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Wu S, Wang S, Ding Y, Zhang Z. Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection: A Retrospective Clinical Study. J Multidiscip Healthc 2024; 17:3889-3905. [PMID: 39155978 PMCID: PMC11328859 DOI: 10.2147/jmdh.s470204] [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: 03/22/2024] [Accepted: 08/05/2024] [Indexed: 08/20/2024] Open
Abstract
Objective Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. Methods We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. Results The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768-0.820), 0.823 (95% CI 0.799-0.847) and 0.817 (95% CI 0.792-0.842) in the training cohort and 0.702 (95% CI 0.655-0.748), 0.705 (95% CI 0.659-0.752) and 0.747 (95% CI 0.703-0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. Conclusion We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications.
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Affiliation(s)
- Shanshan Wu
- Department of Anesthesiology, Liaocheng People’s Hospital, Shandong University, Liaocheng, People’s Republic of China
- Department of Anesthesiology, Liaocheng People’s Hospital, Liaocheng, People’s Republic of China
| | - Shuren Wang
- Department of Anesthesiology, Dongchangfu District Maternal and Child Health Hospital, Liaocheng, People’s Republic of China
| | - Yonghong Ding
- Department of Anesthesiology, Liaocheng People’s Hospital, Liaocheng, People’s Republic of China
| | - Zongwang Zhang
- Department of Anesthesiology, Liaocheng People’s Hospital, Shandong University, Liaocheng, People’s Republic of China
- Department of Anesthesiology, Liaocheng People’s Hospital, Liaocheng, People’s Republic of China
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Yang Y, Zhang Y, Zhou G, Yang Z, Yan H, Zhang J. Efficacy of epidural esketamine on postoperative sleep quality after laparoscopic and robotic lower abdominal surgeries: a study protocol for randomised, double-blind, controlled trial. BMJ Open 2024; 14:e081589. [PMID: 38417951 PMCID: PMC10900385 DOI: 10.1136/bmjopen-2023-081589] [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] [Indexed: 03/01/2024] Open
Abstract
INTRODUCTION Postoperative sleep disturbances significantly impair postoperative recovery. The administration of intravenous esketamine has been shown to potentially improve postoperative sleep quality. However, the effectiveness of epidural esketamine in improving postoperative sleep quality remains to be elucidated. This study aims to explore the impact of both intraoperative and postoperative use of epidural esketamine on the postoperative sleep quality of patients undergoing minimally invasive lower abdominal surgeries. METHODS AND ANALYSIS This randomised, double-blind, parallel-group, placebo-controlled trial will be conducted at the Fudan University Shanghai Cancer Centre. A total of 128 adults undergoing minimally invasive lower abdominal surgeries will be randomly allocated in a 1:1 ratio to either the esketamine group or the placebo group. In the esketamine group, epidural esketamine will be administered intraoperatively (0.2 mg/kg) and postoperatively (25 mg). Postoperatively, all patients will receive epidural analgesia. The primary outcome of the study is the incidence of poor sleep quality on the third day after surgery. The sleep quality assessment will be conducted using the Pittsburgh Sleep Quality Index and a Numeric Rating Scale of sleep. The main secondary outcomes include postoperative pain and anxiety and depression scores. The postoperative pain, both rest pain and movement pain, will be assessed using a Numerical Rating Scale within 5 days after surgery. Anxiety and depression scores will be evaluated using the Hospital Anxiety and Depression Scale both before and after the surgery. Safety outcomes will include delirium, fidgeting, hallucinations, dizziness and nightmares. The analyses will be performed in accordance with intention-to-treat principle ETHICS AND DISSEMINATION: Ethics approval has been obtained from the Ethics Committee of the Shanghai Cancer Centre (2309281-9). Prior to participation, all patients will provide written informed consent. The results of the trial are intended to be published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER ChiCTR2300076862.
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Affiliation(s)
- Yuecheng Yang
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Yunkui Zhang
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Guoxia Zhou
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Zaixian Yang
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Han Yan
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Jun Zhang
- Department of Anaesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
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Wu X, Deng J, Li X, Yang L, Zhao G, Yin Q, Shi Y, Tong Z. Effects of Propofol on Perioperative Sleep Quality in Patients Undergoing Gastrointestinal Endoscopy: A Prospective Cohort Study. J Perianesth Nurs 2023; 38:787-791. [PMID: 37269278 DOI: 10.1016/j.jopan.2023.02.001] [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: 07/14/2022] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE Some patients experience sleep disturbances after endoscopy performed under sedation. This study aimed to evaluate the effects of propofol on sleep quality after gastrointestinal endoscopy (GE). DESIGN This study was a prospective cohort study. METHODS This study enrolled 880 patients who underwent GE. Patients who chose to undergo GE under sedation received intravenous propofol, whereas the control group did not. The Pittsburgh Sleep Quality Index (PSQI) was measured before GE (PSQI-1) and 3 weeks (PSQI-2) after GE. The Groningen Sleep Score Scale (GSQS) was used before GE (GSQS-1) and 1 (GSQS-2) and 7 days (GSQS-3) after GE. FINDINGS There was a significant increase in GSQS scores from baseline to days 1 and 7 after GE (GSQS-2 vs GSQS-1, P < .001, GSQS-3 vs GSQS-1, P = .008). However, no significant changes were observed in the control group (GSQS-2 vs GSQS-1, P = .38, GSQS-3 vs GSQS-1, P = .66). On day 21, there were no significant changes in the baseline PSQI scores over time in either group (sedation group, P = .96; control group, P = .95). CONCLUSIONS GE with propofol sedation negatively affected sleep quality for 7 days after GE but not 3 weeks after GE.
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Affiliation(s)
- Xiaofei Wu
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Jinhe Deng
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Xiaona Li
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Li Yang
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Gaofeng Zhao
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Qing Yin
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Yongyong Shi
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Zhilan Tong
- Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
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Zhang X, Zhang L. Risk prediction of sleep disturbance in clinical nurses: a nomogram and artificial neural network model. BMC Nurs 2023; 22:289. [PMID: 37641040 PMCID: PMC10463587 DOI: 10.1186/s12912-023-01462-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/23/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Sleep disturbance occur among nurses at a high incidence. AIM To develop a Nomogram and a Artificial Neural Network (ANN) model to predict sleep disturbance in clinical nurses. METHODS A total of 434 clinical nurses participated in the questionnaire, a cross-sectional study conducted from August 2021 to June 2022.They were randomly distributed in a 7:3 ratio between training and validation cohorts.Nomogram and ANN model were developed using predictors of sleep disturbance identified by univariate and multivariate analyses in the training cohort; The 1000 bootstrap resampling and receiver operating characteristic curve (ROC) were used to evaluate the predictive accuracy in the training and validation cohorts. RESULTS Sleep disturbance was found in 180 of 304 nurses(59.2%) in the training cohort and 80 of 130 nurses (61.5%) in the validation cohort.Age, chronic diseases, anxiety, depression, burnout, and fatigue were identified as risk factors for sleep disturbance. The calibration curves of the two models are well-fitted. The sensitivity and specificity (95% CI) of the models were calculated, resulting in sensitivity of 83.9%(77.5-88.8%)and 88.8% (79.2-94.4%) and specificity of83.1% (75.0-89.0%) and 74.0% (59.4-84.9%) for the training and validation cohorts, respectively. CONCLUSIONS The sleep disturbance risk prediction models constructed in this study have good consistency and prediction efficiency, and can effectively predict the occurrence of sleep disturbance in clinical nurses.
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Affiliation(s)
- Xinyu Zhang
- The First Affiliated Hospital of Jinzhou Medical University, 121001, Jinzhou, People's Republic of China
| | - Lei Zhang
- Department of Nursing, Jinzhou Medical University, No.40, Section 3, Songpo Road, Linghe District, 121001, Jinzhou, People's Republic of China.
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Du J, Zhang H, Ding Z, Wu X, Chen H, Ma W, Qiu C, Zhu S, Kang X. Development and validation of a nomogram for postoperative sleep disturbance in adults: a prospective survey of 640 patients undergoing spinal surgery. BMC Anesthesiol 2023; 23:154. [PMID: 37142982 PMCID: PMC10157914 DOI: 10.1186/s12871-023-02097-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/19/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Postoperative sleep disturbance (PSD) is a prevalent clinical complication that may arise due to various factors. The purpose of this investigation is to identify the risk factors for PSD in spinal surgery and establish a risk prediction nomogram. METHODS The clinical records of individuals who underwent spinal surgery from January 2020 to January 2021 were gathered prospectively. The least absolute shrinkage and selection operator (LASSO) regression, along with multivariate logistic regression analysis, was employed to establish independent risk factors. A nomogram prediction model was devised based on these factors. The nomogram's effectiveness was evaluated and verified via the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). RESULTS A total of 640 patients who underwent spinal surgery were analyzed in this investigation, among which 393 patients experienced PSD with an incidence rate of 61.4%. After conducting LASSO regression and logistic regression analyses using R software on the variables in training set, 8 independent risk factors associated to PSD were identified, including female, preoperative sleep disorder, high preoperative anxiety score, high intraoperative bleeding volume, high postoperative pain score, dissatisfaction with ward sleep environment, non-use of dexmedetomidine and non-use of erector spinae plane block (ESPB). The nomogram and online dynamic nomogram were constructed after incorporating these variables. In the training and validation sets, the area under the curve (AUC) in the receiver operating characteristic (ROC) curves were 0.806 (0.768-0.844) and 0.755 (0.667-0.844), respectively. The calibration plots indicated that the mean absolute error (MAE) values in both sets were respectively 1.2% and 1.7%. The decision curve analysis demonstrated the model had a substantial net benefit within the range of threshold probabilities between 20% and 90%. CONCLUSIONS The nomogram model proposed in this study included eight frequently observed clinical factors and exhibited favorable accuracy and calibration. TRIAL REGISTRATION The study was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2200061257, 18/06/2022).
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Affiliation(s)
- Jin Du
- Department of Anesthesiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Honggang Zhang
- Department of Anesthesiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhe Ding
- Department of Anesthesiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaobin Wu
- Department of Anesthesiology, China Coast Guard Hospital of the People ' s Armed Police Force, Jiaxing, China
| | - Hua Chen
- Department of Anesthesiology, China Coast Guard Hospital of the People ' s Armed Police Force, Jiaxing, China
| | - Weibin Ma
- Department of Anesthesiology, China Coast Guard Hospital of the People ' s Armed Police Force, Jiaxing, China
| | - Canjin Qiu
- Department of Anesthesiology, China Coast Guard Hospital of the People ' s Armed Police Force, Jiaxing, China
| | - Shengmei Zhu
- Department of Anesthesiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xianhui Kang
- Department of Anesthesiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Yang D, Yang L, Li Q, Zuo Y. Pharmacotherapy for improving postoperative sleep quality: a protocol for a systematic review and network meta-analysis. BMJ Open 2023; 13:e069724. [PMID: 36822805 PMCID: PMC9950894 DOI: 10.1136/bmjopen-2022-069724] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
INTRODUCTION Improving the quality of sleep may promote enhanced recovery in surgical patients. In addition to controversial or conflicting study conclusions, the current clinical studies on pharmacotherapy for improving postoperative sleep quality are mostly limited to evaluating the effect of a specific drug or supplement compared with placebo, and they lack comparisons between drugs or supplements. Therefore, we plan to conduct a systematic review and network meta-analysis to compare the efficacy of different drugs or supplements for improving postoperative sleep quality. METHODS AND ANALYSIS We will search the MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CNKI and Wanfang databases from the dates of their inception to December 2022. We will only include randomised controlled trials, irrespective of language and publication status. The primary outcome is postoperative sleep quality assessed by any validated tools or polysomnography. We will assess the quality of all included trials according to version 2 of the Cochrane risk-of-bias tool for randomised trials. We will use the GeMTC package of R software to perform direct and indirect comparisons via a Bayesian framework using a random-effects model. We will use the Confidence in Network Meta-Analysis approach to evaluate the quality of evidence. ETHICS AND DISSEMINATION Ethical approval is not required for this protocol because we will only be pooling published data. We plan to submit our review to academic conferences and peer-reviewed academic journals. PROSPERO REGISTRATION NUMBER CRD42022356508.
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Affiliation(s)
- Di Yang
- Department of Anaesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Anaesthesiology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Lei Yang
- Department of Anaesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qian Li
- Department of Anaesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yunxia Zuo
- Department of Anaesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Jin F, Liu W, Qiao X, Shi J, Xin R, Jia HQ. Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study. Front Oncol 2023; 13:1114302. [PMID: 36910602 PMCID: PMC9996165 DOI: 10.3389/fonc.2023.1114302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Background The prediction model of postoperative pneumonia (POP) after lung cancer surgery is still scarce. Methods Retrospective analysis of patients with lung cancer who underwent surgery at The Fourth Hospital of Hebei Medical University from September 2019 to March 2020 was performed. All patients were randomly divided into two groups, training cohort and validation cohort at the ratio of 7:3. The nomogram was formulated based on the results of multivariable logistic regression analysis and clinically important factors associated with POP. Concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram. Results A total of 1252 patients with lung cancer was enrolled, including 877 cases in the training cohort and 375 cases in the validation cohort. POP was found in 201 of 877 patients (22.9%) and 89 of 375 patients (23.7%) in the training and validation cohorts, respectively. The model consisted of six variables, including smoking, diabetes mellitus, history of preoperative chemotherapy, thoracotomy, ASA grade and surgery time. The C-index from AUC was 0.717 (95%CI:0.677-0.758) in the training cohort and 0.726 (95%CI:0.661-0.790) in the validation cohort. The calibration curves showed the model had good agreement. The result of DCA showed that the model had good clinical benefits. Conclusion This proposed nomogram could predict the risk of POP in patients with lung cancer surgery in advance, which can help clinician make reasonable preventive and treatment measures.
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Affiliation(s)
- Fan Jin
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, China.,Department of Anesthesiology, Zhuji People's Hospital, Shaoxing, Zhejiang, China
| | - Wei Liu
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xi Qiao
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jingpu Shi
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Rui Xin
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hui-Qun Jia
- Department of Anesthesiology, The Fourth hospital of Hebei Medical University, Shijiazhuang, Hebei, 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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Zhang Q, Shen F, Wei Q, Liu H, Li B, Zhang Q, Zhang Y. Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study. Risk Manag Healthc Policy 2022; 15:1-12. [PMID: 35023976 PMCID: PMC8747787 DOI: 10.2147/rmhp.s347401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/23/2021] [Indexed: 12/17/2022] Open
Abstract
Purpose The aim of this study was to explore the associated risk factors of perioperative respiratory adverse events (PRAEs) in children undergoing airway surgery and establish and validate a nomogram prediction model for PRAEs. Patients and Methods This study involved 709 children undergoing airway surgery between November 2020 and July 2021, aged ≤18 years in the affiliated hospital of Xuzhou Medical University. They were divided into training (70%; n = 496) and validation (30%; n = 213) cohorts. The least absolute shrinkage and selection operator (LASSO) was used to develop a risk nomogram model. Concordance index values, calibration plot, decision curve analysis, and the area under the curve (AUC) were examined. Results PRAEs were found in 226 of 496 patients (45.6%) and 88 of 213 patients (41.3%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PRAEs were age, obesity, degree of upper respiratory tract infection, premedication, and passive smoking. The risk nomogram model showed good discrimination power, and the AUC generated to predict survival in the training cohort was 0.760 (95% confidence interval, 0.695–0.875). In the validation cohort, the AUC of survival predictions was 0.802 (95% confidence interval, 0.797–0.895). Calibration plots and decision curve analysis showed good model performance in both datasets. The sensitivity and specificity of the risk nomogram model were calculated, and the result showed the sensitivity of 69.5% and 64.8% and specificity of 73.3% and 81.6% for the training and validation cohorts, respectively. Conclusion The present study showed the proposed nomogram achieved an optimal prediction of PRAEs in patients undergoing airway surgery, which can provide a certain reference value for predicting the high-risk population of perioperative respiratory adverse events and can lead to reasonable preventive and treatment measures.
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Affiliation(s)
- Qin Zhang
- 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
| | - Qingfeng Wei
- 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
| | - Bo Li
- Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China
| | - Qian Zhang
- Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu 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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Yang J, Miao H, Qiao H, Li T. Commentary on the Paper by Yang et al: Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study [Letter]. Nat Sci Sleep 2022; 14:179-180. [PMID: 35173499 PMCID: PMC8840835 DOI: 10.2147/nss.s357538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/02/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Jiaojiao Yang
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of China
| | - Huihui Miao
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of China
| | - Hui Qiao
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of China
| | - Tianzuo Li
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of China
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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: 5] [Impact Index Per Article: 1.7] [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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