Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Efficacy of epidural esketamine on postoperative sleep quality after laparoscopic and robotic lower abdominal surgeries: a study protocol for randomised, double-blind, controlled trial

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.

Effects of Propofol on Perioperative Sleep Quality in Patients Undergoing Gastrointestinal Endoscopy: A Prospective Cohort Study

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.

Risk prediction of sleep disturbance in clinical nurses: a nomogram and artificial neural network model

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.

Development and validation of a nomogram for postoperative sleep disturbance in adults: a prospective survey of 640 patients undergoing spinal surgery

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).

Pharmacotherapy for improving postoperative sleep quality: a protocol for a systematic review and network meta-analysis

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.

Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study

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.

Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study

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.

Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

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.

Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance

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.