Problem-based learning and masculinities : unique opportunities for Mr. Fix It? Well, maybe

I watched a smoker of 30 years being admitted to the Coronary Care unit following an acute Myocardial Infarction (heart attack). The message from the male clinician was simple, accurate, but somewhat behaviourist: " the death of part of your heart muscle is the result of your smoking, if you don’t stop smoking the damage will continue and you will die." A global, proactive and humanistic consultation demonstrating an understanding of the man’s addiction to a legal and accessible drug and illuminating prevention strategies may have been more appropriate. Maybe the interaction was about competing masculinities, the risk taker and the problem solver. The irony? As I left the hospital that night I observed the same clinician strategically positioned in a secluded hospital doorway drawing heavily on a cigarette. Hypocrite? No, invincible late 20’s male? Maybe. Smoking was someone else’s problem – at least today.

In my 16 years as a clinician such scenarios are common. Clinical practice based predominantly on problem solving potentiates hegemonic masculine approaches to treating men in clinical practice, often justified by limited health resources and increasing patient acuity. Ironically, Problem-based Learning (PBL) curriculums commonly used in health sciences higher education encourages, nurtures and rewards such problem solving approaches. As a teaching academic with current clinical practice it occurs to me that health science education and PBL has an opportunity if not obligation to empower clinicians to establish holistic approaches to male health presentations.

This paper explores the interconnections of Problem-based Learning (PBL) curriculums, health promotion, male nurses’ health-related behaviours and the implications and specificities of masculinity. The pilot study offers an insight into the perceptions of three male nurses that completed undergraduate nursing studies in PBL curriculums. The data obtained introduces some connections that could be illuminated by further research.

Ischaemic heart disease and Australian immigrants: the influence of birthplace and language skills on treatment and use of health services

Admission rates for ischaemic heart disease (IHD), and the use of invasive cardiovascular procedures, separation mode and length of stay (LOS) were compared between Australians from non-English speaking background (NESB; n=8627) and English speaking background (ESB; n=13162) aged 20 years and over admitted to Victorian urban public hospitals. The study covered the period from 1993 to 1998. It was found that, compared with their ESB counterparts, the incidence of admission for acute myocardial infarction was significantly higher for NESB men and women before and after controlling for confounding factors. The age-adjusted ratios for NESB women compared with their ESB counterparts ranged from 1.23 to 1.89 for cardiac catheterisation, from 0.23 to 0.27 for percutaneous transluminal coronary angioplasty (PTCA), and from 1.04 to 1.80 for coronary artery bypass grafting (CABG).
Procedure rates were comparable in men for cardiac catheterisation and CABG but higher for PTA rates in NESB men (OR: 1.29, 95%CI: 1.11-1.50) than their ESB counterparts. Both NESB men (β=0.04, 95%CI: 0.01-0.07) and women (β=0.03, 95%CI: 0.02-0.08) experienced significantly longer hospital stays than their ESB counterparts. These findings indicate there may be systematic differences in patients’ treatment and service utilisation in Victorian public hospitals. The extent to which physicians’ bias and
patients’ choice could explain these differences requires further investigation.

Role of P-glycoprotein in the intestinal absorption of tanshinone IIA, a major active ingredient in the root of Salvia miltiorrhiza Bunge

The extracts from the roots of Salvia miltiorrhiza Bunge (Danshen) are widely and traditionally used in the treatment of angina pectoris, acute myocardial infarct, hyperlipidemia and stroke in China and other Asian countries. In this study, we have investigated the role of P-glycoprotein (P-gp) in the intestinal absorption of tanshinone IIA (TSA), a major active constituent of Danshen, using several in vitro and in vivo models. The oral bioavailability of TSA was about 2.9

Financial, family, and social factors impacting on cardiac rehabilitation attendance

Background
Attendance in phase 2 cardiac rehabilitation program after acute myocardial infarction is poor.

Objective
To identify and explore the demographic factors that influence peoples’ decisions to attend cardiac rehabilitation programs.

Methods
A descriptive-interpretive design was used. Semi-structured interviews were conducted with 10 people post infarction in Victoria, Australia after their first scheduled appointment to attend outpatient cardiac rehabilitation. The interview transcripts were thematically analysed.

Results
The perceived relevance of cardiac rehabilitation related to the context of people’s lives, namely their financial, family and social situation, and how important program outcomes were seen to be relevant to this context.

Conclusion
The findings of this study suggest that there are a proportion of people unlikely to attend outpatient cardiac rehabilitation programs following an AMI despite encouragement to attend. It may be unrealistic to aim for 100% referral and uptake into cardiac rehabilitation programs and therefore an inappropriate endpoint by which to evaluate such programs.

Relationship between platelet phospholipid FA and mean platelet volume in healthy men

Increased mean platelet volume (MPV) has been suggested as an independent risk factor for acute myocardial infarction and the increased reactivity of large platelets. The aim of this study was to investigate the correlation between platelet phospholipid (PL) PUFA composition and MPV in 139 free-living healthy men ages 20-55 yr (vegans, n = 18; ovolacto vegetarians, n = 43; moderate meat-eaters, n = 60; and high meateaters, n = 18). Each subject completed a semiquantitative Food Frequency Questionnaire and gave a blood sample. Platelet PL FA composition and MPV were determined by standard methods. MPV was significantly greater in the vegans than in the ovolacto vegetarian, moderate, or high meat-eater groups (P < 0.01). Both vegan and ovolacto vegetarian groups had significantly higher platelet PL 18:2n-6 and 22:4n-6, and lower 20:5n-3 and 22:6n-3 compared with the moderate and high meat-eater groups. The vegans demonstrated a significant reduction in 20:4n-6 and 22:5n-3 compared with the ovolacto vegetarian, high meat-eater, and moderate meat-eater groups. Bivariate analysis results showed that MPV was significantly positively correlated with platelet PL 18:2n-6 (P = 0.048) and negatively correlated with 20:3n-6 (P = 0.02), 20:5n-3 (P = 0.005), and 22:5n-3 (P< 0.0001), respectively. In a multiple linear regression analysis, after controlling for potential confounding factors such as dietary group, age, exercise, body mass index, and dietary polyunsaturated and saturated fat, cholesterol, carbohydrate, and fiber intake, the MPV was still strongly negatively correlated with platelet PL 20:3n-6 (P = 0.003) and 22:5n-3 (P = 0.001). The present data suggest that 22:5n-3 and 20:3n-6 may play a role in the structural function of the platelet membrane.

Autonomous capillary microfluidic system with embedded optics for improved troponin I cardiac biomarker detection

 Cardiovascular diseases are the most prevalent medical conditions affecting the modern world, reducing the quality of life for those affected and causing an ever increasing burden on clinical resources. Cardiac biomarkers are crucial in the diagnosis and management of patient outcomes. In that respect, such proteins are desirable to be measured at the point of care, overcoming the shortcomings of current instrumentation. We present a CO2 laser engraving technique for the rapid prototyping of a polymeric autonomous capillary system with embedded on-chip planar lenses and biosensing elements, the first step towards a fully miniaturised and integrated cardiac biosensing platform. The system has been applied to the detection of cardiac Troponin I, the gold standard biomarker for the diagnosis of acute myocardial infarction. The devised lab-on-a-chip device was demonstrated to have 24 pg/ml limit of detection, which is well within the minimum threshold for clinically applicable concentrations. Assays were completed within approximately 7–9 min. Initial results suggest that, given the portability, low power consumption and high sensitivity of the device, this technology could be developed further into point of care instrumentation useful in the diagnosis of various forms of cardiovascular diseases. 2014 Elsevier B.V. All rights reserved.

Comorbid depression and health-related quality of life in patients with coronary artery disease.

This article reviews recent studies relating to the impact of depression and its treatment on the health-related quality of life (HRQOL) of patients with coronary artery disease (CAD).

Validity of the Hospital Anxiety and Depression Scale and Patient Health Questionnaire-9 to screen for depression in patients with coronary artery disease.

Depression is common but frequently undetected in patients with coronary artery disease (CAD). Self-report screening instruments for assessing depression such as the Hospital Anxiety and Depression Scale (HADS) and the Patient Health Questionnaire-9 (PHQ-9) are available but their validity is typically determined in depressed patients without comorbid somatic illness. We investigated the validity of these instruments relative to a referent diagnostic standard in recently hospitalized patients with CAD.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Understanding the experience of inpatient rehabilitation: insights into patient-centred care from patients and family members

The aim of the study was describe the experiences, needs and preferences of recent inpatients of a rehabilitation centre, and the needs of their families. Data were collected in four focus groups, two with patients (n = 13) who had recently completed inpatient rehabilitation following an illness, injury or elective surgery, and two with family members (n = 11). During the focus groups, two researchers facilitated discussion on any topic that participants considered important to the experience of inpatient rehabilitation; participants were encouraged to describe their care, needs and preferences. The focus group discussions were audio-taped and transcribed verbatim. Field notes were hand recorded. Data were analysed and collated into themes. Six key themes emerged. Participants wanted: interactions with friendly, empathetic staff; regular contact with senior staff and all staff to introduce themselves by name and profession; timely communication of accurate information; and rehabilitation services seven days a week. The physical environment had both positive and negative effects on patient well-being. Patients with complex or atypical circumstances required special attention to ensure their needs were met. In conclusion, patients and families identified six important issues that need to be considered during inpatient rehabilitation.

Risk factors for coronary heart disease among Asian Indians living in Australia

The aim of this study was to assess the coronary heart disease risk factors in the Asian Indian community living in a large city in Australia. A cross-sectional survey was conducted at the Australia India Friendship Fair in 2010. All people of Asian Indian descent who attended the Fair and visited the health promotion stall were eligible to participate in the study if they self-identified as of Asian Indian origin, were aged between 18 and 80 years, and were able to speak English. Blood pressure, blood glucose, waist circumference, height, and weight were measured by a health professional. Smoking, cholesterol levels, and physical activity status were obtained through self-reports. Data were analyzed for 169 participants. More than a third of the participants under the age of 65 years had high blood pressure. Prevalence of diabetes (16%) and obesity (61%) was significantly higher compared with the national average. Ten women identified themselves as smokers. Physical activity patterns were similar to that of the wider Australian population. The study has provided a platform for raising awareness among nurses and promoting advocacy on the cardiovascular risk among Asian Indians. Strategies involving Asian Indian nurses and other Asian Indian health professionals as well as support from the private and public sectors can assist in the reduction of the coronary heart disease risk factors among this extremely susceptible population.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.