Investigation of Effort-Reward Imbalance Model as predictor of Counterproductive Work Behaviors

Introduction: Nowadays, counterproductive behaviors have become a common and costly position for many organizations, and Managers of organizations are always looking for a suitable and practical solution to reduce this type of behavior in their organization. Due to the importance of the subject, the present study aims to investigate the imbalance of effort and reward as a predictor of counterproductive behaviors. Materials and Methods: The present study is a cross-sectional study. The target population was all nurses working in hospitals in Hamadan, and according to the simple random sampling method, 320 people were selected as the research sample. The tools used in this study were the Imbalance of Effort-Reward questionnaire and the counterproductive questionnaire. Data analysis was performed using the Pearson correlation method using SPSS18. Results: The results showed that the effort-reward imbalance model at a significance level of 0.05 is able to predict individual counterproductive behaviors in nurses (P = 0.036). Among the studied variables, the reward variable is able to predict individual counterproductive behaviors (β = -0.179 and P = 0.006) and organizational (β=-0.171 and P = 0.009) and the over-commitment variable is able to predict individual counterproductive behaviors. (β= 0.145 and P = 0.05). According to the results, the effort-reward imbalance model could not predict organizational counterproductive behaviors. Conclusion: Based on the results, it can be concluded that job stress is an important factor in creating Counterproductive behaviors in personnel and the components of the model used in this study can be used to reduce the incidence of these behaviors among nurses.

Identifying the value of RVEF for the prediction of major cardiovascular outcomes: a study of 7,131 patients undergoing cardiovascular magnetic resonance imaging

Background

Right ventricular (RV) function remains poorly recognized for its value in predicting cardiovascular events at a population level. Cardiovascular Magnetic Resonance (CMR) imaging is the gold standard for RV assessment.

Purpose

To define the independent prognostic value of RVEF for the prediction of major adverse cardiovascular events (MACE) as primary outcome in patients with known or suspected cardiovascular disease.

Methods

Data was obtained from the Cardiovascular Imaging Registry of Calgary (CIROC). Patients underwent standardized CMR imaging protocols and analysis. Clinical events were identified from administrative data.

Results

7,131 patients were included. 870 primary outcome events occurred over 2.5 years follow-up. RVEF provided equivalent predictive utility versus LVEF (Table 1). There was an increase in events with worsening severity of RVEF (Figure 1), with a significant “threshold-effect” at an RVEF of 40%.

Conclusions

RVEF is a strong and independent predictor of MACE at a population level.

Figure 1

Funding Acknowledgement

Type of funding source: None

Development and validation of a risk model for the prediction of cardiovascular hospital admission using CMR-based phenotype in patients with known or suspected cardiovascular disease

Background

Cardiovascular diseases remain the leading cause of morbidity worldwide and impose the highest economic burden among noncommunicable diseases. Much of these costs are related to hospitalizations for adverse cardiovascular events, which may be reduced by targeted management of high-risk patients. Cardiac markers derived from CMR imaging have been shown to be strong independent predictors of prognosis within specific cohorts. However, its capacity to broadly contribute to risk models aimed at predicting incident cardiac hospitalization has not been demonstrated.

Purpose

Using a large clinical outcomes registry of patients clinically referred for CMR, develop and validate a nomogram for prediction of cardiovascular hospital admission.

Methods

A total of 7127 consecutive patients were prospectively recruited between 02/2015 and 07/2019. All patients completed standardized health questionnaires and CMR imaging protocols. A nomogram was developed for prediction of cardiovascular hospitalization, inclusive of admission for heart failure, MI, cardiac arrest, heart transplant, LVAD implantation, or stroke. The risk model was derived from 80% (n=5702) of the cohort using Cox modelling that included CMR, medication, laboratory, and patient-reported health variables. Model validation was assessed by discrimination and calibration procedures applied to the remaining 20% of patients (n=1425). A minimum follow-up of six months was mandated.

Results

The derivation cohort was comprised of 38% females with a median age of 56 (IQR 44–65) years. During a median follow-up of 934 days, 514 (9.0%) events occurred. The validation cohort was similarly comprised of 37% females with a median age of 57 (IQR 44–66) years. During a median follow-up of 970 days, 142 (10.0%) events occurred. Numerous CMR parameters were significantly different between those experiencing versus not experiencing the primary composite outcome, including: LVEF (44% vs 59%, p<0.0001), RVEF (52% vs 55%, p<0.0001), LV mass (65g/m2 vs 56g/m2, p<0.0001), and LA volume (43mL/m2 vs 34mL/m2, p<0.0001). These and other CMR-derived characteristics were independently predictive of the composite outcome by univariate modelling (Figure 1A). An eight-variable nomogram (Figure 1B) was developed using a stepwise multivariate model that exhibited high discrimination in both the derivation and validation cohorts (C-index 0.81 and 0.83, respectively). Continuous model calibration curves indicated satisfactory external performance. The model was able to discriminate risk of hospitalization at 1-year with a dynamic range of 20–99%.

Conclusion

Using data available at time of CMR imaging, we derived and validated a Cox-based nomogram that offers robust prediction of future cardiovascular admissions. This tool may provide value for the identification of patients who may benefit from targeted surveillance and management strategies, and may offer a foundation for improved patient-specific cost modelling.

Figure 1

Funding Acknowledgement

Type of funding source: None

Birjand longitudinal aging study (BLAS): the objectives, study protocol and design (wave I: baseline data gathering)

Objectives

The pace of population aging is growing rapidly around the world. Aging is associated with the emergence of different health status including geriatric syndrome such as frailty, diabetes, cardiovascular diseases, and dementia. These conditions are the most prominent challenges for health care systems and also elderly people. Therefore, understanding these changes can help scientists to prevent and treat significant health issues and also improve the functional ability of older adults.

Methods

This is a protocol of the first wave of Birjand Longitudinal Aging Study that is an ongoing community-based prospective cohort study with a following up at least 10 years. This study carries out on aged population ≥ 60 years which were residents in Birjand County (urban and rural older subjects). The selection of the participants of this study in urban areas is based on an age group weighted multistage stratified random sample while in the rural region the sample was selected from all ten rural regions of Birjand County by simple random sampling. The rural region sampling was based on the list of the aged population which were under the coverage of the rural health center. Sociodemographic, past medical history, lifestyle, sleep, activities of daily living, cognitive function, quality of life, and social capital were evaluated by interviewing with the participants and one of the informants. Anthropometric measures, electrocardiography, and interpretation of ophthalmologic examination were carried out by experts. Fasting Blood samples were collected and bio-banked in - 80 °C. then finally biochemical and hematologic markers were measured.

Results

This is the protocol of stage one baseline of Birjand Longitudinal Aging Study (BLAS). The BLAS is an enjoining study, the first phase of its baseline was carried out on a community- dwelling aged population sample ≥ 60 years who were residents in urban and rural regions of Birjand County. This is a community based prospective cohort study with at least 10 years follow up of participants. The data for 65% of older subjects (response rate = 65%) that lived in clusters were collected.

Conclusions

This study can help scientists to recognize some risk factors related to the aging process and also aware policymakers about the necessity to create heath care services at regional and even national levels.

4179Feasibility and validation of routine CMR-based phenotyping for the prediction of heart failure admission or death in patients with systolic dysfunction

Background

Standardized patient phenotyping using cardiovascular magnetic resonance (CMR) imaging has been shown to be of clinical value for prediction of adverse events in patients with heart failure and reduced ejection fraction (HFrEF). Studies have validated the prognostic capacity of function (LV, RV and LA) and replacement fibrosis burden in patients with ischemic and non-ischemic cardiomyopathy. The translation and validation of routine CMR-based phenotyping into clinical practice has yet to be demonstrated in prospective studies.

Purpose

This study was designed to explore feasibility and prognostic value of routine CMR-based patient phenotyping in a high-volume clinical referral center for patients with HFrEF.

Methods

One thousand three hundred and ninety-three consecutive patients with chronic HFrEF were prospectively recruited between January 2015 and June 2018. Chronic HFrEF was defined by LVEF≤50% by CMR, with no recent (within 90 days) acute myocardial infarction or myocarditis diagnosis. Patients with congenital heart disease and those without LGE CMR protocol were excluded. All patients underwent standardized CMR protocols with multi-chamber volumetric analysis and regional myocardial fibrosis coding. Pharmacy, ECG, laboratory and patient reported data was used for statistical modelling. A minimum three-month follow-up was mandated to identify the composite clinical outcome of heart failure hospitalization or death.

Results

The cohort had a median age of 61 years with 23% being female. The median follow-up was 737 days with 146 patients (10.5%) experiencing the composite outcome. Numerous imaging and non-imaging variables were significantly different between patients with and without the composite outcome, including: median LVEF (32% vs 39%, p<0.0001), RVEF (46% vs 51% p<0.0001), LV mass (77g/m2 vs. 65g/m2, p<0.0001), digoxin (19% vs. 9%, p<0.0001) and diuretic (63% vs 41%, p<0.0001) use. Presence of replacement fibrosis (HR=2.09, p=0.001), particularly midwall striae (HR=2.01, p<0.0001), diffuse (HR=3.88, p<0.0001) and RV insertion site fibrosis (HR=1.54, p=0.022) patterns, were significantly associated with the combined endpoint. A stepwise multivariable model was applied using all eligible variables and resulted in robust accuracy for prediction of the combined outcome with a concordance index of 0.751 (Figure 1).

Conclusions

This study demonstrates the feasibility and prognostic value of automated patient phenotyping that captures patient reported data, imaging, and administrative data for risk prediction modelling in HFrEF. The incremental application of machine learning is being explored.

Acknowledgement/Funding

J White: Early Investigator Award (Heart and Stroke Foundation of Alberta), Calgary Health Trust

4941Machine learning based automated diagnosis of ischemic vs non-ischemic dilated cardiomyopathy using 3D myocardial deformation analysis

Background

Late Gadolinium Enhancement (LGE) imaging is a reference standard technique for the differentiation of ischemic cardiomyopathy (ICM) from non-ischemic dilated cardiomyopathy (NIDCM) in patients with heart failure and reduced ejection fraction (HFrEF). 3D myocardial deformation analysis (3D-MDA) offers highly reproducible phenotypic assessments of regional architecture and function that may provide value for artificial-intelligence-assisted cardiomyopathy diagnosis without need for LGE imaging.

Purpose

In this study, we trained and validated a machine-learning-based model to enable automated diagnosis of ischemic versus non-ischemic dilated cardiomyopathy exclusively using regional patterns of deformation among patients otherwise matched by age, sex and global contractile dysfunction.

Methods

100 ICM and 100 NIDCM patients matched for age, sex, and LVEF underwent standard cine SSFP and LGE imaging. Patient diagnoses were established using a combination of clinical and LGE-based criteria. 3D-MDA was performed using validated software (GIUSEPPE) to compute regional 3D strain measures at each cardiac phase in both conventional and principal strain directions. Principal Component Analysis (PCA) was performed on the composite 3D-MDA dataset. The first 20 components were chosen, accounting for approximately 65% of the population variance. Subsequently, a support-vector-machine-based algorithm was used with 10-fold cross-validation to discriminate ICM from NIDCM.

Results

Patients were 63±10 years (ICM: 63±10 years, NIDCM: 63±10 years, p=0.955), 74% male (ICM: 74%, NIDCM: 74%, p=1.000), and had a mean LVEF of 27±8% (ICM: 27±7%, NIDCM: 28±7%, p=0.688). Global time to peak strain was significantly shorter in ICM patients relative to NIDCM patients across all surfaces and in all directions (p<0.05). The highest single-variable Area Under the Curve (AUC) achieved for the classification of ICM versus NIDCM from global data was for minimum principal strain (ICM: 43.7±7.8, NIDCM: 48.3±7.5, p<0.001, AUC: 0.682) (Figure 1). However, a multi-feature machine-learning-based model exposed to all available regional 3D deformation data achieved an AUC of 0.903 (sensitivity 87.7%, specificity 75.5%).

Conclusions

Machine learning-based analyses of3D regionaldeformation patterns allows for robust discrimination of ICM versus NIDCM. Further expansion of the presented findings is planned on a wider, multi-centre cohort.

Acknowledgement/Funding

Dr. White was supported by an award from Heart and Stroke Foundation of Alberta. This study was funded in part by Calgary Health Trust.