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Mohammadyari P, Vieceli Dalla Sega F, Fortini F, Minghini G, Rizzo P, Cimaglia P, Mikus E, Tremoli E, Campo G, Calore E, Schifano SF, Zambelli C. Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement. Sci Rep 2024; 14:10902. [PMID: 38740898 DOI: 10.1038/s41598-024-61685-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution by surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI). These procedures are associated with high post-intervention mortality, then the corresponding risk assessment is relevant from a clinical standpoint. This study compares the traditional Cox Proportional Hazard (CPH) against Machine Learning (ML) based methods, such as Deep Learning Survival (DeepSurv) and Random Survival Forest (RSF), to identify variables able to estimate the risk of death one year after the intervention, in patients undergoing either to SAVR or TAVI. We found that with all three approaches the combination of six variables, named albumin, age, BMI, glucose, hypertension, and clonal hemopoiesis of indeterminate potential (CHIP), allows for predicting mortality with a c-index of approximately 80 % . Importantly, we found that the ML models have a better prediction capability, making them as effective for statistical analysis in medicine as most state-of-the-art approaches, with the additional advantage that they may expose non-linear relationships. This study aims to improve the early identification of patients at higher risk of death, who could then benefit from a more appropriate therapeutic intervention.
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Affiliation(s)
| | | | | | - Giada Minghini
- Department of Environmental and Prevention Sciences, Università di Ferrara, Ferrara, Italy
| | - Paola Rizzo
- Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy.
- Department of Translational Medicine, Università di Ferrara, Ferrara, Italy.
- Laboratory for Technologies of Advanced Therapies (LTTA), Ferrara, Italy.
| | - Paolo Cimaglia
- Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy
| | - Elisa Mikus
- Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy
| | - Elena Tremoli
- Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy
| | - Gianluca Campo
- Department of Translational Medicine, Università di Ferrara, Ferrara, Italy
- Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy
| | - Enrico Calore
- Istituto Nazionale di Fisica Nucleare (INFN), Ferrara, Italy
| | - Sebastiano Fabio Schifano
- Istituto Nazionale di Fisica Nucleare (INFN), Ferrara, Italy.
- Department of Environmental and Prevention Sciences, Università di Ferrara, Ferrara, Italy.
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Heltø ALK, Rosager EV, Aasbrenn M, Maule CF, Petersen J, Nielsen FE, Suetta C, Gregersen R. Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours. Clin Epidemiol 2023; 15:707-719. [PMID: 37324726 PMCID: PMC10264096 DOI: 10.2147/clep.s405485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/03/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from short, acute hospitalizations and to examine how model performance changed with an increasing amount of information. Methods This registry-based study included acute hospitalizations in Denmark for 2016-2018 lasting ≤24 hours where patients were permanent residents, ≥65 years old, and discharged alive. Utilizing many different predictor variables, we developed random forest models with an increasing amount of information, compared their performance, and examined important variables. Results We included 107,132 patients with a median age of 75 years. Of these, 3.3% (n=3575) died within 30 days of discharge. Model performance improved especially with the addition of laboratory results and information on prior acute admissions (AUROC 0.835), and again with comorbidities and number of prescription drugs (AUROC 0.860). Model performance did not improve with the addition of sociodemographic variables (AUROC 0.861), apart from age and sex. Important variables included age, dementia, number of prescription drugs, C-reactive protein, and eGFR. Conclusion The best model accurately estimated the risk of short-term mortality for older patients following short, acute hospitalizations. Trained on a large and heterogeneous dataset, the model is applicable to most acute clinical settings and could be a useful tool for physicians prior to discharge.
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Affiliation(s)
- Amalia Lærke Kjær Heltø
- Department of Emergency Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emilie Vangsgaard Rosager
- Department of Emergency Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Martin Aasbrenn
- Department of Geriatrics and Palliative Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Cathrine Fox Maule
- Center of Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Janne Petersen
- Center of Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Finn Erland Nielsen
- Department of Emergency Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Charlotte Suetta
- Department of Geriatrics and Palliative Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Rasmus Gregersen
- Department of Emergency Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Center of Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Discharge readiness as an infrastructure: Negotiating the transfer of care for elderly patients in medical wards. Soc Sci Med 2022; 312:115388. [DOI: 10.1016/j.socscimed.2022.115388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022]
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Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. PSYCHOTHERAPY AND PSYCHOSOMATICS 2022; 91:8-35. [PMID: 34991091 DOI: 10.1159/000521288] [Citation(s) in RCA: 406] [Impact Index Per Article: 203.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 11/19/2022]
Abstract
The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.
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Affiliation(s)
- Mary E Charlson
- Division of Clinical Epidemiology and Evaluative Sciences Research, Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Danilo Carrozzino
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
| | - Jenny Guidi
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
| | - Chiara Patierno
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
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Lehmann F, Schenk LM, Bernstock JD, Bode C, Borger V, Gessler F, Güresir E, Hamed M, Potthoff AL, Putensen C, Schneider M, Zimmermann J, Vatter H, Schuss P, Hadjiathanasiou A. Admission Dehydration Status Portends Adverse Short-Term Mortality in Patients with Spontaneous Intracerebral Hemorrhage. J Clin Med 2021; 10:jcm10245939. [PMID: 34945232 PMCID: PMC8708142 DOI: 10.3390/jcm10245939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/13/2021] [Accepted: 12/16/2021] [Indexed: 01/25/2023] Open
Abstract
The impact of dehydration at admission of patients with spontaneous intracerebral hemorrhage (ICH) on short-term mortality remains ambiguous due to scarce data. All of the consecutive patients with spontaneous ICH, who were referred to our neurovascular center in 2018/19, were assessed for hydration status on admission. Dehydration was defined by a blood urea-to-creatinine ratio > 80. In a cohort of 249 patients, 76 patients (31%) were dehydrated at the time of admission. The following factors were significantly and independently associated with increased 30-day mortality in multivariate analysis: “signs of cerebral herniation” (p = 0.008), “initial midline shift > 5 mm” (p < 0.001), “ICH score > 3” (p = 0.007), and “admission dehydration status” (p = 0.007). The results of the present study suggest that an admission dehydration status might constitute a significant and independent predictor of short-term mortality in patients with spontaneous ICH.
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Affiliation(s)
- Felix Lehmann
- Department of Anesthesiology and Intensive Care, University Hospital Bonn, 53127 Bonn, Germany; (C.B.); (C.P.)
- Correspondence: ; Tel.: +49-228-287-14119
| | - Lorena M. Schenk
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Christian Bode
- Department of Anesthesiology and Intensive Care, University Hospital Bonn, 53127 Bonn, Germany; (C.B.); (C.P.)
| | - Valeri Borger
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Florian Gessler
- Department of Neurosurgery, University Hospital Rostock, 18055 Rostock, Germany;
| | - Erdem Güresir
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Motaz Hamed
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Anna-Laura Potthoff
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Christian Putensen
- Department of Anesthesiology and Intensive Care, University Hospital Bonn, 53127 Bonn, Germany; (C.B.); (C.P.)
| | - Matthias Schneider
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Julian Zimmermann
- Department of Neurology, University Hospital Bonn, 53127 Bonn, Germany;
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Patrick Schuss
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
| | - Alexis Hadjiathanasiou
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany; (L.M.S.); (V.B.); (E.G.); (M.H.); (A.-L.P.); (M.S.); (H.V.); (P.S.); (A.H.)
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Aasbrenn M, Christiansen CF, Esen BÖ, Suetta C, Nielsen FE. Correction to: Mortality of older acutely admitted medical patients after early discharge from emergency departments: a nationwide cohort study. BMC Geriatr 2021; 21:517. [PMID: 34587915 PMCID: PMC8480043 DOI: 10.1186/s12877-021-02420-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Martin Aasbrenn
- CopenAge - Copenhagen Center for Clinical Age Research, University of Copenhagen, Copenhagen, Denmark. .,Geriatric Research Unit, Department of Geriatric and Palliative Medicine, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.
| | | | - Buket Öztürk Esen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Charlotte Suetta
- CopenAge - Copenhagen Center for Clinical Age Research, University of Copenhagen, Copenhagen, Denmark.,Geriatric Research Unit, Department of Geriatric and Palliative Medicine, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Geriatric Research Unit, Department of Medicine, Herlev-Gentofte Hospitals, Herlev, Denmark
| | - Finn Erland Nielsen
- Department of Emergency Medicine, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Department of Emergency Medicine, Slagelse Hospital, Slagelse, Denmark.,Copenhagen Center for Translational Research, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
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