Congruity between WHO ISH laboratory and non-laboratory-based charts in an urban population in India: A
Jyoti Pradhan1, Amal Korambeth2
1Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Hyderabad, Telangana, India.
Insights
Non-laboratory cardiovascular disease (CVD) risk charts show good agreement with laboratory-based charts in Indian adults. These findings support the use of non-laboratory charts for CVD risk assessment in low-resource settings.
Area of Science:
- Public Health
- Epidemiology
- Cardiology
Background:
- Cardiovascular diseases (CVDs) cause a significant portion of global mortality.
- Non-laboratory risk prediction charts offer a practical alternative for CVD risk assessment, especially where laboratory facilities are limited.
- Ensuring comparability between non-laboratory and laboratory-based charts is crucial for reliable risk prediction.
Purpose of the Study:
- To evaluate the congruity between WHO ISH laboratory and non-laboratory-based CVD risk prediction charts.
- To assess the accuracy of non-laboratory charts for predicting CVD risk in adults aged 40 and above in an urban Indian population.
Main Methods:
- A cross-sectional study was conducted on adults aged 40 years and older.
- Data collection involved semi-structured questionnaires, anthropometry, and biochemical measurements.
- Statistical analyses included Pearson's correlation, scatter plots, Cohen's Kappa coefficient, and Receiver Operator Curve (ROC) analysis.
Main Results:
- A very strong association was found between the risk scores from both charts (correlation coefficient = 0.861, P < 0.001).
- The agreement level between the two charts, measured by Kappa statistics, was 75.89%.
- The ROC curve analysis indicated a high area under the curve (0.964), suggesting good diagnostic accuracy.
Conclusions:
- The non-laboratory-based WHO ISH risk prediction chart demonstrates reasonable accuracy for assessing cardiovascular disease risk in the Indian population.
- These findings support the applicability of non-laboratory charts in low- and middle-income countries, facilitating wider CVD risk screening.
Background:
Cardiovascular diseases (CVDs) are responsible for nearly one-third of all global deaths. WHO ISH non-laboratory-based risk prediction charts are useful for predicting the individual risk of CVD, especially in places without laboratory facilities. However, their results must be comparable to the laboratory-based charts.
Materials And Methods:
This cross-sectional study aimed to measure the congruity between the WHO ISH laboratory and non-laboratory-based charts in adults ≥40 years in an urban population in India. Data were collected using a semi-structured questionnaire, including details about behavioral and environmental risk factors, anthropometry, and biochemical measurements.
Statistical Analysis:
Pearson's correlation and scatter plots were applied to measure the association between the risk scores in the two charts. Cohen's Kappa coefficient was applied to test the congruity between the risk categories given by the two charts. Receiver operator curve (ROC) plot for non-laboratory-based chart was plotted taking the laboratory-based chart as the gold standard.
Results:
The mean CVD risk scores of the nonlaboratory and the laboratory-based charts are 6.62 ± 4.62% (IQR 3-9) and 7.99 ± 6.05% (IQR 3-11.75), respectively. The mean CVD risk score in males came to be more than that of females. Scatter plots showed a very strong association with a correlation coefficient of 0.861 (P < 0.001). The level of agreement between the two charts using Kappa statistics was 75.89%. The ROC curve showed a high area under the curve of 0.964.
Conclusions:
The non-laboratory-based chart can be applied to the population in low-and-middle-income countries like India, with reasonable accuracy.
Related Concept Videos
01:25Interpreting Run Charts
01:16Bioequivalence Data: Statistical Interpretation
01:29Bias in Epidemiological Studies
01:02The R Chart
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
01:22Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...