Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Bazoukis, George; Stavrakis, Stavros; Zhou, Jiandong; Bollepalli, Sandeep Chandra; Tse, Gary; Zhang, Qingpeng; Singh, Jagmeet P.; Armoundas, Antonis A.

doi:10.1007/s10741-020-10007-3

Cited by 71 publications

(55 citation statements)

References 38 publications

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“…Indeed, HF is often complicated by various co‐morbidities that adversely affect prognosis 9–13 . Therefore, targeting co‐morbidities has been increasingly advocated as being relevant to HF care 14 . According to the ESC Heart Failure Pilot Survey, 74% of patients with HF had at least one co‐morbidity, and HF patients commonly have multiple co‐morbidities 15 .…”

Section: Discussionmentioning

confidence: 99%

Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction

et al. 2021

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Aims The H 2 FPEF score is a convenient risk stratification tool for diagnosing heart failure with preserved ejection fraction (HFpEF). This study examined the value of the H 2 FPEF score for predicting all-cause mortality and rehospitalization in HFpEF patients. Methods and results This was a retrospective cohort study of patients diagnosed with HFpEF by echocardiography at a single tertiary centre between 1 January 2015 and 30 April 2018. According to the H 2 FPEF score, the subjects were divided into low (0-1 points), intermediate (2-5 points), and high (6-9 points) score groups. The primary outcomes were all-cause mortality and rehospitalization. A total of 476 patients (mean age: 70.5 ± 8.4 years, 60.7% female) were included. Of these, 47 (9.9%), 262 (55.0%), and 167 (35.1%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow-up of 27.5 months, 63 patients (13.2%) died, and 311 patients (65.3%) were rehospitalized. The mortality rates were 3 (6.4%), 29 (11.1%), and 31 (18.6%), and the number of patients with rehospitalization was 28 (59.6%), 159 (60.7%), and 124 (74.3%) for the low, intermediate, and high score groups, respectively. Multivariate Cox regression identified H 2 FPEF score as an independent predictor of all-cause mortality (hazard ratio [HR]: 1.46, 95% CI: 1.23-1.73, P < 0.0001) and rehospitalization (HR: 1.15, 95% CI: 1.08-1.22, P < 0.0001). Receiver operating characteristic (ROC) analysis demonstrated the H 2 FPEF score can effectively predict all-cause mortality (AUC 0.67, 95% CI: 0.60-0.73, P < 0.0001) and rehospitalization (AUC 0.59, 95% CI: 0.54-0.65, P = 0.001) after adjusting for age and NYHA class. With a cutoff value of 5.5, the sensitivity and specificity were 68.3% and 55.4% for all-cause mortality and 50.5% and 66.7% for rehospitalization. Conclusions The H 2 FPEF score can be used to predict prognosis in HFpEF patients. Higher scores are associated with higher all-cause mortality and rehospitalization.

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Section: Discussionmentioning

confidence: 99%

Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction

et al. 2021

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“…Risk stratification in HF is an important clinical problem ( 24 , 25 ). Previous studies have elucidated that the demographics of patients with HFmrEF lied in between those of HFpEF and HFrEF patients, but in general were more similar to HFpEF patients, with a heavier burden of hypertension and atrial fibrillation/flutter ( 10 , 12 , 13 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

Heart Failure With Midrange Ejection Fraction: Prior Left Ventricular Ejection Fraction and Prognosis

Zhang

Sun

Chen

et al. 2021

Front. Cardiovasc. Med.

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Aims: Evidence-based guidelines for heart failure management depend mainly on current left ventricular ejection fraction (LVEF). However, fewer studies have examined the impact of prior LVEF. Patients may enter the heart failure with midrange ejection fraction (HFmrEF) category when heart failure with preserved ejection fraction (HFpEF) deteriorates or heart failure with reduced ejection fraction (HFrEF) improves. In this study, we examined the association between change in LVEF and adverse outcomes.Methods: HFmrEF patients with at least two or more echocardiograms 3 months apart at the First Affiliated Hospital of Dalian Medical University between September 1, 2015 and November 30, 2019 were identified. According to the prior LVEF, the subjects were divided into improved group (prior LVEF < 40%), stable group (prior LVEF between 40 and 50%), and deteriorated group (prior LVEF ≥ 50%). The primary outcomes were cardiovascular death, all-cause mortality, hospitalization for worsening heart failure, and composite event of all-cause mortality or all-cause hospitalization.Results: A total of 1,168 HFmrEF patients (67.04% male, mean age 63.60 ± 12.18 years) were included. The percentages of improved, stable, and deteriorated group were 310 (26.54%), 334 (28.60%), and 524 (44.86%), respectively. After a period of follow-up, 208 patients (17.81%) died and 500 patients met the composite endpoint. The rates of all-cause mortality were 35 (11.29%), 55 (16.47%), and 118 (22.52%), and the composite outcome was 102 (32.90%), 145 (43.41%), and 253 (48.28%) for the improved, stable, and deteriorated groups, respectively. Cox regression analysis showed that the deterioration group had higher risk of cardiovascular death (HR: 1.707, 95% CI: 1.064–2.739, P = 0.027), all-cause death (HR 1.948, 95% CI 1.335–2.840, P = 0.001), and composite outcome (HR 1.379, 95% CI 1.096–1.736, P = 0.006) compared to the improvement group. The association still remained significant after fully adjusted for both all-cause mortality (HR = 1.899, 95% CI 1.247–2.893, P = 0.003) and composite outcome (HR: 1.324, 95% CI: 1.020–1.718, P = 0.035).Conclusion: HFmrEF patients are heterogeneous with three different subsets identified, each with different outcomes. Strategies for managing HFmrEF should include previously measured LVEF to allow stratification based on direction changes in LVEF to better optimize treatment.

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“…This is difficult and time consuming. In fact, the black-box issue has been a major reason for the slow adoption of machine learning in clinical practice, despite machine learning often producing the highest prediction accuracy among all predictive modeling methods [33,[123][124][125][126][127].…”

Section: Gap 2: No Information Given On the Reason Why A Patient Is Deemed High Risk And The Potential Interventions To Reduce The Riskmentioning

confidence: 99%

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

et al. 2021

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Background Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. Objective To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. Methods We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. Results We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. Conclusions Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. International Registered Report Identifier (IRRID) PRR1-10.2196/27065

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Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Cited by 71 publications

References 38 publications

Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction

Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction

Heart Failure With Midrange Ejection Fraction: Prior Left Ventricular Ejection Fraction and Prognosis

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

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Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Cited by 71 publications

References 38 publications

Predictive value of H2FPEF score in patients with heart failure with preserved ejection fraction

Predictive value of H2FPEF score in patients with heart failure with preserved ejection fraction

Heart Failure With Midrange Ejection Fraction: Prior Left Ventricular Ejection Fraction and Prognosis

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

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Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction

Predictive value of H₂FPEF score in patients with heart failure with preserved ejection fraction