OBJECTIVE: In this retrospective case-control study, an attempt was made to assess the predictive efficacy of Framingham's risk prediction algorithm in Indian perspective. METHODS: A total of 350 patients and 293 age- and sex-matched controls were considered in the study. Those patients, who were presenting for the first time with acute coronary syndrome (ACS) and who did not have any prior manifestation of coronary heart disease (CHD) formed the patient group. The risk prediction algorithm was applied to obtain the risk score and the corresponding 10-year risk in each patient and control. They were divided into two groups: diabetic and nondiabetic. Depending on the 10-year risk, they were further grouped into high risk (10-year risk > 20%), moderately high risk (10-year risk between 10% to 20%), and low risk (10-year risk < 10%). The results were compared and statistically analyzed. RESULTS: In the diabetic patients with ACS, 14.29% qualified as high risk, 32.79% as moderately high risk, and 52.94% as low risk. The corresponding figures for diabetic subjects without ACS were 3.26%, 54.35%, and 42.39%, respectively. In nondiabetic patients with ACS, 19.91% were in the high-risk group, 38.96% in moderately high risk, and 41.13% in the low-risk group; while among the controls, the corresponding figures were 9.95%, 21.89%, and 68.16%, respectively. In nondiabetic subjects, the mean risk was significantly higher for patients compared to controls (14.13 vs. 8.61, p < 0.01). However, in diabetic subjects, there was no significant difference in the mean projected risk between those with ACS and those without ACS (11.37 vs. 10.41, p = NS). CONCLUSION: In the Indian perspective, Framingham's risk prediction protocol has a fair amount of predictive efficacy since the difference of mean risk score between the patients and controls was statistically significant. However, it fails to identify a large proportion of high-risk nondiabetic patients. Hence, a better protocol for the Indian perspective is badly needed.
OBJECTIVE: In this retrospective case-control study, an attempt was made to assess the predictive efficacy of Framingham's risk prediction algorithm in Indian perspective. METHODS: A total of 350 patients and 293 age- and sex-matched controls were considered in the study. Those patients, who were presenting for the first time with acute coronary syndrome (ACS) and who did not have any prior manifestation of coronary heart disease (CHD) formed the patient group. The risk prediction algorithm was applied to obtain the risk score and the corresponding 10-year risk in each patient and control. They were divided into two groups: diabetic and nondiabetic. Depending on the 10-year risk, they were further grouped into high risk (10-year risk > 20%), moderately high risk (10-year risk between 10% to 20%), and low risk (10-year risk < 10%). The results were compared and statistically analyzed. RESULTS: In the diabeticpatients with ACS, 14.29% qualified as high risk, 32.79% as moderately high risk, and 52.94% as low risk. The corresponding figures for diabetic subjects without ACS were 3.26%, 54.35%, and 42.39%, respectively. In nondiabetic patients with ACS, 19.91% were in the high-risk group, 38.96% in moderately high risk, and 41.13% in the low-risk group; while among the controls, the corresponding figures were 9.95%, 21.89%, and 68.16%, respectively. In nondiabetic subjects, the mean risk was significantly higher for patients compared to controls (14.13 vs. 8.61, p < 0.01). However, in diabetic subjects, there was no significant difference in the mean projected risk between those with ACS and those without ACS (11.37 vs. 10.41, p = NS). CONCLUSION: In the Indian perspective, Framingham's risk prediction protocol has a fair amount of predictive efficacy since the difference of mean risk score between the patients and controls was statistically significant. However, it fails to identify a large proportion of high-risk nondiabetic patients. Hence, a better protocol for the Indian perspective is badly needed.
Authors: Mythili Kalladka; Barbara L Greenberg; Shreenivasa Murthy Padmashree; Nagathihally Thirumalegowda Venkateshaiah; Shilpa Yalsangi; Bangalore Nagarajachar Raghunandan; Michael Glick Journal: Int J Public Health Date: 2013-12-19 Impact factor: 3.380