Clinical research in cardiology : official journal of the German Cardiac Society · 2023
A machine-learning-based prediction of non-home discharge among acute heart failure patients.
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Okada Akira, Kaneko Hidehiro, Konishi Masaaki, Kamiya Kentaro, Sugimoto Tadafumi, Matsuoka Satoshi, Yokota Isao, Suzuki Yuta, Yamaguchi Satoko, Itoh Hidetaka, Fujiu Katsuhito, Michihata Nobuaki, Jo Taisuke, Matsui Hiroki, Fushimi Kiyohide, Takeda Norifumi, Morita Hiroyuki, Yasunaga Hideo, Komuro Issei
Аннотация
BACKGROUND: Scarce data on factors related to discharge disposition in patients hospitalized for acute heart failure (AHF) were available, and we sought to develop a parsimonious and simple predictive model for non-home discharge via machine learning.
METHODS: This observational cohort study using a Japanese national database included 128,068 patients admitted from home for AHF between April 2014 and March 2018. The candidate predictors for non-home discharge were patient demographics, comorbidities, and treatment performed within 2 days after hospital admission. We used 80% of the population to develop a model using all 26 candidate variables and using the variable selected by 1 standard-error rule of Lasso regression, which enhances interpretability, and 20% to validate the predictive ability.
RESULTS: We analyzed 128,068 patients, and 22,330 patients were not discharged to home; 7,879 underwent in-hospital death and 14,451 were transferred to other facilities. The machine-learning-based model consisted of 11 predictors, showing a discrimination ability comparable to that using all the 26 variables (c-statistic: 0.760 [95% confidence interval, 0.752-0.767] vs. 0.761 [95% confidence interval, 0.753-0.769]). The common 1SE-selected variables identified throughout all analyses were low scores in activities of daily living, advanced age, absence of hypertension, impaired consciousness, failure to initiate enteral alimentation within 2 days and low body weight.
CONCLUSIONS: The developed machine learning model using 11 predictors had a good predictive ability to identify patients at high risk for non-home discharge. Our findings would contribute to the effective care coordination in this era when HF is rapidly increasing in prevalence.
METHODS: This observational cohort study using a Japanese national database included 128,068 patients admitted from home for AHF between April 2014 and March 2018. The candidate predictors for non-home discharge were patient demographics, comorbidities, and treatment performed within 2 days after hospital admission. We used 80% of the population to develop a model using all 26 candidate variables and using the variable selected by 1 standard-error rule of Lasso regression, which enhances interpretability, and 20% to validate the predictive ability.
RESULTS: We analyzed 128,068 patients, and 22,330 patients were not discharged to home; 7,879 underwent in-hospital death and 14,451 were transferred to other facilities. The machine-learning-based model consisted of 11 predictors, showing a discrimination ability comparable to that using all the 26 variables (c-statistic: 0.760 [95% confidence interval, 0.752-0.767] vs. 0.761 [95% confidence interval, 0.753-0.769]). The common 1SE-selected variables identified throughout all analyses were low scores in activities of daily living, advanced age, absence of hypertension, impaired consciousness, failure to initiate enteral alimentation within 2 days and low body weight.
CONCLUSIONS: The developed machine learning model using 11 predictors had a good predictive ability to identify patients at high risk for non-home discharge. Our findings would contribute to the effective care coordination in this era when HF is rapidly increasing in prevalence.