OBJECTIVE: To determine if a risk prediction model for patients with unstable angina would predict resource utilization. METHODS AND RESULTS: Four hundred sixty-five consecutive patients admitted for unstable angina to a tertiary care university-based medical center were prospectively evaluated from June 1, 1992, to June 30, 1995. The proportion of patients receiving coronary angiography, coronary angioplasty, and coronary artery bypass grafting were analyzed according to four risk groups on the basis of a previously published model: Group 1, <2% risk of major complication; Group 2, 2.1% to 5% risk; Group 3, 5.1 % to 15% risk; and Group 4, >15.1 % risk. Hospital length of stay and estimated cost of hospitalization based on DRG and specific payer ratio of cost-to-charge were also compared between groups. Multiple linear regression analysis was used to determine the influence of estimated risk and procedures on hospital costs. The four groups were well matched for gender, hypertension, tobacco history, and previous percutaneous transluminal coronary angioplasty and myocardial infarction. Group 4 had a higher incidence of previous coronary bypass grafting (35% vs 10%, p=0.001) and triple vessel or left main coronary artery disease compared with Group 1 (44% vs 13%, p=0.041). Group 4 patients were more likely to be admitted to the coronary care unit compared with Group 2 or Group 1 patients (80% vs Group 1: 51% [p= 0.001]; and vs Group 2: 53% [p=0.001]), more likely to receive heparin (87% vs 71%, p=0.007), and more likely to receive a beta-blocker or calcium channel blocker (89% vs 74%, p=0.008) than Group 1. Coronary angioplasty rates were similar for all groups, but Group 4 patients were more likely to receive coronary bypass grafting than Group 2 or Group 1 (27% vs Group 2: 12%, p=0.004 and vs Group 1: 8%, p=0.002). Hospital length of stay was highest in Group 4 and lowest for Group 1. Average hospital costs were significantly less in Group 3 than in Group 4, but higher than in Group 1. Multivariate analysis determined a dependency of costs on risk group with Group 2 having costs 31.4% (95% CI=9.8 to 57.2), Group 3 46.7% (24, 3 to 73.1), and Group 4 75% (46.9 to 110.7) higher than Group 1. The use of procedures also significantly increased costs, with PTCA-treated patients having a 44.9% (26.7 to 65.7) increase in costs compared with medically treated patients, and surgically treated patients having a 204.7% increase in costs. CONCLUSION: Resource utilization as assessed by the use of revascularization procedures, length of stay, and hospital costs are influenced by patient acuity estimated from a prediction model on the basis of estimated risk of cardiac complications. The model exerts independent influence on cost even after adjustment for various procedures. The use of revascularization procedures, especially coronary artery surgery, remains a large determinant of hospital cost.
OBJECTIVE: To determine if a risk prediction model for patients with unstable angina would predict resource utilization. METHODS AND RESULTS: Four hundred sixty-five consecutive patients admitted for unstable angina to a tertiary care university-based medical center were prospectively evaluated from June 1, 1992, to June 30, 1995. The proportion of patients receiving coronary angiography, coronary angioplasty, and coronary artery bypass grafting were analyzed according to four risk groups on the basis of a previously published model: Group 1, <2% risk of major complication; Group 2, 2.1% to 5% risk; Group 3, 5.1 % to 15% risk; and Group 4, >15.1 % risk. Hospital length of stay and estimated cost of hospitalization based on DRG and specific payer ratio of cost-to-charge were also compared between groups. Multiple linear regression analysis was used to determine the influence of estimated risk and procedures on hospital costs. The four groups were well matched for gender, hypertension, tobacco history, and previous percutaneous transluminal coronary angioplasty and myocardial infarction. Group 4 had a higher incidence of previous coronary bypass grafting (35% vs 10%, p=0.001) and triple vessel or left main coronary artery disease compared with Group 1 (44% vs 13%, p=0.041). Group 4 patients were more likely to be admitted to the coronary care unit compared with Group 2 or Group 1 patients (80% vs Group 1: 51% [p= 0.001]; and vs Group 2: 53% [p=0.001]), more likely to receive heparin (87% vs 71%, p=0.007), and more likely to receive a beta-blocker or calcium channel blocker (89% vs 74%, p=0.008) than Group 1. Coronary angioplasty rates were similar for all groups, but Group 4 patients were more likely to receive coronary bypass grafting than Group 2 or Group 1 (27% vs Group 2: 12%, p=0.004 and vs Group 1: 8%, p=0.002). Hospital length of stay was highest in Group 4 and lowest for Group 1. Average hospital costs were significantly less in Group 3 than in Group 4, but higher than in Group 1. Multivariate analysis determined a dependency of costs on risk group with Group 2 having costs 31.4% (95% CI=9.8 to 57.2), Group 3 46.7% (24, 3 to 73.1), and Group 4 75% (46.9 to 110.7) higher than Group 1. The use of procedures also significantly increased costs, with PTCA-treated patients having a 44.9% (26.7 to 65.7) increase in costs compared with medically treated patients, and surgically treated patients having a 204.7% increase in costs. CONCLUSION: Resource utilization as assessed by the use of revascularization procedures, length of stay, and hospital costs are influenced by patient acuity estimated from a prediction model on the basis of estimated risk of cardiac complications. The model exerts independent influence on cost even after adjustment for various procedures. The use of revascularization procedures, especially coronary artery surgery, remains a large determinant of hospital cost.
Authors: José Carlos Nicolau; Gilson Soares Feitosa Filho; João Luiz Petriz; Remo Holanda de Mendonça Furtado; Dalton Bertolim Précoma; Walmor Lemke; Renato Delascio Lopes; Ari Timerman; José A Marin Neto; Luiz Bezerra Neto; Bruno Ferraz de Oliveira Gomes; Eduardo Cavalcanti Lapa Santos; Leopoldo Soares Piegas; Alexandre de Matos Soeiro; Alexandre Jorge de Andrade Negri; Andre Franci; Brivaldo Markman Filho; Bruno Mendonça Baccaro; Carlos Eduardo Lucena Montenegro; Carlos Eduardo Rochitte; Carlos José Dornas Gonçalves Barbosa; Cláudio Marcelo Bittencourt das Virgens; Edson Stefanini; Euler Roberto Fernandes Manenti; Felipe Gallego Lima; Francisco das Chagas Monteiro Júnior; Harry Correa Filho; Henrique Patrus Mundim Pena; Ibraim Masciarelli Francisco Pinto; João Luiz de Alencar Araripe Falcão; Joberto Pinheiro Sena; José Maria Peixoto; Juliana Ascenção de Souza; Leonardo Sara da Silva; Lilia Nigro Maia; Louis Nakayama Ohe; Luciano Moreira Baracioli; Luís Alberto de Oliveira Dallan; Luis Augusto Palma Dallan; Luiz Alberto Piva E Mattos; Luiz Carlos Bodanese; Luiz Eduardo Fonteles Ritt; Manoel Fernandes Canesin; Marcelo Bueno da Silva Rivas; Marcelo Franken; Marcos José Gomes Magalhães; Múcio Tavares de Oliveira Júnior; Nivaldo Menezes Filgueiras Filho; Oscar Pereira Dutra; Otávio Rizzi Coelho; Paulo Ernesto Leães; Paulo Roberto Ferreira Rossi; Paulo Rogério Soares; Pedro Alves Lemos Neto; Pedro Silvio Farsky; Rafael Rebêlo C Cavalcanti; Renato Jorge Alves; Renato Abdala Karam Kalil; Roberto Esporcatte; Roberto Luiz Marino; Roberto Rocha Corrêa Veiga Giraldez; Romeu Sérgio Meneghelo; Ronaldo de Souza Leão Lima; Rui Fernando Ramos; Sandra Nivea Dos Reis Saraiva Falcão; Talia Falcão Dalçóquio; Viviana de Mello Guzzo Lemke; William Azem Chalela; Wilson Mathias Júnior Journal: Arq Bras Cardiol Date: 2021-07 Impact factor: 2.667