Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

doi:10.21203/rs.3.rs-4853180/v1

Background

Frailty is common in patients with hypertension and predisposes patients to poor postoperative outcomes. However, there is a lack of a prediction model for frailty in patients with hypertension. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services.

Methods

The patients diagnosed with hypertension were collected from the the China Health and Retirement Longitudinal Study (CHARLS) database and were randomly divided into a training cohort and a validation group at a ratio of 7:3. The independent risk factors of frailty were determined by LASSO regression and multivariable logistic regression and a nomogram predict model for frailty was established. The performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

The content of the nomogram includes age, health status, cognitive function, and the Center for Epidemiological Studies Depression (CES-D). The AUCs of the training cohort and validation cohort (0.830 and 0.854) suggested good discrimination of the nomogram. Calibration curves and DCA curves proved accuracy and clinical applicability.

Conclusion

The nomogram we created for the frailty in hypertensive patients had good performance and applicability, which could help clinicians in the medical decision-making process.

hypertension

frailty

nomogram

Lasso-logistic regression

Hypertension is the main risk factor for inducing cardiovascular diseases (CVDs), which are the most common cause of death from non-communicable diseases^1,2. Hypertension has emerged as the leading cause of death, accounting for 9.4 million deaths each year³. In China, high systolic BP (SBP) was the top risk factor for both the number of deaths and the percentage of the disability-adjusted life years (DALYs)⁴. Although 46.9% of hypertensive patients were aware of their condition and 40.7% were taking prescription antihypertensive medications, only 15.3% achieved blood control⁵. Hypertension results in substantial disability and places a heavy burden on the healthcare system.

The pathophysiology of hypertension is shaped by a combination of environmental, genetic, anatomical, neurologic, endocrine, humoral, and hemodynamic factors⁶. In addition, psychosocial factors, including occupational stress, socioeconomic stress, anxiety, and depression, are possible enhancers and triggers of hypertension. Higher levels of psychosocial stress occur in those with hypertension compared to those with normal blood pressure⁷.

Defined as a state of decreased physiological reserve and increased vulnerability to possible intrinsic or extrinsic stressors, frailty involves several domains: physical, psychological, social, and others, which amplifies the risk of adverse health outcomes^8–10. Moreover, frailty has been proven to be associated with cardiovascular mortality and morbidity, and multiple mechanisms (physical inactivity, subclinical vascular and cardiac alterations, oxidative stress, deoxyribonucleic acid damage and telomere length shortening inflammatory markers, endocrine dysregulation, accelerated cellular senescence, and epigenetic modifications) contribute to the association between frailty and symptomatic cardiovascular disease^11–13. Recognition of frailty status can help physicians determine surgical risk and prognosis¹⁴. However, there is a lack of a prediction model for frailty in patients with hypertension. Since frailty is reversible, early intervention can improve patient prognosis. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services that can slow the progression of frailty and improve the quality of life in the elderly population.

2.1 Participant characteristics

In 2013 and 2015, 4725 and 5193 patients with hypertension were recruited, removing duplicates for a total of 4501 patients from the China Health and Retirement Longitudinal Study (CHARLS). The mean age of the 4501 enrolled adults with hypertension was 61.8 years (SD: ± 9.3), with 2103 (46.7%) male and 2398 (53.5%) female. For health factors, there were 583 (13.0%) patients with diabetes and 374 (8.3%) with kidney disease. In addition, the mean cognition score was 10.7 (SD: ± 4.1), and 1640 (36.4%) of the participants had depression (Table 1).

The prevalence of frailty in hypertension was 11.9% (534/4501). Most variables were significantly different when compared between older hypertension patients with frailty and non-frailty (Table 1). Baseline data showed that compared to the non-frailty patients, the frailty patients mainly comprised women (66.5% VS. 51.5%, P < 0.001) and patients of a higher average age (64.4 ± 9.5 VS. 61.4 ± 9.2, P < 0.001). It was noteworthy that patients with frailty had more chronic conditions, along with poor cognition score (8.4 ± 4.2 VS. 11.0 ± 4.0, P < 0.001) and a significant rate of depression (22.3% VS. 69.1%, P < 0.001).

All patients were completely randomized in a ratio of 7:3 into a training cohort (N = 3151) and a validation cohort (N = 1350). There was no significant difference among the following variables between the training cohort and validation cohort (P > 0.05), indicating that the random grouping was rational (Table 2).

2.2 Prediction model built based on Lasso-logistic regression

2.2.1 Independent risk factors for hypertension patients with frailty

The training cohort was used to establish a prognostic model, and the validation cohort was used for validation purposes. The parameters were firstly screened by a LASSO regression analysis, and the variation characteristics of the coefficient of these variables were shown in Fig. 1. The model performance was excellent with the λ of the standard error (0.02293). Variables based on LASSO regression were further analyzed by multivariate logistic regression to screen independent risk factors for frailty, including age (OR: 1.04, 95%CI: 1.02–1.05, P < 0.001), health status (OR: 0.53, 95%CI: 0.43–0.65, P < 0.001), cognitive function (OR: 0.93, 95%CI: 0.43–0.96, P < 0.001), and the Center for Epidemiological Studies Depression (CES-D, OR: 1.15, 95%CI: 1.13–1.17, P < 0.001) (Table 3).

2.2.2 Construct the nomogram

A nomogram was developed based on the independent factors of the LASSO-logistic regression (Fig. 2). The AUC of the predictive model was 0.830 (Fig. 3). The calibration curve showed the agreement between the predicted and actual results (Fig. 4). Additionally, the clinical validity of the nomogram was evaluated by the decision curve analysis (DCA). From the DCA curve, the nomogram could acquire the net benefit in most of the reasonable threshold probabilities (Fig. 5).

2.2.3 Validate the nomogram

In order to verify the robustness of the nomogram, an internal validation cohort was performed. The AUC in the validation set was 0.854 (Fig. 3). Good linearity and clinical applicability of the nomogram were found in the calibration and DCA curves (Figs. 4 and 5).

Frailty represents a global health issue with high prevalence and adverse health effects^15,16. In our study, the probability of frailty in hypertensive patients was 11.9%. For patients with hypertension, accurate and prompt diagnosis of frailty is important because it allows for effective interventions and treatments¹⁷. Therefore, we established a reliable model to identify the risk of frailty in hypertensive patients and assist in its management to help clinicians and patients share decisions.

In our research, we utilized LASSO regression to screen variables and adjust for complexity. Compared to the univariate regression analysis, Lasso regression can minimize the multicollinearity in variables. The nomogram contains four risk factors, including age, health status, cognitive function, and CES-D. The use of the nomogram is that the corresponding predicted values are first transformed into the corresponding nomogram scores. The scores are summed, and the probability of a hypertensive patient being frail is derived from the realistic values at the intersection points. The AUCs and calibration curves suggested that the nomogram had good discrimination and accuracy in both the training and validation cohorts, while the DCA curves further showed that the nomogram conferred high clinical net benefits.

At present, most of the studies on frailty have been done on the Western population; few have been done on the Chinese or oriental populations. Recently, standardized definitions of frailty have been proposed, which were characterized by unintentional weight loss, self-reported fatigue, weakness, slower walking speed, and reduced physical activity^8,18. Due to the aging and increasingly complex nature of cardiovascular patients, there is growing awareness of frailty in cardiovascular medicine^14,19. The selection of hypertension treatment might also be influenced by frailty. Frail older adults are always excluded from randomized controlled trials (RCTs) for cardiovascular disease, including hypertension²⁰. This limited the generalizability of the results, making it difficult to accurately evaluate the safety and effectiveness of chronic disease treatments for frail individuals. Secondly, frailty is associated with reduced life expectance and lifetime morbidity. According to the results of the SHARE study, the life expectance of males was 0.1–0.8 years, and females was 0.4–5.5 years in frail individuals at age 70²¹. Thus, the duration of benefit from specific treatments may exceed life expectancy in frail individuals²². Finally, frailty is linked to poor adherence to antihypertensive medication²³.

Aging is the primary risk factor for the majority of chronic diseases²⁴. Over increasing timescales, all people accumulate molecular and cellular damage, and these aging processes, including inflammation, genomic instability, and epigenetic changes, are highly inter-correlated^25,26. Many of these processes are related to frailty. The prevalence of frailty among community-dwelling elderly increases with advanced age: 4% for elderly aged 65 to 69 years, 7% for elderly aged 70 to 74 years, 9% for elderly aged 75 to 79 years, 16% for elderly aged 80 to 84 years, and 26% for elderly aged 85 and over. Besides, we found that frailty has a tight correlation with cognition. Previously, it was demonstrated that changes in physical activity and disease are highly correlated with cognitive and functional outcomes in the elderly^27–29. Cognitive function and frailty may be related by pathogenic mechanisms such as chronic inflammation and oxidative stress³⁰. Reduced cognitive function can decrease the self-care ability and adherence to hypertension treatment, which further exacerbates disease progression and leads to a higher risk of frailty. Depressive symptoms were assessed using the CESD score. Depression predicts frailty due to reduced social relationships, gait speed, and physical activity³¹. Subclinical vascular disease (white matter disease) in patients with late life depression has been considered a key factor in pre-frailty^32,33. Accumulating evidence supports a positive association between frailty and inflammatory cytokines such as IL-6, which is also elevated in depressed patients^34,35. Mitochondrial dysfunction has been observed in several neurodegenerative diseases. Muscle biopsies obtained from depressed participants had decreased ATP production and impaired mitochondrial respiration, which is strongly associated with symptoms of frailty³⁶. Furthermore, depression can adversely affect psychological status and exacerbate debilitating episodes by reducing social activities. Health status plays a direct role in frailty. The comorbidities of cardiovascular disease can exacerbate the clinical course, compromise treatment, and worsen outcomes^37,38. The burden of cardiovascular disease is associated with increased short and long-term morbidity^39,40.

However, our study still has some limitations. First, the CHARLS data did not include a number of potential predictors in hypertension, such as metabolic syndrome and family history. Moreover, the nomogram was constructed based on the Chinese population, and its applicability to populations in other countries requires further validation by an external validation cohort. Lastly, originating from a retrospective cohort, the nomogram needs to be verified using a larger sample size and prospective set.

4.1 Study design

The population of the study data was gathered from the CHARLS. The aim of CHARLS is to collect high-quality data about Chinese residents aged 45 and older to analyze the problem of population aging in China and promote interdisciplinary research on aging issues⁴¹. The multistage, stratified, probability proportional to size (PPS) sampling was done on randomly selected subjects in 28 provinces in 2011, 2013, 2015, and 2018. Eligible participants were interviewed face-to-face using computer-assisted personal interviews (CAPI). The questionnaire collected demographics, health status and functioning, old age security, and health care information. The CHARLS study was approved by the institutional review board of Peking University, and all participants gave written informed consent. For further details on this procedure, see the previously published study⁴¹. All the methods were carried out in accordance with relevant guidelines and regulations.

For the present study, data form patients from 2013 and 2015 were extracted from the CHARLS study. We included individuals who met all of the following criteria: (1) aged ≥ 45 years; (2) patients with hypertension; (3) individuals with complete baseline information.

4.2 Data collection

4.2.1 The definition of hypertension and frailty

Hypertension was defined as systolic blood pressure (SBP) ≥ 140mmHg and/or diastolic blood pressure (DBP) ≥ 90mmHg and /or taking antihypertensive medications⁴². Frailty status was measured through the frailty index (FI), a commonly used tool for assessing frailty^43,44. FI can reflect changes in biological aging and the health trajectory of patients over time, which is constructed based on the accumulation of a range of physical, psychological, cognitive, and functional deficits in individuals. Frailty encompasses slowness, weakness, low physical activity, exhaustion, and shrinking, the details of which are below:

Measurement of weakness using the self-report of “having difficulty in lifting or carrying something as heavy as 5 kg”⁴⁵;

Slowness was considered to be present if the individuals had difficulty walking 100 meters or climbing several flights of stairs without resting;

Exhaustion was assessed if a subject answered “often” or “most of the time” the question from the Center for Epidemiological Studies Depression (CES-D) Sale (Chinese version): “In the last week, I felt that everything I did was an effort” and “In the last week I could out not get going”⁸;

Low physical activity was defined if the respondent answered “No” when asked, “During a usual week, did you walk at least 10 minutes continuously?”

Weight loss was measured by unintentional loss of 5 kg in the past six months or a current body mass index (BMI) < 18.5 kg/m². It turns out that, in fact, weight loss is a better indicator of frailty than BMI and energy intake⁴⁶.

Patients were classified as non-frail (having 0 indicators), pre-fail (having 1–2 indicators), and frail (having three or more indicators).

4.2.2 Socio-demographic Factors

Socia-demographic Factors included age, gender, marital status, education level, and insurance. Gender was male or female. Marital status was categorized as “married” if an individual lived with spouse and “unmarried” if an individual had never married or was widowed or divorced. Insurance was grouped “yes” or “no.”

4.2.3 Behavioral Factors

Behavioral factors encompassed social activities, sleep activity, sleep duration, and life satisfaction. Smoking history, drinking history, and social activities were recorded as “Yes” or “No”. According to the response of, “My sleep was restless,” sleep quality was categorized into four groups. Total nighttime sleep duration was obtained from the question, “In the past month, on an average night, how many hours of actual sleep did you get at night?”.

4.2.4 Health factors

Health status consisted of self-reported health, history of chronic diseases (dyslipidemia, diabetes, cancer, chronic lung disease, kidney disease, digestive disease, stroke, asthma, arthritis or rheumatism, and mental disease), vision, hearing, cognitive score, and depression. History of chronic diseases was classified as “yes” or “no” based on self-reported diagnosis. Vision, hearing, and self-reported health status were categorized as “good,” “fair,” and “poor.” Cognitive functions include attention, memory, orientation, and visuospatial skills. A total score can range from 0 to 21, where a higher score indicates better cognition. Individuals were screened for depression with the Centre for Epidemiologic Studies Depression Scale ( CES-D), which consisted of 21 items. The scores of 10 or greater signified the presence of depression.

4.3 Statistical methods

Continuous variables were reported as means ± standard deviations, and the categorical variables were expressed as frequency (percentage). Comparisons between groups were carried out with a t-test, ANOVA, and Chi-square test. Lasso regression was performed for risk factor selection, and the independent risk factors for frailty were identified by multivariate logistic regression analysis. The identified independent risk factors were used to develop the nomogram, and the nomogram was validated in the validation cohort. The receiver operating characteristic (ROC) curves were drawn, and the area under the ROC curve (AUC) was calculated to evaluate the predictive ability of the nomogram. The calibration curve (Hosmer-Lemeshow test) was utilized to assess the predictive accuracy of the nomogram. Decision Curve Analysis (DCA) curves were performed to demonstrate the clinical validity of the nomogram by quantifying the net benefits under different threshold probabilities.

All data were analyzed using R software (version 4.4.1), and P-value less than 0.05 (two-sided) was considered to be statistically significant.

In our study, we created a reliable nomogram to predict frailty in hypertensive patients based on LASSO-logistic regression analysis, incorporating age, health status, cognitive function, and CESD. The nomogram achieved good performance and applicability, which could help clinicians in the medical decision-making process.

Ethics approval and consent to participate

Ethics approval for the study was granted by the Ethics Review Committee of Peking University (approved number: IRB00001052–11015), and all the participants provided signed informed consent at the time of participation.

Consent for publication

Not applicable.

Availability of data and materials

The data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn.

Competing interests

The authors declare that they have no competing interests

Funding

This study was supported by the Research Project of Shanxi Provincial Health Commission (Grant No: 2021111, GW).

Authors' contributions

Conceived and designed the protocol: W.G.P and W.H.R; Collected data: C.X.W and W.H.R; Analyzed data: W.G.P; Wrote the manuscript: W.G.P and C.X.W; Critically revised the manuscript: C.R.Z and S.Z.X; All authors contributed to the article and approved the submitted version.

Acknowledgements

The authors highly appreciate the China Health and Retirement Longitudinal Study (CHARLS) team for providing high-quality data.

Data avaliability statement

The data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn.

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Table 1 Baseline characteristics of study participants

Overall

Non-frailty

Frailty

P-value

Age

Gender (%)

Male

Female

61.8 ± 9.3

2103 (46.7)

2398 (53.3)

61.4 ± 9.2

1924 (48.5)

2043 (51.5)

64.4 ± 9.5

179 (33.5)

355 (66.5)

<0.001

Marital status (%)

Unmarried

Married

630 (14.0)

3871 (86.0)

523 (13.2)

3444 (86.8)

107 (20.0)

427 (80.0)

<0.001

Smoking (%)

0.141

Yes

No

261 (5.8)

4240 (94.2)

238 (6.0)

3729 (94.0)

23 (4.3)

511 (95.7)

Social activity (%)

<0.001

Yes

No

Sleep duration (h)

Dyslipidemia (%)

Diabetes (%)

Cancer (%)

Liver disease (%)

Heart disease (%)

Stroke (%)

Kidney disease (%)

Digestive disease (%)

Mental disease (%)

Arthritis or rheumatism (%)

Asthma (%)

Waistline (cm)

Health status (%)

Poor

Fair

Good

Vision (%)

Poor

Fair

Good

Hearing (%)

Poor

Fair

Good

Depression

Yes

No

Cognitive function

2690 (59.8)

1811 (40.2)

6.2 ± 1.9

1023 (22.7)

583 (13.0)

37 (0.8)

186 (4.1)

1049 (23.3)

196 (4.4)

374 (8.3)

977 (21.7)

49 (1.1)

1572 (34.9)

162 (3.6)

90.5 ± 11.9

1564 (34.7)

2223 (49.4)

714 (15.9)

495 (11.0)

3240 (72.0)

766 (17.0)

692 (15.4)

2281 (50.7)

1528 (33.9)

1640 (36.4)

2861 (63.6)

10.7 ± 4.1

2450 (61.8)

1517 (38.2)

6.3 ± 1.8

892 (22.5)

487 (12.3)

31 (0.8)

157 (4.0)

862 (21.7)

158 (4.0)

299 (7.5)

802 (20.2)

36 (0.9)

1299 (32.7)

128 (3.2)

90.9 ± 11.9

1206 (30.4)

2071 (52.2)

690 (17.4)

367 (9.3)

2885 (72.7)

715 (18.0)

535 (13.5)

2029 (51.1)

1403 (35.4)

1225 (30.9)

2742 (69.1)

11.0 ± 4.0

240 (44.9)

294 (55.1)

5.2 ± 2.4

131 (24.5)

96 (18.0)

6 (1.1)

29 (5.4)

187 (35.0)

38 (7.1)

75 (14.0)

175 (32.8)

13 (2.4)

273 (51.1)

34 (6.4)

87.6 ± 11.9

358 (67.0)

152 (28.5)

24 ( 4.5)

128 (24.0)

355 (66.5)

51 (9.6)

157 (29.4)

252 (47.2)

125 (23.4)

415 (77.7)

119 (22.3)

8.4 ± 4.2

<0.001

0.315

<0.001

0.571

0.136

<0.001

0.003

<0.001

Notes: Continuous variables were reported as mean and standard deviation (mean ± SD), while categorical variables were expressed as frequency (percentage).

Table 2 Characteristics for training and validation cohorts

Training cohort

(N=3151)

Validation cohort

(N=1350)

P-value

Age

61.8 ± 9.3

61.8 ± 9.2

0.876

Gender (%)

Male

Female

1474 (46.8)

1677 (53.2)

629 (46.6)

721 (53.4)

0.935

Marital status (%)

Unmarried

Married

449 (14.2)

2702 (85.8)

181 (13.4)

1169 (86.6)

0.484

Smoking (%)

0.101

Yes

No

195 (6.2)

2956 (93.8)

66 (4.9)

1284 (95.1)

Social activity (%)

0.878

Yes

No

Sleep duration (h)

Dyslipidemia (%)

Diabetes (%)

Cancer (%)

Liver disease (%)

Heart disease (%)

Stroke (%)

Kidney disease (%)

Digestive disease (%)

Mental disease (%)

Arthritis or rheumatism (%)

Asthma (%)

Waistline (cm)

Health status (%)

Poor

Fair

Good

Vision (%)

Poor

Fair

Good

Hearing (%)

Poor

Fair

Good

Depression

Yes

No

Cognitive function

1265 (40.1)

1886 (59.9)

6.2±1.9

713 (22.6)

393 (12.5)

27 (0.9)

131 (4.2)

734 (23.3)

145 (4.6)

264 (8.4)

675 (21.4)

40 (1.3)

1106 (35.1)

110 (3.5)

90.5 ± 11.8

1112 (35.3)

1530 (48.6)

509 (16.2)

348 (11.0)

2268 (72.0)

535 (17.0)

476 (15.1)

1608 (51.0)

1067 (33.9)

1171 (37.2)

1980 (62.8)

10.6 ± 4.1

546 (40.4)

804 (59.6)

6.3±1.9

310 (23.0)

190 (14.1)

10 (0.7)

55 (4.1)

315 (23.3)

51 (3.8)

110 (8.1)

302 (22.4)

9 (0.7)

466 (34.5)

52 (3.9)

90.5 ± 12.1

452 (33.5)

693 (51.3)

205 (15.2)

147 (10.9)

972 (72.0)

231 (17.1)

216 (16.0)

673 (49.9)

461 (34.1)

469 (34.7)

881 (65.3)

10.9 ± 4.2

0.050

0.836

0.156

0.830

0.963

1.000

0.245

0.844

0.504

0.103

0.733

0.611

0.950

0.232

0.985

0.679

0.130

0.091

Table 3 Multivariate Logistic regression analysis based on the results of Lasso regression

Variables	β	S.E	OR (95%CI)	P value
Age	0.036	0.007	1.04 (1.02-1.05)	<0.001
Health status	-0.636	0.108	0.53 (0.43-0.65)	<0.001
Cognitive function	-0.071	0.016	0.93 (0.90-0.96)	<0.001
CES-D	0.140	0.010	1.15 (1.13-1.17)	<0.001

Abbreviations: β, regression coefficient; S.E., standard error; OR, odds ratio; CES-D: center for epidemiological survey, depression scale;

No competing interests reported.

Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Result

2.1 Participant characteristics

2.2 Prediction model built based on Lasso-logistic regression

2.2.1 Independent risk factors for hypertension patients with frailty

2.2.2 Construct the nomogram

2.2.3 Validate the nomogram

Discussion

Method

4.1 Study design

4.2 Data collection

4.2.1 The definition of hypertension and frailty

4.2.2 Socio-demographic Factors

4.2.3 Behavioral Factors

4.2.4 Health factors

4.3 Statistical methods

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1