Abstract
Stroke is a severe neurological disorder that significantly impacts patients’ recovery and quality of life. Stroke patients frequently experience sleep disorders, including difficulty initiating sleep, insomnia, vivid dreams, and sleep apnea. These disorders not only disrupt nighttime rest but also significantly affect stroke recovery and prognosis, increasing the risks of recurrence and mortality. Currently, there are few studies on this topic, and most rely on Logistic regression models, which can identify risk factors but cannot quantify risks. Therefore, it is essential to develop a tool that can comprehensively assess multiple risk factors and provide individualized predictions. Nomogram models can quantify risk factors and intuitively present them, thereby providing clinicians with comprehensive assessments. This study aims to develop and validate a new nomogram model to predict the risk of sleep disorders in stroke patients, enabling early identification and personalized interventions to support patient recovery and improve quality of life. A cohort of 156 stroke patients (January–August 2023) was utilized for model development, comprising 70 with sleep disorders and 86 without. An external validation set included 72 patients (September–December), with 34 experiencing sleep disorders. Patient data was analyzed using Lasso regression; the “rms” package in R facilitated model construction. Model performance was assessed through Hosmer–Lemeshow goodness-of-fit tests, calibration curves, and receiver operating characteristic analyses. Gender bias, co-morbidities (hypertension and coronary heart disease), depression, and anxiety scales differentiated the groups significantly. Key predictors included female gender, hypertension, coronary heart disease, and psychological distress. The model yielded impressive predictive capabilities, with area under the curves of 0.950 (modeling group) and 0.966 (validation group). Calibration curves matched closely with ideals, confirming robustness across both sets. Net benefit rates indicated strong utility over a wide probability spectrum. Female gender, specific co-morbidities, heightened depressive and anxiety states signify elevated sleep disorder risks poststroke. Our nomogram effectively predicts these conditions, offering valuable insights for timely detection and intervention in susceptible stroke survivors.
Keywords: forecast, nomogram, sleep disorders, stroke
1. Introduction
Stroke is a significant neurological disorder that poses a severe threat to human health.[1] It mainly results from insufficient blood supply to the brain or bleeding caused by ruptured blood vessels, which in turn leads to various neurological impairments, including limb paralysis, aphasia, and cognitive deficits.[2,3] Stroke has a high incidence rate and is one of the leading causes of long-term disability and mortality globally.[4] One of the critical factors that can hinder the recovery and rehabilitation of stroke patients is the presence of sleep disorders.[5] Sleep disorders are highly prevalent among stroke patients, affecting approximately 30% to 70% of individuals, as reported in early studies.[6] These disorders include difficulty falling asleep, frequent awakenings, insomnia, and sleep apnea.[6] The impact of sleep disorders on stroke patients is multifaceted. Poor sleep quality can exacerbate neurological deficits, prolong hospital stays, and increase the risk of stroke recurrence and mortality.[7] Moreover, sleep disorders can negatively influence the overall quality of life and mental health of stroke patients, making it essential to identify and manage these conditions promptly.[8]
The pathophysiology of sleep disorders in stroke patients is complex and multifactorial. Neuronal damage and inflammatory responses triggered by stroke can disrupt normal sleep patterns.[9] Additionally, poststroke complications such as pain, disability, and psychological distress (including depression and anxiety) can further contribute to the development of sleep disorders.[10] The disruption of neuroendocrine and autonomic functions following a stroke can also impact sleep regulation.[11]
Despite the significant impact of sleep disorders on stroke recovery, there is a scarcity of effective predictive tools to identify patients at high risk of developing these conditions. Early identification of sleep disorders in stroke patients is crucial for timely intervention and improved outcomes. However, current methods for assessing the risk of sleep disorders in stroke patients are limited, often relying on subjective assessments or basic clinical evaluations. Logistic regression models, which are commonly used, cannot quantify the risk of sleep disorders effectively.[12] In this context, the development of a nomogram model offers a promising solution. Nomograms are statistical tools that can quantify multiple risk factors and present them in a visual format, allowing for a comprehensive assessment of disease risk for individual patients.[13] By incorporating various clinical and demographic factors, a nomogram can provide a more accurate and personalized prediction of sleep disorders in stroke patients. This study aims to fill the gap in the literature by developing and validating a nomogram model to predict the risk of sleep disorders in stroke patients, thereby facilitating early identification and personalized interventions for those at risk.
2. Materials and methods
2.1. General information
This study included 156 stroke patients admitted to our hospital from January to August 2023 for model development, comprising 70 patients with sleep disorders and 86 without sleep disorders. External validation was performed on 72 patients admitted from September to December, of whom 34 had sleep disorders and 38 had no sleep disorders (Fig. 1).
Figure 1.
Study flow diagram. ROC = receiver operating characteristic.
2.2. Inclusion and exclusion criteria
Inclusion criteria[14]: meeting the diagnostic criteria for stroke; being diagnosed with ischemic stroke via head magnetic resonance imaging or CT; having stable vital signs for at least 24 consecutive hours; having no sleep disorder prior to stroke onset; and the patient and their family being informed of the study-related information and providing informed consent.
Exclusion criteria: combined with serious heart, lung, liver, or kidney dysfunction; combined with multiple sclerosis, infection, poisoning, and other causes of white matter disease; a history of depression, anxiety, and other mental disorders; and patients with visual or hearing impairment who are unable to complete the cognitive function assessment. This study has been approved by the Ethics Committee of Shanghai Public Health Clinical Center (Public Health Review 2023-S052-01).
2.3. Research method
The following data will be collected in this study: basic information of the subjects (gender, age, body mass index [BMI], and marital status) and related information of co-existing diseases (hypertension, diabetes, coronary heart disease, hyperlipidemia, atrial fibrillation, smoking history, drinking history, stroke history, etc). The risk factors of sleep disorders in stroke patients were further analyzed, and 14 possible risk factors in this group were included in Lasso regression model as independent variables for screening.
2.4. Observation indicators and evaluation criteria
Sleep quality evaluation: the evaluation was conducted by the Pittsburgh Sleep Quality Index,[15] which consists of 9 self-rated and 5 other-rated items, covering 7 factors of 18 items through self-rated and other-rated sleep quality of individuals in the past month. The total score is between 0 and 21, with higher scores indicating poorer sleep quality.
Depression assessment: Self-Rating Depression Scale[16] (SDS) is used for assessment, which contains 20 items in total. Subjects need to choose the answer options related to each question according to their own situation. Each response option has a specific score value, and the corresponding score values are added up according to the answer selected by the subject to obtain the final SDS total. The total SDS score ranges from 20 to 80, with higher scores indicating more severe depressive symptoms.
Anxiety assessment: Self-Rating Anxiety Scale[16] (SAS) is used for assessment. SAS contains 20 items in total, and subjects need to choose the answer options related to each question according to their own situation. Each response option has a specific point value, and the corresponding points are added up to give the final SAS total score based on the answer selected by the subject. The total SAS score ranges from 20 to 80, with higher scores indicating more severe anxiety symptoms.
2.5. Statistical method
SPSS 23.0 and R4.3.1 software were used for data analysis in this study. For measurement data conforming to normal distribution and homogeneity of variance, the mean ± standard deviation was used for comparison between groups. The count data were expressed as a percentage and compared using the χ² test. Lasso regression was used to analyze the influencing factors of sleep disorders in stroke patients, and the model was established by R4.3.1 software and rms package. The model was verified by repeated sampling 1000 times using Bootstrap method. The identification of the model was calculated by C-index (CI), and the performance of the model was evaluated by receiver operating characteristic curve. Calibration curves and Hosmer–Lemeshow goodness of fit tests were used to assess the accuracy of the model. The significance level was set at P < .05 to test the statistical significance of the differences.
3. Results
3.1. Baseline clinical characteristics
In both the modeling and verification groups, the sleep disorder group had a higher proportion of women, a higher prevalence of smoking, drinking, and co-existing diseases (hypertension, diabetes, coronary heart disease, hyperlipidemia, and atrial fibrillation), as well as higher self-rating scale scores for depression and anxiety than the non-sleep disorder group, with statistically significant differences (P < .05). There were no significant differences in age, BMI, marital status, or stroke history between the 2 groups (P > .05; Tables 1 and 2).
Table 1.
Comparison of basic clinical data in modeling group.
| Variables | Sleep disorder group (n = 70) | Non-sleep disorder group (n = 86) | P value |
|---|---|---|---|
| Age (years old) | 67.40 ± 8.45 | 65.49 ± 8.38 | .160 |
| BMI (kg/m2) | 24.10 ± 2.83 | 24.47 ± 3.18 | .437 |
| Gender (case [%]) | <.001 | ||
| Female | 55 (78.57) | 36 (41.86) | |
| Male | 15 (21.43) | 50 (58.14) | |
| Marital status (case [%]) | .587 | ||
| Be married | 63 (90.00) | 75 (87.21) | |
| Unmarried/widowed/other | 7 (10.00) | 11 (12.79) | |
| Hypertension (case [%]) | .001 | ||
| Yes | 54 (77.14) | 43 (50.00) | |
| No | 16 (22.86) | 43 (50.00) | |
| Diabetes (case [%]) | <.001 | ||
| Yes | 30 (42.86) | 12 (13.95) | |
| No | 40 (57.14) | 74 (86.05) | |
| Coronary heart disease (case [%]) | <.001 | ||
| Yes | 31 (44.29) | 9 (10.47) | |
| No | 39 (55.71) | 77 (89.53) | |
| Hyperlipidemia (case [%]) | .001 | ||
| Yes | 39 (55.71) | 25 (29.07) | |
| No | 31 (44.29) | 61 (70.93) | |
| Atrial fibrillation (case [%]) | .007 | ||
| Yes | 14 (20.00) | 5 (5.81) | |
| No | 56 (80.00) | 81 (94.19) | |
| History of stroke (case [%]) | .176 | ||
| Yes | 6 (8.57) | 3 (3.49) | |
| No | 64 (91.43) | 83 (96.51) | |
| Smoking (case [%]) | <.001 | ||
| Yes | 34 (48.57) | 17 (19.77) | |
| No | 36 (51.43) | 69 (80.23) | |
| Drinking (case [%]) | .149 | ||
| Yes | 14 (20.00) | 10 (11.63) | |
| No | 56 (80.00) | 76 (88.37) | |
| Self-Rating Depression Scale score (score) | 49.06 ± 4.44 | 39.11 ± 6.28 | <.001 |
| Self-Rating Anxiety Scale score (score) | 46.16 ± 4.71 | 37.78 ± 5.31 | <.001 |
BMI = body mass index.
Table 2.
Comparison of basic clinical data in the verification group.
| Variables | Sleep disorder group (n = 70) | Non-sleep disorder group (n = 86) | P value |
|---|---|---|---|
| Age (years old) | 67.79 ± 8.83 | 63.92 ± 7.75 | .051 |
| BMI (kg/m2) | 23.93 ± 2.79 | 25.14 ± 3.71 | .126 |
| Gender (case [%]) | .001 | ||
| Female | 27 (79.41) | 16 (42.11) | |
| Male | 7 (20.59) | 22 (57.89) | |
| Marital status (case [%]) | .559 | ||
| Be married | 31 (91.18) | 33 (86.84) | |
| Unmarried/widowed/other | 3 (8.82) | 5 (13.16) | |
| Hypertension (case [%]) | .041 | ||
| Yes | 25 (73.53) | 19 (50.00) | |
| No | 9 (26.47) | 19 (50.00) | |
| Diabetes (case [%]) | .001 | ||
| Yes | 17 (50.00) | 5 (13.16) | |
| No | 17 (50.00) | 33 (86.84) | |
| Coronary heart disease (case [%]) | <.001 | ||
| Yes | 19 (55.88) | 4 (10.53) | |
| No | 15 (44.12) | 34 (89.47) | |
| Hyperlipidemia (case [%]) | .001 | ||
| Yes | 21 (61.76) | 9 (23.68) | |
| No | 13 (38.24) | 29 (76.32) | |
| Atrial fibrillation (case [%]) | .050 | ||
| Yes | 7 (20.59) | 2 (5.26) | |
| No | 27 (79.41) | 36 (94.74) | |
| History of stroke (case [%]) | .128 | ||
| Yes | 4 (11.76) | 1 (2.63) | |
| No | 30 (88.24) | 37 (97.37) | |
| Smoking (case [%]) | .002 | ||
| Yes | 16 (47.06) | 5 (13.16) | |
| No | 18 (52.94) | 33 (86.84) | |
| Drinking (case [%]) | .139 | ||
| Yes | 8 (23.53) | 4 (10.53) | |
| No | 26 (76.47) | 34 (89.47) | |
| Self-Rating Depression Scale score (score) | 49.47 ± 4.55 | 38.42 ± 5.66 | <.001 |
| Self-Rating Anxiety Scale score (score) | 46.12 ± 3.98 | 37.00 ± 5.16 | <.001 |
BMI = body mass index.
3.2. Lasso regression screens characteristic variables
In this study, the author used 10× cross-validation to select the optimal Lambda parameter and found the Lambda value that could minimize the cross-validation error (Fig. 2). In addition, this study also calculates the number of variables with a nonzero regression coefficient under this Lambda value. Variable assignment: dependent variable (0 = no sleep disorder, 1 = sleep disorder); female (0 = no, 1 = yes); marital status (0 = unmarried/widowed/other, 1 = married); combined with hypertension (0 = no, 1 = yes); combined with diabetes (0 = no, 1 = yes); combined with coronary heart disease (0 = no, 1 = yes); combined with hyperlipidemia (0 = no, 1 = yes); combined with atrial fibrillation (0 = no, 1 = yes); history of stroke (0 = no, 1 = yes); smoking history (0 = no, 1 = yes); drinking history (0 = no, 1 = yes); and age, BMI, depression self-rating scale score, anxiety self-rating scale score (measured value). The Lasso regression results indicated that being female, having hypertension, having coronary heart disease, and having higher self-rating scale scores for depression and anxiety were risk factors for sleep disorders in stroke patients.
Figure 2.
Lasso regression screening results of risk factors for in-hospital sleep disorders in stroke patients. (A) Coefficient path map of Lasso regression. (B) Cross-validation graph of Lasso regression.
3.3. To establish a risk nomogram model for stroke patients with sleep disorders
Based on the results of Lasso regression analysis, female, combined hypertension, combined coronary heart disease, self-rating scales for depression, and self-rating scales for anxiety were introduced into R4.3.1 software to establish a nematographic prediction model (Fig. 3). The bootstrapping method was used to sample the model 1000 times. The results showed that CI of the modeling group was 0.942, and CI of the verification group was 0.949. Receiver operating characteristic curve analysis was performed on the model that predicted the risk of sleep disorders in stroke patients (Fig. 4), and the results showed that the area under the curve of the modeling group was 0.950 (95% CI [0.920–0.980]), and that of the verification group was 0.966 (95% CI [0.933–0.998]) with good identification. In addition, the calibration curve (Fig. 5) was close to the ideal curve, and the Hosmer–Lemeshow goodness of fit test results were χ²=0.582 and P = .748 in the modeling group and 0.389 and P = .823 in the verification group, indicating that the model had good clinical prediction efficacy (Fig. 6).
Figure 3.
Nomogram model for predicting the risk of stroke patients with sleep disorders. The nomogram was developed in the training set, with clinical characteristics in stroke patients.
Figure 4.
ROC curve of stroke patients with sleep disorder risk predicted by a histogram model. (A) ROC curve of the modeling group. (B) ROC curve of the validation group. ROC = receiver operating characteristic.
Figure 5.
Calibration curve of the nomogram model predicting the risk of sleep disorders in stroke patients. (A) Calibration curve of the modeling group. (B) Calibration curve of the verification group. ROC = receiver operating characteristic.
Figure 6.
Decision curve of the nomogram model for predicting the risk of sleep disorders in stroke patients. (A) Decision curve of the modeling group. (B) The decision curve of the verification group.
4. Discussion
The incidence of sleep disorders in stroke patients is relatively high. Early studies have shown that approximately 30% to 48% of stroke patients experience difficulty falling asleep or suffer from insomnia, a finding that has been corroborated by subsequent research.[17] Our study revealed a similar incidence rate of 44.87% for sleep disorders among stroke patients, aligning with prior research.[17] Through Lasso regression analysis, we identified several key risk factors for sleep disorders in this patient population, including female gender, the presence of hypertension, coronary heart disease, elevated depression scale scores, and increased anxiety scale scores. These findings underscore the multifactorial nature of sleep disorders in stroke patients and highlight the necessity for comprehensive risk assessment.
The higher incidence of sleep disorders in female stroke patients can be attributed to biological characteristics, hormonal changes, psychological stress, and social roles.[18] Firstly, from a biological and physiological perspective, the neuroendocrine system in women is more complex. Hormonal fluctuations, such as changes in estrogen and progesterone levels, can affect the sleep cycle, particularly during specific periods like menstruation and menopause, with a particularly significant impact.[19] Secondly, psychological research indicates that women often experience higher levels of emotional stress, including but not limited to anxiety and depression, and the rehabilitation process after stroke increases this burden and affects sleep quality.[20] In addition, societal roles and expectations can impose further stress, such as family responsibilities, occupational pressures, etc, which are important factors affecting sleep.[20] Hypertension can cause damage and hardening of the blood vessel wall, leading to arteriolar disease, ischemic disease, and other vascular dysfunctions.[21] This may lead to neurovascular dysregulation, affecting the normal regulation of sleep. In addition, high blood pressure may indirectly interfere with normal sleep patterns by affecting the sensitivity and responsiveness of blood vessels to specific neurotransmitters. Increased activity of the sympathetic nervous system is a key link, which leads to increased levels of catecholamine neurotransmitters such as adrenaline and norepinephrine, which causes blood vessels to constrict and blood pressure to rise, and also activates brain regions associated with wakefulness and alertness, thus disrupting the initiation and maintenance of sleep[22]; Hypertension may also reduce nitric oxide, an important vasodilator. Reducing the production of nitric oxide will weaken its sleep-promoting effect and further worsen sleep quality.[23] These imbalances in the balance of neurotransmitters together constitute an important biological mechanism by which hypertension affects sleep. Hypertension can also cause inflammation and oxidative stress responses that may also affect sleep.[24] In patients with coronary heart disease, angina pectoris or myocardial ischemia is caused by insufficient blood supply to the heart. In patients with mild coronary heart disease, angina pectoris symptoms may occur only briefly after physical activity, which has limited influence on sleep quality at night. However, as the disease progresses to moderate and severe, persistent myocardial ischemia can cause angina pectoris at night, paroxysmal dyspnea at night, and even heart failure, which seriously affects the continuity and depth of sleep. Especially in advanced patients with cardiac dysfunction, fluid retention leads to pulmonary edema, and the superposition of anxiety and fear often leads to severe sleep fragmentation and excessive daytime sleepiness. Therefore, the degree of coronary heart disease directly determines the breadth and depth of its impact on sleep disorders, from occasional sleep disruption to continuous sleep deprivation, reflecting the dynamic erosion of patients’ sleep health during the course of the disease.[25] Moreover, patients with coronary heart disease often experience anxiety, depression, and other psychological problems. Combined with the psychological stress and worry about the future caused by the diagnosis and treatment of heart disease, these psychological factors may interfere with patients’ sleep.[26] Individuals with depression may have abnormal levels of neurotransmitters, such as serotonin, dopamine, and norepinephrine, which play a crucial role in sleep and mood regulation. These abnormal levels may lead to problems with falling asleep, experiencing light sleep, and waking up at night.[27] People with depression often face negative emotions such as troubled thinking, sadness, and hopelessness, which can interfere with falling and staying asleep. And lack of sleep or poor quality sleep can worsen depressive symptoms, creating a vicious cycle.[28] Anxiety causes people to focus on negative thoughts and neglect relaxation and rest, making it difficult to relax mentally and fall asleep.[29] In the state of anxiety, the body increases sympathetic nerve activity in response to pressure, speeds up the heart rate and breathing rate, and has symptoms such as chest tightness and shortness of breath, which further affect falling asleep and staying asleep. In an anxious state, patients may frequently wake up during the night or experience nightmares, which can also affect sleep quality.[30]
Based on the above risk factors, a nomogram model for predicting the risk of stroke patients with sleep disorders was established. After further verification, the model was more accurate in predicting the ability of stroke patients to develop sleep disorders, and the prediction results were basically consistent with the actual incidence. The area under the curve value of this model is 0.950, which indicates that it has good prediction performance.
5. Limitations
Objectively, our study has some limitations. First, the sample size is small, and it is a single-center study, which may have bias. Second, there was no external verification; third, different types of sleep disorders were not analyzed.
6. Conclusions
Being female, having hypertension, having coronary heart disease, having a higher self-rating scale score for depression, and having a self-rating scale score for anxiety are risk factors for sleep disorders in stroke patients. Based on the aforementioned factors, the nomogram model for predicting the risk of sleep disorders in stroke patients is helpful for early detection of those at high risk of sleep disorders. It has certain predictive value and good accuracy. However, large sample sizes and multi-center studies are still needed to further explore the factors affecting the occurrence of sleep disorders in stroke patients.
Author contributions
Conceptualization: Yu-Hong Zhou.
Data curation: Yu-Hong Zhou, Guang Tu, Yan Wu, Juan Wu, Lei Shen.
Formal analysis: Guang Tu, Lei Shen.
Project administration: Yu-Ling Lei.
Supervision: Yu-Ling Lei.
Writing – original draft: Yu-Hong Zhou, Guang Tu, Yan Wu, Juan Wu, Lei Shen, Yu-Ling Lei.
Abbreviations:
- BMI
- body mass index
- CI
- C-index
- SAS
- Self-Rating Anxiety Scale
- SDS
- Self-Rating Depression Scale
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Zhou Y-H, Tu G, Wu Y, Wu J, Shen L, Lei Y-L. Development and validation of a nomogram diagnostic model for sleep disorders in stroke patients: A cross-sectional study. Medicine 2025;104:36(e44353).
Contributor Information
Guang Tu, Email: tuguang060666@163.com.
Yan Wu, Email: wujuan@shphc.org.cn.
Juan Wu, Email: wujuan@shphc.org.cn.
Lei Shen, Email: shenlei@shphc.org.cn.
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