Determining clinical pharmacy workload by patient disease classification in medical and surgical patients

Aim: To determine the time needed to provide clinical pharmacy services to individual patient episodes for medical and surgical patients and the effect of patient presentation and complexity on the clinical pharmacy workload. Method: During a 5-month period in 2006 at two general hospitals, pharmacists recorded a defined range of activities that they provided for patients, including the actual times required for these tasks. A customised database linked to the two hospitals' patient administration systems stored the data according to the specific patient episode number. The influence of patient presentation and complexity on the clinical pharmacy activities provided was also examined. Results: The average time required by pharmacists to undertake a medication history interview and medication reconciliation was 9.6 (SD 4.9) minutes. Interventions required 5.7 (SD 4.6) minutes, clinical review of the medical record 5.5 (SD 4.0) minutes and medication order review 3.5 (SD 2.0) minutes. For all of these activities, the time required for medical patients was greater than for surgical patients and greater for 'complicated' patients. The average time required to perform all clinical pharmacy activities for 1071 completed patient episodes was 14.4 (SD 10.9) minutes and was greater for medical and 'complicated' patients. Conclusion: The time needed to provide clinical pharmacy services was affected by whether the patients were medical or surgical. The existence of comorbidities or complications affected these times. The times required to perform clinical pharmacy activities may not be consistent with recently proposed staff ratios for the provision of a basic clinical pharmacy service.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

The global burden of musculoskeletal conditions for 2010: an overview of methods.

The objective of this paper is to provide an overview of methods used for estimating the burden from musculoskeletal (MSK) conditions in the Global Burden of Diseases 2010 study. It should be read in conjunction with the disease-specific MSK papers published in Annals of Rheumatic Diseases. Burden estimates (disability-adjusted life years (DALYs)) were made for five specific MSK conditions: hip and/or knee osteoarthritis (OA), low back pain (LBP), rheumatoid arthritis (RA), gout and neck pain, and an 'other MSK conditions' category. For each condition, the main disabling sequelae were identified and disability weights (DW) were derived based on short lay descriptions. Mortality (years of life lost (YLLs)) was estimated for RA and the rest category of 'other MSK', which includes a wide range of conditions such as systemic lupus erythematosus, other autoimmune diseases and osteomyelitis. A series of systematic reviews were conducted to determine the prevalence, incidence, remission, duration and mortality risk of each condition. A Bayesian meta-regression method was used to pool available data and to predict prevalence values for regions with no or scarce data. The DWs were applied to prevalence values for 1990, 2005 and 2010 to derive years lived with disability. These were added to YLLs to quantify overall burden (DALYs) for each condition. To estimate the burden of MSK disease arising from risk factors, population attributable fractions were determined for bone mineral density as a risk factor for fractures, the occupational risk of LBP and elevated body mass index as a risk factor for LBP and OA. Burden of Disease studies provide pivotal guidance for governments when determining health priority areas and allocating resources. Rigorous methods were used to derive the increasing global burden of MSK conditions.

Medulloblastoma Down Under 2013 : a report from the third annual meeting of the International Medulloblastoma Working Group.

Medulloblastoma is curable in approximately 70% of patients. Over the past decade, progress in improving survival using conventional therapies has stalled, resulting in reduced quality of life due to treatment-related side effects, which are a major concern in survivors. The vast amount of genomic and molecular data generated over the last 5-10 years encourages optimism that improved risk stratification and new molecular targets will improve outcomes. It is now clear that medulloblastoma is not a single-disease entity, but instead consists of at least four distinct molecular subgroups: WNT/Wingless, Sonic Hedgehog, Group 3, and Group 4. The Medulloblastoma Down Under 2013 meeting, which convened at Bunker Bay, Australia, brought together 50 leading clinicians and scientists. The 2-day agenda included focused sessions on pathology and molecular stratification, genomics and mouse models, high-throughput drug screening, and clinical trial design. The meeting established a global action plan to translate novel biologic insights and drug targeting into treatment regimens to improve outcomes. A consensus was reached in several key areas, with the most important being that a novel classification scheme for medulloblastoma based on the four molecular subgroups, as well as histopathologic features, should be presented for consideration in the upcoming fifth edition of the World Health Organization's classification of tumours of the central nervous system. Three other notable areas of agreement were as follows: (1) to establish a central repository of annotated mouse models that are readily accessible and freely available to the international research community; (2) to institute common eligibility criteria between the Children's Oncology Group and the International Society of Paediatric Oncology Europe and initiate joint or parallel clinical trials; (3) to share preliminary high-throughput screening data across discovery labs to hasten the development of novel therapeutics. Medulloblastoma Down Under 2013 was an effective forum for meaningful discussion, which resulted in enhancing international collaborative clinical and translational research of this rare disease. This template could be applied to other fields to devise global action plans addressing all aspects of a disease, from improved disease classification, treatment stratification, and drug targeting to superior treatment regimens to be assessed in cooperative international clinical trials.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Towards a classification of biomarkers of neuropsychiatric disease: from encompass to compass

There is currently considerable imprecision in the nosology of biomarkers used in the study of neuropsychiatric disease. The neuropsychiatric field lags behind others such as oncology, wherein, rather than using 'biomarker' as a blanket term for a diverse range of clinical phenomena, biomarkers have been actively classified into separate categories, including prognostic and predictive tests. A similar taxonomy is proposed for neuropsychiatric diseases in which the core biology remains relatively unknown. This paper divides potential biomarkers into those of (1) risk, (2) diagnosis/trait, (3) state or acuity, (4) stage, (5) treatment response and (6) prognosis, and provides illustrative exemplars. Of course, biomarkers rely on available technology and, as we learn more about the neurobiological correlates of neuropsychiatric disorders, we will realize that the classification of biomarkers across these six categories can change, and some markers may fit into more than one category.Molecular Psychiatry advance online publication, 28 October 2014; doi:10.1038/mp.2014.139.

Hybrid methods to select informative gene sets in microarray data classification

One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)–are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.

Clinical pharmacy input for hospital inpatients according to disease state

This study measured the clinical activities performed and times taken by hospital pharmacists to provide medication monitoring services to individual medical and surgical patients. Linking these data to hospital Patient Administration Systems showed how clinical pharmacy manpower needs are guided by patient partition, disease complexity and Diagnosis Related Group classification.

Determinants of health-related quality of life in people with Parkinson’s disease: a path analysis

Purpose:
To identify the demographic factors, impairments and activity limitations that contribute to health-related quality of life (HRQOL) in people with idiopathic Parkinson’s disease (PD).

Method:
Two hundred and ten individuals with idiopathic PD who participated in the baseline assessment of a randomized clinical trial were included. The Parkinson’s Disease Questionnaire-39 summary index was used to quantify HRQOL. In order to provide greater clarity regarding the determinants of HRQOL, path analysis was used to explore the relationships between the various predictors in relation to the functioning and disability framework of the International Classification of Functioning model.

Results:
The two models of HRQOL that were examined in this study had a reasonable fit with the data. Activity limitations were found to be the strongest predictor of HRQOL. Limitations in performing self-care activities contributed the most to HRQOL in Model 1 (β = 0.38; p < 0.05), while limitations in functional mobility had the largest contribution in Model 2 (β = −0.31; p < 0.0005). Self-reported history of falls was also found to have a significant and direct relationship with HRQOL in both models (Model 1 β = −0.11; p < 0.05; Model 2 β = −0.21; p < 0.05).

Conclusions:
Health-related quality of life in PD is associated with self-care limitations, mobility limitations, self-reported history of falls and disease duration. Understanding how these factors are inter-related may assist clinicians focus their assessments and develop strategies that aim to minimize the negative functional and social sequelae of this debilitating disease.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.

Venous thromboembolism and underutilisation of anticoagulant thromboprophylaxis in hospitalised patients with inflammatory bowel disease

Background: Venous thromboembolism (VTE) is a well-recognised extra-intestinal manifestation of inflammatory bowel disease (IBD). Despite the widespread support for anticoagulant prophylaxis in hospitalised IBD patients, the utilisation and efficacy in clinical practice are unknown. Aims: The aim of this study was to assess the prevalence and clinical features of VTE among hospitalised IBD patients and ascertain whether appropriate thromboprophylaxis had been administered. Methods: All patients with a discharge diagnosis of Crohn disease or ulcerative colitis and VTE were retrospectively identified using International Classification of Diseases, tenth revision codes from medical records at our institution from July 1998 to December 2009. Medical records were then reviewed for clinical history and utilisation of thromboprophylaxis. Statistical analysis was performed by Mann-Whitney test and either χ2 tests or Fisher's exact tests. Results: Twenty-nine of 3758 (0.8%) IBD admissions suffered VTE, 13 preadmission and 16 during admission. Of these 29 admissions (in 25 patients), 24% required intensive care unit and 10% died. Of the 16 venous thrombotic events that occurred during an admission, eight (50%) did not receive anticoagulant thromboprophylaxis and eight (50%) occurred despite thromboprophylaxis. Most thromboembolism despite prophylaxis occurred post-intestinal resection (n = 5, 63%). Conclusion: Thromboprophylaxis is underutilised in half of IBD patients suffering VTE. Prescription of thromboprophylaxis for all hospitalised IBD patients, including dual pharmacological and mechanical prophylaxis in postoperative patients, may lead to a reduction in this preventable complication of IBD. © 2014 Royal Australasian College of Physicians.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

Microvascular disease after renal transplantation

Background/Aims: Individuals who reach end-stage kidney disease (CKD5) have a high risk of vascular events that persists even after renal transplantation. This study compared the prevalence and severity of microvascular disease in transplant recipients and patients with CKD5. Methods: Individuals with a renal transplant or CKD5 were recruited consecutively from renal clinics, and underwent bilateral retinal photography (Canon CR5-45, Canon). Their retinal images were deidentified and reviewed for hypertensive/microvascular signs by an ophthalmologist and a trained grader (Wong and Mitchell classification), and for vessel caliber at a grading centre using a computer-assisted method and Knudtson’s modification of the Parr-Hubbard formula. Results: Ninety-two transplant recipients (median duration 6.4 years, range 0.8 to 28.8) and 70 subjects with CKD5 were studied. Transplant recipients were younger (p<0.001), with a higher eGFR (p< 0.001), but were just as likely to have a moderate-severe hypertensive/microvascular retinopathy (46/92, 50%) as subjects with CKD5 (38/70, 54%; OR 0.84, CI 0.45 to 1.57, p=0.64), and had similar mean arteriole and venular calibres (135.1 ± 7.5 μm and 137.9 ± 14.9 μm, p=0.12; and 199.1 ± 17.8 μm and 202.4 ± 27.8 μm, p=0.36, respectively). Arteriole and venular caliber were not different in nine patients examined before and after transplantation (p=0.62 and p=0.11, respectively). Conclusions: Hypertensive/microvascular disease occurred just as often and was generally as severe in transplant recipients and subjects with CKD5. Microvascular disease potentially contributes to increased cardiac events post- transplantation.

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.