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ARTICLE IN PRESS Journal of Cardiology xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Journal of Cardiology journal homepage: www.elsevier.com/locate/jjcc
Original article
New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems Hirotake Okazaki (MD) a , Akihiro Shirakabe (MD, PhD) a,∗ , Noritake Hata (MD, PhD) a , Masanori Yamamoto (MD, PhD) a , Nobuaki Kobayashi (MD, PhD) a , Takuro Shinada (MD, PhD) a , Kazunori Tomita (MD) a , Masafumi Tsurumi (MD) a , Masato Matsushita (MD) a , Yoshiya Yamamoto (MD) a , Shinya Yokoyama (MD, PhD) a , Kuniya Asai (MD, PhD) b , Wataru Shimizu (MD, PhD, FJCC) b a b
Division of Intensive Care Unit, Chiba Hokusoh Hospital, Nippon Medical School, Chiba, Japan Department of Cardiovascular Medicine, Nippon Medical School, Tokyo, Japan
a r t i c l e
i n f o
Article history: Received 2 December 2013 Received in revised form 4 February 2014 Accepted 19 February 2014 Available online xxx Keywords: Acute heart failure syndrome Mortality Prognosis Scoring
a b s t r a c t Background: No scoring system for assessing acute heart failure (AHF) has been reported. Methods and results: Data for 824 AHF patients were analyzed. The subjects were divided into an alive (n = 750) and a dead group (n = 74). We constructed a predictive scoring system based on eight significant APACHE II factors in the alive group [mean arterial pressure (MAP), pulse, sodium, potassium, hematocrit, creatinine, age, and Glasgow Coma Scale (GCS); giving each one point], defined as the APACHE-HF score. The patients were assigned to five groups by the APACHE-HF score [Group 1: point 0 (n = 70), Group 2: points 1 and 2 (n = 343), Group 3: points 3 and 4 (n = 294), Group 4: points 5 and 6 (n = 106), and Group 5: points 7 and 8 (n = 11)]. A higher optimal balance was observed in the APACHE-HF between sensitivity and specificity [87.8%, 63.9%; area under the curve (AUC) = 0.779] at 2.5 points than in the APACHE II (47.3%, 67.3%; AUC = 0.558) at 17.5 points. The multivariate Cox regression model identified belonging to Group 5 [hazard ratio (HR): 7.764, 95% confidence interval (CI) 1.586–38.009], Group 4 (HR: 6.903, 95%CI 1.940–24.568) or Group 3 (HR: 5.335, 95%CI 1.582–17.994) to be an independent predictor of 3-year mortality. The Kaplan–Meier curves revealed a poorer prognosis, including all-cause death and HF events (death, readmission-HF), in Group 5 and Group 4 than in the other groups, in Group 3 than in Group 2 or Group 1, and in Group 2 than in Group 1. Conclusions: The new scoring system including MAP, pulse, sodium, potassium, hematocrit, creatinine, age, and GCS (APACHE-HF) can be used to predict adverse outcomes of AHF. © 2014 Published by Elsevier Ltd on behalf of Japanese College of Cardiology.
Introduction The Acute Physiology and Chronic Health Evaluation (APACHE) scoring system was first established in 1981 to predict the prognosis in patients receiving intensive care (Fig. 1A) [1]. Subsequently, the APACHE II, III, and IV systems were published over the past 20 years [2–4].
∗ Corresponding author at: Division of Intensive Care Unit, Chiba Hokusoh Hospital, Nippon Medical School, 1715 Kamagari, Inzai, Chiba 270-1694, Japan. Tel.: +81 476 99 1111; fax: +81 476 99 1911. E-mail address: [email protected] (A. Shirakabe).
The APACHE II system consists of three parts, including the acute physiology score, chronic health points, and age points. The total number of points for the three parts is calculated as the APACHE II score. This score has been reported to be predictive of adverse outcomes in patients requiring intensive care, such as those with respiratory disease, severe pancreatitis, or severe sepsis [5–9]. However, this scoring system involves many factors, as described above; therefore, it cannot be applied easily, and clinicians hesitate to use it in every patient. In previous observational studies, various predictive factors for detecting adverse outcomes in acute heart failure (AHF) patients have been identified, including age [10], anemia [11], renal insufficiency [12,13], poor liver function [14], high uric acid [15], high lactate [16], low cholesterol [12,16], elevated blood glucose [17],
http://dx.doi.org/10.1016/j.jjcc.2014.03.002 0914-5087/© 2014 Published by Elsevier Ltd on behalf of Japanese College of Cardiology.
Please cite this article in press as: Okazaki H, et al. New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems. J Cardiol (2014), http://dx.doi.org/10.1016/j.jjcc.2014.03.002
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ARTICLE IN PRESS H. Okazaki et al. / Journal of Cardiology xxx (2014) xxx–xxx
Fig. 1. Definition of each scoring system. (A) The APACHE II scoring system was defined in this study. (B) The Modified APACHE II scoring system was constructed based on the significant APACHE II factors in the alive group [mean blood pressure (BP), sodium, potassium, creatinine, age, and Glasgow Coma Scale (GCS)] and was given points based on the APACHE II system. (C) The APACHE-HF scoring system was constructed based on the significant APACHE II factors in the alive group (mean BP, pulse, sodium, potassium, creatinine, hematocrit, age, and GCS) and was given one point for each cut-off value. The cut-off value for each factor was defined by the receiver-operating characteristic (ROC) curve as follows: mean BP [91.5 mmHg, area under the ROC curve (AUC) = 0.678, p < 0.001), pulse (110.5 beats/min, AUC = 0.594, p = 0.008), sodium (137.5 mmol/L, AUC = 0.613, p = 0.001), potassium (4.85 mmol/L, AUC = 0.601, p = 0.004), hematocrit (36.95 mg/dL, AUC = 0.617, p = 0.001), creatinine (1.475 mg/dL, AUC = 0.676, p < 0.001), age (71.5 years, AUC = 0.572, p = 0.042) and GCS (13.5, AUC = 0.567, p = 0.058)].
Please cite this article in press as: Okazaki H, et al. New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems. J Cardiol (2014), http://dx.doi.org/10.1016/j.jjcc.2014.03.002
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ARTICLE IN PRESS H. Okazaki et al. / Journal of Cardiology xxx (2014) xxx–xxx
hyperkalemia [12], hyponatremia [18], brain-type natriuretic peptide (BNP), and left ventricular ejection fraction. With respect to patients with AHF, no predictive scoring system has been established, and evaluations of the APACHE II system have rarely been reported. We therefore evaluated the efficacy of the APACHE II and our newly established scoring system for AHF patients. Methods Subjects Clinical data were collected from 824 patients with AHF who were admitted to the intensive care unit at Chiba Hokusoh Hospital, Nippon Medical School between January 2000 and July 2012. AHF was defined as either new-onset HF or decompensation of chronic HF with symptoms sufficient to warrant hospitalization [19]. HF was diagnosed according to the Framingham criteria for a clinical diagnosis of HF based on the satisfaction of two major criteria or one major and two minor criteria [20]. All patients had a New York Heart Association (NYHA) functional class of either Class III or IV. AHF patients with one of the following criteria were admitted to the intensive care unit (ICU) by physician’s decision in the present study: (1) patients who need high projectile oxygen inhalation (including mechanical support) to treat orthopnea, (2) patients who need intrope or mechanical support with low blood pressure, (3) patients who need many types of diuretics to improve the general or lung edema. Patients with HF caused by acute coronary syndrome were excluded from the study. All data were retrospectively retrieved from hospital medical records.
3
in the group with lowest point as the referent was assessed using a Cox regression hazard model. Statistical analysis All data were statistically analyzed using the SPSS 20.0J software program (SPSS Japan Institute, Tokyo, Japan). All numerical data were expressed as the mean ± standard deviation or median (range or 25–75% interquartile range) depending on normality. Unpaired Student’s t-test or the Mann–Whitney U-test was used to compare the two groups. Normality was assessed using the Shapiro–Wilk Wtest. Comparisons of all proportions were made using a chi-square analysis. A p-value of less than 0.05 was considered to be statistically significant. ROC curves were calculated to predict the cut-off values, and the sensitivity, specificity and area, under the ROC curve (AUC) were determined. The survival rates were analyzed between the groups assigned based on the cut-off values of the ROC curves for each scoring system using Kaplan–Meier curves, and significant differences were calculated using the log-rank test. A Cox regression analysis was performed to obtain the hazard ratios (HRs) for 90-day mortality and 90-day HF events. Subsequently, a multivariate analysis was performed using the variables with a p-value of 140 mmHg (yes, %) SBP 100–140 mmHg (yes, %) SBP < 100 mmHg (yes, %) Diastolic blood pressure (mmHg) LVEF (%) LVEF > 40% (%) NYHA (IV, %) Laboratory data Total bilirubin (mg/dL) Urinary acid (mg/dL) BUN (mg/dL) Hemoglobin (g/dL) CRP (mg/dL) BNP (pg/mL) Respiratory support Endotracheal intubation (yes, %) NPPV (yes, %) Medication (cases) during the first 5 days Furosemide (yes, %) Nitroglycerin (yes, %) Nicorandil (yes, %) Carperitide (yes, %) Dopamine (yes, %) Dobutamine (yes, %) ACE-I/ARB (yes, %) -Blocker (yes, %) Spironolactone (yes, %) Outcome ICU hospitalization (days) Total hospitalization (days)
74 (65–80) 554 (67.2%) 544 (66.0%)
74 (64–80) 44 (59.5%) 43 (58.1%)
76 (70–81) 510 (68.0%) 501 (66.8%)
0.041 0.153 0.157
554 (67.2%) 141 (17.1%) 143 (17.4%) 117 (21.5%) 22 (2.7%)
44 (59.5%) 14 (18.9%) 7 (9.5%) 20 (27.0%) 2 (2.7%)
510 (68.0%) 127 (16.9%) 136 (18.1%) 157 (20.9%) 20 (2.7%)
1.000 0.630 0.076 0.236 1.000
612 (74.3%) 341 (41.4%) 374 (45.4%)
47 (63.5%) 32 (43.2%) 25 (33.8%)
565 (75.3%) 309 (41.2%) 349 (46.5%)
0.036 0.805 0.038
160 (132–186) 559 (67.8%) 232 (28.2%) 66 (8.0%) 90 (70–100) 35.0 (25–46) 295 (36.0%) 667 (80.9%) 0.6 (0.4–0.8) 6.8 (5.5–8.1) 23.2 (17.9–33.1) 12.5 (10.7–14.5) 0.56 (0.19–1.78) 805 (415–1403)
Dead group (n = 74)
131 (107–168) 31 (41.9%) 29 (39.2%) 13 (17.6%) 80 (62–90) 32 (22–45) 23 (31.9%) 66 (89.2%) 0.7 (0.4–1.0) 7.7 (5.9–9.6) 36.4 (21.5–48.7) 11.3 (10.3–14.2) 1.44 (0.56–4.58) 1363 (927–1787)
Alive group (n = 750)
p-Value
162 (138–188) 528 (70.4%) 203 (27.1%) 53 (7.1%) 90 (72–100) 35 (24–46) 272 (36.4%) 601 (80.1%)
ARTICLE IN PRESS Journal of Cardiology xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Journal of Cardiology journal homepage: www.elsevier.com/locate/jjcc
Original article
New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems Hirotake Okazaki (MD) a , Akihiro Shirakabe (MD, PhD) a,∗ , Noritake Hata (MD, PhD) a , Masanori Yamamoto (MD, PhD) a , Nobuaki Kobayashi (MD, PhD) a , Takuro Shinada (MD, PhD) a , Kazunori Tomita (MD) a , Masafumi Tsurumi (MD) a , Masato Matsushita (MD) a , Yoshiya Yamamoto (MD) a , Shinya Yokoyama (MD, PhD) a , Kuniya Asai (MD, PhD) b , Wataru Shimizu (MD, PhD, FJCC) b a b
Division of Intensive Care Unit, Chiba Hokusoh Hospital, Nippon Medical School, Chiba, Japan Department of Cardiovascular Medicine, Nippon Medical School, Tokyo, Japan
a r t i c l e
i n f o
Article history: Received 2 December 2013 Received in revised form 4 February 2014 Accepted 19 February 2014 Available online xxx Keywords: Acute heart failure syndrome Mortality Prognosis Scoring
a b s t r a c t Background: No scoring system for assessing acute heart failure (AHF) has been reported. Methods and results: Data for 824 AHF patients were analyzed. The subjects were divided into an alive (n = 750) and a dead group (n = 74). We constructed a predictive scoring system based on eight significant APACHE II factors in the alive group [mean arterial pressure (MAP), pulse, sodium, potassium, hematocrit, creatinine, age, and Glasgow Coma Scale (GCS); giving each one point], defined as the APACHE-HF score. The patients were assigned to five groups by the APACHE-HF score [Group 1: point 0 (n = 70), Group 2: points 1 and 2 (n = 343), Group 3: points 3 and 4 (n = 294), Group 4: points 5 and 6 (n = 106), and Group 5: points 7 and 8 (n = 11)]. A higher optimal balance was observed in the APACHE-HF between sensitivity and specificity [87.8%, 63.9%; area under the curve (AUC) = 0.779] at 2.5 points than in the APACHE II (47.3%, 67.3%; AUC = 0.558) at 17.5 points. The multivariate Cox regression model identified belonging to Group 5 [hazard ratio (HR): 7.764, 95% confidence interval (CI) 1.586–38.009], Group 4 (HR: 6.903, 95%CI 1.940–24.568) or Group 3 (HR: 5.335, 95%CI 1.582–17.994) to be an independent predictor of 3-year mortality. The Kaplan–Meier curves revealed a poorer prognosis, including all-cause death and HF events (death, readmission-HF), in Group 5 and Group 4 than in the other groups, in Group 3 than in Group 2 or Group 1, and in Group 2 than in Group 1. Conclusions: The new scoring system including MAP, pulse, sodium, potassium, hematocrit, creatinine, age, and GCS (APACHE-HF) can be used to predict adverse outcomes of AHF. © 2014 Published by Elsevier Ltd on behalf of Japanese College of Cardiology.
Introduction The Acute Physiology and Chronic Health Evaluation (APACHE) scoring system was first established in 1981 to predict the prognosis in patients receiving intensive care (Fig. 1A) [1]. Subsequently, the APACHE II, III, and IV systems were published over the past 20 years [2–4].
∗ Corresponding author at: Division of Intensive Care Unit, Chiba Hokusoh Hospital, Nippon Medical School, 1715 Kamagari, Inzai, Chiba 270-1694, Japan. Tel.: +81 476 99 1111; fax: +81 476 99 1911. E-mail address: [email protected] (A. Shirakabe).
The APACHE II system consists of three parts, including the acute physiology score, chronic health points, and age points. The total number of points for the three parts is calculated as the APACHE II score. This score has been reported to be predictive of adverse outcomes in patients requiring intensive care, such as those with respiratory disease, severe pancreatitis, or severe sepsis [5–9]. However, this scoring system involves many factors, as described above; therefore, it cannot be applied easily, and clinicians hesitate to use it in every patient. In previous observational studies, various predictive factors for detecting adverse outcomes in acute heart failure (AHF) patients have been identified, including age [10], anemia [11], renal insufficiency [12,13], poor liver function [14], high uric acid [15], high lactate [16], low cholesterol [12,16], elevated blood glucose [17],
http://dx.doi.org/10.1016/j.jjcc.2014.03.002 0914-5087/© 2014 Published by Elsevier Ltd on behalf of Japanese College of Cardiology.
Please cite this article in press as: Okazaki H, et al. New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems. J Cardiol (2014), http://dx.doi.org/10.1016/j.jjcc.2014.03.002
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ARTICLE IN PRESS H. Okazaki et al. / Journal of Cardiology xxx (2014) xxx–xxx
Fig. 1. Definition of each scoring system. (A) The APACHE II scoring system was defined in this study. (B) The Modified APACHE II scoring system was constructed based on the significant APACHE II factors in the alive group [mean blood pressure (BP), sodium, potassium, creatinine, age, and Glasgow Coma Scale (GCS)] and was given points based on the APACHE II system. (C) The APACHE-HF scoring system was constructed based on the significant APACHE II factors in the alive group (mean BP, pulse, sodium, potassium, creatinine, hematocrit, age, and GCS) and was given one point for each cut-off value. The cut-off value for each factor was defined by the receiver-operating characteristic (ROC) curve as follows: mean BP [91.5 mmHg, area under the ROC curve (AUC) = 0.678, p < 0.001), pulse (110.5 beats/min, AUC = 0.594, p = 0.008), sodium (137.5 mmol/L, AUC = 0.613, p = 0.001), potassium (4.85 mmol/L, AUC = 0.601, p = 0.004), hematocrit (36.95 mg/dL, AUC = 0.617, p = 0.001), creatinine (1.475 mg/dL, AUC = 0.676, p < 0.001), age (71.5 years, AUC = 0.572, p = 0.042) and GCS (13.5, AUC = 0.567, p = 0.058)].
Please cite this article in press as: Okazaki H, et al. New scoring system (APACHE-HF) for predicting adverse outcomes in patients with acute heart failure: Evaluation of the APACHE II and Modified APACHE II scoring systems. J Cardiol (2014), http://dx.doi.org/10.1016/j.jjcc.2014.03.002
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ARTICLE IN PRESS H. Okazaki et al. / Journal of Cardiology xxx (2014) xxx–xxx
hyperkalemia [12], hyponatremia [18], brain-type natriuretic peptide (BNP), and left ventricular ejection fraction. With respect to patients with AHF, no predictive scoring system has been established, and evaluations of the APACHE II system have rarely been reported. We therefore evaluated the efficacy of the APACHE II and our newly established scoring system for AHF patients. Methods Subjects Clinical data were collected from 824 patients with AHF who were admitted to the intensive care unit at Chiba Hokusoh Hospital, Nippon Medical School between January 2000 and July 2012. AHF was defined as either new-onset HF or decompensation of chronic HF with symptoms sufficient to warrant hospitalization [19]. HF was diagnosed according to the Framingham criteria for a clinical diagnosis of HF based on the satisfaction of two major criteria or one major and two minor criteria [20]. All patients had a New York Heart Association (NYHA) functional class of either Class III or IV. AHF patients with one of the following criteria were admitted to the intensive care unit (ICU) by physician’s decision in the present study: (1) patients who need high projectile oxygen inhalation (including mechanical support) to treat orthopnea, (2) patients who need intrope or mechanical support with low blood pressure, (3) patients who need many types of diuretics to improve the general or lung edema. Patients with HF caused by acute coronary syndrome were excluded from the study. All data were retrospectively retrieved from hospital medical records.
3
in the group with lowest point as the referent was assessed using a Cox regression hazard model. Statistical analysis All data were statistically analyzed using the SPSS 20.0J software program (SPSS Japan Institute, Tokyo, Japan). All numerical data were expressed as the mean ± standard deviation or median (range or 25–75% interquartile range) depending on normality. Unpaired Student’s t-test or the Mann–Whitney U-test was used to compare the two groups. Normality was assessed using the Shapiro–Wilk Wtest. Comparisons of all proportions were made using a chi-square analysis. A p-value of less than 0.05 was considered to be statistically significant. ROC curves were calculated to predict the cut-off values, and the sensitivity, specificity and area, under the ROC curve (AUC) were determined. The survival rates were analyzed between the groups assigned based on the cut-off values of the ROC curves for each scoring system using Kaplan–Meier curves, and significant differences were calculated using the log-rank test. A Cox regression analysis was performed to obtain the hazard ratios (HRs) for 90-day mortality and 90-day HF events. Subsequently, a multivariate analysis was performed using the variables with a p-value of 140 mmHg (yes, %) SBP 100–140 mmHg (yes, %) SBP < 100 mmHg (yes, %) Diastolic blood pressure (mmHg) LVEF (%) LVEF > 40% (%) NYHA (IV, %) Laboratory data Total bilirubin (mg/dL) Urinary acid (mg/dL) BUN (mg/dL) Hemoglobin (g/dL) CRP (mg/dL) BNP (pg/mL) Respiratory support Endotracheal intubation (yes, %) NPPV (yes, %) Medication (cases) during the first 5 days Furosemide (yes, %) Nitroglycerin (yes, %) Nicorandil (yes, %) Carperitide (yes, %) Dopamine (yes, %) Dobutamine (yes, %) ACE-I/ARB (yes, %) -Blocker (yes, %) Spironolactone (yes, %) Outcome ICU hospitalization (days) Total hospitalization (days)
74 (65–80) 554 (67.2%) 544 (66.0%)
74 (64–80) 44 (59.5%) 43 (58.1%)
76 (70–81) 510 (68.0%) 501 (66.8%)
0.041 0.153 0.157
554 (67.2%) 141 (17.1%) 143 (17.4%) 117 (21.5%) 22 (2.7%)
44 (59.5%) 14 (18.9%) 7 (9.5%) 20 (27.0%) 2 (2.7%)
510 (68.0%) 127 (16.9%) 136 (18.1%) 157 (20.9%) 20 (2.7%)
1.000 0.630 0.076 0.236 1.000
612 (74.3%) 341 (41.4%) 374 (45.4%)
47 (63.5%) 32 (43.2%) 25 (33.8%)
565 (75.3%) 309 (41.2%) 349 (46.5%)
0.036 0.805 0.038
160 (132–186) 559 (67.8%) 232 (28.2%) 66 (8.0%) 90 (70–100) 35.0 (25–46) 295 (36.0%) 667 (80.9%) 0.6 (0.4–0.8) 6.8 (5.5–8.1) 23.2 (17.9–33.1) 12.5 (10.7–14.5) 0.56 (0.19–1.78) 805 (415–1403)
Dead group (n = 74)
131 (107–168) 31 (41.9%) 29 (39.2%) 13 (17.6%) 80 (62–90) 32 (22–45) 23 (31.9%) 66 (89.2%) 0.7 (0.4–1.0) 7.7 (5.9–9.6) 36.4 (21.5–48.7) 11.3 (10.3–14.2) 1.44 (0.56–4.58) 1363 (927–1787)
Alive group (n = 750)
p-Value
162 (138–188) 528 (70.4%) 203 (27.1%) 53 (7.1%) 90 (72–100) 35 (24–46) 272 (36.4%) 601 (80.1%)
