The fundamentals of Indian personality: An investigation of the big five

Context

The Big Five model is a well-accepted model of personality but there is scant research on the factor structure of personality from Asian populations. It is unclear whether the Big Five personality model can account for cross-cultural variation in personality structures.

Aim

To explore the factor structure of personality by analyzing the Big Five personality factors in a sample from Karnataka, South India.

Settings and Design

This was a cross-structural observational study conducted in Udupi and Mangalore.

Methods and Material

400 community participants (200 women) from diverse socio economic backgrounds were recruited for the study and were assessed on either an English or Kannada version of the BFI-2-S. The reliability of the translated version of BFI-2-S was established.

Statistical Analysis Used

Exploratory factor analysis using Principal Component Analysis with Varimax Rotation and Kaiser Normalization was carried out.

Results

Factor Analysis revealed a four-factor and a five-factor solution that varied distinctly from the original Big Five. None of the identified factors fit into the original five factors. The four-factor solution explained 36.86% of the variance and the five-factor solution explained 41.74%. The five factors were named as - Social Effectiveness, Interpersonal Ability, Altruism, Emotional Instability, and Innovativeness. The translated tool showed good temporal stability.

Conclusions

The Five factors identified in the present study differ from the Big Five model or the General Factor of Personality. This raises questions about the cross-cultural validity of the Big Five model as well as highlighting the need to adopt more culturally adaptive methods of assessing personality.

Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario

Software Defined Networking represents a mature technology for the control of optical networks, though all open controller implementations present in the literature still lack the adequate level of maturity and completeness to be considered for (pre)-production network deployments. This work aims at experimenting on, assessing and discussing the use of the OneM2M open-source platform in the context of optical networks. Network elements and devices are implemented as IoT devices, and the control application is built on top of an OneM2M-compliant server. The work concretely addresses the scalability and flexibility performances of the proposed solution, accounting for the expected growth of optical networks. The two experiment scenarios show promising results and confirm that the OneM2M platform can be adopted in such a context, paving the way to other researches and studies.

Clinical risk model for predicting 1-year mortality after transcatheter aortic valve replacement

Objectives

Estimating 1‐year life expectancy is an essential factor when evaluating appropriate indicators for transcatheter aortic valve replacement (TAVR).

Background

It is clinically useful in developing a reliable risk model for predicting 1‐year mortality after TAVR.

Methods

We evaluated 2,588 patients who underwent TAVR using data from the Optimized CathEter vAlvular iNtervention (OCEAN) Japanese multicenter registry from October 2013 to May 2017. The 1‐year clinical follow‐up was achieved by 99.5% of the entire population (n = 2,575). Patients were randomly divided into two cohorts: the derivation cohort (n = 1,931, 75% of the study population) and the validation cohort (n = 644). Considerable clinical variables including individual patient's comorbidities and frailty markers were used for predicting 1‐year mortality following TAVR.

Results

In the derivation cohort, a multivariate logistic regression analysis demonstrated that sex, body mass index, Clinical Frailty Scale, atrial fibrillation, peripheral artery disease, prior cardiac surgery, serum albumin, renal function as estimated glomerular filtration rate, and presence of pulmonary disease were independent predictors of 1‐year mortality after TAVR. Using these variables, a risk prediction model was constructed to estimate the 1‐year risk of mortality after TAVR. In the validation cohort, the risk prediction model revealed high discrimination ability and acceptable calibration with area under the curve of 0.763 (95% confidence interval, 0.728–0.795, p < .001) in the receiver operating characteristics curve analysis and a Hosmer–Lemeshow χ² statistic of 5.96 (p = .65).

Conclusions

This risk prediction model for 1‐year mortality may be a reliable tool for risk stratification and identification of adequate candidates in patients undergoing TAVR.

The incidence, predictive factors and prognosis of acute pulmonary complications after transcatheter aortic valve implantation

OBJECTIVES

Although acute pulmonary complications (APCs), such as the exacerbation of pulmonary disease (PD) or a newly developed pulmonary event, are thought to be catastrophic after invasive therapy, little is known about the occurrence of APCs after transcatheter aortic valve implantation (TAVI). This study aims to clarify the incidence, predictive factors and impact of APCs on prognosis after TAVI.

METHODS

We identified 749 patients who underwent TAVI, using data from the Optimized CathEter vAlvular iNtervention (OCEAN-TAVI) Japanese multicentre registry. APCs were defined as exacerbation of a comorbidity or newly developed PD during hospitalization. Patients were divided into 2 groups: an APC group (1.5%, 11/749) and a non-APC group (98.5%, 738/749). Clinical and prognostic outcomes were compared, and predictive factors for APCs were assessed.

RESULTS

Procedure-related death did not differ between the groups (0.4% vs 0.0%, P = 1.00), although 30-day mortality was significantly higher in the APC group than in the non-APC group (27.3% vs 1.6%, P = 0.001) and the difference in cumulative 1-year mortality increased further (72.7% vs 8.6%, log-rank test: P < 0.001). In particular, concomitant PD and transapical (TA) approach were identified as predictors of APCs after TAVI [univariable odds ratio (uOR) = 24.2, 95% confidence interval (CI) = 3.08-189.9, P = 0.002; uOR = 3.69, 95% CI = 1.11-12.3, P = 0.033, respectively].

CONCLUSIONS

Although rare, the occurrence of APCs after TAVI was associated with extremely poor prognosis. Patients undergoing TAVI with concomitant PD and/or TA require careful consideration to avoid the risk of APCs.