Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks.

PURPOSE: While adjuvant therapy with capecitabine and oxaliplatin (CAPOX) has been proven to be effective in stage III colon cancer, capecitabine monotherapy (CapMono) might be equally effective in elderly patients. Unfortunately, the elderly are under-represented in clinical trials and patients included may not be representative of the routine care population. Observational data might alleviate this problem but is sensitive to biases such as confounding by indication. Here, we build causal models using Bayesian Networks (BNs), identify confounders, and estimate the effect of adjuvant chemotherapy using survival analyses. METHODS: Patients 70 years and older were selected from the Netherlands Cancer Registry (N = 982). We developed several BNs using constraint-based, score-based, and hybrid algorithms while precluding noncausal relations. In addition, we created models using a limited set of recurrence and survival nodes. Potential confounders were identified through the resulting graphs. Several Cox models were fitted correcting for confounders and for propensity scores. RESULTS: When comparing adjuvant treatment with surgery only, pathological lymph node classification, physical status, and age were identified as potential confounders. Adjuvant treatment was significantly associated with survival in all Cox models, with hazard ratios between 0.39 and 0.45; CIs overlapped. BNs investigating CAPOX versus CapMono did not find any association between the treatment choice and survival and thus no confounders. Analyses using Cox models did not identify significant association either. CONCLUSION: We were able to successfully leverage BN structure learning algorithms in conjunction with clinical knowledge to create causal models. While confounders differed depending on the algorithm and included nodes, results were not contradictory. We found a strong effect of adjuvant therapy on survival in our cohort. Additional oxaliplatin did not have a marked effect and should be avoided in elderly patients.

Identifying Confounders Using Bayesian Networks and Estimating Treatment Effect in Prostate Cancer With Observational Data.

PURPOSE: Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is sensitive to biases such as confounding by indication. Structure learning algorithms for Bayesian Networks (BNs) can be used to discover the underlying model from data. This enables identification of confounders through graph analysis, although the model might contain noncausal edges. We propose using a blacklist to aid structure learning in finding causal relationships. This is illustrated by an analysis into the effect of active treatment (v observation) in localized prostate cancer. METHODS: In total, 4,121 prostate cancer records were obtained from the Netherlands Cancer Registry. Subsequently, we developed a (causal) BN using structure learning while precluding noncausal relations. Additionally, we created several Cox proportional hazards models, each correcting for a different set of potential confounders (including propensity scores). Model predictions for overall survival were compared with expected survival on the basis of the general population using data from Statistics Netherlands (Centraal Bureau voor de Statistiek). RESULTS: Structure learning precluding noncausal relations resulted in a causal graph but did not identify significant edges toward treatment; they were added manually. Graph analysis identified year of diagnosis and age as confounders. The BN predicted a treatment effect of 1 percentage point at 10 years. Chi-squared analysis found significant associations between year of diagnosis, age, stage, and treatment. Propensity score correction was successful. Adjusted Cox models predicted significant treatment effect around 3 percentage points at 10 years. CONCLUSION: A blacklist in conjunction with structure learning can result in a causal BN that can be used for confounder identification. Treatment effect found here is close to the 5 percentage point found in the literature.

Need for numbers: assessing cancer survivors' needs for personalized and generic statistical information.

BACKGROUND: Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors' need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors' considerations for (not) wanting statistical information. METHODS: Cancer survivors' need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale ('not at all'- 'very much'). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson's correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively. RESULTS: Overall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients. CONCLUSIONS: In light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.

Using guideline-based clinical decision support in oncological multidisciplinary team meetings: A prospective, multicenter concordance study.

BACKGROUND: Multidisciplinary team meetings formulate guideline-based individual treatment plans based on patient and disease characteristics and motivate reasons for deviation. Clinical decision trees could support multidisciplinary teams to adhere more accurately to guidelines. Every clinical decision tree is tailored to a specific decision moment in a care pathway and is composed of patient and disease characteristics leading to a guideline recommendation. OBJECTIVE: This study investigated (1) the concordance between multidisciplinary team and clinical decision tree recommendations and (2) the completeness of patient and disease characteristics available during multidisciplinary team meetings to apply clinical decision trees such that it results in a guideline recommendation. METHODS: This prospective, multicenter, observational concordance study evaluated 17 selected clinical decision trees, based on the prevailing Dutch guidelines for breast, colorectal and prostate cancers. In cases with sufficient data, concordance between multidisciplinary team and clinical decision tree recommendations was classified as concordant, conditional concordant (multidisciplinary team specified a prerequisite for the recommendation) and non-concordant. RESULTS: Fifty-nine multidisciplinary team meetings were attended in 8 different hospitals, and 355 cases were included. For 296 cases (83.4%), all patient data were available for providing an unconditional clinical decision tree recommendation. In 59 cases (16.6%), insufficient data were available resulting in provisional clinical decision tree recommendations. From the 296 successfully generated clinical decision tree recommendations, the multidisciplinary team recommendations were concordant in 249 (84.1%) cases, conditional concordant in 24 (8.1%) cases and non-concordant in 23 (7.8%) cases of which in 7 (2.4%) cases the reason for deviation from the clinical decision tree generated guideline recommendation was not motivated. CONCLUSION: The observed concordance of recommendations between multidisciplinary teams and clinical decision trees and data completeness during multidisciplinary team meetings in this study indicate a potential role for implementation of clinical decision trees to support multidisciplinary team decision-making.

Communication in decision aids for stage I-III colorectal cancer patients: a systematic review.

OBJECTIVES: To assess the communicative quality of colorectal cancer patient decision aids (DAs) about treatment options, the current systematic review was conducted. DESIGN: Systematic review. DATA SOURCES: DAs (published between 2006 and 2019) were identified through academic literature (MEDLINE, Embase, CINAHL, Cochrane Library and PsycINFO) and online sources. ELIGIBILITY CRITERIA: DAs were only included if they supported the decision-making process of patients with colon, rectal or colorectal cancer in stages I-III. DATA EXTRACTION AND SYNTHESIS: After the search strategy was adapted from similar systematic reviews and checked by a colorectal cancer surgeon, two independent reviewers screened and selected the articles. After initial screening, disagreements were resolved with a third reviewer. The review was conducted in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DAs were assessed using the International Patient Decision Aid Standards (IPDAS) and Communicative Aspects (CA) checklist. RESULTS: In total, 18 DAs were selected. Both the IPDAS and CA checklist revealed that there was a lot of variation in the (communicative) quality of DAs. The findings highlight that (1) personalisation of treatment information in DAs is lacking, (2) outcome probability information is mostly communicated verbally and (3) information in DAs is generally biased towards a specific treatment. Additionally, (4) DAs about colorectal cancer are lengthy and (5) many DAs are not written in plain language. CONCLUSIONS: Both instruments (IPDAS and CA) revealed great variation in the (communicative) quality of colorectal cancer DAs. Developers of patient DAs should focus on personalisation techniques and could use both the IPDAS and CA checklist in the developmental process to ensure personalised health communication and facilitate shared decision making in clinical practice.

Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.

OBJECTIVE: The interpretation and clinical application of guidelines can be challenging and time-consuming, which may result in noncompliance to guidelines. The aim of this study was to convert the Dutch guideline for colorectal cancer (CRC) into decision trees and subsequently implement decision trees in an online decision support environment to facilitate guideline application. METHODS: The recommendations of the Dutch CRC guidelines (published in 2014) were translated into decision trees consisting of decision nodes, branches and leaves that represent data items, data item values and recommendations, respectively. Decision trees were discussed with experts in the field and published as interactive open access decision support software (available at www.oncoguide.nl). Decision tree validation and a concordance analysis were performed using consecutive reports (January 2016-January 2017) from CRC multidisciplinary tumour boards (MTBs) at Amsterdam University Medical Centers, location AMC. RESULTS: In total, we developed 34 decision trees driven by 101 decision nodes based on the guideline recommendations. Decision trees represented recommendations for diagnostics (n = 1), staging (n = 10), primary treatment (colon: n = 1, rectum: n = 5, colorectal: n = 9), pathology (n = 4) and follow-up (n = 3) and included one overview decision tree for optimal navigation. We identified several guideline information gaps and areas of inconclusive evidence. A total of 158 patients' MTB reports were eligible for decision tree validation and resulted in treatment recommendations in 80% of cases. The concordance rate between decision tree treatment recommendations and MTB advices was 81%. Decision trees reported in 22 out of 24 non-concordant cases (92%) that no guideline recommendation was available. CONCLUSIONS: We successfully converted the Dutch CRC guideline into decision trees and identified several information gaps and areas of inconclusive evidence, the latter being the main cause of the observed disagreement between decision tree recommendations and MTB advices. Decision trees may contribute to future strategies to optimize quality of care for CRC patients.

Prognostic factors analysis for oral cavity cancer survival in the Netherlands and Taiwan using a privacy-preserving federated infrastructure.

The difference in incidence of oral cavity cancer (OCC) between Taiwan and the Netherlands is striking. Different risk factors and treatment expertise may result in survival differences between the two countries. However due to regulatory restrictions, patient-level analyses of combined data from the Netherlands and Taiwan are infeasible. We implemented a software infrastructure for federated analyses on data from multiple organisations. We included 41,633‬ patients with single-tumour OCC between 2004 and 2016, undergoing surgery, from the Taiwan Cancer Registry and Netherlands Cancer Registry. Federated Cox Proportional Hazard was used to analyse associations between patient and tumour characteristics, country, treatment and hospital volume with survival. Five factors showed differential effects on survival of OCC patients in the Netherlands and Taiwan: age at diagnosis, stage, grade, treatment and hospital volume. The risk of death for OCC patients younger than 60 years, with advanced stage, higher grade or receiving adjuvant therapy after surgery was lower in the Netherlands than in Taiwan; but patients older than 70 years, with early stage, lower grade and receiving surgery alone in the Netherlands were at higher risk of death than those in Taiwan. The mortality risk of OCC in Taiwanese patients treated in hospitals with higher hospital volume (≥ 50 surgeries per year) was lower than in Dutch patients. We conducted analyses without exchanging patient-level information, overcoming barriers for sharing privacy sensitive information. The outcomes of patients treated in the Netherlands and Taiwan were slightly different after controlling for other prognostic factors.

Clinical decision trees support systematic evaluation of multidisciplinary team recommendations.

PURPOSE: EUSOMA's recommendation that "each patient has to be fully informed about each step in the diagnostic and therapeutic pathway" could be supported by guideline-based clinical decision trees (CDTs). The Dutch breast cancer guideline has been modeled into CDTs ( www.oncoguide.nl ). Prerequisites for adequate CDT usage are availability of necessary patient data at the time of decision-making and to consider all possible treatment alternatives provided in the CDT. METHODS: This retrospective single-center study evaluated 394 randomly selected female patients with non-metastatic breast cancer between 2012 and 2015. Four pivotal CDTs were selected. Two researchers analyzed patient records to determine to which degree patient data required per CDT were available at the time of multidisciplinary team (MDT) meeting and how often multiple alternatives were actually reported. RESULTS: The four selected CDTs were indication for magnetic resonance imaging (MRI) scan, preoperative and adjuvant systemic treatment, and immediate breast reconstruction. For 70%, 13%, 97% and 13% of patients, respectively, all necessary data were available. The two most frequent underreported data-items were "clinical M-stage" (87%) and "assessable mammography" (28%). Treatment alternatives were reported by MDTs in 32% of patients regarding primary treatment and in 28% regarding breast reconstruction. CONCLUSION: Both the availability of data in patient records essential for guideline-based recommendations and the reporting of possible treatment alternatives of the investigated CDTs were low. To meet EUSOMA's requirements, information that is supposed to be implicitly known must be explicated by MDTs. Moreover, MDTs have to adhere to clear definitions of data-items in their reporting.

Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network.

PURPOSE: The TNM classification system is used for prognosis, treatment, and research. Regular updates potentially break backward compatibility. Reclassification is not always possible, is labor intensive, or requires additional data. We developed a Bayesian network (BN) for reclassifying the 5th, 6th, and 7th editions of the TNM and predicting survival for non-small-cell lung cancer (NSCLC) without training data with known classifications in multiple editions. METHODS: Data were obtained from the Netherlands Cancer Registry (n = 146,084). A BN was designed with nodes for TNM edition and survival, and a group of nodes was designed for all TNM editions, with a group for edition 7 only. Before learning conditional probabilities, priors for relations between the groups were manually specified after analysis of changes between editions. For performance evaluation only, part of the 7th edition test data were manually reclassified. Performance was evaluated using sensitivity, specificity, and accuracy. Two-year survival was evaluated with the receiver operating characteristic area under the curve (AUC), and model calibration was visualized. RESULTS: Manual reclassification of 7th to 6th edition stage group as ground truth for testing was impossible in 5.6% of the patients. Predicting 6th edition stage grouping using 7th edition data and vice versa resulted in average accuracies, sensitivities, and specificities between 0.85 and 0.99. The AUC for 2-year survival was 0.81. CONCLUSION: We have successfully created a BN for reclassifying TNM stage grouping across TNM editions and predicting survival in NSCLC without knowing the true TNM classification in various editions in the training set. We suggest binary prediction of survival is less relevant than predicted probability and model calibration. For research, probabilities can be used for weighted reclassification.

Impact on Quality of Documentation and Workload of the Introduction of a National Information Standard for Tumor Board Reporting.

PURPOSE: Tumor boards, clinical practice guidelines, and cancer registries are intertwined cancer care quality instruments. Standardized structured reporting has been proposed as a solution to improve clinical documentation, while facilitating data reuse for secondary purposes. This study describes the implementation and evaluation of a national standard for tumor board reporting for breast cancer on the basis of the clinical practice guideline and the potential for reusing clinical data for the Netherlands Cancer Registry (NCR). METHODS: Previously, a national information standard for breast cancer was derived from the corresponding Dutch clinical practice guideline. Using data items from the information standard, we developed three different tumor board forms: preoperative, postoperative, and postneoadjuvant-postoperative. The forms were implemented in Amphia Hospital's electronic health record. Quality of clinical documentation and workload before and after implementation were compared. RESULTS: Both draft and final tumor board reports were collected from 27 and 31 patients in baseline and effect measurements, respectively. Completeness of final reports increased from 39.5% to 45.4% (P = .04). The workload for tumor board preparation and discussion did not change significantly. Standardized tumor board reports included 50% (61/122) of the data items carried in the NCR. An automated process was developed to upload information captured in tumor board reports to the NCR database. CONCLUSION: This study shows implementation of a national standard for tumor board reports improves quality of clinical documentation, without increasing clinical workload. Simultaneously, our work enables data reuse for secondary purposes like cancer registration.

Pathology Image Exchange: The Dutch Digital Pathology Platform for Exchange of Whole-Slide Images for Efficient Teleconsultation, Telerevision, and Virtual Expert Panels.

Among the many uses of digital pathology, remote consultation, remote revision, and virtual slide panels may be the most important ones. This requires basic slide scanner infrastructure in participating laboratories to produce whole-slide images. More importantly, a software platform is needed for exchange of these images and functionality to support the processes around discussing and reporting on these images without breaching patient privacy. This poses high demands on the setup of such a platform, given the inherent complexity of the handling of digital pathology images. In this article, we describe the setup and validation of the Pathology Image Exchange project, which aimed to create a vendor-independent platform for exchange of whole-slide images between Dutch pathology laboratories to facilitate efficient teleconsultation, telerevision, and virtual slide panels. Pathology Image Exchange was released in April 2018 after technical validation, and a first successful validation in real life has been performed for hematopathology cases.

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands.

BACKGROUND: Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform ( https://www.evidencio.com ) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation. METHODS: Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation. RESULTS: Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods. CONCLUSIONS: This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees.

PURPOSE: The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems. METHODS: All recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations. RESULTS: In total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations. CONCLUSION: By defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.

New image processing and noise reduction technology allows reduction of radiation exposure in complex electrophysiologic interventions while maintaining optimal image quality: a randomized clinical trial.

BACKGROUND: Despite their carcinogenic potential, X-rays remain indispensable for electrophysiologic (EP) procedures. OBJECTIVE: The purpose of this study was to evaluate the dose reduction and image quality of a novel X-ray technology using advanced image processing and dose reduction technology in an EP laboratory. METHODS: In this single-center, randomized, unblinded, parallel controlled trial, consecutive patients undergoing catheter ablation for complex arrhythmias were eligible. The Philips Allura FD20 system allows switching between the reference (Allura Xper) and the novel X-ray imaging technology (Allura Clarity). Primary end-point was overall procedural patient dose, expressed in dose area product (DAP) and air kerma (AK). Operator dose, procedural success, and necessity to switch to higher dose settings were secondary end-points. RESULTS: A total of 136 patients were randomly assigned to the novel imaging group (n = 68) or the reference group (n = 68). Baseline characteristics were similar, except patients in the novel imaging group were younger (58 vs 65 years, P < .01). Median DAP and AK were 43% and 40% lower in the novel imaging group, respectively (P < .0001). A 50% operator dose reduction was achieved in the novel imaging group (P < .001). Fluoroscopy time, number of exposure frames, and procedure duration were equivalent between the two groups, indicating that the image quality was similarly adequate in both groups. Procedural success was achieved in 91% of patients in both groups; one pericardial tamponade occurred in the novel imaging group. CONCLUSION: The novel imaging technology, Allura Clarity, significantly reduces patient and operator dose in complex EP procedures while maintaining image quality.

Cardiac resynchronization therapy cures dyssynchronopathy in canine left bundle-branch block hearts.

AIMS: We investigated to what extent biventricular pacing (BVP) can normalize LV function and remodeling, induced by isolated left bundle branch block (LBBB). METHODS AND RESULTS: In 16 dogs LBBB was induced. Eight animals were followed for 16 weeks and in 8 animals BVP was started after 8 weeks. LV pressure, LV geometry (2Dechocardiography), systolic circumferential shortening (CSsys, MRI tagging) and myocardial blood flow (MBF, microspheres) was measured. * and # indicate P < 0.05 compared to pre-LBBB and 8 weeks of LBBB, respectively. Data is presented relative to pre-LBBB values (mean +/- SEM). BVP increased LV dP/dt|max from 78 +/- 5%* to 86 +/- 5%*# (immediately) and 89 +/- 6%# (after 8 weeks) and normalized regional differences in CSsys and MBF. After 8 weeks of BVP, LV end-diastolic volume (EDV) was reduced from 123 +/- 3%* to 109 +/- 6%# and LV lateral wall mass was reduced from 128 +/- 5%* to 113 +/- 3%*#. The acute increase in LV dP/dt|max upon BVP correlated with LV EDV and LV wall mass after 8 weeks of BVP. CONCLUSION: In canine hearts with long-term isolated LBBB, BVP largely reverses global and regional functional and structural abnormalities induced by LBBB.

Calculation of effective VV interval facilitates optimization of AV delay and VV interval in cardiac resynchronization therapy.

BACKGROUND: In hearts with left bundle branch block (LBBB), both atrioventricular (AV) delay and interventricular (VV) interval determine left ventricular (LV) pump function in cardiac resynchronization therapy (CRT). The optimal combination of AV delay and VV interval currently is determined by extensive hemodynamic testing. OBJECTIVES: The purpose of this study was to investigate whether the effective VV interval (VV(eff)) can be used to optimize AV delay and VV interval. METHODS: In eight canine hearts with chronic LBBB, LV pacing was performed at various AV delays as well as biventricular pacing at multiple AV delays and VV intervals. LV pump function was assessed from LVdP/dt(max) and stroke volume (conductance catheter). Interventricular asynchrony was calculated from the timing difference between upslope of LV and RV pressure curves. VV(eff) was defined as the time delay between activation of the RV apex and LV lateral wall, irrespective of the source of RV activation (RV pacing or intrinsic conduction). VV(eff) was determined from pacemaker settings and surface ECGs recorded during biventricular pacing at various AV delays (positive values denote LV preexcitation). RESULTS: For all animals, the relationship between VV(eff) and LVdP/dt(max) as well as LV stroke work was parabolic. Maximal improvement in LVdP/dt(max) was similar during LV pacing, simultaneous biventricular pacing, and sequential biventricular pacing and was obtained at similar values of VV(eff). VV(eff) was strongly correlated with interventricular asynchrony (R = 0.97 +/- 0.03). Optimum LVdP/dt(max) occurred at VV(eff) ranging from -24 to 12 ms (mean -6 +/- 13 ms). For each experiment, the optimal VV(eff) was virtually equal to the value halfway between its minimum (during LV pacing at short AV delay) and maximum (during LBBB) value (R = 0.91). CONCLUSION: Use of VV(eff) facilitates determination of the best combination of AV delay and VV interval during biventricular pacing. For each individual heart, VV(eff), resulting in optimum LV pump function, can be estimated using surface ECGs recorded during biventricular pacing.

Tailoring cardiac resynchronization therapy using interventricular asynchrony. Validation of a simple model.

This study explores the use of interventricular asynchrony (interVA) for optimizing cardiac resynchronization therapy (CRT), an idea emerging from a simple pathway model of conduction in the ventricles. Measurements were performed in six dogs with chronic left bundle branch block (LBBB) and in 29 patients of the Pacing Therapies for Congestive Heart Failure (PATH-CHF)-I study. In the dogs, intraventricular asynchrony (intraVA) was determined using left ventricular (LV) endocardial activation maps. In dogs and patients, the maximum rate of rise of LV pressure (LV dP/dt(max)) and the pulse pressure (PP) and interVA [time delay between upslope of LV and right ventricular (RV) pressure curves] were measured during LV, RV, and biventricular (BiV) pacing with various atrioventricular (AV) delays. Measurements in the canine hearts supported the pathway model in that optimal resynchronization occurred at approximately 50% reduction of intraVA and at an interVA value halfway that during LBBB and LV pacing. In patients with significant hemodynamic response during pacing (n = 22), intrinsic interVA and interVA at peak improvement (interVA(p)) varied widely between patients (from -83 to -15 ms and from -42 to +31 ms, respectively). However, the model predicted individual interVA(p) accurately (SD of +/-6 ms and +/-12 ms for LV dP/dt(max) and PP, respectively). At equal interVA, LV and BiV pacing produced equal hemodynamic response, but in 11 of 22 responders, BiV pacing reduced interVA insufficiently to reach the maximum hemodynamic response. LV pacing at short AV delay proved to result in better hemodynamics than predicted by the model, indicating that additional factors determine hemodynamics during LV preexcitation. Guided by a simple pathway model, interVA measurements accurately predict optimal hemodynamic performance in individual CRT patients.

Acute hemodynamic benefit of left ventricular apex pacing in children.

BACKGROUND: Despite the fact that pacing at the right ventricular apex acutely and chronically decreases left ventricular contractile function, this pacing site is still conventionally used in adults and children. Because animal studies showed beneficial effects of left ventricular pacing, we compared the hemodynamic performance of left ventricular apex, left ventricular free wall, and right ventricular apex pacing in children. METHODS: Studies were performed in 10 children (median age, 2.5 years; range, 2 months to 17 years) undergoing surgery for congenital heart disease with normal systemic left ventricular anatomy and intraventricular conduction. High-fidelity left ventricular and arterial pressure measurements were performed during epicardial right ventricular apex and left ventricular apex and free wall pacing. RESULTS: Left ventricular apex pacing increased the maximum rate of rise of left ventricular pressure and pulse pressure significantly relative to right ventricular apex pacing (by 7.7% +/- 7.2% and 7.7% +/- 7.0%, respectively) without changes in end-diastolic left ventricular pressure. Left ventricular free wall pacing did not significantly improve hemodynamics as compared with right ventricular apex pacing. The QRS duration was not different among pacing at the three sites. CONCLUSIONS: In this short-term study left ventricular apex pacing is hemodynamically superior to right ventricular apex and left ventricular free wall pacing in children. Therefore, the left ventricular apex appears a favorable pacing site after pediatric cardiac surgery.

Adaptation to mechanical load determines shape and properties of heart and circulation: the CircAdapt model.

With circulatory pathology, patient-specific simulation of hemodynamics is required to minimize invasiveness for diagnosis, treatment planning, and followup. We investigated the advantages of a smart combination of often already known hemodynamic principles. The CircAdapt model was designed to simulate beat-to-beat dynamics of the four-chamber heart with systemic and pulmonary circulation while incorporating a realistic relation between pressure-volume load and tissue mechanics and adaptation of tissues to mechanical load. Adaptation was modeled by rules, where a locally sensed signal results in a local action of the tissue. The applied rules were as follows: For blood vessel walls, 1) flow shear stress dilates the wall and 2) tensile stress thickens the wall; for myocardial tissue, 3) strain dilates the wall material, 4) larger maximum sarcomere length increases contractility, and 5) contractility increases wall mass. The circulation was composed of active and passive compliances and inertias. A realistic circulation developed by self-structuring through adaptation provided mean levels of systemic pressure and flow. Ability to simulate a wide variety of patient-specific circumstances was demonstrated by application of the same adaptation rules to the conditions of fetal circulation followed by a switch to the newborn circulation around birth. It was concluded that a few adaptation rules, directed to normalize mechanical load of the tissue, were sufficient to develop and maintain a realistic circulation automatically. Adaptation rules appear to be the key to reduce dramatically the number of input parameters for simulating circulation dynamics. The model may be used to simulate circulation pathology and to predict effects of treatment.

Left bundle branch block induces ventricular remodelling and functional septal hypoperfusion.

AIMS: Left ventricular (LV) dilatation, hypertrophy, and septal perfusion defects are frequently observed in patients with left bundle branch block (LBBB). We investigated whether isolated LBBB causes these abnormalities. METHODS AND RESULTS: In eight dogs, LBBB was induced by radio frequency ablation. Two-dimensional echocardiography showed that 16 weeks of LBBB decreased LV ejection fraction (by 23+/-14%) and increased LV cavity volume (by 25+/-19%) and wall mass (by 17+/-16%). The LV septal-to-lateral wall mass ratio decreased by 6+/-9%, indicating asymmetric hypertrophy. After onset of LBBB, myocardial blood flow (MBF, fluorescent microspheres) and systolic circumferential shortening [CS(sys), magnetic resonance (MR) tagging] decreased in the septum to 83+/-16% and -11+/-20% of baseline, respectively, and increased in LV lateral wall to 118+/-12% and 180+/-90% of baseline, respectively. MBF and CS(sys) values did not change over 16 weeks of LBBB. Changes in external mechanical work paralleled those in CS(sys). Glycogen content was not significantly different between septum and LV lateral wall of LBBB hearts (16 weeks) and control samples, indicating absence of hibernation. CONCLUSIONS: The asynchronous ventricular activation during LBBB leads to redistribution of circumferential shortening and myocardial blood flow and, in the long run, LV remodelling. Septal hypoperfusion during LBBB appears to be primarily determined by reduced septal workload.