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Cardelli M, Marchegiani F, Stripoli P, Piacenza F, Recchioni R, Di Rosa M, Giacconi R, Malavolta M, Galeazzi R, Arosio B, Cafarelli F, Spannella F, Cherubini A, Lattanzio F, Olivieri F. Plasma cfDNA abundance as a prognostic biomarker for higher risk of death in geriatric cardiovascular patients. Mech Ageing Dev 2024; 219:111934. [PMID: 38604436 DOI: 10.1016/j.mad.2024.111934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 04/08/2024] [Indexed: 04/13/2024]
Abstract
The management of geriatric cardiovascular disease (CVD) patients with multimorbidity remains challenging and could potentially be improved by integrating clinical data with innovative prognostic biomarkers. In this context, the analysis of circulating analytes, including cell-free DNA (cfDNA), appears particularly promising. Here, we investigated circulating cfDNA (measured through the quantification of 247 bp and 115 bp Alu genomic fragments) in a cohort of 244 geriatric CVD patients with multimorbidity hospitalised for acute CVD or non-CVD events. Survival analysis showed a direct association between Alu 247 cfDNA abundance and risk of death, particularly evident in the first six months after admission for acute CVD events. Higher plasma cfDNA concentration was associated with mortality in the same period of time. The cfDNA integrity (Alu 247/115), although not associated with outcome, appeared to be useful in discriminating patients in whom Alu 247 cfDNA abundance is most effective as a prognostic biomarker. The cfDNA parameters were associated with several biochemical markers of inflammation and myocardial damage. In conclusion, an increase in plasma cfDNA abundance at hospital admission is indicative of a higher risk of death in geriatric CVD patients, especially after acute CVD events, and its analysis may be potentially useful for risk stratification.
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Affiliation(s)
- Maurizio Cardelli
- Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona 60121, Italy
| | | | - Pierpaolo Stripoli
- Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona 60121, Italy
| | - Francesco Piacenza
- Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona 60121, Italy
| | - Rina Recchioni
- Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona 60121, Italy
| | - Mirko Di Rosa
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, IRCCS INRCA, Ancona 60124, Italy
| | - Robertina Giacconi
- Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona 60121, Italy
| | - Marco Malavolta
- Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona 60121, Italy
| | - Roberta Galeazzi
- Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona 60121, Italy
| | - Beatrice Arosio
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | | | - Francesco Spannella
- Internal Medicine and Geriatrics, IRCCS INRCA, Via della Montagnola 81, Ancona 60127, Italy; Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, Ancona 60126, Italy
| | - Antonio Cherubini
- Geriatria, Accettazione Geriatrica e Centro di Ricerca per L'invecchiamento, IRCCS INRCA, Ancona 60127, Italy
| | | | - Fabiola Olivieri
- Advanced Technology Center for Aging Research, IRCCS INRCA, Ancona 60121, Italy; Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, Ancona 60126, Italy; Scientific Direction, IRCCS INRCA, Ancona, Italy
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Cartuliares MB, Mogensen CB, Rosenvinge FS, Skovsted TA, Lorentzen MH, Heltborg A, Hertz MA, Kaldan F, Specht JJ, Skjøt-Arkil H. Community-acquired pneumonia: use of clinical characteristics of acutely admitted patients for the development of a diagnostic model - a cross-sectional multicentre study. BMJ Open 2024; 14:e079123. [PMID: 38816044 PMCID: PMC11141191 DOI: 10.1136/bmjopen-2023-079123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 05/20/2024] [Indexed: 06/01/2024] Open
Abstract
OBJECTIVES This study aimed to describe the clinical characteristics of adults with suspected acute community-acquired pneumonia (CAP) on hospitalisation, evaluate their prediction performance for CAP and compare the performance of the model to the initial assessment of the physician. DESIGN Cross-sectional, multicentre study. SETTING The data originated from the INfectious DisEases in Emergency Departments study and were collected prospectively from patient interviews and medical records. The study included four Danish medical emergency departments (EDs) and was conducted between 1 March 2021 and 28 February 2022. PARTICIPANTS A total of 954 patients admitted with suspected infection were included in the study. PRIMARY AND SECONDARY OUTCOME The primary outcome was CAP diagnosis assessed by an expert panel. RESULTS According to expert evaluation, CAP had a 28% prevalence. 13 diagnostic predictors were identified using least absolute shrinkage and selection operator regression to build the prediction model: dyspnoea, expectoration, cough, common cold, malaise, chest pain, respiratory rate (>20 breaths/min), oxygen saturation (<96%), abnormal chest auscultation, leucocytes (<3.5×109/L or >8.8×109/L) and neutrophils (>7.5×109/L). C reactive protein (<20 mg/L) and having no previous event of CAP contributed negatively to the final model. The predictors yielded good prediction performance for CAP with an area under the receiver-operator characteristic curve (AUC) of 0.85 (CI 0.77 to 0.92). However, the initial diagnosis made by the ED physician performed better, with an AUC of 0.86 (CI 84% to 89%). CONCLUSION Typical respiratory symptoms combined with abnormal vital signs and elevated infection biomarkers were predictors for CAP on admission to an ED. The clinical value of the prediction model is questionable in our setting as it does not outperform the clinician's assessment. Further studies that add novel diagnostic tools and use imaging or serological markers are needed to improve a model that would help diagnose CAP in an ED setting more accurately. TRIAL REGISTRATION NUMBER NCT04681963.
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Affiliation(s)
- Mariana B Cartuliares
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Christian Backer Mogensen
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Flemming S Rosenvinge
- Department of Clinical Microbiology, Odense Universitetshospital, Odense, Denmark
- Research Unit of Clinical Microbiology, University of Southern Denmark, Odense, Denmark
| | - Thor Aage Skovsted
- Department of Biochemistry and Immunology, University Hospital of Southern Denmark, Aabenraa, Denmark
| | - Morten Hjarnø Lorentzen
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Anne Heltborg
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Mathias Amdi Hertz
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Infectious Diseases, Odense University Hospital, Odense, Denmark
| | - Frida Kaldan
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
| | - Jens Juel Specht
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
| | - Helene Skjøt-Arkil
- Department of Emergency Medicine, University Hospital of Southern Denmark, Aabenraa, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
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Xu CB, Su SS, Yu J, Lei X, Lin PC, Wu Q, Zhou Y, Li YP. Risk factors and predicting nomogram for the clinical deterioration of non-severe community-acquired pneumonia. BMC Pulm Med 2024; 24:57. [PMID: 38280994 PMCID: PMC10821265 DOI: 10.1186/s12890-023-02813-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024] Open
Abstract
BACKGROUND Currently, there remains insufficient focus on non-severe community-acquired pneumonia (CAP) patients who are at risk of clinical deterioration, and there is also a dearth of research on the related risk factors. Early recognition of hospitalized patients at risk of clinical deterioration will be beneficial for their clinical management. METHOD A retrospective study was conducted in The First Affiliated Hospital of Wenzhou Medical University, China, spanning from January 1, 2018 to April 30, 2022, and involving a total of 1,632 non-severe CAP patients. Based on whether their condition worsened within 72 h of admission, patients were divided into a clinical deterioration group and a non-clinical deterioration group. Additionally, all patients were randomly assigned to a training set containing 75% of patients and a validation set containing 25% of patients. In the training set, risk factors for clinical deterioration in patients with non-severe CAP were identified by using LASSO regression analysis and multivariate logistic regression analysis. A nomogram was developed based on identified risk factors. The effectiveness of the nomogram in both the training and validation sets was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Age, body mass index (BMI), body temperature, cardiovascular comorbidity, respiratory rate, LDH level, lymphocyte count and D-dimer level were identified as risk factors associated with the clinical deterioration of non-severe CAP within 72 h of admission. The area under curve (AUC) value of the nomogram was 0.78 (95% CI: 0.74-0.82) in the training set and 0.75 (95% CI: 0.67-0.83) in the validation set. Furthermore, the calibration curves for both the training and validation sets indicated that the predicted probability of clinical deterioration aligned with the actual probability. Additionally, DCA revealed clinical utility for the nomogram at a specific threshold probability. CONCLUSION The study successfully identified the risk factors linked to the clinical deterioration of non-severe CAP and constructed a nomogram for predicting the probability of deterioration. The nomogram demonstrated favorable predictive performance and has the potential to aid in the early identification and management of non-severe CAP patients at elevated risk of deterioration.
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Affiliation(s)
- Cheng-Bin Xu
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Shan-Shan Su
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Jia Yu
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Xiong Lei
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Peng-Cheng Lin
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Qing Wu
- The Center of Laboratory and Diagnosis, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325015, People's Republic of China
| | - Ying Zhou
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China.
| | - Yu-Ping Li
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province, 325015, People's Republic of China.
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Wei C, Wang X, He D, Huang D, Zhao Y, Wang X, Liang Z, Gong L. Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study. BMC Pulm Med 2024; 24:38. [PMID: 38233787 PMCID: PMC10795228 DOI: 10.1186/s12890-024-02852-x] [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: 10/07/2023] [Accepted: 01/07/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients. METHODS The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model. RESULTS Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719-0.768) in the training cohort and 0.731 (95% CI 0.694-0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality. CONCLUSIONS The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.
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Affiliation(s)
- Chang Wei
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Xinyu Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Dingxiu He
- Department of Emergency Medicine, The People's Hospital of Deyang, Deyang, Sichuan, China
| | - Dong Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Yue'an Zhao
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China
| | - Xinyuan Wang
- Department of Orthopaedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zong'an Liang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China.
| | - Linjing Gong
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, 610041, Chengdu, Sichuan, Sichuan, China.
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Gupta K, Sinhal R, Badhiye SS. Remote photoplethysmography-based human vital sign prediction using cyclical algorithm. JOURNAL OF BIOPHOTONICS 2024; 17:e202300286. [PMID: 37614208 DOI: 10.1002/jbio.202300286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 08/03/2023] [Indexed: 08/25/2023]
Abstract
This article aims to predict vital signs like heart rate (HR), respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the cascade residual CNN-FPNR technique for preprocessing and SNR enhancement using energy variance maximization. The image cascade network (ICNet) facilitates segmentation, achieving strong segmentation in low-light ambient videos. Remote photoplethysmography (iPPG) enables noncontact vital sign monitoring, predicting HR and respiratory rate (RR). An innovative noninvasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time-warping-based optimization method is used for precise region selection. The study introduces an intensity variance-based threshold analysis for arterial oxygen saturation level determination. Ultimately, the support vector machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.
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Affiliation(s)
- Kapil Gupta
- Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India
| | - Ruchika Sinhal
- Reporting Engineer, Kagool Data Pvt. Ltd, Hyderabad, India
| | - Sagarkumar S Badhiye
- Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India
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Shang N, Li Q, Liu H, Li J, Guo S. Erector spinae muscle-based nomogram for predicting in-hospital mortality among older patients with severe community-acquired pneumonia. BMC Pulm Med 2023; 23:346. [PMID: 37710218 PMCID: PMC10500910 DOI: 10.1186/s12890-023-02640-z] [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: 04/21/2023] [Accepted: 09/07/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP. METHODS Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. RESULTS A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA). CONCLUSIONS The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes. TRIAL REGISTRATION This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).
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Affiliation(s)
- Na Shang
- Department of Emergency Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
- Department of Emergency Medicine, Capital Medical University School of Rehabilitation Medicine, Beijing Bo'Ai Hospital, China Rehabilitation Research Center, Beijing, 100068, China
| | - Qiujing Li
- Department of Emergency Medicine, Capital Medical University, Beijing Shijitan Hospital, Beijing, 100038, China
| | - Huizhen Liu
- Department of Emergency Medicine, Capital Medical University School of Rehabilitation Medicine, Beijing Bo'Ai Hospital, China Rehabilitation Research Center, Beijing, 100068, China
| | - Junyu Li
- Department of Emergency Medicine, Capital Medical University School of Rehabilitation Medicine, Beijing Bo'Ai Hospital, China Rehabilitation Research Center, Beijing, 100068, China
| | - Shubin Guo
- Department of Emergency Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China.
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Yao K, Wang J, Ma B, He L, Zhao T, Zou X, Weng Z, Yao R. A nomogram for predicting risk of death during hospitalization in elderly patients with Alzheimer's disease at the time of admission. Front Neurol 2023; 14:1093154. [PMID: 36873432 PMCID: PMC9978216 DOI: 10.3389/fneur.2023.1093154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Background and objectives Elderly patients with Alzheimer's disease (AD) often have multiple underlying disorders that lead to frequent hospital admissions and are associated with adverse outcomes such as in-hospital mortality. The aim of our study was to develop a nomogram to be used at hospital admission for predicting the risk of death in patients with AD during hospitalization. Methods We established a prediction model based on a dataset of 328 patients hospitalized with AD -who were admitted and discharged from January 2015 to December 2020. A multivariate logistic regression analysis method combined with a minimum absolute contraction and selection operator regression model was used to establish the prediction model. The identification, calibration, and clinical usefulness of the predictive model were evaluated using the C-index, calibration diagram, and decision curve analysis. Internal validation was evaluated using bootstrapping. Results The independent risk factors included in our nomogram were diabetes, coronary heart disease (CHD), heart failure, hypotension, chronic obstructive pulmonary disease (COPD), cerebral infarction, chronic kidney disease (CKD), anemia, activities of daily living (ADL) and systolic blood pressure (SBP). The C-index and AUC of the model were both 0.954 (95% CI: 0.929-0.978), suggesting that the model had accurate discrimination ability and calibration. Internal validation achieved a good C-index of 0.940. Conclusion The nomogram including the comorbidities (i.e., diabetes, CHD, heart failure, hypotension, COPD, cerebral infarction, anemia and CKD), ADL and SBP can be conveniently used to facilitate individualized identification of risk of death during hospitalization in patients with AD.
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Affiliation(s)
- Kecheng Yao
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Junpeng Wang
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Baohua Ma
- Department of Medical Record, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Ling He
- Department of General Practice, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Tianming Zhao
- Department of Respiratory and Critical Care Medicine, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Xiulan Zou
- Department of Geriatrics, The People's Hospital of China Three Gorges University, Yichang, Hubei, China
| | - Zean Weng
- Department of Neurology, The First College of Clinical Medical Sciences, Three Gorges University, Yichang, Hubei, China
| | - Rucheng Yao
- Department of Hepatopancreatobilary Surgery, The First College of Clinical Medical Sciences, Three Gorges University, Yichang, Hubei, China
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