Distinct Electrogram Features and Ventricular Arrhythmia Induction Modes Between Repolarization and Conduction Heterogeneities.

BACKGROUND: Recent clinical studies have indicated the presence of localized electrical abnormalities in idiopathic ventricular fibrillation and J-wave syndrome patients. OBJECTIVES: This study aims to characterize the specific electrical signatures of localized repolarization and conduction heterogeneities and their respective role in vulnerability to arrhythmias. METHODS: Optical mapping was performed in porcine right ventricles with local: 1) repolarization shortening; 2) conduction slowing; or 3) structural heterogeneity induced by locally perfusing: 1) pinacidil (20 µmol/L, n = 13); or 2) flecainide (2 µmol/L, n = 13) via an epicardial catheter; or 3) by local epicardial tissue destruction (9 radiofrequency lesions n = 12). Electrograms were recorded (n = 5 in each group) and spontaneous and induced arrhythmias were quantified and optically mapped. RESULTS: Electrograms were normal in (1) but showed local fragmentation in 40% of preparations in (2) with greater effects observed at high pacing frequencies dependent on the wavefront direction. In (3), the structural substrate alone increased the width and number of peaks in the electrograms, and addition of flecainide induced pronounced fragmentation (≥3 peaks and ≥70 ms) in all cases. Occurrence of spontaneous arrhythmias was significantly increased in (1) and (2) (P < 0.0001 and 0.05, respectively, vs baseline) and were triggered by ectopies. Vulnerability to arrhythmias at high pacing frequencies (≥2 Hz) was the lowest in (1) and greatest in (2). CONCLUSIONS: Microstructural substrates have the most pronounced impact on electrograms, especially when combined with sodium channel blockers, whereas local action potential duration shortening does not lead to electrogram fragmentation even though it is associated with the highest prevalence of spontaneous arrhythmias.

Implantable loop recorders in patients with Brugada syndrome: the BruLoop study.

BACKGROUND AND AIMS: Available data on continuous rhythm monitoring by implantable loop recorders (ILRs) in patients with Brugada syndrome (BrS) are scarce. The aim of this multi-centre study was to evaluate the diagnostic yield and clinical implication of a continuous rhythm monitoring strategy by ILRs in a large cohort of BrS patients and to assess the precise arrhythmic cause of syncopal episodes. METHODS: A total of 370 patients with BrS and ILRs (mean age 43.5 ± 15.9, 33.8% female, 74.1% symptomatic) from 18 international centers were included. Patients were followed with continuous rhythm monitoring for a median follow-up of 3 years. RESULTS: During follow-up, an arrhythmic event was recorded in 30.7% of symptomatic patients [18.6% atrial arrhythmias (AAs), 10.2% bradyarrhythmias (BAs), and 7.3% ventricular arrhythmias (VAs)]. In patients with recurrent syncope, the aetiology was arrhythmic in 22.4% (59.3% BAs, 25.0% VAs, and 15.6% AAs). The ILR led to drug therapy initiation in 11.4%, ablation procedure in 10.9%, implantation of a pacemaker in 2.5%, and a cardioverter-defibrillator in 8%. At multivariate analysis, the presence of symptoms [hazard ratio (HR) 2.5, P = .001] and age >50 years (HR 1.7, P = .016) were independent predictors of arrhythmic events, while inducibility of ventricular fibrillation at the electrophysiological study (HR 9.0, P < .001) was a predictor of VAs. CONCLUSIONS: ILR detects arrhythmic events in nearly 30% of symptomatic BrS patients, leading to appropriate therapy in 70% of them. The most commonly detected arrhythmias are AAs and BAs, while VAs are detected only in 7% of cases. Symptom status can be used to guide ILR implantation.

The Use of Electrocardiogram Smartwatches in Patients with Cardiac Implantable Electrical Devices.

Unlimited access to ECGs using an over-the-counter smartwatch constitutes a real revolution for our discipline, and the application is rapidly expanding to include patients with cardiac implantable electronic devices (CIEDs) such as pacemakers (PMs) and implantable cardioverter defibrillators (ICDs). CIEDs require periodic evaluation and adjustment by healthcare professionals. In addition, implanted patients often present with symptoms that may be related to their PMs or ICDs. An ECG smartwatch could reveal information about device functioning, confirm normal device function, or aid in the case of device troubleshooting. In this review, we delve into the available evidence surrounding smartwatches with ECG registration and their integration into the care of patients with implanted pacemakers and ICDs. We explore safety considerations and the benefits and limitations associated with these wearables, drawing on relevant studies and case series from our own experience. By analyzing the current landscape of this emerging technology, we aim to provide a comprehensive overview that facilitates informed decision-making for both healthcare professionals and patients.

Current Role of Electrocardiographic Imaging in Patient Selection for Cardiac Resynchronization Therapy.

Cardiac resynchronization therapy (CRT) is a recognized therapy for heart failure with altered ejection fraction and abnormal left ventricular activation time. Since the introduction of the therapy, a 30% rate of non-responders is observed and unchanged. The 12-lead ECG remains the only recommended tool for patient selection to CRT. The 12-lead ECG is, however, limited in its inability to provide a precise pattern of regional electrical activity. Electrocardiographic imaging (ECGi) provides a non-invasive detailed mapping of cardiac activation and therefore appears as a promising tool for CRT candidates. The non-invasive ventricular activation maps acquired by ECGi have been primarily explored for the diagnosis and guidance of therapy in patients with atrial or ventricular tachyarrhythmia. However, the accuracy of the system in this field is lacking and needs further improvement before considering a clinical application. On the other hand, its use for patient selection for CRT is encouraging. In this review, we introduce the technical considerations and we describe how ECGi can precisely characterize ventricular activation, especially in patients with left bundle branch block, thus identifying the electrical substrate responsive to CRT.

Local VA index for the differential diagnosis of supraventricular tachycardia.

BACKGROUND: Differentiating between atypical atrioventricular nodal reentrant tachycardia (AVNRT) and orthodromic reciprocating tachycardia utilizing a septal accessory pathway is a complex challenge. OBJECTIVE: The purpose of this study was to describe the "local VA index," a straightforward method based on signals from the coronary sinus catheter, to distinguish between these arrhythmias during tachycardia and entrainment. The ventriculoatrial (VA) interval on the coronary sinus catheter is measured during tachycardia and entrainment, at the site of earliest atrial activity. The difference between these 2 situations defines the "local VA index." We also propose a mechanism to clarify the limitations of historical pacing maneuvers, such as postpacing interval minus tachycardia cycle length (PPI-TCL) and stimulus-atrial interval minus ventriculoatrial interval (SA-VA), by examining nodal decrement and intraventricular conduction delay. METHODS: In a retrospective study of 75 patients referred for supraventricular tachycardia evaluation, 37 were diagnosed with atrioventricular reentrant tachycardia (AVRT) with orthodromic reciprocating tachycardia, and 38 with AVNRT (27 typical, 11 atypical). RESULTS: In comparison to AVRT patients, AVNRT patients exhibited longer PPI-TCL (176 ± 47 ms vs 113 ± 42 ms; P <.01) and SA-VA (138 ± 47 ms vs 64 ± 28 ms; P <.01). The AVRT group had mean local VA index of -1 ± 13 ms, whereas the AVNRT group had a significantly longer index of 91 ± 46 ms (P <.01). An optimal threshold for differentiation was a local VA index of 40 ms. Importantly, there was no significant correlation between pacing cycle length and nodal decrement as well as intraventricular delay related to pathway location. This interindividual variability might explain misleading interpretations of PPI-TCL and SA-VA. CONCLUSION: This novel approach is advantageous because of its simplicity and effectiveness, requiring only 2 diagnostic catheters. A local VA interval difference <40 ms provides a clear distinction for AVRT.

The use of smartwatch electrocardiogram beyond arrhythmia detection.

The adoption of wearables in medicine has expanded worldwide with a rapidly growing number of consumers and new features capable of real-time monitoring of health parameters such as the ability to record and transmit a single-lead electrocardiogram (ECG). Smartwatch ECGs are increasingly used but current smartwatches only screen for atrial fibrillation (AF). Most of the literature has focused on analyzing the smartwatch ECG accuracy for the detection of AF or other tachycardias. As with the conventional ECG, this tool may be used for many more purposes than only detection of AF. The objectives of this review are to describe the published literature regarding the accuracy and clinical value of recording a smartwatch ECG in other situations than diagnosis of tachycardia and discuss possible techniques to optimize the diagnostic yield.

Triggers of Ventricular Fibrillation in Patients With Inferolateral J-Wave Syndrome.

BACKGROUND: There are few data on ventricular fibrillation (VF) initiation in patients with inferolateral J waves. OBJECTIVES: This multicenter study investigated the characteristics of triggers initiating spontaneous VF in inferolateral J-wave syndrome. METHODS: A total of 31 patients (age 37 ± 14 years, 24 male) with spontaneous VF episodes associated with inferolateral J waves were evaluated to determine the origin and characteristics of triggers. The J-wave pattern was recorded in inferior leads in 11 patients, lateral leads in 3, and inferolateral leads in 17. RESULTS: The VF triggers (n = 37) exhibited varying QRS durations (176 ± 21 milliseconds, range 119-219 milliseconds) and coupling intervals (339 ± 46 milliseconds, range 250-508 milliseconds) with a right (70%) or left (30%) bundle branch block (BBB) pattern. Trigger patterns were associated with J-wave location: left BBB triggers with inferior J waves and right BBB triggers with lateral J waves. Electrophysiologic study was performed for 22 VF triggers in 19 patients. They originated from the left or right Purkinje system in 6 and from the ventricular myocardium in 10 and were undetermined in 6. Purkinje vs myocardial triggers showed distinct electrocardiographic characteristics in coupling interval and QRS-complex duration and morphology. Abnormal epicardial substrate associated with fragmented electrograms was identified in 9 patients, with triggers originating from the same region in 7 patients. Catheter ablation resulted in VF suppression in 15 patients (79%). CONCLUSIONS: VF initiation in inferolateral J-wave syndrome is associated with significant individual heterogeneity in trigger characteristics. Myocardial triggers have electrocardiographic features distinct from Purkinje triggers, and their origin often colocalizes with an abnormal epicardial substrate.

Cardiac structure discontinuities revealed by ex-vivo microstructural characterization. A focus on the basal inferoseptal left ventricle region.

BACKGROUND: While the microstructure of the left ventricle (LV) has been largely described, only a few studies investigated the right ventricular insertion point (RVIP). It was accepted that the aggregate cardiomyocytes organization was much more complex due to the intersection of the ventricular cavities but a precise structural characterization in the human heart was lacking even if clinical phenotypes related to right ventricular wall stress or arrhythmia were observed in this region. METHODS: MRI-derived anatomical imaging (150 µm3) and diffusion tensor imaging (600 µm3) were performed in large mammalian whole hearts (human: N = 5, sheep: N = 5). Fractional anisotropy, aggregate cardiomyocytes orientations and tractography were compared within both species. Aggregate cardiomyocytes orientation on one ex-vivo sheep whole heart was then computed using structure tensor imaging (STI) from 21 µm isotropic acquisition acquired with micro computed tomography (MicroCT) imaging. Macroscopic and histological examination were performed. Lastly, experimental cardiomyocytes orientation distribution was then compared to the usual rule-based model using electrophysiological (EP) modeling. Electrical activity was modeled with the monodomain formulation. RESULTS: The RVIP at the level of the inferior ventricular septum presented a unique arrangement of aggregate cardiomyocytes. An abrupt, mid-myocardial change in cardiomyocytes orientation was observed, delimiting a triangle-shaped region, present in both sheep and human hearts. FA's histogram distribution (mean ± std: 0.29 ± 0.06) of the identified region as well as the main dimension (22.2 mm ± 5.6 mm) was found homogeneous across samples and species. Averaged volume is 0.34 cm3 ± 0.15 cm3. Both local activation time (LAT) and morphology of pseudo-ECGs were strongly impacted with delayed LAT and change in peak-to-peak amplitude in the simulated wedge model. CONCLUSION: The study was the first to describe the 3D cardiomyocytes architecture of the basal inferoseptal left ventricle region in human hearts and identify the presence of a well-organized aggregate cardiomyocytes arrangement and cardiac structural discontinuities. The results might offer a better appreciation of clinical phenotypes like RVIP-late gadolinium enhancement or uncommon idiopathic ventricular arrhythmias (VA) originating from this region.

Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity.

Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This "irregularly irregular" approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.

T-Wave Oversensing with Contemporary Implantable Cardioverter-Defibrillators.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs) need to reliably detect ventricular tachycardia (VT) and ventricular fibrillation (VF) while avoiding T-wave oversensing (TWOS), which is associated with a risk of inappropriate therapies. The incidence of TWOS with endovascular ICDs appears to differ between manufacturers. OBJECTIVES: We aimed to evaluate the incidence and clinical consequences of TWOS with contemporary Medtronic and Boston Scientific ICDs. METHODS: Consecutive patients implanted with a recent Medtronic or Boston Scientific ICD and remotely monitored at three French centers were included. All transmitted EGMs labelled as VF, VT, non-sustained VT (NSVT), or ventricular oversensing (Medtronic) were screened for TWOS. RESULTS: Among 7589 transmitted episodes from 674 patients with a Boston Scientific ICD, we did not identify a single case of TWOS. Among 16,790 transmitted episodes from 1733 patients with a Medtronic ICD, we identified 60 patients (3.4%) with at least one episode of TWOS. In 46 patients, TWOS was intermittent (NSVT episodes). In the remaining 14 patients, TWOS resulted in 60 sustained episodes (completed counters). No inappropriate therapies were delivered in 12 of these patients because no therapies were programmed (in monitor zones, 11 episodes) or because therapies were inhibited by the morphology discriminator (Wavelet, 19 episodes) or by the anti-TWOS algorithm (26 episodes). Two patients received inappropriate therapies due to TWOS (0.1% of patients with Medtronic ICDs). CONCLUSION: On review of 24,379 transmitted episodes from 2407 patients with endovascular ICDs, we found no case of TWOS with Boston Scientific devices, whereas TWOS was not uncommon with Medtronic devices. However, the risk of inappropriate therapy with Medtronic ICDs was very low (0.1%) due to the often intermittent nature of this phenomenon, the morphology discriminator, and the anti-TWOS algorithm.

Remote management of worsening heart failure to avoid hospitalization in a real-world setting.

AIMS: From a patient and health system perspective, managing worsening heart failure (WHF) as an outpatient has become a priority. Remote management allows early detection of WHF, enabling timely intervention with the aim of preventing hospitalization. The objective of the study was to evaluate the feasibility and safety of remotely managing WHF events using a multiparametric platform. METHODS AND RESULTS: All patients enrolled in the heart failure remote management programme of the Bordeaux University Hospital Telemedicine Center between 1 January and 31 December 2021 were included in the study. Follow-up data were collected until 1 March 2022. Inclusion criteria were chronic heart failure (HF) with New York Heart Association ≥II symptoms and an elevated B-type natriuretic peptide (BNP > 100 pg/mL or N-terminal-pro-BNP > 1000 pg/mL). Patient assessments were performed remotely and included measurements of body weight, blood pressure, heart rate, symptoms, biochemical parameters, and data from cardiac implantable electronic devices when available. In total, 161 patients (71 ± 11 years old, 79% male) were followed for a mean of 291 ± 66 days with a mean adherence to the remote monitoring system of 80 ± 20%. Over this period, 52 (32.3%) patients had 105 WHF events, of which 66 (63%) were successfully managed remotely, the remaining requiring hospitalization. Freedom from WHF events and hospitalization at 300 days were 66% and 85%, respectively (P < 0.001 for the difference). Increased level of BNP was associated with an increased risk of WHF event [hazard ratio (HR) per unit increase in BNP: 1.001; 95% confidence interval (CI) 1-1.002; P = 0.001] and hospitalization (HR 1.002; 95% CI 1.002-1.003; P = 0.002). A decrease in the level of glomerular filtration rate was associated with an increased risk of hospitalization (HR per unit decrease in estimated glomerular filtration rate: 0.946; 95% CI 0.906-0.989; P = 0.014). WHF event recurrence and (re)hospitalization rates at 1-month were similar among patients managed remotely (18% and 12%, respectively) and those requiring hospitalization (21% and 10%, respectively). Iatrogenic complications occurred more often during hospitalization than remote management (26% vs. 3%, P < 0.001). CONCLUSIONS: Our study suggests that remote management of WHF events based on a multiparametric approach led by a telemedical centre is feasible and safe. Adopting such a strategy for patients with chronic HF could reduce HF-related hospitalizations with expected benefits for patients, care providers, and health care systems.

Abnormal Atrial Potentials Recorded During Sinus Rhythm or Pacing Represent Substrates for Reentrant Atrial Tachycardia.

BACKGROUND: Abnormal atrial potentials (AAPs) recorded during sinus rhythm/atrial pacing may indicate areas of slow conduction capable of supporting reentrant atrial tachycardia (AT). Therefore, we sought to examine the relationship between AAPs and AT circuits. METHODS: One hundred twenty-three reentrant ATs in 104 patients were analyzed. AAPs, consisting of fragmented potentials and split potentials, were assessed using the Rhythmia LUMIPOINT algorithm. RESULTS: There was 93±13% overlap between areas with AAPs during sinus rhythm/atrial pacing and areas of slow conduction along the reentry circuit during AT. The cumulative area of AAPs was smaller in patients with localized-reentrant ATs compared with anatomic macro-reentrant ATs (20.0 [14.6-30.5] versus 28.9 [21.8-35.6] cm2; P=0.021). Patients with perimitral ATs had larger areas of AAPs on the lateral wall whereas patients with roof-dependent ATs had larger areas of AAPs on the roof and posterior wall (P≤0.018 for all comparisons). The patchy scar that was associated with localized-reentrant AT exhibited a larger area of AAPs at its periphery than the scar that did not participate in localized-reentrant AT (3.1 [2.4-4.5] versus 1.0 [0.7-1.6] cm2; P<0.001). CONCLUSIONS: AAPs recorded during sinus rhythm/atrial pacing are associated with areas of slow conduction during reentrant AT. The burden and distribution of AAPs may provide actionable insights into AT circuit features, including in cases in which ATs are difficult to map.

Electrophysiologic Determinants of Isoelectric Intervals on Surface Electrocardiograms During Atrial Tachycardia: Insights From High-Density Mapping.

BACKGROUND: Substrate abnormalities can alter atrial activation during atrial tachycardias (ATs) thereby influencing AT-wave morphology on the surface electrocardiogram. OBJECTIVES: This study sought to identify determinants of isoelectric intervals during ATs with complex atrial activation patterns. METHODS: High-density activation maps of 126 ATs were studied. To assess the impact of the activated atrial surface on the presence of isoelectric intervals, this study measured the minimum activated area throughout the AT cycle, defined as the smallest activated area within a 50-millisecond period, by using signal processing algorithms (LUMIPOINT). RESULTS: ATs with isoelectric intervals (P-wave ATs) included 23 macro-re-entrant ATs (40%), 26 localized-re-entrant ATs (46%), and 8 focal ATs (14%), whereas those without included 46 macro-re-entrant ATs (67%), 21 localized-re-entrant ATs (30%), and 2 focal ATs (3%). Multivariable regression identified smaller minimum activated area and larger very low voltage area as independent predictors of P-wave ATs (OR: 0.732; 95% CI: 0.644-0.831; P < 0.001; and OR: 1.042; 95% CI: 1.006-1.080; P = 0.023, respectively). The minimum activated area with the cutoff value of 10 cm2 provided the highest predictive accuracy for P-wave ATs with sensitivity, specificity, and positive and negative predictive values of 96%, 97%, 97%, and 95%, respectively. In re-entrant ATs, smaller minimum activated area was associated with lower minimum conduction velocity within the circuit and fewer areas of delayed conduction outside of the circuit (standardized ß: 0.524; 95% CI: 0.373-0.675; P < 0.001; and standardized ß: 0.353; 95% CI: 0.198-0.508; P < 0.001, respectively). CONCLUSIONS: Reduced atrial activation area and voltage were associated with isoelectric intervals during ATs.

Omnipolar versus bipolar mapping to guide ventricular tachycardia ablation.

BACKGROUND: Omnipolar technology (OT) was recently proposed to generate electroanatomic voltage maps with orientation-independent electrograms. We describe the first cohort of patients undergoing ventricular tachycardia (VT) ablation guided by OT. OBJECTIVE: The purpose of this study was to compare omnipolar and bipolar high-density maps with regard to voltage amplitude, late potential (LP) annotation, and isochronal late activation mapping distribution. METHODS: A total of 24 patients (16 [66%] ischemic cardiomyopathy and 12 [50%] redo cases) underwent VT ablation under OT guidance. Twenty-seven sinus rhythm substrate maps and 10 VT activation maps were analyzed. Omnipolar and bipolar (HD Wave Solution algorithm, Abbott, Abbott Park, IL) voltages were compared. Areas of LPs were correlated with the VT isthmus areas, and late electrogram misannotation was evaluated. Deceleration zones based on isochronal late activation maps were analyzed by 2 blinded operators and compared to the VT isthmuses. RESULTS: OT maps had higher point density (13.8 points/cm2 vs 8.0 points/cm2). Omnipolar points had 7.1% higher voltages than bipolar points within areas of dense scar and border zone. The number of misannotated points was significantly lower for OT maps (6.8% vs 21.9%; P = .01), showing comparable sensitivity (53% vs 59%) but higher specificity (79% vs 63%). The sensitivity and specificity of detection of the VT isthmus in the deceleration zones were, respectively, 75% and 65% for OT and 35% and 55% for bipolar mapping. At 8.4 months, 71% freedom from VT recurrence was achieved. CONCLUSION: OT is a valuable tool for guiding VT ablation, providing more accurate identification of LPs and isochronal crowding due to slightly higher voltages.

Artificial intelligence for detection of ventricular oversensing: Machine learning approaches for noise detection within nonsustained ventricular tachycardia episodes remotely transmitted by pacemakers and implantable cardioverter-defibrillators.

BACKGROUND: Pacemakers (PMs) and implantable cardioverter-defibrillators (ICDs) increasingly automatically record and remotely transmit nonsustained ventricular tachycardia (NSVT) episodes, which may reveal ventricular oversensing. OBJECTIVES: We aimed to develop and validate a machine learning algorithm that accurately classifies NSVT episodes transmitted by PMs and ICDs in order to lighten health care workload burden and improve patient safety. METHODS: PMs or ICDs (Boston Scientific, St Paul, MN) from 4 French hospitals with ≥1 transmitted NSVT episode were split into 3 subgroups: training set, validation set, and test set. Each NSVT episode was labeled as either physiological or nonphysiological. Four machine learning algorithms-2DTF-CNN, 2D-DenseNet, 2DTF-VGG, and 1D-AgResNet-were developed using training and validation data sets. Accuracies of the classifiers were compared with an analysis of the remote monitoring team of the Bordeaux University Hospital using F2 scores (favoring sensitivity over predictive positive value) using an independent test set. RESULTS: A total of 807 devices transmitted 10,471 NSVT recordings (82% ICD; 18% PM), of which 87 devices (10.8%) transmitted 544 NSVT recordings with nonphysiological signals. The classification by the remote monitoring team resulted in an F2 score of 0.932 (sensitivity 95%; specificity 99%) The 4 machine learning algorithms showed high and comparable F2 scores (2DTF-CNN: 0.914; 2D-DenseNet: 0.906; 2DTF-VGG: 0.863; 1D-AgResNet: 0.791), and only 1D-AgResNet had significantly different labeling from that of the remote monitoring team. CONCLUSION: Machine learning algorithms were accurate in detecting nonphysiological signals within electrograms transmitted by PMs and ICDs. An artificial intelligence approach may render remote monitoring less resourceful and improve patient safety.