Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics

Ghazalbash, Somayeh; Zargoush, Manaf; Mowbray, Fabrice; Παπαϊωάννου, Αλεξάνδρα

doi:10.1016/j.ijmedinf.2021.104597

Cited by 5 publications

(12 citation statements)

References 58 publications

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“…Most studies (n = 23) 23 – 25 , 27 , 29 – 40 , 42 – 47 , 49 were conducted in urban acute care hospital settings including university teaching hospitals (n = 9), 23 , 27 , 29 , 32 , 36 , 37 , 46 , 47 , 49 academic medical centers (n = 1), 40 skilled nursing facilities (n = 1), 41 and geriatric rehabilitation facilities (n = 2). 39 , 48 Some studies involved collaboration between community acute care hospitals in rural regions (n = 6).…”

Section: Resultsmentioning

confidence: 99%

“… 24 , 25 , 32 We identified more prospective studies (n = 6) 39 , 41 , 42 , 46 – 48 than retrospective studies (n = 2). 43 , 49 The remaining articles described case studies (n = 4), 23 , 27 , 28 , 37 randomized controlled trials (RCTs) (n = 3), 35 , 40 , 45 quasi-experimental trials (n = 2), 29 , 44 and a population-based study, 36 prospective non-RCT with blinded outcome evaluation, 30 comparative process evaluation, 26 predictability and prognostication analysis, 38 and a study protocol. 31 …”

Section: Resultsmentioning

confidence: 99%

“… 40 The remaining articles did not disclose details of the admitting diagnoses. 26 , 28 – 31 , 33 – 36 , 38 , 39 , 41 , 43 – 45 , 49 …”

Section: Resultsmentioning

confidence: 99%

“…Other notable causes for delayed discharge included multimorbidity (n = 3), 29 , 32 , 38 dementia (n = 1), 36 and QOL (n = 11). 26 , 29 , 30 , 36 , 39 , 42 – 46 , 48 The manner in which the efficacy of outcomes from CDP was measured varied among studies.…”

Section: Resultsmentioning

confidence: 99%

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Outcomes of complex discharge planning in older adults with complex needs: a scoping review

Rameli

Rajendran

2022

J Int Med Res

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In this scoping review, we aimed to evaluate the effectiveness of integrated multidisciplinary team discharge planning and identify common outcomes among older adults with complex needs, focusing on a safe transition from the hospital to the community. We performed a literature search for relevant articles using seven electronic databases and agreed search terms. Only articles published in English were included. In total, 23,772 articles were identified, with 27 articles meeting the inclusion criteria. A preponderance of patients aged ≥65 years and women was inferred based on population demographics. Initiatives on complex discharge planning were noted across most Western countries. Common outcomes of complex discharge planning were functionality (n = 11) including frailty (n = 4), quality of life (n = 11), and patient-centered factors including psychosocial needs (n = 9). Various outcomes from complex discharge planning initiatives and pathways were explored in this scoping review. None of the selected studies covered all nine domains of outcome assessment. Further research is needed involving follow-up studies after complex discharge planning interventions to assess their true effectiveness or value.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“… 40 The remaining articles did not disclose details of the admitting diagnoses. 26 , 28 – 31 , 33 – 36 , 38 , 39 , 41 , 43 – 45 , 49 …”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Outcomes of complex discharge planning in older adults with complex needs: a scoping review

Rameli

Rajendran

2022

J Int Med Res

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“…In addition, it is of interest to us that the tree model can be used to generate easy-to-understand rules to guide the prevention and control of MetS. Currently, DT models have been applied in clinical practice, including disease diagnosis, risk of disease occurrence, regression and prognosis [ 38 – 40 ], health economics evaluation [ 41 ], clinical decision support system (CDSS) [ 42 ], and rational and safe drug use [ 43 ], and DT has shown powerful performance and high accuracy in all these aspects. Although studies have shown that the prediction performance of LR is lower than that of the DT, LR is not prone to overfitting and has good generalization ability [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Zhang

Razbek

et al. 2022

BMC Public Health

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Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.

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A systematic literature review of predicting patient discharges using statistical methods and machine learning

Pahlevani,

Taghavi,

Vanberkel

2024

Health Care Manag Sci

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Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

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Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics

Cited by 5 publications

References 58 publications

Outcomes of complex discharge planning in older adults with complex needs: a scoping review

Outcomes of complex discharge planning in older adults with complex needs: a scoping review

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

A systematic literature review of predicting patient discharges using statistical methods and machine learning

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