Abstract
Background
This research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients.
Methods
309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model’s predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model.
Results
Based on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784–0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability.
Conclusion
The online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society.
Keywords: High care dependency, Nomogram, Stroke, Hospital-family transition period, Lasso
Background
About 12 million new cases and 6.5 million stroke-related fatalities occurred globally in 2019 [1], making stroke one of the leading illnesses posing a danger to public health. Stroke holds the somber distinction of being the second leading global cause of both mortality and prolonged disability [2]. Furthermore, it reigns as the paramount reason for death and incapacity among the adult population in China [3]. The majority of stroke survivors suffer from neurological deficits of varying degrees, resulting in cognitive, speech, motor, and swallowing dysfunctions, which seriously threaten the life quality of the patients and impose a huge burden of care on society and families [4–6]. Increasing population aging has led to a rise in the prevalence of chronic diseases and disabilities, as well as an increasing demand for health care and long-term social security programs. As an age-related disease, the burden of stroke will continue to increase. Since 1990, there has been a 32.0% increase in the disability rate and an 85.0% increase in the absolute global prevalence of stroke [1]. The health of older stroke patients is therefore becoming an important public health issue.
It has been reported that more than 50% of stroke patients who receive acute care in the hospital will transition directly to home for rehabilitation [7]. The hospital-family transition period is defined as the entire process of transitioning a patient to the home during hospitalization, and there is no clear time definition [8]. Effective transitional care is critical to patient prognosis, assisting patients to maximize adaptation to changes in the treatment environment and care needs, promoting functional recovery, improving quality of life, and reducing unplanned readmission rates [9–11]. For stroke survivors and those who care for them, the hospital-family transition phase is crucial, but it’s also the most vulnerable moment [7]. At a period when the majority of patients are unprepared to take care of themselves at home [12]. Significant financial, psychological, and physical strains are also placed on the caregivers throughout the hospital-family transition [13]. The hospital-family transition period is a challenging period of post-stroke rehabilitation and recovery that still requires further study.
The theory of basic human needs proposed by Henderson and others in 1966 emphasizes that basic human needs are the basis for health. Once basic needs are not met, individuals become dependent on the outside world, which is known as “care dependency” (CD) [14]. Older patients with stroke due to increasing age, changing psychological state [15, 16] and neurological damage, which leads to the continuous decline of their body functions, are unable to meet their needs in daily life [17, 18]. As a result, they need to rely on others in various aspects and become a high incidence of care dependency.
Care dependency was found to be an independent predictor of a poorer prognosis for patient survival [19]. As patients’ functional dependency behaviors increase, they lose their ability to care for themselves, and their quality of life decreases, making it impossible for them to reintegrate into society. High care dependency also increases the risk of death among the older population [20], and it also causes the risk of depression and burnout of caregivers [21–23], putting the health of both the caregiver and the cared-for at risk. Care dependency can be reduced if early interventions are detected, improving patient prognosis [24]. Thus, it is especially crucial to identify and recognize care dependency early in older stroke patients.
There is a dearth of pertinent early identification tools, and the majority of recent studies have concentrated on analyzing the existing state and influencing factors of care reliance in stroke patients [25, 26]. Early identification and rapid screening are essential for raising stroke victims’ chances of survival, so it is necessary to develop convenient and fast early warning tools. Maximizing the standard of living for older stroke victims, encouraging the recovery of patients, reducing stroke recurrence, lowering the readmission rate, and thus reducing the social burden of stroke.
Methods
Selection of participants
309 older stroke patients during the hospital-family transition period who were followed up in the Neurology Department of three general hospitals in Jinzhou from June to December 2023 were selected as the training set, and 133 older stroke patients during the hospital-family transition period who were followed up in the Neurology department of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set. The participant inclusion criteria were: (1) Patients were diagnosed with stroke and confirmed by MRI or CT, (2) Age ≥ 60 years, (3) In the hospital-family transition period (patients who were reviewed for the first time in the outpatient clinic after discharge from the hospital), and (4) Consented voluntarily to take part in this investigation after being well informed. The exclusion criteria were: (1) patients with severe visual, hearing, speech, cognitive, and psychiatric disorders who were unable to answer the questionnaire; (2) patients combined with other serious physical diseases, such as severe heart, lung, kidney disease, blood disease, malignant tumors, or serious circulatory diseases; and (3) congenital disability or disability not caused by stroke.
Calculation of sample size
This study is a cross-sectional study, according to the sample size calculation formula of cross-sectional study [27] 
, α/2 = 0.05, Uα/2 = 1.96, and δ is the tolerance error, which takes the value of 0.08. According to the results of the pre-survey, the incidence of high care dependency among 30 stroke patients was 53.33% (16/30), p = 0.533, and the minimum sample size of the training set was calculated to be 167 cases, taking into account a 10% sample attrition rate. The minimum sample size required for the validation set was 72 cases, as required by the 7:3 ratio of the training set to the validation set. A total of 442 cases of total samples were finally included in this study, including 309 cases (70%) in the training set and 133 cases (30%) in the validation set.
Research tools
Patient general situation questionnaire
The researcher designed the questionnaire based on meta-analysis, systematic review, and consultation with experts in the relevant field. The content included social demographic data: gender, age, education level, marital status, smoking, and drinking, and disease-related information: stroke type, history of stroke, course of stroke, chronic disease, and falls in the past 6 months.
Care dependency scale (CDS)
This scale was developed by Dutch scholar Dijkstra in 1996 [28], and the Chinese version of the care dependency scale was translated and revised by Zhang Shuqi et al. [29]. It comprises 15 items, divided into physical and psychosocial dimensions, and assesses patients’ care dependency. The scale is scored on the Likert 5-point scale, with lower scores indicating a higher degree of care dependency. This study defined a total score of ≤ 59 as high care dependency.
Tilburg frailty indicator (TFI)
This scale was developed by Dutch scholar Gobbens et al. in 2010 [30]. It comprises 15 items categorized into physical, psychological, and social frailty. It is used to assess the frailty status of older people. The scale is scored on a yes/no dichotomous scale. A total score of ≥ 5 is considered frailty.
Hamilton anxiety rating scale (HAMA)
This scale was developed by Hamilton in 1959 [31]. It comprises 17 items categorized into somatic anxiety and mental anxiety. It is used to assess and quantify the degree of anxiety in patients. The scale is scored on a Likert 5-point scale, with a total score of ≥ 14 indicating the presence of anxiety symptoms.
Hamilton depression rating scale (HAMD)
This scale, developed by Hamilton in 1960 [32], is the most common scale used to assess the state of depression in clinical practice. It is commonly used to diagnose depression in patients, evaluate its severity, and assess the effectiveness of antidepressant drugs. In this study, a version with 17 entries was used, with a total score of ≥ 7 indicating the presence of depressive symptoms in the patients.
Mini nutritional assessment-short form (MNA-SF)
This scale was simplified by Rubenstein et al. in 2001 based on the 18-item Mini Nutritional Assessment (MNA) [33]. The scale consists of 6 items: change in food intake in the last three months, change in body weight in the last three months, mobility, acute illness or trauma in the last three months, psychosomatic problems, and body mass index. A total score of ≤ 7 indicated that the patient was malnourished.
Data collection
The investigators were the researcher herself and two professionally trained graduate nursing students. Before the formal survey, team members explained the purpose, significance, potential risks and benefits, and completion requirements of the study to patients who met the inclusion and exclusion criteria using uniform and standardized instructional language. Questionnaires were distributed after obtaining patients’ informed consent, completed anonymously, and returned on the spot. For those unable to complete the questionnaire independently due to advanced age or education level, the questionnaire was conducted with the assistance of the researcher, i.e., the researcher asked the questions item by item, the patient answered, and the researcher recorded the answers. There was no guiding language throughout the process and no interference with the patient’s answers.309 of the 315 surveys that were distributed in the training set of this study were successfully retrieved, yielding an effective recovery rate of 98.09%. 140 questionnaires were issued in the validation set, and 133 of them were successfully recovered, yielding an effective recovery rate of 95.00%.
Statistical analysis
Referring to the related literature [34], this study first excludes the samples with more than 20% missing items in the questionnaire from the data set and does not analyze them. Secondly, for samples with less than 20% missing items, the columns with continuous values in the dataset were replaced with the mean of the remaining values in the columns. If the missing values are from categorical columns, we replace the missing values with the most common category.
R 4.3.3 and IBM SPSS 27.0 were the statistical analytic tools utilized. Frequency and percentage were used to express count data. The rank data was compared using the Mann-Whitney U test, and the difference in count data across groups was compared using the χ2 test. It was deemed statistically significant at P < 0.05.
Spearman correlation analysis was performed using R (package “correlation”), and regression analysis using Lasso regression can be considered for variables with strong correlations. Lasso regression is an effective method to solve the problem of multicollinearity between variables, which can avoid over-fitting and realize the optimal selection of variables. Lasso regression analyses were performed using R software (package “glmnet”) in the training set. The relationship between partial likelihood deviation (binomial deviation) and the logarithm (Lambda) was plotted according to a 10-fold cross-validation of the minimum criterion, with the high care dependency as the outcome variable and the 15 candidate factors collected as explanatory variables. Vertical lines were plotted at lambda.min and lambda.1se to mark the optimal values. The non-zero coefficient variables from the LASSO regression were included in the logistic regression analysis, which ultimately led to the selection of predictors with modeling power. R software (package “rms” and “regplot”) was used to establish a dynamic nomogram for high care dependency in the hospital-family transition period of older stroke patients. The “DynNom” package and Shiny apps software were used to visualize the dynamic nomogram.
Internal validation was performed using R (“rms” and “pROC” packages) with 1000 repeated samples by the Bootstrap method. 133 older stroke patients were selected for external validation. The model’s performance was assessed using the calibration plot, decision curve analysis (DCA), and receiver-operating characteristics (ROC) area under curve (AUC). Figure 1 shows the flowchart of our research.
Fig. 1.
The flowchart of our research
Results
Data comparison between the training and validation sets
442 older stroke patients were included in our study, including 309 (70%) in the training set and 133 (30%) in the validation set. There were 263 (59.5%) males and 179 (40.5%) females. The incidence of high care dependency was 52.1% in the training set and 54.9% in the validation set. The differences in demographic characteristics and the prevalence of high care dependency between the training and validation sets were not statistically significant, indicating homogeneity (Table 1).
Table 1.
Data comparison between the training and validation sets
| Variables | Total (n = 442) |
Training set (n = 309) |
Validation set (n = 133) |
Statistic value | P |
|---|---|---|---|---|---|
| High care dependency | |||||
| No | 208(47.1) | 148(47.9) | 60(45.1) | 0.289a) | 0.591 |
| Yes | 234(52.9) | 161(52.1) | 73(54.9) | ||
| Gender | |||||
| Male | 263(59.5) | 181(58.6) | 82(61.7) | 0.366a) | 0.545 |
| Female | 179(40.5) | 128(41.4) | 51(38.3) | ||
| Age (years) | |||||
| 60 ∼ 69 | 177(40.0) | 120(38.8) | 57(42.9) | -0.419b) | 0.679 |
| 70 ∼ 79 | 180(40.7) | 131(42.4) | 49(36.8) | ||
| 80 ∼ 89 | 85(19.2) | 58(18.8) | 27(20.3) | ||
| Marital status | |||||
| Married | 199(45.0) | 147(47.6) | 52(39.1) | 2.698a) | 0.100 |
| Single/divorced/widowed | 243(55.0) | 162(52.4) | 81(60.9) | ||
| Education Level | |||||
| Primary school and below | 113(25.6) | 83(26.9) | 30(22.6) | -0.545b) | 0.586 |
| Junior high school | 183(41.4) | 125(40.5) | 58(43.6) | ||
| High school | 98(22.2) | 66(21.4) | 32(24.1) | ||
| College graduate or above | 48(10.9) | 35(11.3) | 13(9.8) | ||
| Smoking | |||||
| No | 375(84.8) | 256(82.8) | 119(89.5) | 3.174a) | 0.075 |
| Yes | 67(15.2) | 53(17.2) | 14(10.5) | ||
| Drinking | |||||
| No | 362(81.9) | 251(81.2) | 111(83.5) | 0.312a) | 0.577 |
| Yes | 80(18.1) | 58(18.8) | 22(16.5) | ||
| Stroke type | |||||
| Ischemic | 354(80.1) | 254(82.2) | 100(75.2) | 2.868a) | 0.090 |
| Hemorrhagic | 88(19.9) | 55(17.8) | 33(24.8) | ||
| Chronic disease (species) | |||||
| 0 | 73(73) | 47(15.2) | 26(19.5) | -1.185b) | 0.069 |
| 1–2 | 270(61.1) | 186 (60.2) | 84(63.2) | ||
| ≥ 3 | 99(22.4) | 76(24.6) | 23(17.3) | ||
| History of stroke | |||||
| No | 271(61.3) | 195(63.1) | 76(57.1) | 1.394a) | 0.238 |
| Yes | 171(38.7) | 114(36.9) | 57(42.9) | ||
| Course of stroke (years) | |||||
| <1 | 300(67.9) | 218(70.6) | 82(61.7) | -1.657b) | 0.098 |
| 1–3 | 75(17.0) | 46(14.9) | 29(21.8) | ||
| >3 | 67(15.2) | 45(14.6) | 22(16.5) | ||
| Falls in the past 6 months | |||||
| No | 304(68.8) | 212(68.6) | 92(69.2) | 0.014a) | 0.906 |
| Yes | 138(31.2) | 97(31.4) | 41(30.8) | ||
| Anxiety | |||||
| No | 249(56.3) | 171(55.3) | 78(58.6) | 0.413a) | 0.520 |
| Yes | 193(43.7) | 138(44.7) | 55(41.4) | ||
| Depression | |||||
| No | 291(65.8) | 201(65.0) | 90(67.7) | 0.284a) | 0.594 |
| Yes | 151(34.2) | 108(35.0) | 43(32.3) | ||
| Malnutrition | |||||
| No | 338(76.5) | 238(77.0) | 100(75.2) | 0.174a) | 0.677 |
| Yes | 104(23.5) | 71(23.0) | 33(24.8) | ||
| Frailty | |||||
| No | 209(47.3) | 147(47.6) | 62(46.6) | 0.034a) | 0.853 |
| Yes | 233(52.7) | 162(52.4) | 71(53.4) | ||
Note (a) χ2 test, (b) Mann-Whitney U test
Construction of the dynamic nomogram
Variables were down-screened using LASSO tenfold cross-validation from 15 candidate factors such as gender, age, and education, and vertical lines were plotted at lambda.min and lambda.1se, and 6 risk factors were screened at log(λ) = -2.938, including history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and debility (Fig. 2). The independent influencing factors of high care dependency in hospital-family transition in older stroke patients were screened using multivariable logistic regression analysis with the occurrence of high care dependency as the dependent variable (no = 0, yes = 1) and variables with statistically significant (P < 0.05) results in lasso regression analysis as the independent variables. Table 2 shows the multivariable logistic regression analysis’s findings. The nomogram for the hospital-family transition period in older stroke patients (Fig. 3) was created based on 6 independent risk factors, including the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty. The online page can be accessed at https://dynamicnomogramfyrl.shinyapps.io/dynnomapp-1/.
Fig. 2.
(A) The path diagram of regression coefficients of 15 variables. (B) 10-fold cross-validation curve
Table 2.
Multivariate logistic regression analysis of risk factors for high care dependency in older stroke patients
| Variables | B | SE | Wald χ2 | OR | 95%CI | P |
|---|---|---|---|---|---|---|
| (Intercept) | -3.263 | 0.556 | 34.467 | 0.038 | 0.012–0.110 | <0.001 |
| History of Stroke | 1.290 | 0.302 | 18.265 | 3.635 | 2.029–6.650 | <0.001 |
| Chronic disease (species) | 0.743 | 0.242 | 9.434 | 2.101 | 1.319–3.416 | 0.002 |
| Falls in the past 6 months | 1.085 | 0.328 | 10.922 | 2.959 | 1.568–5.707 | <0.001 |
| Depression | 1.052 | 0.303 | 12.025 | 2.864 | 1.589–5.242 | <0.001 |
| Malnutrition | 0.787 | 0.362 | 4.727 | 2.197 | 1.090–4.540 | 0.030 |
| Frailty | 1.035 | 0.286 | 13.123 | 2.815 | 1.620–4.979 | <0.001 |
Fig. 3.
Nomogram for high care dependency. Note Nomogram used to predict high care dependency for the hospital-family transition period in older stroke patients. The specific values of each of the six independent risk factors for the person being assessed correspond to the top “points” (0–100) to derive the corresponding score for that factor. The corresponding scores for each factor are added together to give a total score, and then extrapolated downward from the “Total Points” to give a predicted probability
Validation of the dynamic nomogram
The model appeared to have high discrimination, as evidenced by the AUC under the ROC curves of the training set was 0.830 (95%CI: 0.784–0.875) and the validation set was 0.833 (95%CI: 0.766-0.900) (Fig. 4). The calibration curve demonstrated that the expected and actual results agreed well, indicating that the model performed well (Fig. 5). The nomogram net benefit rate (red line) was found to be above both the All and None lines using decision curve analysis (DCA), indicating that the model had strong clinical practicability (Fig. 6).
Fig. 4.
(A) The receiver operator characteristic (ROC) curve of the training set. (B) The receiver operator characteristic (ROC) curve of the validation set
Fig. 5.
(A) Calibration curve of the training set. (B) Calibration curve of the validation set
Fig. 6.
(A) The decision curve analysis (DCA) curve of the training set. (B) The decision curve analysis (DCA) curve of the validation set
Discussion
This study investigated 442 older stroke patients in the hospital-family transition with care dependency scores (57.76 ± 7.00), of which 309 were in the training set with a high care dependency prevalence of 52.1% and 133 were in the validation set with a high care dependency prevalence of 54.9%, which is higher than that of previous studies [25]. It may be because our study population consisted of older stroke patients, who tend to show a higher level of care dependency than the general population due to physiologic decline, coexisting chronic diseases, and functional impairments that may remain after a stroke. This finding further highlights the importance of developing specialized care plans and interventions for older stroke patients. The above nomogram demonstrates that psychological factors are significant predictors that should not be disregarded, in addition to clinical and demographic traits. Therefore, this not only requires healthcare professionals to strengthen the assessment and intervention of patients’ mental health in daily practice but also suggests that policymakers and healthcare institutions need to incorporate mental health services into the standardized process of stroke rehabilitation and transitional care to ensure the provision of more comprehensive and personalized care programs.
Risk factors of high care dependency for the hospital-family transition period in older stroke patients
In this study, a history of stroke, falls in the last 6 months, and the number of comorbid chronic conditions were found to be risk factors for high care dependency in older stroke patients during the hospital-family transition. This implies that past stroke experience increases patients’ high need for care from others during post-discharge recovery, speculating that this may be due to cumulative damage to neurological function, physical decline, or a compounding effect due to multiple strokes. Falls are common accidents in the older [35], and in stroke patients, falls not only reflect deficits in balance, muscle strength, or sensory perception [36, 37], but may also be an indication of cognitive decline [38]. Patients who have had recent falls have impaired self-care and mobility safety and need more supervision and assistance to prevent future falls and related injuries. Older stroke patients are often accompanied by a range of chronic conditions like diabetes, heart disease, and hypertension, the accumulation of which exacerbates physical decline and increases the risk of complications, leading to a higher care dependency, consistent with the findings of previous studies [39]. These findings have important implications for clinical practice, suggesting that healthcare professionals should pay special attention to these high-risk factors during patients‘ discharge planning, and adopt targeted interventions, such as strengthening rehabilitation training, implementing fall prevention programs, and optimizing chronic disease management strategies, to alleviate patients’ care dependency and ensure a safe transition to the home environment, as well as enhance their quality of life and rehabilitation outcomes.
Our study also found malnutrition, frailty, and depression to be risk factors for high care dependency. Numerous studies have demonstrated that in stroke patients, malnutrition is an independent risk factor for mortality and a worse prognosis [40–43], which in turn exacerbates care dependency, consistent with our findings. There has been less research examining the connection between care dependency and frailty. Our study assessed the current status of frailty in older stroke patients during the hospital-family transition and discovered that frailty was a separate risk factor for high care dependency, possibly because frailty is a specific manifestation of population aging [44]. Frailty implies a decline in physical functioning, leading to a decrease in self-care, exacerbation of the severity of pre-existing conditions, and consequently a higher degree of care dependency [45, 46]. Depression is prevalent in stroke patients [47], which not only undermines treatment adherence but also reduces motivation to participate in rehabilitation and can significantly impair social and cognitive performance [48–50], all of which may lead to an increased level of care dependency. These findings emphasize the importance of comprehensive assessment and targeted interventions for nutritional status, frailty, and mental health in the management and care of older stroke patients. Through personalized nutritional support, debilitating state reversal programs, and psychological counseling, patients’ care dependency can be effectively reduced, their functional recovery facilitated, the strain on families and society lessened, and their standard of living increased.
Evaluation and analysis of the nomogram
The advantage of this study is that it integrates patients’ clinical data more comprehensively than existing studies and establishes a specific screening tool for high care dependency in older stroke patients during the hospital-family transition period. The nomogram model developed in this study performed well in terms of identification, calibration, and clinical application. The dynamic nomogram we constructed solves the problem that ordinary nomograms need to be manually measured, and the results cannot be accurately transformed. It is more convenient and faster than the ordinary nomogram model. It greatly improves the ability of early screening and provides a reliable basis for long-term follow-up and verification. It is of great significance for optimizing patient care plans and guiding clinical decision-making.
Limitations
Despite the importance of the predictive model developed in this study for the treatment and prognosis of older stroke patients during the hospital-family transition, some limitations remain. First, our study excluded patients with hearing, speech, and cognitive impairments who could not answer the questionnaires, which may produce selection bias and affect the generalizability of the results. Secondly, this study was a retrospective questionnaire survey. Although the investigators had been strictly trained and the questionnaire content was reviewed promptly in the field, recall bias was still unavoidable. Finally, the sample selection in this study was limited to Liaoning province only, which may produce regional bias. In the future, samples can be selected in different geographical areas to verify the model’s validity further.
Conclusion
We developed a convenient dynamic online nomogram by integrating six demographic and clinical characteristics and commonly used indicators to assess high care dependency for the hospital-family transition period in older stroke patients. The model has good identifiability, accuracy, and clinical practicability. Provide the basis for early identification of high care dependency, help optimize the allocation of resources, and improve China’s older intervention in patients with stroke.
Acknowledgements
Sincere thanks to all the older stroke patients and their carers who participated in this study, your co-operation was vital to our research.
Author contributions
LFY, ZL research designed and wrote the manuscript, LFY, SL, GHH data collection, LFY, ZRL, LYY, ZTL data analysis, ZL revised and improved the manuscript, and all authors reviewed and approved the final manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to the protection of the individual privacy of the participants but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the institutional review board’s medical ethics committee at Jinzhou Medical University(JZMULL2023140). The purpose, content, methods, potential risks, and benefits of this study were explained to the study subjects, and they voluntarily signed an informed consent form.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to the protection of the individual privacy of the participants but are available from the corresponding author on reasonable request.