Patterns of clinical and imaging presentations in patients with spontaneous intracranial hypotension due to spinal cerebrospinal fluid venous fistula: A single-center retrospective cross-sectional study.

OBJECTIVE: To identify distinct clinical or imaging subtypes of spontaneous intracranial hypotension (SIH) due to spinal cerebrospinal fluid (CSF) venous fistula (CVF). BACKGROUND: Spontaneous intracranial hypotension is classically understood to present clinically with an orthostatic headache and stereotyped brain magnetic resonance imaging (MRI) findings; however, most prior literature examining clinical and brain MRI features of SIH has focused on all types of spinal CSF leaks concurrently. This study aimed to evaluate whether data support the possibility of internally consistent subtypes based on brain imaging features and clinical symptoms analogous to those seen in primary headache syndromes. METHODS: This retrospective cross-sectional single-institution study included 48 consecutive patients meeting the International Classification of Headache Disorders, 3rd edition criteria for SIH due to CVF. Clinical symptoms, pre-treatment brain MRI, and symptom duration were analyzed. Clinical and MRI data were analyzed to identify patterns and associations between symptoms and imaging findings. RESULTS: A total of 20 males and 28 females were evaluated, with a mean (standard deviation) age of 61 (10) years. In all, 44/48 (92%) patients experienced headaches, though 18/48 (40%) did not endorse relief when flat, including six of the 48 (13%) with worsening symptoms when flat. In all, 19/48 (40%) patients reported at least one migraine symptom, and six of the 48 (13%) presented with at least one migraine symptom and had no relief when flat. Clinical symptoms clustered primarily into a "classic" presentation consisting of relief when flat, occipital head pain, comorbid neck pain, a pressure/throbbing headache quality, and an "atypical" presentation that was characterized by having several differences: less relief when flat (nine of 22 (41%) vs. 20/23 (87.0%), p = 0.002; odds ratio [OR] 0.110, 95% confidence interval [CI] 0.016-0.53), more frontal head pain (14/22 (64%) vs. one of 23 (4%), p < 0.001; OR 35.0, 95% CI 4.2-1681.0), less neck pain (two of 21 (4.5%) vs. nine of 13 (69.6%), p < 0.001; OR 0.023, 95% CI 0.0005-0.196), and more stabbing/sharp headache quality (nine of 22 (41%) vs. two of 23 (9%), p = 0.017; OR 7.0, 95% CI 1.18-75.9). Brain MRI findings clustered into three groups: those presenting with most imaging findings of SIH concurrently, those with brain sag but less pachymeningeal/venous engorgement, and those with pachymeningeal/venous engorgement but less brain sag. CONCLUSION: This study highlights the clinical and imaging diversity among patients with SIH due to CVF, challenging the reliance on classic orthostatic headache alone for diagnosis. The findings suggest the existence of distinct SIH subtypes based on clinical and imaging presentations, underscoring the need for comprehensive evaluation in patients with suspected CVF. Future research should further elucidate the relationship between clinical symptoms and imaging findings, aiming to refine diagnostic criteria and enhance understanding of SIH's pathophysiology.

Long-Term Epidural Patching Outcomes and Predictors of Benefit in Patients With Suspected CSF Leak Nonconforming to ICHD-3 Criteria.

BACKGROUND AND OBJECTIVES: Spinal CSF leaks lead to spontaneous intracranial hypotension (SIH). While International Classification of Headache Disorders, Third Edition (ICHD-3) criteria necessitate imaging confirmation or low opening pressure (OP) for SIH diagnosis, their sensitivity may be limited. We offered epidural blood patches (EBPs) to patients with symptoms suggestive of SIH, with and without a documented low OP or confirmed leak on imaging. This study evaluates the efficacy of this strategy. METHODS: We conducted a prospective cohort study with a nested case-control design including all patients who presented to a tertiary headache clinic with clinical symptoms of SIH who completed study measures both before and after receiving an EBP between August 2016 and November 2018. RESULTS: The mean duration of symptoms was 8.7 ± 8.1 years. Of 85 patients assessed, 69 did not meet ICHD-3 criteria for SIH. At an average of 521 days after the initial EBP, this ICHD-3-negative subgroup experienced significant improvements in Patient-Reported Outcomes Measurement Information System (PROMIS) Global Physical Health score of +3.3 (95% CI 1.5-5.1), PROMIS Global Mental Health score of +1.8 (95% CI 0.0-3.5), Headache Impact Test (HIT)-6 head pain score of -3.8 (95% CI -5.7 to -1.8), Neck Disability Index of -4.8 (95% CI -9.0 to -0.6) and PROMIS Fatigue of -2.3 (95% CI -4.1 to -0.6). Fifty-four percent of ICHD-3-negative patients achieved clinically meaningful improvements in PROMIS Global Physical Health and 45% in HIT-6 scores. Pain relief following lying flat prior to treatment was strongly associated with sustained clinically meaningful improvement in global physical health at an average of 521 days (odds ratio 1.39, 95% CI 1.1-1.79; p < 0.003). ICHD-3-positive patients showed high rates of response and previously unreported, treatable levels of fatigue and cognitive deficits. DISCUSSION: Patients who did not conform to the ICHD-3 criteria for SIH showed moderate rates of sustained, clinically meaningful improvements in global physical health, global mental health, neck pain, fatigue, and head pain after EBP therapy. Pre-treatment improvement in head pain when flat was associated with later, sustained improvement after EBP therapy among patients who did not meet the ICHD-3 criteria. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that epidural blood patch is an effective treatment of suspected CSF leak not conforming to ICHD-3 criteria for SIH.

EEG response of dexmedetomidine during drug induced sleep endoscopy.

Introduction: Dexmedetomidine is one of the anesthetics of choice for drug induced sleep endoscopy (DISE), with advantages including limited respiratory depression, analgesia, and decreased incidence of emergence delirium. However, challenges with determining sedation levels and prolonged recovery have limited its usage. An improved understanding of the effect of dexmedetomidine on the level of sedation and the corresponding electroencephalographic (EEG) changes could help overcome these barriers. Methods: Fifty-one patients received dexmedetomidine sedation with Richmond Agitation-Sedation Scale (RASS) score assessment and continuous EEG monitoring via SedLine for DISE. We constructed a pharmacokinetic model to determine continuous dexmedetomidine blood concentration. From the SedLine, we extracted the patient state index (PSI), and from the EEG we calculated the spectral edge frequency 95% (SEF95) and the correlation dimension (CD), a type of fractal dimension used to assess the complexity of a system. These metrics were subsequently compared against one another and with the dexmedetomidine concentration. Results: Our pharmacokinetic model yielded a two-compartment model with volumes of 51.8 L and 106.2 L, with clearances of 69.5 and 168.9 L/h, respectively, and a time to effect of 9 min, similar to prior studies. Based on this model, decreasing RASS score, SEF95, CD, and PSI were all significantly associated with increasing dexmedetomidine concentration (p < 0.001, p = 0.006, p < 0.001 respectively). The CD, SEF95, and PSI better captured the effects of increasing dexmedetomidine concentration as compared to the RASS score. Simulating dexmedetomidine concentration based on titration to target levels derived from CD and PSI confirmed commonly used dexmedetomidine infusion dosages. Conclusion: Dexmedetomidine use for DISE confirmed previous pharmacokinetic models seen with dexmedetomidine. Complex EEG metrics such as PSI and CD, as compared to RASS score and SEF95, better captured changes in brain state from dexmedetomidine and have potential to improve the monitoring of dexmedetomidine sedation.

Evaluation of patient state index, bispectral index, and entropy during drug induced sleep endoscopy with dexmedetomidine.

Multiple electroencephalographic (EEG) monitors and their associated EEG markers have been developed to aid in assessing the level of sedation in the operating room. While many studies have assessed the response of these markers to propofol sedation and anesthetic gases, few studies have compared these markers when using dexmedetomidine, an alpha-2 agonist. Fifty-one patients underwent drug induced sleep endoscopy with dexmedetomidine sedation. Continuous EEG was captured using SedLine (Masimo, Inc), and a playback system was used to extract the bispectral index (BIS) (Medtronic Inc), the patient state index (PSI) (Masimo, Inc), the state and response Entropy (GE Healthcare), and calculate the spectral edge frequency 95% (SEF95). Richmond Agitation-Sedation Scale (RASS) scores were assessed continually throughout the procedure and in recovery. We assessed the correlation between EEG markers and constructed ordinal logistic regression models to predict the RASS score and compare EEG markers. All three commercial EEG metrics were significantly associated with the RASS score (p < 0.001 for all metrics) whereas SEF95 alone was insufficient at characterizing dexmedetomidine sedation. PSI and Entropy achieved higher accuracy at predicing deeper levels of sedation as compared to BIS (PSI: 58.3%, Entropy: 58.3%, BIS: 44.4%). Lightening secondary to RASS score assessment is significantly captured by all three commercial EEG metrics (p < 0.001). Commercial EEG monitors can capture changes in the brain state associated with the RASS score during dexmedetomidine sedation. PSI and Entropy were highly correlated and may be better suited for assessing deeper levels of sedation.

Distinct clinical phenotypes for Crohn's disease derived from patient surveys.

BACKGROUND: Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions. METHODS: We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn's disease and studied their associations with drug responses. RESULTS: Using the patients' self-reported information, we identified two subpopulations of Crohn's disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn's disease subtypes. These subtypes show a trend towards differential genotype signatures. CONCLUSION: Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease.

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores. METHODS AND RESULTS: We retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in nonstroke controls were compared against the past 30 days of remote monitoring before stroke in cases. We trained 3 types of models on our data: (1) convolutional neural networks, (2) random forest, and (3) L1 regularized logistic regression (LASSO). We calculated the CHA2DS2-VASc score for each patient and compared its performance against machine learned indices based on AF burden in separate test cohorts. Finally, we investigated the effect of combining our AF burden models with CHA2DS2-VASc. We identified 3114 nonstroke controls and 71 stroke cases, with no significant differences in baseline characteristics. Random forest performed the best in the test data set (area under the curve [AUC]=0.662) and convolutional neural network in the validation dataset (AUC=0.702), whereas CHA2DS2-VASc had an AUC of 0.5 or less in both data sets. Combining CHA2DS2-VASc with random forest and convolutional neural network yielded a validation AUC of 0.696 and test AUC of 0.634, yielding the highest average AUC on nontraining data. CONCLUSIONS: This proof-of-concept study found that machine learning and ensemble methods that incorporate daily AF burden signature provided incremental prognostic value for risk stratification beyond CHA2DS2-VASc for near-term risk of stroke.

Mendelian Disease Associations Reveal Novel Insights into Inflammatory Bowel Disease.

Background: Monogenic diseases have been shown to contribute to complex disease risk and may hold new insights into the underlying biological mechanism of Inflammatory Bowel Disease (IBD). Methods: We analyzed Mendelian disease associations with IBD using over 55 million patients from the Optum's deidentified electronic health records dataset database. Using the significant Mendelian diseases, we performed pathway enrichment analysis and constructed a model using gene expression datasets to differentiate Crohn's disease (CD), ulcerative colitis (UC), and healthy patient samples. Results: We found 50 Mendelian diseases were significantly associated with IBD, with 40 being significantly associated with both CD and UC. Our results for CD replicated those from previous studies. Pathways that were enriched consisted of mainly immune and metabolic processes with a focus on tolerance and oxidative stress. Our 3-way classifier for UC, CD, and healthy samples yielded an accuracy of 72%. Conclusions: Mendelian diseases that are significantly associated with IBD may reveal novel insights into the genetic architecture of IBD.

Identification of UT-A1- and AQP2-interacting proteins in rat inner medullary collecting duct.

The urea channel UT-A1 and the water channel aquaporin-2 (AQP2) mediate vasopressin-regulated transport in the renal inner medullary collecting duct (IMCD). To identify the proteins that interact with UT-A1 and AQP2 in native rat IMCD cells, we carried out chemical cross-linking followed by detergent solubilization, immunoprecipitation, and LC-MS/MS analysis of the immunoprecipitated material. The analyses revealed 133 UT-A1-interacting proteins and 139 AQP2-interacting proteins, each identified in multiple replicates. Fifty-three proteins that were present in both the UT-A1 and the AQP2 interactomes can be considered as mediators of housekeeping interactions, likely common to all plasma membrane proteins. Among proteins unique to the UT-A1 list were those involved in posttranslational modifications: phosphorylation (protein kinases Cdc42bpb, Phkb, Camk2d, and Mtor), ubiquitylation/deubiquitylation (Uba1, Usp9x), and neddylation (Nae1 and Uba3). Among the proteins unique to the AQP2 list were several Rab proteins (Rab1a, Rab2a, Rab5b, Rab5c, Rab7a, Rab11a, Rab11b, Rab14, Rab17) involved in membrane trafficking. UT-A1 was found to interact with UT-A3, although quantitative proteomics revealed that most UT-A1 molecules in the cell are not bound to UT-A3. In vitro incubation of UT-A1 peptides with the protein kinases identified in the UT-A1 interactome revealed that all except Mtor were capable of phosphorylating known sites in UT-A1. Overall, the UT-A1 and AQP2 interactomes provide a snapshot of a dynamic process in which UT-A1 and AQP2 are produced in the rough endoplasmic reticulum, processed through the Golgi apparatus, delivered to endosomes that move into and out of the plasma membrane, and are regulated in the plasma membrane.

A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.

Summary: Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn's disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC. Availability and Implementation: PROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor. Contact: rbaltman@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival. Microscopic genetic changes may manifest as macroscopic morphological changes in the brain tumors that can be detected using magnetic resonance imaging (MRI), which can serve as noninvasive biomarkers for determining methylation of MGMT regulatory regions. In this research, we use a compendium of brain MRI scans of GBM patients collected from The Cancer Imaging Archive (TCIA) combined with methylation data from The Cancer Genome Atlas (TCGA) to predict the methylation state of the MGMT regulatory regions in these patients. Our approach relies on a bi-directional convolutional recurrent neural network architecture (CRNN) that leverages the spatial aspects of these 3-dimensional MRI scans. Our CRNN obtains an accuracy of 67% on the validation data and 62% on the test data, with precision and recall both at 67%, suggesting the existence of MRI features that may complement existing markers for GBM patient stratification and prognosis. We have additionally presented our model via a novel neural network visualization platform, which we have developed to improve interpretability of deep learning MRI-based classification models.

Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

OBJECTIVE: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. METHODS: FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process. RESULTS: Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall's tau of 0.24 ( P = .002). CONCLUSION: Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload.

Imputing gene expression to maximize platform compatibility.

Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples; the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis. Availability and Implementation: The gene inference model described in this paper is available as a R package (affyImpute), which can be downloaded at http://simtk.org/home/affyimpute. Contact: rbaltman@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Predicting hospital visits from geo-tagged Internet search logs.

The steady rise in healthcare costs has deprived over 45 million Americans of healthcare services (1, 2) and has encouraged healthcare providers to look for opportunities to improve their operational efficiency. Prior studies have shown that evidence of healthcare seeking intent in Internet searches correlates well with healthcare resource utilization. Given the ubiquitous nature of mobile Internet search, we hypothesized that analyzing geo-tagged mobile search logs could enable us to machine-learn predictors of future patient visits. Using a de-identified dataset of geo-tagged mobile Internet search logs, we mined text and location patterns that are predictors of healthcare resource utilization and built statistical models that predict the probability of a user's future visit to a medical facility. Our efforts will enable the development of innovative methods for modeling and optimizing the use of healthcare resources-a crucial prerequisite for securing healthcare access for everyone in the days to come.

Beat-to-beat spatiotemporal variability in the T vector is associated with sudden cardiac death in participants without left ventricular hypertrophy: the Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: Despite advances in prevention and treatment of cardiovascular disease, sudden cardiac death (SCD) remains a clinical challenge. Risk stratification in the general population is needed. METHODS AND RESULTS: Beat-to-beat spatiotemporal variability in the T vector was measured as the mean angle between consecutive T-wave vectors (mean TT' angle) on standard 12-lead ECGs in 14 024 participants in the Atherosclerosis Risk in Communities (ARIC) study. Subjects with left ventricular hypertrophy, atrial arrhythmias, frequent ectopy, ventricular pacing, or QRS duration ≥120 ms were excluded. The mean spatial TT' angle was 5.21±3.55°. During a median of 14 years of follow-up, 235 SCDs occurred (1.24 per 1000 person-years). After adjustment for demographics, coronary heart disease risk factors, and known ECG markers for SCD, mean TT' angle was independently associated with SCD (hazard ratio 1.089; 95% CI 1.044 to 1.137; P<0.0001). A mean TT' angle >90th percentile (>9.57°) was associated with a 2-fold increase in the hazard for SCD (hazard ratio 2.01; 95% CI 1.28 to 3.16; P=0.002). In a subgroup of patients with T-vector amplitude ≥0.2 mV, the association with SCD was almost twice as strong (hazard ratio 3.92; 95% CI 1.91 to 8.05; P<0.0001). A significant interaction between mean TT' angle and age was found: TT' angle was associated with SCD in participants aged <55 years (hazard ratio 1.096; 95% CI 0.043 to 1.152; P<0.0001) but not in participants aged ≥55 years (P(interaction)=0.009). CONCLUSIONS: In a large, prospective, community-based cohort of left ventricular hypertrophy-free participants, increased beat-to-beat spatiotemporal variability in the T vector, as assessed by increasing TT' angle, was associated with SCD.

Repolarization lability measured on 10-second ECG by spatial TT' angle: reproducibility and agreement with QT variability.

BACKGROUND: Reproducibility of spatial TT' angle on the 10-second ECG and its agreement with QT variability has not been previously studied. METHODS: We analyzed 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 172 healthy IDEAL study participants (age 38.1±15.2years, 50% male, 94% white). Repolarization lability was measured by the QT variance (QTV), short-term QT variability (STV(QT)), and spatial TT' angle. Bland-Altman analysis was used to assess the agreement between different log-transformed metrics of repolarization lability, and to assess the reproducibility. RESULTS: The heart rate showed a very high reproducibility (bias 0.14%, Lin's rho_c=0.99). As expected, noise suppression by averaging improves reproducibility. Agreement between two 10-second LogQTV was poor (bias -0.04; 95% limits of agreement [-1.89; 1.81]), while LogSTV(QT) (0.04 [-1.01; 1.10]), and especially LogTT' angle (-0.009 [-0.84; 0.82]) was better. CONCLUSION: TT' angle is a satisfactory reproducible metric of repolarization lability on the 10-second ECG.

Mechanical alternans is associated with mortality in acute hospitalized heart failure: prospective mechanical alternans study (MAS).

BACKGROUND: Acute hospitalized heart failure (AHHF) is associated with 40% to 50% risk of death or rehospitalization within 6 months after discharge. Timely (before hospital discharge) risk stratification of patients with AHHF is crucial. We hypothesized that mechanical alternans (MA) and T-wave alternans (TWA) are associated with postdischarge outcomes in patients with AHHF. METHODS AND RESULTS: A prospective cohort study was conducted in the intensive cardiac care unit and enrolled 133 patients (59.6±15.7 years; 65% men) admitted with AHHF. Surface ECG and peripheral arterial blood pressure waveform via arterial line were recorded continuously during the intensive cardiac care unit stay. MA and TWA were measured by enhanced modified moving average method. All-cause death or heart transplant served as a combined primary end point. MA was observed in 28 patients (25%), whereas TWA was detected in 33 patients (33%). If present, MA was tightly coupled with TWA. Mean TWA amplitude was larger in patients with both TWA and MA when compared with patients with lone TWA (median, 37 [interquartile range, 26-61] versus 22 [21-23] µV; P=0.045). After a median of 10-month postdischarge, 42 (38%) patients died and 2 had heart transplants. MA was associated with the primary end point in univariable Cox model (hazard ratio, 1.84; 95% confidence interval, 1.00-3.40; P=0.05) and after adjustment for left ventricular ejection fraction, New York Heart Association HF class, and implanted implantable cardioverter defibrillator/cardiac resynchronization therapy defibrillator (hazard ratio, 2.12 95% confidence interval, 1.13-3.98; P=0.020). TWA without consideration of simultaneous MA was not significantly associated with primary end point (hazard ratio, 1.42; 95% confidence interval, 0.77-2.64; P=0.260). CONCLUSIONS: MA is independently associated with outcomes in AHHF. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01557465.

Comparison of sum absolute QRST integral, and temporal variability in depolarization and repolarization, measured by dynamic vectorcardiography approach, in healthy men and women.

BACKGROUND: Recently we showed the predictive value of sum absolute QRST integral (SAI QRST) and repolarization lability for risk stratification of sudden cardiac death (SCD) in heart failure patients. The goal of this study was to compare SAI QRST and metrics of depolarization and repolarization variability in healthy men and women. METHODS: Orthogonal ECGs were recorded at rest for 10 minutes in 160 healthy men and women (mean age 39.6±14.6, 80 men). Mean spatial TT' angle, and normalized variances of T loop area, of spatial T vector amplitude, of QT interval and Tpeak-Tend area were measured for assessment of repolarization lability. Normalized variances of spatial QRS vector and QRS loop area characterized variability of depolarization. In addition, variability indices (VI) were calculated to adjust for normalized heart rate variance. SAI QRST was measured as the averaged arithmetic sum of areas under the QRST curve. RESULTS: Men were characterized by shorter QTc (430.3±21.7 vs. 444.7±22.2 ms; P<0.0001) and larger SAI QRST (282.1±66.7 vs. 204.9±58.5 mV*ms; P<0.0001). Repolarization lability negatively correlated with spatial T vector amplitude. Adjusted by normalized heart rate variance, QT variability index was significantly higher in women than in men (-1.54±0.38 vs. -1.70±0.33; P = 0.017). However, in multivariate logistic regression after adjustment for body surface area, QTc, and spatial T vector amplitude, healthy men had 1.5-3 fold higher probability of having larger repolarization lability, as compared to healthy women (T vector amplitude variability index odds ratio 3.88 (95%CI 1.4-11.1; P = 0.012). CONCLUSIONS: Healthy men more likely than women have larger repolarization lability.