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Maimaitituerxun R, Chen W, Xiang J, Xie Y, Kaminga AC, Wu XY, Chen L, Yang J, Liu A, Dai W. The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus. J Diabetes 2023; 15:448-458. [PMID: 37057310 PMCID: PMC10172024 DOI: 10.1111/1753-0407.13384] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/09/2023] [Accepted: 03/21/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment (MCI) in T2DM patients. METHODS Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM-related information, and history of depression and anxiety. Cognitive function was assessed using the Mini-Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. RESULTS A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%-38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815-0.883), and the threshold probability ranged from 35.0% to 85.0%. CONCLUSIONS Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.
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Affiliation(s)
- Rehanguli Maimaitituerxun
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Wenhang Chen
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China
| | - Jingsha Xiang
- Human Resources Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yu Xie
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Atipatsa C Kaminga
- Department of Mathematics and Statistics, Mzuzu University, Mzuzu, Malawi
| | - Xin Yin Wu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Letao Chen
- Infection Control Center, Xiangya Hospital, Central South University, Changsha, China
| | - Jianzhou Yang
- Department of Preventive Medicine, Changzhi Medical College, Changzhi, China
| | - Aizhong Liu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Wenjie Dai
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
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Xu R, Miao L, Ni J, Ding Y, Song Y, Yang C, Zhu B, Jiang R. Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study. Front Neurol 2022; 13:955352. [PMID: 35959399 PMCID: PMC9360761 DOI: 10.3389/fneur.2022.955352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives This study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients. Methods In this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure. Results The prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1*age (OR = 1.476, 95% CI: 1.103–1.975, P = 0.009), lower albumin (OR = 0.863, 95% CI: 0.771–0.965, P = 0.010), and lower 10*calcium levels (OR = 0.747, 95% CI: 0.615–0.907, P = 0.003). In addition, higher 0.1*age, lower albumin levels, and anxiety were independently associated with severe sleep disturbance in MHD patients. A risk prediction model of sleep disturbance in MHD patients showed that the concordance index after calibration is 0.736, and the calibration curve is approximately distributed along the reference line. Conclusions Older age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration.
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Affiliation(s)
- Rongpeng Xu
- Department of Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
- Department of Critical Care Medicine, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Liying Miao
- Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jiayuan Ni
- Department of Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yuan Ding
- Department of Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yuwei Song
- Department of Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Chun Yang
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bin Zhu
- Department of Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China
- *Correspondence: Bin Zhu
| | - Riyue Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Riyue Jiang
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Li Y, Zhao L, Wang Y, Zhang X, Song J, Zhou Q, Sun Y, Yang C, Wang H. Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance. CNS Neurosci Ther 2021; 28:554-565. [PMID: 34951135 PMCID: PMC8928914 DOI: 10.1111/cns.13772] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Neurocognitive disorders (NCDs) and sleep disturbance are highly prevalent in the perioperative period and intensive care unit (ICU). There has been a lack of individualized evaluation tools designed for the high-risk NCDs in critically ill patients with sleep disturbance. OBJECTIVES The aim of this study was to develop and validate prediction models for NCDs among adult patients with sleep disturbance. METHODS The R software was used to analyze the dataset of adult patients admitted to the ICU with sleep disturbance, who were diagnosed following the codes of the International Classification of Diseases, 9th Revision (ICD-9) and 10th Revision (ICD-10) using the MIMIC-IV database. We used logistic regression and LASSO analyses to identify important risk factors associated with NCDs and develop nomograms for NCDs predictions. We measured the performances of the nomograms using the bootstrap resampling procedure, sensitivity, specificity of the receiver operating characteristic (ROC), area under the ROC curves (AUC), and decision curve analysis (DCA). RESULTS The prediction models shared the 10 risk factors (age, gender, midazolam, morphine, glucose, diabetes diseases, potassium, international normalized ratio, partial thromboplastin time, and respiratory rate). Cardiovascular diseases were included in the logistic regression, the sensitivity was 74.1%, and specificity was 64.6%. When platelet and Glasgow Coma Score (GCS) were included and cardiovascular diseases were removed in the LASSO prediction model, the sensitivity was 86.1% and specificity was 82.8%. Discriminative abilities of the logistic prediction and LASSO prediction models for NCDs in the validation set were evaluated as the AUC scores, which were 0.730 (95% CI 0.716-0.743) and 0.920 (95% CI 0.912-0.927). Net benefits of the prediction models were observed at threshold probabilities of 0.567 and 0.914. CONCLUSIONS The LASSO prediction model showed better performance than the logistic prediction model and should be preferred for nomogram-assisted decisions on clinical risk management of NCDs among adult patients with sleep disturbance in the ICU.
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Affiliation(s)
- Yun Li
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China.,Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Lina Zhao
- Emergency Department, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Ye Wang
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China.,Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Xizhe Zhang
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Jiannan Song
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Qi Zhou
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Yi Sun
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Chenyi Yang
- Department of Anesthesiology, The Third Central Hospital of Tianjin, The Third Central Clinical College of Tianjin Medical University, Nankai University Affinity The Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Haiyun Wang
- The Third Central Clinical College of Tianjin Medical University, Tianjin, China.,Department of Anesthesiology, The Third Central Hospital of Tianjin, The Third Central Clinical College of Tianjin Medical University, Nankai University Affinity The Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China
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Yang S, Zhang Q, Xu Y, Chen F, Shen F, Zhang Q, Liu H, Zhang Y. Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study. Nat Sci Sleep 2021; 13:1473-1483. [PMID: 34466046 PMCID: PMC8403031 DOI: 10.2147/nss.s319339] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/28/2021] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To develop a risk prediction nomogram of postoperative sleep disturbance (PSD) in patients undergoing non-cardiac surgery. PATIENTS AND METHODS Data on 881 consecutive patients who underwent non-cardiac surgery at the Affiliated Hospital of Xuzhou Medical University between June 2020 and April 2021 were prospectively collected. Of these, we randomly divided 881 non-cardiac patients into two groups, training cohort (n = 617) and validation cohort (n = 264) at the ratio of 7:3. Characteristic variables were selected based on the data of training cohort through least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to identify the independent risk factors associated with PSD that then were incorporated into the nomogram. The predictive performance of the nomogram was measured by concordance index (C index), receiver operating characteristic (ROC) curve, and calibration with 1000 bootstrap samples to decrease the over-fit bias. RESULTS PSD was found in 443 of 617 patients (71.8%) and 190 of 264 patients (72.0%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PSD were female sex, anxiety, dissatisfaction of ward environment, absence of combined regional nerve block, postoperative nausea and vomiting (PONV), the longer duration stayed in post anesthesia care unit (PACU), the higher dose of midazolam and sufentanil, the higher postoperative numeric rating score for pain (NRS) score. Incorporating these 9 factors, the nomogram achieved good concordance indexes of 0.82 (95% confidence interval [CI], 0.78-0.85) and 0.80 (95% CI, 0.74-0.85) in predicting PSD in the training and validation cohorts, respectively, and obtained well-fitted calibration curves. The sensitivity and specificity (95% CIs) of the nomogram were calculated, resulting in sensitivity of 74.0% (70.0-78.2%) and 75.3% (68.4-81.7%) and specificity of 79.3% (72.5-85.2%) and 70.3% (58.4-80.7%) for the training and validation cohorts, respectively. Patients who had a nomogram score of less than 262 or 262 or greater were considered to have low or high risks of PSD presence, respectively. CONCLUSION The proposed nomogram achieved an optimal prediction of PSD in patients undergoing non-cardiac surgery. The risks for an individual patient to harbor PSD can be determined by this model, which can lead to a reasonable preventive and treatment measures.
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Affiliation(s)
- Shuting Yang
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Qian Zhang
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Yifan Xu
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Futeng Chen
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Fangming Shen
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Qin Zhang
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - He Liu
- Department of Anesthesiology, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine; Huzhou Central Hospital, Huzhou City, Zhejiang Province, People's Republic of China
| | - Yueying Zhang
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
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