BACKGROUND AND OBJECTIVE: Although research has identified women's prodromal and acute myocardial infarction (MI) symptoms, diagnosing coronary heart disease in women remains challenging. Knowing how individual symptoms cluster by race and other characteristics would provide key diagnostic information. We performed a secondary analysis to: (a) generate naturally occurring symptom clusters based on prodromal and acute MI symptom scores separately, (b) examine the association between women's characteristics and symptom clusters, and (c) describe the percentage of women who reported experiencing the same symptoms in both prodromal and acute MI phases. SUBJECT AND METHODS: The database contained retrospective self-reported data obtained by telephone survey from 1270 women (43% black, 42% white, 15% Hispanic) with a confirmed MI recruited from 15 geographically diverse sites. Data included frequency and severity of 33 prodromal symptoms, intensity of 37 acute MI symptoms, and comorbidities/risk factors. We used both bivariate and multivariate analyses to examine associations between cluster assignment and characteristics/risk factors. Because of the possibility of complex interactions, we explored nonlinear interactions with recursive partitioning. RESULTS: Cluster analysis yielded 3 naturally occurring clusters for each of the prodromal and acute symptom sets. Each cluster contained women who reported increasing frequency and severity of symptoms. Six characteristics (age, race, body mass index, personal history of heart disease, diabetes, smoking status) were strongly associated with the clusters. Body mass index was the most important factor in classifying prodromal symptoms, whereas age was for acute symptoms. CONCLUSIONS: Black women younger than 50 years were more likely to report frequent and intense prodromal symptoms, whereas older white women reported the least. Younger, obese, diabetic black women reported the most acute symptoms, whereas older nonobese, nondiabetic white women reported the fewest. Symptom clusters and characteristics of women in these clusters provide valuable diagnostic information. Further research with a control group is needed.
BACKGROUND AND OBJECTIVE: Although research has identified women's prodromal and acute myocardial infarction (MI) symptoms, diagnosing coronary heart disease in women remains challenging. Knowing how individual symptoms cluster by race and other characteristics would provide key diagnostic information. We performed a secondary analysis to: (a) generate naturally occurring symptom clusters based on prodromal and acute MI symptom scores separately, (b) examine the association between women's characteristics and symptom clusters, and (c) describe the percentage of women who reported experiencing the same symptoms in both prodromal and acute MI phases. SUBJECT AND METHODS: The database contained retrospective self-reported data obtained by telephone survey from 1270 women (43% black, 42% white, 15% Hispanic) with a confirmed MI recruited from 15 geographically diverse sites. Data included frequency and severity of 33 prodromal symptoms, intensity of 37 acute MI symptoms, and comorbidities/risk factors. We used both bivariate and multivariate analyses to examine associations between cluster assignment and characteristics/risk factors. Because of the possibility of complex interactions, we explored nonlinear interactions with recursive partitioning. RESULTS: Cluster analysis yielded 3 naturally occurring clusters for each of the prodromal and acute symptom sets. Each cluster contained women who reported increasing frequency and severity of symptoms. Six characteristics (age, race, body mass index, personal history of heart disease, diabetes, smoking status) were strongly associated with the clusters. Body mass index was the most important factor in classifying prodromal symptoms, whereas age was for acute symptoms. CONCLUSIONS: Black women younger than 50 years were more likely to report frequent and intense prodromal symptoms, whereas older white women reported the least. Younger, obese, diabetic black women reported the most acute symptoms, whereas older nonobese, nondiabetic white women reported the fewest. Symptom clusters and characteristics of women in these clusters provide valuable diagnostic information. Further research with a control group is needed.
Authors: Debra K Moser; Laura P Kimble; Mark J Alberts; Angelo Alonzo; Janet B Croft; Kathleen Dracup; Kelly R Evenson; Alan S Go; Mary M Hand; Rashmi U Kothari; George A Mensah; Dexter L Morris; Arthur M Pancioli; Barbara Riegel; Julie Johnson Zerwic Journal: Circulation Date: 2006-06-26 Impact factor: 29.690
Authors: Lars B Andersen; Colin A G Boreham; Ian S Young; George Davey Smith; Alison M Gallagher; Liam Murray; Peter McCarron Journal: Prev Med Date: 2005-12-05 Impact factor: 4.018
Authors: Andrea M Barsevick; Kyra Whitmer; Lillian M Nail; Susan L Beck; William N Dudley Journal: J Pain Symptom Manage Date: 2006-01 Impact factor: 3.612
Authors: Jean C McSweeney; Leanne L Lefler; Ellen P Fischer; Albert Joe Naylor; Laura K Evans Journal: J Cardiovasc Nurs Date: 2007 Jul-Aug Impact factor: 2.083
Authors: Anneke de Torbal; Eric Boersma; Jan A Kors; Gerard van Herpen; Jaap W Deckers; Deirdre A M van der Kuip; Bruno H Stricker; Albert Hofman; Jacqueline C M Witteman Journal: Eur Heart J Date: 2006-02-14 Impact factor: 29.983
Authors: Jean C McSweeney; Patricia O'Sullivan; Mario A Cleves; Leanne L Lefler; Marisue Cody; Debra K Moser; Kelly Dunn; Margaret Kovacs; Patricia B Crane; Lois Ramer; Patricia R Messmer; Bonnie J Garvin; Weizhi Zhao Journal: Am J Crit Care Date: 2010-01 Impact factor: 2.228
Authors: Heather Dunn; Laurie Quinn; Susan J Corbridge; Kamal Eldeirawi; Mary Kapella; Eileen G Collins Journal: West J Nurs Res Date: 2017-05-16 Impact factor: 1.967
Authors: Jean C McSweeney; Anne G Rosenfeld; Willie M Abel; Lynne T Braun; Lora E Burke; Stacie L Daugherty; Gerald F Fletcher; Martha Gulati; Laxmi S Mehta; Christina Pettey; Jane F Reckelhoff Journal: Circulation Date: 2016-02-29 Impact factor: 29.690
Authors: Anne G Rosenfeld; Elizabeth P Knight; Alana Steffen; Larisa Burke; Mohamud Daya; Holli A DeVon Journal: Heart Lung Date: 2015-06-26 Impact factor: 2.210
Authors: Jean McSweeney; Mario A Cleves; Ellen P Fischer; Debra K Moser; Jeanne Wei; Christina Pettey; Martha O Rojo; Narain Armbya Journal: J Cardiovasc Nurs Date: 2014 Nov-Dec Impact factor: 2.083
Authors: Catherine J Ryan; Karen M Vuckovic; Lorna Finnegan; Chang G Park; Lani Zimmerman; Bunny Pozehl; Paula Schulz; Susan Barnason; Holli A DeVon Journal: West J Nurs Res Date: 2019-01-22 Impact factor: 1.967