Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.

Digital messaging to support control for type 2 diabetes (StAR2D): a multicentre randomised controlled trial

Background

Failure to take medicines for diabetes as prescribed contributes to poor outcomes from the condition. Mobile phones are ubiquitous and short message service (SMS) texts have shown promise as a low-cost intervention. We tested the effectiveness of SMS-text messaging in improving outcomes in adults with type 2 diabetes.

Methods

StAR2D was a 12-month two-arm randomised trial of SMS-text messaging and usual care in Cape Town, South Africa and Lilongwe, Malawi. Messages used behaviour change theory and were developed with patients and staff. The intervention group received four messages each week. The primary outcome was change in HbA1c. Secondary outcomes were the proportion of patients who collected > 80% medication and changes in systolic blood pressure, lipids, cardiovascular risk, and the proportion of the participants reaching treatment goals.

Results

The trial took place between 1 October, 2016 and 1 October 2018, 1186 participants were randomised to intervention (593) and control (593) groups. 91% of participants completed follow-up. There was a reduction in HbA1c (DCCT) in both groups but not in mean change (95% CI) between groups (− 0.08% (− 0.31 to 0.16) (IFCC − 0.82 mmol/mol (− 3.44 to 1.79). There was a small but not significant increase in the proportions of participants likely to have collected 80% or more of medication (Relative risk 1.11 (0.84 to 1.47; P = 0.47). There was a significant difference between groups in change in systolic blood pressure from baseline of 3.46 mmHg (1.48 to 5.44, P = 0.001) in favour of the intervention group. The between group difference in change in 10-year risk of coronary heart disease was − 0.71% (− 1.46 to 0.04, P = 0.064). The proportion of participants meeting treatment goals in the intervention group was 36.0% and in the control group 26.8% (Relative risk 1.36 (1.13 to 1.63, P = 0.001). Participants reported many challenges to adherence despite finding messages acceptable and useful.

Conclusions

Whilst SMS text messages do not lead to improved glycaemia in these low-resource settings there appeared to be an impact on blood pressure and achievement of treatment goals but the mechanisms for this are unclear. Text messages alone, may be unsuccessful unless accompanied by health system strengthening and other forms of self-management support for type 2 diabetes.

Trial registration

Trial registration: ISRCTN, ISRCTN70768808. Registered 1 July 2015, http://www.isrctn.com/I ISRCTN70768808.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11874-7.

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study

OBJECTIVES

The role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households and COVID-19 in New York City (NYC).

STUDY DESIGN

Cohort study.

METHODS

We conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as the proportion of the estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was an adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), the prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering.

RESULTS

39,923 suspected COVID-19 cases were presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant (β_interaction = 0.99, 95% CI: 0.99-1.00).

CONCLUSIONS

Overcrowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of the surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform

Background

Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform.

Methods and Results

Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate‐to‐severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate‐to‐severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%.

Conclusions

The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front‐line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease.

Registration

URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones

OBJECTIVE

Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency.

METHODS

Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization.

RESULTS

Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD.

SIGNIFICANCE

Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.

Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus

Autonomic nervous system dysfunction (ANSD) is a significant cause of mortality in tetanus. Currently, diagnosis relies on nonspecific clinical signs. Heart rate variability (HRV) may indicate underlying autonomic nervous system activity and represents a potentially valuable noninvasive tool for ANSD diagnosis in tetanus. HRV was measured from three 5-minute electrocardiogram recordings during a 24-hour period in a cohort of patients with severe tetanus, all receiving mechanical ventilation. HRV measurements from all subjects—five with ANSD (Ablett Grade 4) and four patients without ANSD (Ablett Grade 3)—showed HRV was lower than reported ranges for healthy individuals. Comparing different severities of tetanus, raw data for both time and frequency measurements of HRV were reduced in those with ANSD compared with those without. Differences were statistically significant in all except root mean square SD, indicating HRV may be a valuable tool in ANSD diagnosis.

Severity detection tool for patients with infectious disease

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.

Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning

BACKGROUND

Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson's disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PSP may be deployed at a stage when they are likely to have maximal benefit. Analysis of gait and related tasks is one possible means of discrimination.

RESEARCH QUESTION

Here we investigate a wearable sensor array coupled with machine learning approaches as a means of disease classification.

METHODS

21 participants with PSP, 20 with PD, and 39 healthy control (HC) subjects performed a two minute walk, static sway test, and timed up-and-go task, while wearing an array of six inertial measurement units. The data were analysed to determine what features discriminated PSP from PD and PSP from HC. Two machine learning algorithms were applied, Logistic Regression (LR) and Random Forest (RF).

RESULTS

17 features were identified in the combined dataset that contained independent information. The RF classifier outperformed the LR classifier, and allowed discrimination of PSP from PD with 86 % sensitivity and 90 % specificity, and PSP from HC with 90 % sensitivity and 97 % specificity. Using data from the single lumbar sensor only resulted in only a modest reduction in classification accuracy, which could be restored using 3 sensors (lumbar, right arm and foot). However for maximum specificity the full six sensor array was needed.

SIGNIFICANCE

A wearable sensor array coupled with machine learning methods can accurately discriminate PSP from PD. Choice of array complexity depends on context; for diagnostic purposes a high specificity is needed suggesting the more complete array is advantageous, while for subsequent disease tracking a simpler system may suffice.

A Deep Learning Framework for the Remote Detection of Parkinson'S Disease Using Smart-Phone Sensor Data

The assessment of Parkinson's disease (PD) using wearable sensors in non-clinical environments presents an opportunity for objective disease classification and severity prediction on a high-frequency and longitudinal basis. However, many challenges exist in analysing remotely collected data due to many sources of data corruption. Using a cohort of 1,815 participants (866 controls and 949 with PD) we implement a range of classification algorithms on Alternate Finger Tapping test data collected on smart-phones in remote environments. We compare the disease classification ability of two traditional machine learning methods against two state-of-the-art deep learning approaches, determining if the latter is successful without the definition of an explicit feature set. We find the deep learning approaches capable of disease classification, often outperforming traditional methods. We show similarities between the participants successfully classified through use of a manually extracted feature set, and the features learnt by a convolutional neural network. Finally, we discuss the broader challenges of analysing remotely collected datasets whilst highlighting the suitability of deep learning for assessing PD when large participant numbers are available.

Mobile Messaging Support Versus Usual Care for People With Type 2 Diabetes on Glycemic Control: Protocol for a Multicenter Randomized Controlled Trial

Background

Health outcomes for people treated for type 2 diabetes could be substantially improved in sub-Saharan Africa. Failure to take medicine regularly to treat diabetes has been identified as a major problem. Resources to identify and support patients who are not making the best use of medicine in low- and middle-income settings are scarce. Mobile phones are widely available in these settings, including among people with diabetes; linked technologies, such as short message service (SMS) text messaging, have shown promise in delivering low-cost interventions efficiently. However, evidence showing that these interventions will work when carried out at a larger scale and measuring the extent to which they will improve health outcomes when added to usual care is limited.

Objective

The objective of this trial is to test the effectiveness of sending brief, automated SMS text messages for improving health outcomes and medication adherence in patients with type 2 diabetes compared to an active control.

Methods

We will carry out a randomized trial recruiting from clinics in two contrasting settings in sub-Saharan Africa: Cape Town, South Africa, and Lilongwe, Malawi. Intervention messages will advise people about the benefits of their diabetes treatment and offer motivation and encouragement around lifestyle and use of medication. We allocated patients, using randomization with a minimization algorithm, to receive either three to four intervention messages per week or non-health-related messages every 6 weeks. We will follow up with participants for 12 months, measuring important risk factors for poor health outcomes and complications in diabetes. This will enable us to estimate potential health benefits, including the primary outcome of hemoglobin A_1c (HbA_1c) levels as a marker for long-term blood glucose control and a secondary outcome of blood pressure control. We will record the costs of performing these activities and estimate cost-effectiveness. We will also use process evaluation to capture the collection of medication and assess the reception of the intervention by participants and health care workers.

Results

Recruitment to the trial began in September 2016 and follow-up of participants was completed in October 2018. Data collection from electronic health records and other routinely collected sources is continuing. The database lock is anticipated in June 2019, followed by analysis and disclosing of group allocation.

Conclusions

The knowledge gained from this study will have wide applications and advance the evidence base for effectiveness of mobile phone-based, brief text messaging on clinical outcomes and in large-scale, operational settings. It will provide evidence for cost-effectiveness and acceptability that will further inform policy development and decision making. We will work with a wide network that includes patients, clinicians, academics, industry, and policy makers to help us identify opportunities for informing people about the work and raise awareness of what is being developed and studied.

Trial Registration

ISRCTN Registry ISRCTN70768808; http://www.isrctn.com/ISRCTN70768808 (Archived by WebCite at http://www.webcitation.org/786316Zqk)

International Registered Report Identifier (IRRID)

DERR1-10.2196/12377

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data

As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) using smartphones. This contribution presents multi-source ensemble learning, a methodology which combines dataset deconstruction with ensemble learning and enables participants with incomplete data (i.e., where not all sensor data is available) to be included in the training of machine learning models and achieves a 100% participant retention rate. We demonstrate the proposed method on a cohort of 1513 participants, 91.2% of which contributed incomplete data in tapping, gait, voice, and/or memory tests. The use of multi-source ensemble learning, alongside convolutional neural networks (CNNs) capitalizing on the amount of available data, increases PD classification accuracy from 73.1% to 82.0% as compared to traditional techniques. The increase in accuracy is found to be partly caused by the use of multi-channel CNNs and partly caused by developing models using the large cohort of participants. Furthermore, through bootstrap sampling we reveal that feature selection is better performed on a large cohort of participants with incomplete data than on a small number of participants with complete data. The proposed method is applicable to a wide range of wearable/remote monitoring datasets that suffer from missing data and contributes to improving the ability to remotely monitor PD via revealing novel methods of accounting for symptom heterogeneity.

Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes

OBJECTIVE

To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones.

APPROACH

Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another.

MAIN RESULTS

We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects.

SIGNIFICANCE

Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.

Abstract C59: Lipid expression dynamics in epithelial cells undergoing HGF-induced EMT

Cancer metastasis occurs when cells detach from the primary tumor, invade through local tissues, migrate to distant sites, and colonize new tumors. The morphological change within the cell is termed epithelial-mesenchymal transition (EMT), as cells change from their epithelial state and derive more primitive, mesenchymal characteristics. A cellular signaling pathway that stimulates EMT is initiated by hepatocyte growth factor (HGF), which binds and activates the c-Met receptor tyrosine kinase. Activation of HGF signaling induces dramatic changes in cell morphology and behavior. HGF signaling exerts its effect by altering gene transcription and proteome profiles in cells triggered to undergo EMT. The proteomic changes in HGF-induced cell lines is well documented, however changes in lipid expression and their contribution to EMT are poorly understood. Here we develop and employ mass spectrometry-based approaches to analyze lipid expression changes in cells. This method allows for analysis of thousands of lipids in a single sample and the determination of the relative abundance in each lipid. We have found that seventy lipids undergo dramatic regulatory expression changes in MDCK cells undergoing HGF-induced EMT. This effort could provide important clues into the identity of lipid modification or synthesis programs that are required for EMT and, more broadly speaking, for cellular events associated with cancer progression. Understanding their contribution and function could allow for identification of therapeutic targets for intervention in cellular processes required for cancer metastasis.

Citation Format: Kristen Alexander, Brendan Coutu, John Prince, Marc Hansen. Lipid expression dynamics in epithelial cells undergoing HGF-induced EMT. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Invasion and Metastasis; Jan 20-23, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;73(3 Suppl):Abstract nr C59.

Comparison of speech production in upright and supine position

Speech is usually produced in an upright sitting or standing posture. Measurements and judgments of speech may be made in conditions requiring a supine position, however. These conditions include MRI recordings, and oral procedures, such as, adjustments to dental appliances, medical and surgical procedures. It is of interest, therefore, to see whether gravity has strong or systematic effects on tongue behavior. In the present study, 13 subjects repeated several words, which contained extreme consonant and vowel tongue positions, during upright and supine condition. Ultrasound imaging provided midsagittal tongue contours, in each condition, for comparison. A neck brace was used to stabilize transducer placement and the palate was used as a physiological reference to register the data sets. Results showed a significant subject effect. In supine position the tongue was more posterior than upright for seven subjects, more anterior for two subjects and varied by phoneme for four subjects. However, there was no significant phoneme effect. The direction of change and the amount of change were not directly related. Most subjects had small upright-supine differences. The largest differences, less than 3 mm on average, were in the posterior tongue.

Antiproliferative activity and induction of apoptosis in human colon cancer cells treated in vitro with constituents of a product derived from Pistacia lentiscus L. var. chia

In this report, we demonstrate that a 50% ethanol extract of the plant-derived product, Chios mastic gum (CMG), contains compounds which inhibit proliferation and induce death of HCT116 human colon cancer cells in vitro. CMG-treatment induces cell arrest at G(1), detachment of the cells from the substrate, activation of pro-caspases-8, -9 and -3, and causes several morphological changes typical of apoptosis in cell organelles. These events, furthermore, are time- and dose-dependent, but p53- and p21-independent. Apoptosis induction by CMG is not inhibited in HCT116 cell clones expressing high levels of the anti-apoptotic protein, Bcl-2, or dominant-negative FADD, thereby indicating that CMG induces cell death via a yet-to-be identified pathway, unrelated to the death receptor- and mitochondrion-dependent pathways. The findings presented here suggest that CMG (a) induces an anoikis form of cell death in HCT116 colon cancer cells that includes events associated with caspase-dependent pathways; and (b) might be developed into a chemotherapeutic agent for the treatment of human colon and other cancers.

Fibrinolysis of loculated pleural effusion in malignant mesothelioma

Surgical resection is not feasible in most cases of malignant mesothelioma and palliation of symptoms remains the mainstay of treatment. When a pleural effusion is loculated, the standard treatment methods of intercostal tube drainage and pleurodesis may not be helpful. We report a 49-year-old man with malignant mesothelioma in whom intrapleural fibrinolysis was performed using streptokinase. It was successful in breaking the locules and draining the effusion. Intrapleural fibrinolysis should be considered in cases of loculated pleural effusion due to malignant mesothelioma, as it may provide symptom relief and palliation.

An open-label, dose-ranging study of atomoxetine in children with attention deficit hyperactivity disorder

OBJECTIVE

The goal of this study was to evaluate the tolerability and effectiveness of the experimental, noradrenergic specific reuptake inhibitor atomoxetine in the treatment of children with attention deficit hyperactivity disorder (ADHD).

METHODS

This was an open, prospective, dose-ranging study of atomoxetine monotherapy in the treatment of 30 children with ADHD between the ages of 7 and 14 years. Atomoxetine was started at 10-20 mg/day and titrated weekly up to 90 mg over 11 weeks, depending on response and adverse effects. Twenty-two children completed the full 11 weeks. We assessed efficacy with weekly clinician and parent ratings of ADHD and oppositional symptoms and monitored adverse effects, laboratory findings, and cardiovascular parameters.

RESULTS

Treatment with atomoxetine (mean final, total daily dose of 1.9 mg/kg/day) was very well tolerated without meaningful adverse effects. Atomoxetine significantly reduced core symptoms of ADHD (ADHD-Rating Scale-IV; 38.6% decrease vs. baseline, p < 0.001) with significant improvement (p < 0.05) in all but 1 of the 18 individual items in the ADHD-Rating Scale-IV. More than 75% of subjects who completed 10 weeks of treatment showed > 25% decrease in ADHD symptoms.

CONCLUSIONS

These findings extend to children the positive results previously reported in adults diagnosed with ADHD who were treated with atomoxetine. These results support additional controlled trials of atomoxetine in cases of pediatric ADHD.

A retrospective study of citalopram in adolescents with depression

Recent evidence suggests that the selective serotonin reuptake inhibitors are safe and efficacious in treating juveniles with depression. However, citalopram has not been reported in adolescents with depression. This study assessed the effectiveness and tolerability of citalopram in all adolescents with depressive disorders treated naturalistically in a community mental health center during a 1-year interval. Medical charts were retrospectively reviewed for 21 adolescents treated with citalopram for major depression (n = 14), bipolar depression (n = 4), or dysthymia (n = 3). An independent rater compared last visit to baseline depression using the Clinical Global Impression (CGI) Severity and Improvement scales. Adolescents received citalopram for an average of 128.5 +/- 84 days at a final average dose of 26.5 +/- 13.1 mg/day. Sixteen of these 21 adolescents (76%) exhibited much to very much improvement as measured by the CGI, and severity of depression diminished significantly (z = 3.007, p < 0.0026). Mild side effects, including headaches, dizziness, nausea, sedation, agitation, and sweating were reported by 7 (33%) of the patients. These data suggest that citalopram may be effective, safe, and well tolerated in the treatment of adolescents with depressive disorders and that controlled trials are warranted in this population.

Aggressive interactions between freshwater turtle, Chelodina oblonga, hatchlings and freshwater crayfish, Cherax spp.: implications for the conservation of the critically endangered western swamp turtle, Pseudemydura umbrina

Interactions between turtle hatchlings of Chelodina oblonga and the marron, Cherax tenuimanus, the gilgie, C. quinquecarinatus, the koonac, C. preissii (freshwater crayfish native to Western Australia) and the introduced yabby, Cherax. sp., were observed in laboratory-based trials in uncluttered aquaria. Marron, koonacs and yabbies, but not gilgies, showed aggressive and predatory behaviour towards the hatchlings. In total, 59 attacks were observed in 26 of the 80 trials. On 12 occasions, crayfish captured hatchlings in their chelae. On two occasions, the attack of the crayfish was so quick that the hatchling was killed instantly. Compared with movement when alone, movement of hatchlings was significantly greater in the presence of koonacs and yabbies, but significantly less in the presence of marron and gilgies. The range of non-native yabbies currently is expanding into Ellen Brook Nature Reserve which harbours the last naturally persisting population of the critically endangered western swamp turtle, Pseudemydura umbrina. No native crayfish occur in the habitat of P. umbrina in this reserve. The possible invasion by the ecological generalist yabby poses a new threat to the survival of P. umbrina.

Metabolic ecology of cockatoos in the south-west of Western Australia

This study examined the metabolic ecology of six cockatoo taxa endemic to the south-west of Western Australia. As the availability of food is one factor that may influence the abundance and distribution of these cockatoos, we document here their baseline energy requirements and feeding patterns. Evaporative waterloss was also measured as this may correlate with the aridity of the species’ environment. Basal metabolic rate was significantly lower at 0.62 ± 0.13 mL O2 g–1 h–1 for the inland red-tailed black cockatoo than 1.11 ± 0.16 mL O2 g–1 h–1 for the forest red-tailed black cockatoo, but there was no significant difference in metabolic rate between the two white-tailed black cockatoos (0.86 ± 0.18 for Carnaby’s and 0.81 ± 0.11 mL O2 g–1 h–1 for Baudin’s) or the two corellas (0.95 ± 0.12 for Butler’s and 0.70 ± 0.04 mL O2 g–1 h–1 for Muir’s). There were no significant differences between the two white-tailed black cockatoos, and between the two corellas, with respect to evaporative water loss. The inland red-tailed black cockatoo had a significantly lower rate of evaporative water loss (0.44 ± 0.07 mg g–1 h–1) than the forest red-tailed black cockatoo (0.70 ± 0.06 mg g–1 h–1), which is presumably an adaptation to its more arid habitat. The total energy content of assorted native and introduced food items that form significant proportions of the diets for these cockatoos varied from only 0.17 kJ for a 9-mg Emex australis seed to 63.9 kJ for a 3-g Banksia attenuata nut. The energy content of each food item and the estimated daily energy requirements of the cockatoos enabled the calculation of the numbers of nuts/cones/seeds required by each species for a day, which ranged from 11 B. attenuata nuts for a Carnaby’s cockatoo to 3592 Persoonia longifolia seeds for a forest red-tailed black cockatoo.

Efficacy of a mixed amphetamine salts compound in adults with attention-deficit/hyperactivity disorder

BACKGROUND

We report on a controlled trial of a mixed amphetamine salts compound (Adderall, dextroamphetamine sulfate, dextro-, levoamphetamine sulfate, dextroamphetamine aspartate, levoamphetamine aspartate, and dextroamphetamine saccharate) in the treatment of adult attention-deficit/hyperactivity disorder (ADHD).

METHODS

This was a 7-week, randomized, double-blind, placebo-controlled, crossover study of Adderall in 27 well-characterized adults satisfying full DSM-IV criteria for ADHD of childhood onset and persistent symptoms into adulthood. Medication was titrated up to 30 mg twice a day. Outcome measures included the ADHD Rating Scale and the Clinical Global Impression Score. Comorbid psychiatric disorders were assessed to test for potential effects on treatment outcome.

RESULTS

Treatment with Adderall at an average oral dose of 54 mg (administered in 2 daily doses) was effective and well tolerated. Drug-specific improvement in ADHD symptoms was highly significant overall (42% decrease on the ADHD Rating Scale, P<.001), and sufficiently robust to be detectable in a parallel groups comparison restricted to the first 3 weeks of the protocol (P<.001). The percentage of subjects who improved (reduction in the ADHD rating scale of > or =30%) was significantly higher with Adderall treatment than with a placebo (70% vs 7%; P =.001).

CONCLUSIONS

Adderall was effective and well tolerated in the short-term treatment of adults with ADHD. More work is needed to evaluate the long-term effects of Adderall, or other amphetamine compounds, in the treatment of adults with ADHD.

A controlled clinical trial of bupropion for attention deficit hyperactivity disorder in adults

OBJECTIVE

Despite the increasing recognition of attention deficit hyperactivity disorder (ADHD) in adults, there is a paucity of controlled pharmacological trials demonstrating the effectiveness of compounds used in treatment, particularly nonstimulants. The authors report results from a controlled investigation to determine the anti-ADHD efficacy of bupropion in adult patients with DSM-IV ADHD.

METHOD

This was a double-blind, placebo-controlled, randomized, parallel, 6-week trial comparing patients receiving sustained-release bupropion (up to 200 mg b.i.d.) (N=21) to patients receiving placebo (N=19). The authors used standardized structured psychiatric instruments for diagnosis of ADHD. To measure improvement, they used separate assessments of ADHD, depression, and anxiety symptoms at baseline and each weekly visit.

RESULTS

Of the 40 subjects (55% male) enrolled in the study, 38 completed the study. Bupropion treatment was associated with a significant change in ADHD symptoms at the week-6 endpoint (42% reduction), which exceeded the effects of placebo (24% reduction). In analyses using a cutoff of 30% or better reduction to denote response, 76% of the subjects receiving bupropion improved, compared to 37% of the subjects receiving placebo. Similarly, in analyses using Clinical Global Impression scale scores, 52% of the subjects receiving bupropion reported being "much improved" to "very improved," compared to 11% of the subjects receiving placebo.

CONCLUSIONS

These results indicate a clinically and statistically significant effect of bupropion in improving ADHD in adults. The results suggest a therapeutic role for bupropion in the armamentarium of agents for ADHD in adults, while further validating the continuity of pharmacological responsivity of ADHD across the lifespan.

Systematic chart review of the pharmacologic treatment of comorbid attention deficit hyperactivity disorder in youth with bipolar disorder

The objective of this study was to evaluate pharmacological approaches for attention deficit hyperactivity disorder (ADHD) in children with bioplar disorder and comorbid ADHD. The medical charts of 38 patients with diagnoses of both Diagnostic and Statistical Manual of Mental Disorders, 3rd ed., revised ADHD and bipolar disorder were reviewed over multiple visits to assess improvement and prescription patterns. Logistic regression was used to model the probability of improvement at each visit, and robust standard errors were estimated in order to account for correlation among individuals using Huber's correction for clustered data. The proportion of visits at which ADHD symptoms were rated as improved following initial improvement in manic symptoms was 7.5 times greater than before initial improvement of manic symptoms. The recurrence of manic symptoms following their initial stabilization significantly inhibited ADHD response to medication. Although tricyclic antidepressants (TCAs) significantly increased the probability of ADHD improvement following mood stabilization, there was also a significant association between treatment with TCAs and relapse of manic symptoms. Our results support the hypothesis that mood stabilization is a prerequisite for the successful pharmacologic treatment of ADHD in children with both ADHD and manic symptoms. Although TCAs can be helpful in the management of ADHD children with manic symptoms, these drugs should be used with caution since they can also have a destabilizing effect on manic symptoms.

A pilot controlled clinical trial of ABT-418, a cholinergic agonist, in the treatment of adults with attention deficit hyperactivity disorder

OBJECTIVE

Despite the increasing recognition of attention deficit hyperactivity disorder (ADHD) in adults, there is a paucity of controlled pharmacological trials. Recent reports have suggested the potential usefulness of cholinergic agents for ADHD. To this end, the authors completed a controlled study of ABT-418, a novel cholinergic activating agent, for the treatment of adults with ADHD.

METHOD

This was a double-blind, placebo-controlled, randomized, crossover trial that compared a transdermal patch of ABT-418 (75 mg/day) to placebo in adults who met DSM-IV criteria for ADHD. There were two 3-week treatment periods separated by 1 week of washout.

RESULTS

Of the 32 subjects enrolled in the study (88% were men; mean age = 40 years, SD = 9), 29 completed the study. At the endpoint of each active arm (last observation carried forward), a significantly higher proportion of subjects was considered improved while receiving ABT-418 than while receiving placebo (40% versus 13%). Similarly, at endpoint there was a significantly greater reduction in ADHD symptom checklist scores (28% versus 15%). Symptoms reflective of attention, and subjects with less severe ADHD, responded more robustly to ABT-418. Treatment with ABT-418 was relatively well tolerated; dizziness and nausea were the most frequently reported adverse effects.

CONCLUSIONS

The results of this investigation indicate that ABT-418, a nicotinic analog, may be a potentially useful agent for the treatment of ADHD.

Controlled trial of high doses of pemoline for adults with attention-deficit/hyperactivity disorder

Despite the increasing awareness of attention-deficit/hyperactivity disorder (ADHD) in adults, there are a limited number of controlled pharmacologic studies of this disorder. Because the stimulant medication magnesium pemoline (Cylert, Abbott Laboratories, Abbott Park, IL) has been found effective in treating ADHD in pediatric groups, we tested its efficacy in adults with ADHD using higher daily doses than those previously studied. We conducted a 10-week, double-blind, placebo-controlled, crossover design study of pemoline at a target daily dose of 3 mg/kg per day in 35 adult patients with DSM-III-R and -IV ADHD. We used standardized structured psychiatric instruments for diagnosis. To measure improvement, we used separate assessments of ADHD, depressive, and anxiety symptoms at baseline and at each biweekly visit. ADHD outcome was determined using the ADHD symptom checklist and Clinical Global Impression scales of Severity and Improvement. Of the 35 adults with ADHD who were randomized in the trial, 27 (77%) completed the protocol. Treatment with pemoline in the final week of the 4-week active phase was best tolerated at doses substantially lower than the target dose of 3 mg/kg per day (mean dose, 2.2 mg/kg per day; mean+/-SD, 148+/-95 mg). Pemoline was significantly better at reducing ADHD symptoms compared with placebo (z = 2.4,p < 0.02). Using a predefined 30% reduction in symptoms as an indication of improvement, 50% of pemoline-treated subjects and 17% of subjects in the placebo group were considered positive responders (chi2 = 7.1, p = 0.008). These results indicate that pemoline is moderately effective in the treatment of ADHD in adults. Although robust doses were targeted, most adults preferred more moderate dosing (120-160 mg/day). Given the limited efficacy, tolerability, and concerns of hepatic dysfunction, pemoline should be considered as second-line medication for treating ADHD in adults.

Absence of effect of stimulants on the phamacokinetics of desipramine in children

We conducted a retrospective chart review to examine the pharmacokinetic interaction between desipramine and the stimulants methylphenidate and dexedrine using routinely monitored desipramine serum concentrations in children receiving desipramine either alone or with a stimulant. Subjects were 142 children and adolescents (age 6-17 yrs; 113 taking desipramine, 29 taking desipramine-stimulants) in whom 401 dose- and weight-normalized serum concentrations were evaluated (333 desipramine, 68 desipramine-stimulants). Desipramine pharmacokinetic parameters were similar for both groups, including mean weight-corrected dose (3.66+/-1.36 mg/kg, desipramine; 3.66+/-1.09 mg/kg, desipramine-stimulants; p=0.97), weight- and dose-normalized serum concentrations (47.26+/-39.26 [microg/L]/[mg/kg], desipramine, 39.02+/-19.92 [microg/L]/[mg/kg], desipramine-stimulants; p=0.09), and clearance (0.690+/-0.913 [L/kg]/hr, desipramine; 0.613+/-0.514 [L/kg]/hr, desipramine-stimulants; p=0.499). When stratified by age, gender, and type of stimulant, no difference was detected (p>0.05) between groups. Our findings indicate the absence of a clinically significant interaction between desipramine and stimulants.

Erythrocyte and brain methionine adenosyltransferase activities in patients with schizophrenia

The activity of methionine adenosyltransferase (MAT) was investigated in erythrocytes and postmortem brain specimens (cortex gyrus frontalis, hippocampus and thalamus) of patients with schizophrenia treated with neuroleptics. In comparison with the control group, abnormally low values of MAT Vmax and an increased MAT affinity towards methionine (lower Km values) were found in erythrocytes. In the brain, a regionally selective decrease of MAT Km was found in cortex gyrus frontalis but the Vmax values were however, unchanged. In the regions of cortex gyrus frontalis and hippocampus, but not in thalamus, the values of Vmax and Km were inversely correlated with the duration of schizophrenia. In rats treated for 28 days with the typical neuroleptic haloperidol and the atypical clozapine, a significant increase of MAT activity was found in the corpus striatum. There is the possibility that the changes observed in MAT activity in patients with schizophrenia are attributed to the neuroleptic medication.