PubMed Health⌕ Search

PubMed · 12765446

An adaptive interval-based algorithm for withholding ICD therapy during sinus tachycardia.

Abstract

Avoiding inappropriate ICD therapy during supraventricular tachycardia (SVT) while assuring 100% sensitivity for VT/VF remains a challenge. Inappropriate VT/VF therapy during sinus tachycardia (ST) is particularly distressing to the patient because the full sequence of ICD therapies is often delivered. ST or 1:1 atrial tachycardia (AT) with long PR intervals and ST or AT with atrial oversensing of far-field R waves cause the majority of inappropriate therapy in the Medtronic GEM DR (Model 7271) ICD. The goals of the present effort were to define an adaptive interval-based algorithm for withholding VT/VF therapy in dual chamber ICDs during ST and to compare performance of the adaptive algorithm with that of the original ST withholding algorithm in the GEM DR. The adaptive algorithm uses a combination of 1:1 atrial to ventricular conduction pattern, changes in RR intervals and changes in intrinsic PR intervals to establish evidence for or against the presence of ST. Performances of the adaptive and original ST withholding algorithms were compared on 3 databases collected by implanted GEM DR devices. The first database included 684 spontaneous VT/VF episodes. The second database included 216 spontaneous SVT episodes that received inappropriate VT/VF therapy. These databases included up to 2,000 atrial or ventricular sensed or paced events preceding the spontaneous tachycardias. The third database included 320 spontaneous ST/AT episodes for which therapy was appropriately withheld by the GEM DR. Performance of the adaptive algorithm on the third database was predicted rather than directly computed because of record length limitations. VT/VF therapy was classified as "withheld" if evidence of ST remained high for one algorithm (i.e., at least 7 more beats to VT/VF detection) at the point of VT/VF detection by the other algorithm. For the 684 true VT/VF episodes, the original algorithm withheld VT/VF therapy in 5 episodes and the adaptive algorithm withheld VT/VF therapy in 3 episodes. The 95% confidence interval for the difference in VT/VF sensitivity between the adaptive and original algorithms was [-0.5 to + 1.1%]. Twelve of the 320 ST/AT episodes (3.8%) that were appropriately classified by the original algorithm were predicted to receive inappropriate therapy by the adaptive algorithm. However, relative to the original algorithm, the adaptive algorithm appropriately withheld VT/VF therapy for 76 of 216 true SVT episodes (i.e., incremental specificity of 35.2%). For the specific SVT episodes that were the targets for improvement by the adaptive ST algorithm (ST/AT with long PR intervals and ST/AT with intermittent atrial oversensing of far-field R waves), the adaptive algorithm reduced inappropriate therapy by 63.2%.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert W Stadler, Bruce D Gunderson, Jeffrey M Gillberg. 2003. An adaptive interval-based algorithm for withholding ICD therapy during sinus tachycardia.. https://doi.org/10.1046/j.1460-9592.2003.t01-1-00168.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗