A Predictive Model for 6-Month Mortality in Elderly Patients with Heart Failure

Suzuki, Sho; Motoki, Hirohiko; Kanzaki, Yusuke; Maruyama, Takuya; Hashizume, Naoto; Kozuka, Ayako; Yahikozawa, Kumiko; Kuwahara, Koichiro

doi:10.1536/ihj.19-572

Cited by 9 publications

(6 citation statements)

References 23 publications

(28 reference statements)

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“…Some studies of the usefulness of the ALBI score have reported its ability to predict prognosis and assist in decision making regarding treatment choices for patients with hepatocellular carcinoma, as it is a suitable tool for evaluation of hepatic reserve function. 5,6) On the other hand, in the area of heart disease, previous reports have shown that both albumin [7][8][9] and bilirubin 10,11) alone, are prognostic factors in patients with HF. Moreover, albumin is also a major component of nutritional indicators such as the prognostic nutritional index, geriatric nutritional risk index, and controlled nutritional status.…”

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confidence: 99%

Association Between Albumin-Bilirubin Score at Admission and In-Hospital Mortality in Patients with Acute Heart Failure

Kawata¹,

Ikeda²,

Masuda³

et al. 2021

Int. Heart J.

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Liver dysfunction is one of the most recognized complications in patients with acute heart failure (HF) and therefore a liver function score may be useful for risk-stratification in those patients. Recently, the albuminbilirubin (ALBI) score was developed as a new model to assess liver function in liver disease. We explored the association between the ALBI score at admission and in-hospital mortality in patients with acute HF.We enrolled 262 patients (median age, 86 years, 137 males) who were admitted to our hospital for treatment of acute HF. The following data were recorded: vital signs, laboratory data including B-type natriuretic peptide (BNP) level, echocardiographic data at admission, demographic and clinical characteristics, and treatment and prognostic information. The Get With the Guidelines-Heart Failure (GWTG-HF) risk score was calculated as an established risk model for each patient. The primary outcome was all-cause in-hospital mortality.During hospitalization, 37 patients (14.1%) died. The in-hospital mortality rate was significantly higher in patients with ALBI scores > 2.25 compared with patients with ALBI scores ! 2.25 (21.1% versus 4.5%, respectively; P = 0.0001). Multivariate analysis revealed that the GWTG-HF score (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.08-1.25, P < 0.0001), BNP level (OR 1.0007, 95% CI 1.0003-1.001, P = 0.0003) and ALBI score (OR 6.0, 95% CI 1.8-19.6, P = 0.0017) were independently associated with in-hospital mortality.Our results indicated that the ALBI score was independently associated with in-hospital mortality in patients hospitalized for acute HF.

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mentioning

confidence: 99%

Association Between Albumin-Bilirubin Score at Admission and In-Hospital Mortality in Patients with Acute Heart Failure

Kawata¹,

Ikeda²,

Masuda³

et al. 2021

Int. Heart J.

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“…Another advantage of the EPICTER score is the inclusion of only seven easy‐to‐obtain variables. Age, 6 , 11 , 12 , 20 , 24 , 25 , 26 renal function, 12 , 20 , 24 , 25 chronic obstructive pulmonary disease, 6 , 12 and advanced functional NYHA class 12 , 25 , 26 , 27 have shown their predictive ability in numerous studies and therefore have been included in many of the prognostic scores developed in acute patients. Cognitive impairment, although less represented in risk scales, 6 , 26 also predicts a poor prognosis in patients with HF.…”

Section: Discussionmentioning

confidence: 99%

The EPICTER score: a bedside and easy tool to predict mortality at 6 months in acute heart failure

et al. 2022

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Aims Estimating the prognosis in heart failure (HF) is important to decide when to refer to palliative care (PC). Our objective was to develop a tool to identify the probability of death within 6 months in patients admitted with acute HF. Methods and results A total of 2848 patients admitted with HF in 74 Spanish hospitals were prospectively included and followed for 6 months. Each factor independently associated with death in the derivation cohort (60% of the sample) was assigned a prognostic weight, and a risk score was calculated. The accuracy of the score was verified in the validation cohort. The characteristics of the population were as follows: advanced age (mean 78 years), equal representation of men and women, significant comorbidity, and predominance of HF with preserved ejection fraction. During follow-up, 753 patients (26%) died. Seven independent predictors of mortality were identified: age, chronic obstructive pulmonary disease, cognitive impairment, New York Heart Association class III-IV, chronic kidney disease, estimated survival of the patient less than 6 months, and acceptance of a palliative approach by the family or the patient. The area under the ROC curve for 6 month death was 0.74 for the derivation and 0.68 for the validation cohort. The model showed good calibration (Hosmer and Lemeshow test, P value 0.11). The 6 month death rates in the score groups ranged from 6% (low risk) to 54% (very high risk). Conclusions The EPICTER score, developed from a prospective and unselected cohort, is a bedside and easy-to-use tool that could help to identify high-risk patients requiring PC.

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“…Among them, stepwise logistic regression performed best, and the ROC of the model reached 0.74-0.75. Sho Suzuki and others used multiple logistic regression analysis with stepwise variable selection to select mortality prediction variables and determine the weighted score to find the most important influencing factors [13].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Model for Heart Disease Prediction

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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Heart disease is a common disease that seriously threatens the health of mankind, especially the middle-aged population and the elderly. It is characterized by high prevalence, disability rate, and mortality rate. The patients are never completely cured even with the most advanced treatment. However, the prediction of the incidence in advance and the enabling of doctors on formulating scientific treatment plans would significantly improve the cure rate. In light of the current situation, this paper proposes a cardiovascular disease prediction model based on ensemble learning, which integrates several classical machine learning algorithms such as AdaBoost, Random Forest(RF), Support Vector Machine(SVM), Lightgbm, and Gradient Boosting Decision Tree(GBDT). The first four algorithms with low correlation are constructed as base learners and then ensemble into the meta learner Gradient Boosting Decision Tree to build an ensemble learning model. The excellence of the ensemble model is evaluated from the evaluation indexes such as accuracy, precision, and recall. The experimental results show that the accuracy rate of the ensemble model is 90%, the precision rate is 90.1%, and the recall rate is 90.66%. Compared with a single machine learning prediction model, the maximum improvement is 4%. The model will effectively assist doctors in making more accurate predictions of patients' physical conditions and carrying out scientific treatment.

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A Predictive Model for 6-Month Mortality in Elderly Patients with Heart Failure

Cited by 9 publications

References 23 publications

Association Between Albumin-Bilirubin Score at Admission and In-Hospital Mortality in Patients with Acute Heart Failure

Association Between Albumin-Bilirubin Score at Admission and In-Hospital Mortality in Patients with Acute Heart Failure

The EPICTER score: a bedside and easy tool to predict mortality at 6 months in acute heart failure

An Ensemble Learning Model for Heart Disease Prediction

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