Worldwide and regional prevalence rates of co-occurrence of insomnia and insomnia symptoms with obstructive sleep apnea: A systematic review and meta-analysis.

Background Obstructive sleep apnea (OSA) is highly prevalent among patients with type 2 diabetes mellitus (T2DM) in China, but few patients with clinical symptoms of OSA are referred for diagnostic polysomnography (PSG). Thus, this study aimed to develop and validate an easy-to-use nomogram that predicts the severity of OSA in patients with T2DM. Methods This retrospective study included consecutive patients with T2DM admitted to the Endocrinology Department, Third Affiliated Hospital of Soochow University between January 1, 2016 and December 31, 2019. OSA was diagnosed with PSG. Participants were randomly assigned to a training cohort (70%) and a validation cohort (30%). Demographic, anthropometric, and biochemical data were collected. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality and identify factors for inclusion in the nomogram (training cohort). Nomogram validation was performed in the validation cohort. Results The study included 280 participants in the training group and 118 participants in the validation group. OSA prevalence was 58.5%. LASSO regression identified waist-to-hip ratio (WHR), smoking status, body mass index (BMI), serum uric acid (UA), the homeostasis model assessment insulin resistance index (HOMA-IR), and history of fatty liver disease as predictive factors for inclusion in the nomogram. Discrimination and calibration in the training group (C-index =0.88) and validation group (C-index =0.881) were good. The nomogram identified patients with T2DM at risk for OSA with an area under the curve of 0.851 [95% confidence interval (CI), 0.788-0.900]. Conclusions Our nomogram could be used to facilitate individualized prediction of OSA risk in patients with T2DM and help prioritize patients for diagnostic PSG.

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Development and validation of a nomogram for predicting the risk of obstructive sleep apnea in patients with type 2 diabetes

Background Obstructive sleep apnea (OSA) is highly prevalent in bariatric surgery patients and can lead to potential perioperative risks, but some screening tools lack adequate performance in this population. Thus, we aimed to develop and validate a clinical nomogram for predicting OSA in bariatric surgery candidates. Methods We retrospectively collected the data of 482 bariatric surgery patients between September 2015 and January 2020. Patients were randomly classified into training cohort (n=338) and validation cohort (n=144). The Lasso regression was used to select potentially relevant features; then, multivariable logistic regression analysis was used to establish the nomogram. Discrimination, calibration and clinical usefulness of the nomogram were assessed using the C-index, calibration curve and decision curve analysis (DCA). Results The overall prevalence of OSA was 71.0% and higher in males (88.2%) compared to females (60.1%). Of these, 26.1% had mild OSA, 14.9% had moderate OSA, and 44.8% had severe OSA. The nomogram consisted of gender, habitual snoring, type 2 diabetes mellitus (T2DM), neck circumference, body mass index (BMI) and age. The nomogram provided favorable discrimination, with a C-indexes for the training and validation cohort of 0.856 (95% CI: 0.816-0.897) and 0.829 (95% CI: 0.76-0.895), respectively, and good calibration. The DCA displayed that the nomogram was clinically useful. Conclusion We established a concise and practical nomogram that could facilitate the preoperative individualized prediction of OSA before bariatric surgery, which may help clinicians select bariatric surgery patients with high-risk OSA for polysomnography (PSG).

/pdf/development-and-validation-of-a-nomogram-for-predicting-4d7shlc6vi.pdf

Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea in Bariatric Surgery Candidates.

To explore the influencing factors of diabetic foot (DF) in diabetic patients and evaluate the influencing factors. All 674 diabetic patients admitted to our hospital from January 2015 to June 2019 who met the research criteria were included as study subjects. Clinical data were collected retrospectively by filling in the case report form for clinical research, including the general demographic characteristics, disease history, laboratory examination results, and incidence of diabetes-related complications. Logistic regression analysis was used to screen the factors influencing DF: the nomogram method was applied to score the factors, and a scoring model was constructed. A total of 122 patients (18.1%) with newly diagnosed DF were identified. Multivariate analysis showed that long duration of diabetes (OR = 2.355), complicated diabetic nephropathy (OR = 3.073), and high HbA1c levels (OR = 1.885) were risk factors for DF development and that foot care education (OR = 0.473) was a protective factor. Cox proportional risk model analysis showed that, compared to patients with better control of HbA1C, the hazard ratio (HR) of DF in the HbA1 group, HbA2 group, and HbA3 group was 1.25, 1.58, and 4.71, respectively. Nomogram model analysis showed that the total score of each factor ranged from 44 to 176 points, and the corresponding risk rate ranged from 0.10 to 0.90 points. The higher the total score, the greater the risk of DF in diabetic patients; the area under the curve in the nomogram model was 0.742; 95% CI: 0.665~0.820 (P < 0.0001). The key to preventing DF is to enhance diabetic patients’ education regarding DF care, improve patients’ self-management abilities, and improve patients’ blood glucose control.

Nomogram analysis of the influencing factors of diabetic foot in patients with diabetes mellitus

Obstructive sleep apnea is the most common type of sleep breathing disorder. Therefore, the purpose of our research is to construct and verify an objective and easy‐to‐use nomogram that can accurately predict a patient’s risk of obstructive sleep apnea. In this study, we retrospectively collected the data of patients undergoing polysomnography at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University. Participants were randomly assigned to a training cohort (50%) and a validation cohort (50%). Logistic regression and Lasso regression models were used to reduce data dimensions, select factors and construct the nomogram. C‐index, calibration curve, decision curve analysis and clinical impact curve analysis were used to evaluate the identification, calibration and clinical effectiveness of the nomogram. Nomograph validation was performed in the validation cohort. The study included 1035 people in the training cohort and 1078 people in the validation cohort. Logistic and Lasso regression analysis identified age, gender, diastolic blood pressure, body mass index, neck circumference and Epworth Sleepiness Scale as the predictive factors included in the nomogram. The training cohort (C‐index = 0.741) and validation cohort (C‐index = 0.745) had better identification and calibration effects. The areas under the curve of the nomogram and STOP‐Bang were 0.741 (0.713–0.767) and 0.728 (0.700–0.755), respectively. Decision curve analysis and clinical impact curve analysis showed that the nomogram is clinically useful. We have established a concise and practical nomogram that will help doctors better determine the priority of patients referred to the sleep centre.

Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea

Background: The currently available polysomnography (PSG) equipments and operating personnel are facing increasing pressure, such situation may result in the problem that a large number of obstructive sleep apnea (OSA) patients cannot receive timely diagnosis and treatment, we sought to develop a nomogram quantifying the risk of OSA for a better decision of using PSG, based on the clinical syndromes and the demographic and anthropometric characteristics. Methods: The nomogram was constructed through an ordinal logistic regression procedure. Predictive accuracy and performance characteristics were assessed with the area under the curve (AUC) of the receiver operating characteristics and calibration plots, respectively. Decision curve analyses were applied to assess the net benefit of the nomogram. Results: Among the 401 patients, 73 (18.2%) were diagnosed and grouped as the none OSA (apnea-hypopnea index [AHI] Conclusions: The established clinical nomogram provides high accuracy in predicting the individual risk of OSA. This tool may help physicians better make decisions on PSG arrangement for the patients referred to sleep centers.

A Nomogram for Predicting the Likelihood of Obstructive Sleep Apnea to Reduce the Unnecessary Polysomnography Examinations.

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A Nomogram for Predicting the Likelihood of Obstructive Sleep Apnea to Reduce the Unnecessary Polysomnography Examinations.