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Olender RT, Roy S, Jamieson HA, Hilmer SN, Nishtala PS. Drug Burden Index Is a Modifiable Predictor of 30-Day Hospitalization in Community-Dwelling Older Adults With Complex Care Needs: Machine Learning Analysis of InterRAI Data. J Gerontol A Biol Sci Med Sci 2024; 79:glae130. [PMID: 38733108 PMCID: PMC11215698 DOI: 10.1093/gerona/glae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient. METHODS In this retrospective cohort study, a data set of 14 198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization. RESULTS The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization. CONCLUSIONS Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.
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Affiliation(s)
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Hamish A Jamieson
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Sarah N Hilmer
- Faculty of Medicine and Health, Kolling Institute, Northern Clinical School, The University of Sydney and Northern Sydney Local Health District, St Leonards, New South Wales, Australia
| | - Prasad S Nishtala
- Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath, UK
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Yeh JK, Tsou YL, Liu MH, Chen WS, Cheng CI, Pan KL, Wang CH, Hsieh IC. Prognostic Significance of Phenylalanine in Heart Failure: Clinical Insights and Inter-Organ Crosstalk Snapshot. J Clin Med 2024; 13:4251. [PMID: 39064291 PMCID: PMC11277683 DOI: 10.3390/jcm13144251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/14/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Heart failure (HF) remains a leading cause of morbidity and mortality globally, necessitating the identification of reliable prognostic biomarkers to guide therapeutic interventions. Recent clinical observations have underscored phenylalanine (PHE) as a prognostic marker in HF, although the mechanisms involving inter-organ crosstalk remain understood. Methods: This study adopted a dull approach, with a retrospective analysis of 550 HF patients to establish the prognostic value of pre-discharge PHE levels and a study on the inter-organ crosstalk of PHE among 24 patients. We analyzed the correlations between PHE concentrations and clinical outcomes, alongside a comprehensive examination of PHE metabolism across the skeletal muscle, liver, heart, kidney, and lung. Results: In the clinical prognostic analysis of 550 patients hospitalized for acute decompensated HF, elevated PHE levels (≥65.6 μM) were significantly and independently associated with increased all-cause mortality during a median follow-up of 4.5 years (log rank = 36.7, p < 0.001), underscoring its value as a prognostic marker in HF. The inter-organic crosstalk study elucidated the mechanism associated with PHE elevation in patients with HF, characterized by an increase in PHE output in skeletal muscle and a decrease in hepatic and cardiac PHE uptakes. Notably, PHE concentration gradients across these organs were correlated with HF severity, such as the NYHA functional class, B-type natriuretic peptide levels, and the presence of acute HF. Conclusions: Our findings confirm the prognostic significance of PHE in patients with HF and unveil the complex metabolic interplay among key organs that contribute to PHE dysregulation. These insights not only reinforce the importance of metabolic monitoring in HF management but also open avenues for therapeutic targets.
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Affiliation(s)
- Jih-Kai Yeh
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan;
| | - Yi-Liang Tsou
- Heart Failure Research Center, Department of Cardiology, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (Y.-L.T.); (M.-H.L.); (W.-S.C.)
| | - Min-Hui Liu
- Heart Failure Research Center, Department of Cardiology, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (Y.-L.T.); (M.-H.L.); (W.-S.C.)
- Department of Nursing, Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Wei-Siang Chen
- Heart Failure Research Center, Department of Cardiology, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (Y.-L.T.); (M.-H.L.); (W.-S.C.)
| | - Cheng-I Cheng
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan
| | - Kuo-Li Pan
- Division of Cardiovascular Disease, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi 613, Taiwan;
| | - Chao-Hung Wang
- Heart Failure Research Center, Department of Cardiology, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (Y.-L.T.); (M.-H.L.); (W.-S.C.)
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - I-Chang Hsieh
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan;
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
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Olender RT, Roy S, Nishtala PS. Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis. BMC Geriatr 2023; 23:561. [PMID: 37710210 PMCID: PMC10503191 DOI: 10.1186/s12877-023-04246-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 08/19/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. DESIGN Systematic review and meta-analyses. PARTICIPANTS Older adults (≥ 65 years) in any setting. INTERVENTION Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. OUTCOME MEASURES Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. RESULTS Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power. CONCLUSION The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.
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Affiliation(s)
- Robert T Olender
- Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK.
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK
| | - Prasad S Nishtala
- Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath, BA2 7AY, UK
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Gil-Rodrigo A, Luque-Hernández MJ, Gutiérrez-García C, Martínez-Buendía C. Letter on the article "Assessment of clinical and haemodynamic congestion as predictors of mortality in elderly outpatients with heart failure". Rev Clin Esp 2023; 223:120-122. [PMID: 36669738 DOI: 10.1016/j.rceng.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/07/2022] [Indexed: 01/19/2023]
Affiliation(s)
- A Gil-Rodrigo
- Unidad de Corta Estancia y Unidad de Hospitalización a Domicilio, Servicio de Urgencias, Hospital General Universitario Dr. Balmis, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain.
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Gil-Rodrigo A, Verdú-Rotellar JM, Gil V, Alquézar A, Llauger L, Herrero-Puente P, Jacob J, Abellana R, Muñoz MÁ, López-Díez MP, Ivars-Obermeier N, Espinosa B, Rodríguez B, Fuentes M, Tost J, López-Grima ML, Romero R, Müller C, Peacock WF, Llorens P, Miró Ò. Evaluation of the HEFESTOS scale to predict outcomes in emergency department acute heart failure patients. Intern Emerg Med 2022; 17:2129-2140. [PMID: 36031673 DOI: 10.1007/s11739-022-03068-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022]
Abstract
The HEFESTOS scale was developed in 14 Spanish primary care centres and validated in 9 primary care centres of other European countries. It showed good performance to predict death/hospitalisation during the first 30 days after an episode of acute heart failure (AHF), with c-statistics of 0.807/0.730 in the derivation/validation cohorts. We evaluated this scale in the emergency department (ED) setting, comparing it to the EHMRG and MEESSI scales in the ED and the EFFECT and GWTG scales in hospitalised patients, to predict 30-day outcomes, including death and hospitalisation. Consecutive AHF patients were enrolled in 34 Spanish EDs in January-February 2016, 2018, and 2019 with variables needed to calculate outcome scores. Thirty-day hospitalisation/death (together and separately) and post-discharge combined adverse event (ED revisit or hospitalisation for AHF or all-cause death) were determined for patients discharged home after ED care. Predictive capacity was assessed by c-statistic with 95% confidence intervals. Of 10,869 patients, 4,044 were included (median age: 83 years, 54% women). The performance of HEFESTOS was modest for 30-day hospitalisation/death, c-statistic=0.656 (0.637-0.675), hospitalisation, 0.650 (0.631-0.669), and death, 0.610 (0.576-0.644). Of 1,034 patients with scores for the 5 scales, HEFESTOS had the numerically highest c-statistic for hospitalisation/death at 30 days, 0.666 (0.627-0.704), vs. MEESSI= 0.650 (0.612-0.687, p=0.51), EFFECT=0.633 (0.595-0.672, p=0.21), GWTG=0.618 (0.578-0.657, p=0.06) and EHMRG=0.617 (0.577-0.704, p=0.07). Similar modest performances were observed for predicting hospitalisation [ranging from HEFESTOS=0.656 (0.618-0.695) to GWTG=0.603 (0.564-0.643)]. Conversely, prediction of 30-day death was good with the MEESSI=0.787 (0.728-845), EFFECT=0.754 (0.691-0.818) and GWTG=0.749 (0.689-0.809) scales, and modest with EHMRG=0.649 (0.581-0.717) and HEFESTOS=0.610 (0.538-0.683). Although the HEFESTOS scale was numerically better for predicting 30-day hospitalisation/death in ED AHF patients, its modest performance precludes routine use. Only 30-day mortality was adequately predicted by some scales, with the MEESSI achieving the best results.
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Affiliation(s)
- Adriana Gil-Rodrigo
- Emergency Department, Short Stay Unit and Hospitalization at Home Unit, Dr, Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - José María Verdú-Rotellar
- Unitat de Suport a La Recerca de Barcelona, Fundació Institut Universitari Per a La Recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Catalan Institute of Health, Pompeu Fabra University, Barcelona, Spain
| | - Víctor Gil
- Emergency Department, Clinic Barcelona Hospital University, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona, c/ Villarroel 170, 08036, Barcelona, Catalonia, Spain
| | - Aitor Alquézar
- Emergency Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Catalonia, Spain
| | - Lluís Llauger
- Emergency Department, Hospital Universitari de Vic, Barcelona, Catalonia, Spain
| | | | - Javier Jacob
- Emergency Department, Hospital Universitari de Bellvitge, l'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | - Rosa Abellana
- Unitat de Bioestadistica del Departament de Fonaments Clínics, Medical School, University of Barcelona, Barcelona, Catalonia, Spain
| | - Miguel-Ángel Muñoz
- Unitat de Suport a La Recerca de Barcelona, Fundació Institut Universitari Per a La Recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Catalan Institute of Health, Pompeu Fabra University, Barcelona, Spain
| | | | - Nicole Ivars-Obermeier
- Emergency Department, Short Stay Unit and Hospitalization at Home Unit, Dr, Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - Begoña Espinosa
- Emergency Department, Short Stay Unit and Hospitalization at Home Unit, Dr, Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - Beatriz Rodríguez
- Emergency Department, Infanta Leonor University Hospital, Madrid, Spain
| | - Marta Fuentes
- Emergency Department, University Hospital of Salamanca, Salamanca, Spain
| | - Josep Tost
- Emergency Department, Consorci Hospitalari de Terrassa, Barcelona, Catalonia, Spain
| | | | - Rodolfo Romero
- Emergency Department, University Hospital of Getafe, Universidad Europea, Madrid, Spain
| | - Christian Müller
- Cardiology Department, University Hospital of Basel, Cardiovascular Research Institute Basel, Basel, Switzerland
- The GREAT Network, Rome, Italy
| | - WFrank Peacock
- Emergency Department, Baylor School of Medicine, Houston, TX, USA
- The GREAT Network, Rome, Italy
| | - Pere Llorens
- Emergency Department, Short Stay Unit and Hospitalization at Home Unit, Dr, Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
| | - Òscar Miró
- Emergency Department, Clinic Barcelona Hospital University, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona, c/ Villarroel 170, 08036, Barcelona, Catalonia, Spain.
- The GREAT Network, Rome, Italy.
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