Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia

Alzeer, Abdullah; Althemery, Abdullah; Alsaawi, Fahad; Albalawi, Marwan; Alharbi, Abdulaziz; Alzahrani, Somayah; Alabdulaali, Deema; Alabdullatif, Raghad; Tash, Adel

doi:10.1016/j.ijmedinf.2021.104565

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…b. Machine learning: Machine learning (ML) was reported in 46 studies to analyse, classify, diagnose, manage, monitor, and predict different health conditions or diseases (e.g., frailty, back pain, ischemic stroke, cancer, COVID-19, tuberculosis, diabetes, mortality, hypertension, mental health conditions, bacterial vaginosis, and heart disease) ( 21 , 22 , 25 – 27 , 30 , 33 , 37 , 39 , 41 , 43 , 46 , 47 , 50 , 52 , 54 , 56 , 58 , 60 , 63 , 65 , 67 , 70 , 71 , 73 , 76 , 77 , 81 , 82 , 84 , 87 , 92 , 96 , 101 , 103 , 105 , 108 , 110 ). This approach was also used to create patient re-admission files, pre-authorization in health insurance, and for finding missed cases of disease; these all form a significant part in delivering medical care services ( 30 , 76 , 111 ).…”

Section: Resultsmentioning

confidence: 99%

“…Eighteen of these studies focused on COVID-19 while the remaining ones were on tuberculosis. Thirteen studies targeted cardiovascular diseases (including stroke, hypertension, ventricular dysfunction, and heart function) ( 31 , 34 , 39 , 44 , 46 , 51 , 59 , 61 , 71 , 76 , 87 , 95 , 112 ). These were mostly experimental studies conducted in WHO AMR, EMR, EUR, SEAR, and WPR regions for prediction and diagnostic purposes.…”

Section: Resultsmentioning

confidence: 99%

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The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Okeibunor

Jaca

Iwu

et al. 2023

Front. Public Health

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BackgroundArtificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.MethodsWe searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.ResultsSeveral AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.ConclusionsPresently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Okeibunor

Jaca

Iwu

et al. 2023

Front. Public Health

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“…A machine-learning model can be utilized to identify cardiovascular patients with a high risk of readmission. 18 In patients over 65 years with acute myocardial infarction, the readmission rate within one year of hospital discharge was 49.9% and the amounts of readmission and mortality were higher in the early months post-discharge but decreased later. The risk trajectories varied by discharge diagnosis and outcome and thus to reduce adverse outcomes, patients should stay vigilant for health deterioration post discharge with proper medical support.…”

Section: Discussionmentioning

confidence: 96%

The Association Between Medication Non-Adherence and Early and Late Readmission Rates for Patients with Acute Coronary Syndrome

Murad¹,

Basheikh²,

Zayed³

et al. 2022

IJGM

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Unplanned hospital readmission forms costly, but preventable burdens on healthcare system. This study was designed to evaluate cardiovascular-related readmission rate after discharge of acute coronary syndrome (ACS) patients and its relationship with medication adherence at a university hospital, Saudi Arabia. Methods: A total of 370 consecutive patients presenting with ACS were involved. The inclusion criteria were clinical and coronary angiography diagnostic data of ACS. Exclusion criteria included heart valve disease, myocarditis, hepatic disease, and history of acute infection during the previous two weeks. Patients were divided into index admission group (n = 291) and unplanned readmission group (n = 79). Readmission and medication adherence rates were evaluated during 1-30, 31-180, 181-365, and 366-548 days post-ACS discharge. Medication adherence was estimated with a (yes/no) questionnaire. Results: The overall readmission rate was 21.4%; individual rates were 30.4%, 38.0%, 27.8%, and 3.8% and the overall medication adherence rate was 62.03%, while individual rates were 54.2%, 70.0%, 63.6%, and 33.3% during the four periods, respectively. There were strong correlations between medication non-adherence and readmission rates. Heart failure, ST-elevated myocardial infarction, unstable angina, cerebrovascular accident, and arrhythmia represented the top causes. Body mass index was higher in readmission group. There were significant correlations between smoking, hypertension, cerebrovascular accident, ischemic heart disease, previous stent, instent restenosis, and LDL-cholesterol as predictor factors and readmission rate. Conclusion:The cardiovascular-related unplanned readmission rate post-ACS discharge was 21.4%, and medication non-adherence rate was 37.97%. There were strong correlations between them in the time frames from 1-month to 1.5-year post-discharge. The individual rates decreased by time, but the first month showed lower rates than the following 5 months and this indicated the role of factors other than medication non-adherence in readmission. The rates are generally consistent with the international levels but utilizing technology can further improve medication adherence and reduce readmission rates.

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“…Although several models for predicting angina-related risk have been reported [ 21 , 22 , 23 ], there are no readily available tools for predicting all-cause readmission of individual, elderly angina patients. Several studies have evaluated readmission following cardiovascular disease (CVD) [ 24 , 25 ]. Okere et al .…”

Section: Discussionmentioning

confidence: 99%

“…Another study used 5 ML algorithms to predict 30-day all-cause readmission in a cohort of 1962 patients with CVD. This decision tree model showed a high F1-value (64%), precision (57%), and recall (71%) [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina

Luo,

Song,

Tong

2024

Rev. Cardiovasc. Med.

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Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. Methods: The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. Results: A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. Conclusions: An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients.

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Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia

Cited by 5 publications

References 31 publications

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

The Association Between Medication Non-Adherence and Early and Late Readmission Rates for Patients with Acute Coronary Syndrome

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina

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