be used to develop a monitoring tool that provides tobacco regulators and researchers with timely information about changes in tobacco use trends.The results show that cessation rates follow a u-shaped curve with respect to age. In other words, the rate is higher among the 25–44 and 65 + age groups than among 45–64-year-olds. This is consistent with , where tOur study estimated lower average smoking cessation rates during the 2009–2017 period compared with those reported in , may havIn 2022, Mendez et. al.  predicteThis work has some limitations. First, the initiation rate was chosen based on the smoking prevalence of 25-year-olds, meaning that the contribution of people who started smoking after the age of 25 was not considered. We believe this has minimal impact on our results since very few smokers initiate after age 25 . Secondl


With the increasing acceptance of face-to-face classes transitioning to web-based learning due to COVID-19, there is an increasing need to have educators trained and equipped to teach online. The ability to teach in-person may not necessarily mean that one is ready teach in a web-based environment.The objective of our study was to investigate the readiness of health care professionals in Singapore to teach online and their technology-related teaching needs.t test was performed to analyze the differences between respondents younger than 40 years and those older than 41 years.This was a quantitative cross-sectional pilot study conducted among health care administrative staff and professionals in medicine, nursing, allied health, and dentistry. Participants were recruited via an open invitation email to all staff members of Singapore’s largest group of health care institutions. Data were collected using a web-based questionnaire. Differences in the readiness of the professionals to teach online were analyzed using analysis of variance, and a 1-sided independent sample P=.77) among all the respondents in their readiness to teach online. There was an agreement among all professionals in their need for software tools to teach; in particular, there was a significant difference in the software needs among the professionals for streaming videos (P=.01). There was no statistically significant difference in the readiness to teach online between those younger than 40 years and those older than 41 years (P=.48).A total of 169 responses was analyzed. Full-time academic faculty members scored the highest for readiness to teach online (2.97), followed by nursing professionals (2.91), medicine professionals (2.88), administrative staff members (2.83), and allied health professionals (2.76). However, there was no statistically significant difference (Our study shows that there are still some gaps in terms of readiness to teach online among health care professionals. Our findings can be used by policy makers and faculty developers to identify opportunities for development among their educators so that they are ready to teach online with the appropriate software tools. By 2021, teaching online had become a norm in most institutions around the world because of the COVID-19 outbreak. The sudden change from in-person class sessions to web-based teaching platforms was accelerated with the need to socially distance and minimize face-to-face contact. Lecture halls and tutorial rooms that were once filled with students became empty, and classes were replaced with a monitor and a webcam during the COVID-19 pandemic. This caught many by surprise, and even full-time faculty members in academic institutions around the world were unprepared to teach online  could beAlthough full-time faculty members may have received support from their institutions to teach online, it was evident that many medical faculty members did not receive adequate training on being effective educators even when they assumed major educational leadership roles in their institutions . This coThe Academic Medical Centre in Singapore recognizes the work of clinician-educators and places emphasis on faculty development across various professions. Their work has been ongoing with the establishment of the Academic Medicine Education Institute  in 2012 Online learning is becoming increasingly common, and there has been a growth in literature ,14,15 exPrevious studies have argued that readiness to teach online can be conceptualized as the educator’s pedagogical -20 and mThe concept of self-efficacy represents the educator’s confidence in teaching  and refeEducators face a different set of challenges when teaching online compared to that in traditional face-to-face teaching settings. Apart from playing the role of a facilitator and content expert, educators will need to take on the role of a social administrator, technologist, counsellor, and researcher . FortunaStudies ,26 showiThe nature of how learning takes place has changed with the increasing use of technology for teaching and learning. Learning can take place asynchronously, where interaction happens at the learner’s convenience, such as through discussion forums, e-learning modules, or video lectures. Synchronous learning aims to mimic traditional face-to-face learning where the learning takes place in real time, and learners log into a video conferencing system and interact with the educator in real time through audio, text-based chats, or various collaborative workspaces . Online learning can be as effective as face-to-face learning , but theThe way one would teach online is different from the way one would teach in a face-to-face session . In asynConstructive alignment  is a priEducators need to re-examine the role that technology can play in assessments. For example, technology should not be limited to merely automate grading but rather to provide feedback to facilitate the development of reflective practice . This caThis study was conducted with staff members of SingHealth, which is the largest group of public health care institutions in Singapore. SingHealth consists of 4 public hospitals, 3 community hospitals, 5 national specialty centers, and a network of 8 polyclinics.All staff members who were experienced in teaching were included in this study, while those who did not have any experience in teaching were excluded. An invitation to participate in the web-based survey was sent through the SingHealth Corporate Communications Department to approximately 29,894 staff  members The survey was developed through an extensive literature review on similar studies -25, suchThe survey to assess readiness to teach online consisted of 4 questions that measured (1) confidence in using technology for teaching, (2) familiarity with using technology for teaching, (3) ability to use technology effectively for teaching, and (4) ability to use technology tools to measure learner’s performance. The “readiness to teach online score” was calculated only for respondents who answered all the 4 questions in the survey.The survey to assess the software tool needs for teaching and learning measured 5 types of software that could be used for (1) organizing their online teaching, (2) collaborative learning, (3) gaining insights into students’ learning progress, (4) promoting active learning, and (5) video streaming.P value <.05 considered as statistically significant. To compare the mean scores across the 2 age groups, we conducted a 1-sided independent sample t test with a P value <.05 considered as statistically significant.Statistical analysis was performed using SPSS for Mac version 27 (IBM Corp). To compare the mean scores across the various professions, means and standard deviations were calculated and analysis of variance (ANOVA) was used with a This study was approved by the National University of Singapore’s Institutional Review Board . This study was conducted following the Checklist for Reporting Results of Internet E-Surveys guidelines  Multime.In this study, 331 responses were collected, with only 208 valid responses; 39 respondents indicated that they did not have any prior teaching experience and were excluded from the final analysis as they did not meet the inclusion criteria. Therefore, only 169 respondents were included in the final analysis.The largest number of responses was received from nursing professionals (n=65), followed by allied health professionals (n=40), medicine professionals (n=33), full-time academic faculty with no clinical appointment (n=9), administrative staff (n=8), and a small number from dentistry (n=3); 11 respondents did not state their profession. The respondents’ age groups differed across the professions, but 40.8% (69/169) of the respondents were aged 31-40 years followed by 29.6% (50/169) aged 41-50 years. Full-time faculty members were generally in their mid-to-late career, followed by the medical professionals. The age group profiles of the professionals in allied health, administration, and nursing were very similar, with many respondents in their early-to-mid career (31-40 years old). In terms of teaching frequency, 62.1% (105/169) of the respondents taught 1-5 times in the past 12 months, 15.4% (26/169) taught 6-10 times in the past 12 months, while 22.5% (38/169) taught more than 10 times in the past 12 months. More details on the respondents’ age groups and teaching frequency in the past 12 months across professions are shown in Full-time academic faculty members scored the highest for readiness to teach online (2.97), followed by nursing professionals (2.91), medicine professionals (2.88), administrative staff members (2.83), and allied health professionals (2.76). A closer look at the survey on the readiness to teach online shows that full-time academic faculty members reported the highest agreement across the 3 statements of confidence in using technology for teaching, familiarity with using technology tools for teaching, and effectiveness in using technology for teaching, but they reported the lowest confidence in using technology for measuring learning outcomes. Respondents whose primary role was in administration reported agreement on statements relating to their confidence in teaching online and familiarity in using technology for teaching and learning but reported slight disagreement on their ability to use technology effectively in their teaching and for measuring learning.F4,144=2.45; P=.05).Among the 3 health care profession groups, the medicine professionals reported agreement in their confidence in teaching online and familiarity with using technology tools for teaching. However, they reported mild agreement in their ability to use technology effectively for teaching and for measuring learning. For both nursing and allied health professionals, there was a mild agreement across all the 4 statements relating to their readiness to teach online. A 1-way ANOVA on the effect of profession on the readiness to teach online revealed only statistical significance in the ability to use technology to measure learner’s performance  across professions.There was a universal agreement across professions that there was a need for software tools to promote active learning. In fact, most respondents across professions, except those in medicine, expressed a desire to have software tools to support their teaching. For respondents in the medicine profession, there was a slight disagreement on the need for tools for organizing their online teaching, collaborative learning, gaining insight into student learning progress, and video streaming . One-wayt test on the readiness to teach online between respondents aged 40 years or younger, and those older than 40 years showed no significant difference across all the 5 items on the readiness. Similarly, there was no significant difference in the technology-related needs between respondents aged 40 years or below and those older than 40 years.F2,161=4.89; P=.009) and the frequency of teaching in the past 12 months.With face-to-face classes being kept to a minimum in Singapore since 2021, it is important to understand educators’ readiness to teach online and their requirements for software tools for conducting classes online. This survey was designed to be simple with only 9 items scored on a 4-point Likert scale to gauge health care professionals’ readiness to teach online and their needs for software tools to facilitate teaching online. The survey to assess readiness for teaching and learning online consisted of 4 questions developed based on existing literature on web-based learning and measures: (1) confidence in teaching online, (2) familiarity with technology tools, (3) effectiveness in using technology to teach, and (4) ability to use technology for measuring student learning. The survey on the technology-related needs for online teaching was administered to understand educators’ software needs for (1) organizing their online teaching, (2) collaborative learning, (3) gaining insights into students’ learning progress, (4) promoting active learning, and (5) video streaming.When asked to rate their effectiveness to teach online, full-time faculty members rated themselves the highest (3.11); the rest of the health care professionals rated themselves below 2.97. Literature shows that it is common for health care educators to receive little or no training on how to become effective teachers  as compaAge alone does not appear to be a good determinant of one’s readiness to teach online. We found that there was almost no difference in the readiness to teach online between the younger and older cohorts of respondents. This finding was consistent with that reported by Eley et al  who founA study by Yeung et al  and Lee Assessment is an important part of teaching; yet, our findings showed that the ability to assess with technology was consistently rated low. A study by Schempp et al  on experThus, we found that one factor alone cannot be a strong determinant for readiness to teach online. We propose that a better way to understand an educator’s readiness to teach online is to consider multiple factors such as their teaching frequency, profession, and access to pedagogical resources. However, this would mean that we will require a higher response rate to make the findings more meaningful.Although we did not find statistically significant differences among health care professionals in their readiness to teach online or in their technology-related needs for teaching online, our findings are nonetheless important to be reported  and discThe findings of our study are important to help identify training gaps in the corresponding educator training programs across different professions. Although faculty development training is conducted by the Academic Medicine Education Institute , our finOur study uses a 9-question survey to measure health care professionals’ readiness for teaching and learning (4 questions) online and their software needs for teaching and learning (5 questions). This survey was conducted in a health care setting in Singapore with various health care professionals. With online teaching and learning being here to stay for the foreseeable future, this survey will help institutions gauge the readiness of their educators to teach online. Findings from our survey can help future research, policy makers, and faculty developers allocate resources more effectively to address the gaps identified.