Nomogram as a predictive model for depression risk in chronic obstructive pulmonary disease

doi:10.21203/rs.3.rs-2372696/v1

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Nomogram as a predictive model for depression risk in chronic obstructive pulmonary disease

https://doi.org/10.21203/rs.3.rs-2372696/v1

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Background

Depression after chronic obstructive pulmonary disease（COPD）is associated with mortality rates and poor prognosis. This study aimed to develop a nomogram to identify the risk of depression in patients with COPD based on predictors.

Methods

The Cross sectional study included 494 COPD aged >20 years who were come from the 2005–2008 National Health and Nutrition Examination Survey database. The 345 subjects from the 2005–2008 survey comprised the development group, and the remaining 149 subjects comprised the validation group. The least absolute shrinkage and selection operator (LASSO) binomial regression model was used to select the best predictive variables before further screening of multivariate regression model.The performance of the nomogram was evaluated on the basis of receiver operating characteristic curve(ROC), calibration curve, and clinical decision curve analysis (DCA).

Results

We reach a decision that there are 10 item,including

BMI,Race,Sex,Age,Education,marriage,hypertension,diabetes,CRP,MONO by LASSO regression model.Multivariate regression had selected 4 statistically significant variables for inclusion.as follow:Hypertension,MONO,CRP,Age.hypertension(Odds

Ratio[OR],0.836;95%confidence interval [CI],0.206-0.914; P = 0.028),MONO (OR, -2.652; 95% CI, 0.011 to 0.437; P=0.004), CRP (OR,0.238; 95% CI, 1.047 to 1.538; P=0.015) and Age (OR,0.031; 95% CI, 0.947 to 0.992; P=0.009).The AUC area under the curve for the training group was 0.774 whereas the validation group was 0.713, The predictive model was calibrated, and the DCA showed that the proposed nomogram had strong clinical applicability.

Conclusion

We have developed a simple nomogram to predict depression in COPD individuals based on Nomogram. External validation is needed to further demonstrate its predictive ability in primary care settings.

Depression

Chronic Obstructive Pulmonary Disease

NHANES

LASSO regression

Nomogram

Chronic obstructive pulmonary disease (COPD) is a common lung disease that causes high morbidity and mortality worldwide. It is characterized by persistent respiratory symptoms and irreversible airflow limitations^[1]–[2]. It will be the third leading cause of death globally by 2030^[3]. Comorbidity is an important component of COPD. In addition, severe comorbidities may have an impact on COPD morbidity and mortality. The most common comorbidities of COPD include cardiovascular disease, metabolic disorders, osteoporosis, skeletal muscle dysfunction, anxiety/depression, cognitive impairment^[4].Depression is a common complication in patients with COPD^[5].A increasing of prognostic research suggests that mental health problems also significantly increase the risk of death from COPD^[6]. Depression is a particularly strong predictor of COPD mortality^[7], so it is important to further develop personalized treatment strategies and improve clinical outcomes.

Nowadays, Nomogram has been widely considered to predict an individual's disease probability^[8].developing a clinical event plays an important role in advancing personalized diagnosis.There is insufficient early recognition and diagnosis of depression occurrence in COPD patients.In addition, COPD-depression prediction models are limited.Therefore,We explored demographic parameters and common hematological parameters to construct a prediction model from NHANES to predict the risk of COPD with depression.

2.1. Study population

The current cross-sectional study collected raw data of 20497 COPD patients in the NHANES database from 2005 to 2008. The NHANES is an program performed by the National Center for Health Statistics (NCHS) of the Centers for Disease Control (CDC) to evaluate the health and nutritional status and Data are collected every 2 years.In our study, Finally 494 patients were included after exclusion of missing having emphysema and/or chronic bronchitis(N = 9602)、having cancer or any type of malignancy(N = 1106)、having stomach or intestinal illness(N = 1685)、Missing depression(n = 7610). All participants were divided into the training set (n = 345) and the testing set (n = 149). Flow chart of the study is displayed in Fig. 1.

2.2. Depression And Candidate Predictor Variables

The outcome variable was COPD-depression.The Patient Health Questionnaire-9 (PHQ-9) scale as a nine-item depression-screening reliable tool,rated from 0 (not at all) to 3 (nearly every day), giving a total score between 0 and 27 based on the subject's responses to questions about the frequency of depressive symptoms in the past 2 weeks^[9].We defined having symptoms of depression at the time of survey participation as a score of ≥ 10 on the PHQ-9. The participants' demographic characteristics were obtained through a self-reported questionnaire and included age, gender (male or female), Race (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, and other Race), Education (less than high school, high school, and more than high school), marriage status (Marital/living with partner、Widow/never married、Divorced/separated). Hypertension was defined by self-report“Ever told you had high blood pressure”.There was definition as Diabetes self-report“Doctor told you have diabetes”.BMI was calculated as the weight in kilograms divided by the square of height in meters (kg/m2). Drinking status (if they reported drinking < 12 drinks in their lifetime, they were defined as “no”; others were defined as “yes”. Smoking status was divided smoker and non-smoker, Participants who responded “yes”and “no” were defined as sleep disorder based on “Ever told by doctor have sleep disorder?” Other indicators based on Laboratory Data, as Leukocytes, CRP,neutrophils, monocytes, platelets.

2.3. Statistical Analysis

Continuous variables with normal distribution were expressed as Mean standard deviation (Mean ± SD), and T test was used for comparison between groups. In the sensitivity analysis, the differences before and after processing the missing data were compared. All subjects were divided into a training set and a test set with a ratio of 7:3.

We made an applicant the least absolute shrinkage and selection operator (LASSO) method to confirm the optimal variables with non-zero coefficients as risk factors^[10]–[11].Thereafter, Based on the results of LASSO regression analysis, Furthermore Hosmer-Lemeshow test was used to identify variables for the multivariate logistic regression models. Multivariable logistic regression analysis was used to establish a prediction model, and a nomogram was constructed. The area under the curve (AUC) value of the model was calculated to predict the degree of discrimination of the prediction model, with higher AUCs indicating better discrimination and lower AIC values indicating superior model fitting. The calibration curve was drawn to evaluate the calibration of the nomogram by reviewing the predicted versus actual probabilities.All the analyses were performed using R software version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria) and the statistical software package EmpowerStats (http://www.empowerstats.com, X&Y solutions, Inc, Boston, MA). Values of p < 0.05 were considered indicative of statistical significance.

3.1. Clinical characteristic of patients with COPD with depression and non-depression

As shown in Table 1, Race (P = 0.008) and Hypertension (P = 0.014) showed statistically significant differences between patients with depression and those without depression. Gender (P = 0.971), Mean age (56.59 VS 53.64,P = 0.164), Body mass index (82.9kg/m2 VS 88.18kg.m2,P = 0.169), Average leukocyte level (7.85*10⁹/L VS 7.54* 109/L, P = 0.410), lymphocyte (2.29*10⁹/L VS 2.15*10⁹/L, P = 0.607), Platelet average (275.68*10⁹/L VS 276.76*10⁹/L, P = 0.911), The mean levels of neutrophils (4.68*10⁹/L VS 4.59*10⁹/L, P = 0.700) and Monocytes (0.59*10⁹/L VS 0.54*10⁹/L, P = 0.059). There were no statistically significant differences between Participants with and without depression in Education level (P = 0.032), Smoking (P = 0.771), Diabetes (P = 0.155) and sleep disorders (P = 0.186).

Table 1

The characteristics of COPD patients with and without depression
Parameters	Total	Depression
	N = 494	NO(n = 420)	YES(n = 74)	P Value
Sex				0.971
Male	226(45.75%)	192(45.71%)	34(45.95%)
Female	268(54.25%)	228(54.29%)	40(54.05%)
Age(N.%)	56.14 + 16.80	56.59 ± 17.3	53.64 ± 13.43	0.164
Male	-	89(41.2%)	11(32.35%)
Female	-	127(58.8%)	23(67.65%)
Race				0.008
Mexican American	46(9.31%)	41(9.76%)	5(6.76%)
Other Hispanic	34(6.88)	25(5.95%)	9(12.16%)
Non-Hispanic Black	316(63.97%)	279(66.43%)	37(50%)
BMI	83.70 ± 22.86	82.90 ± 22.57	88.18 ± 24.15	0.069
Education				0.454
Below high school	162(32.79%)	132(31.43%)	30(40.54%)
College school	120(24.29%)	103(24.52%)	17(22.97%)
Beyond College school	211(42.71%)	184(43.81%)	27(36.49%)
Marital status				0.106
Marital/living with partner	272(55.06%)	231(55%)	41(55.41%)
Widow/never married	125(25.30%)	112(26.67%)	13(17.57%)
Divorced/separated	97(19.64%)	77(18.33%)	20(27.03%)
Hypertension				0.014
YES	229(45.75%)	185(44.05%)	44(59.46%)
NO	265(53.64%)	235(55.95%)	30(40.54%)
Diabetes				0.155
YES	88(17.81%)	72(17.14%)	16(21.62%)
NO	393(79.55%)	339(80.71%)	54(72.97%)
Alcohol				0.663
YES	350(70.85%)	296(70.48%)	54(72.97%)
NO	144(29.15%)	124(29.52%)	20(27.03%)
Sleep disorders				0.186
YES	75(15.18%)	60(14.29%)	15(20.27%)
NO	419(84.98%)	360(85.71%)	59(79.73%)
Smoking				0.771
YES	340(68.83%)	288(68.57%)	30(40.54%)
NO	154(31.71%)	132(31.43%)	22(29.73%)
CRP,mg/dL	0.64 + 1.24	0.60 ± 1.09	0.88 ± 1.88	0.078
WBC,10^9cells/L	7.80 + 2.92	7.85 ± 3.03	7.54 ± 2.18	0.410
LYM,10^9cells/L	2.27 + 2.15	2.29 ± 2.30	2.15 ± 0.79	0.607
MONO,10^9cells/L	0.58 + 0.21	0.59 ± 0.21	0.54 ± 0.16	0.059
NEUT,10^9cells/L	4.67 + 1.73	4.68 ± 1.71	4.59 ± 1.85	0.700
PLT,10^9cells/L	275.84 + 74.63	275.68 ± 73.43	276.76 ± 81.81	0.911
COPD: chronic obstructive pulmonary disease, WBC: white blood cell, MONO: monocyte, NEUT: neutrophil, PLT: platelet, LYM: lymphocyte

3.2. Characteristic analysis of the Training Set and Testing Set

The subjects were randomly divided into a training set and a test set (7:3). In the training group, the age of training concentration depression was younger (p = 0.015), the prevalence of hypertension was higher (69.09% VS 47.93%.p = 0.004), and the monocyte count was lower (p = 0.004)(Table 2).

Table 2

The equilibrium test of the training set and testing set.
Parameters	Training set (n = 345)		P Value	Testing set (n = 149)		P Value
	Depression(n = 55)	Non-depression(n = 290)		Depression(n = 19)	Non-depression(n = 130)
Sex			0.097			0.006
Male	25(45.45%)	167(57.59%)		9(47.37%)	25(19.23%)
Female	30(54.55%)	123(42.41%)		13(68.42%)	105(80.77%)
Age	52.73 + 12.84	58.64 + 16.51	0.012	53.64 ± 13.43		0.219
Race			0.011			0.364
Mexican American	3(5.45%)	22(7.59%)		2(10.53%)	19(14.62%)
Other Hispanic	4(7.27%)	18(6.21%)		5(26.32%)	7(5.38%)
Non-Hispanic Black	28(50.91%)	198(68.28%)		9(47.37%)	81(62.3%)
BMI	85.15 + 21.42	84.78 + 22.96		76.23 + 25.54	78.72 + 21.15	0.471
Education			0.657			0.672
Below high school	24(43.64%)	103(35.52%)		6(31.58%)	29(22.31%)
College school	12(21.82%)	65(22.41%)		5(26.32%)	38(29.23%)
Beyond College school	19(34.55%)	121(41.72%)		8(42.11%)	63(48.46%)
Marital status			0.208			0.501
Marital/living with partner	28(50.91%)	154(53.1%)		13(68.42%)	77(59.23%)
Widow/never married	10(18.18%)	75(25.86%)		3(15.79%)	37(28.46%)
Divorce/separated	17(30.91%)	61(21.03%)		3(15.79%)	16(12.31%)
Hypertension			0.004			0.745
YES	38(69.09%)	139(47.93%)		6(31.58%)	46(35.38%)
NO	17(30.91%)	151(52.07%)		13(68.42%)	84(64.62%)
Diabetes			0.087			0.655
YES	15(27.27%)	57(19.66%)		1(5.26%)	114(87.69%)
NO	36(65.45%)	225(77.59%)		18(94.74%)	15(11.54%)
Alcohol			0.792			0.374
YES	41(25.45%)	221(76.21%)		13(68.42%)	75(57.6%)
NO	14(74.55%)	69(23.79%)		6(31.58%)	55(42.31%)
Sleep disorders			0.915			0.007
YES	10(18.18%)	51(17.59%)		5(26.32%)	9(6.92%)
NO	45(81.82%)	239(82.41%)		14(73.68%)	121(93.08%)
Smoking			0.445			0.160
YES	49(89.09%)	247(85.17%)		3(84.21%)	41(31.54%)
NO	6(10.91%)	43(14.83%)		16(84.21%)	89(68.46%)
CRP,mg/dL	1.01 + 2.12	0.64 ± 1.22	0.072	0.47 ± 0.52	0.51 ± 0.70	0.813
WBC,10^9cells/L	7.57 + 2.27	8.06 + 3.39	0.308	7.45 + 1.93	7.35 + 1.84	0.839
LYM,10^9cells/L	2.09 + 0.78	2.36 ± 2.71	0.465	2.15 + 0.74	2.13 + 0.71	0.263
MONO,10^9cells/L	0.53 + 0.16	0.62 + 0.22	0.012	0.57 ± 0.18	0.53 ± 0.18	0.575
NEUT,10^9cells/L	4.69 + 1.89	4.8 ± 1.79	0.703	4.28 + 1.72	4.40 ± 1.47	0.750
PLT,10^9cells/L	281.42 + 88.52	273.27 ± 72.06	0.467	262.24 + 55.65	281.36 ± 76.56	0.323
COPD: chronic obstructive pulmonary disease, WBC: white blood cell, MONO: monocyte, NEUT: neutrophil, PLT: platelet, LYM: lymphocyte

3.3.Nomogram model Prediction and construction

LASSO regression analysis was performed to identify ten predictors and calculate the scoring formula:0.0053*BMI-0.49088*HYERTENSION:0 + 0.01342*HYERTENSION:1-1.39336*MONO + 0.12963*CRP + 0.05156*SEX: 0–0.01982*AGE + 0.56148*RACE:4 + 0.01897*EDU:1-0.051*MARRIAGE:2 + 0.20255*MARRIAGE:3-0.01676*DIABETES: 2.

We further carried out multivariate logistic regression analysis and generated a prediction model. The results of multivariate logistic regression analysis are shown in Table 3. Ten variables screened by LASSO regression were incorporated into multiple logistic regression model to establish a prediction model. The results showed that four variables were statistically significant in predicting the risk of depression secondary to COPD. hypertension(Odds Ratio [OR],0.433; 95% confidence interval[CI],0.206–0.914; P = 0.028),MONO (OR, 0.071; 95% CI, 0.011 to 0.437; P = 0.004), CRP (OR,1.269; 95% CI, 1.047 to 1.538; P = 0.015) and Age (OR,0.970; 95% CI, 0.947 to 0.992; P = 0.009). So the four indicators are included in the nomogram.

Table 3

Predictors of depression in COPD patients by multivariate logistic regression analysis.
Variable	β	S.E	Wald	P	OR	lower	upper
Hypertension	0.836	0.381	4.821	0.028	0.433	0.206	0.914
MONO	−2.652	0.930	8.124	0.004	0.071	0.011	0.437
CRP	0.238	0.098	5.911	0.015	1.269	1.047	1.538
Age	0.031	0.012	6.914	0.009	0.970	0.947	0.992
Abbreviation:
MONO,Monocyte cell ; CRP, C-reactive protein

Based on the four screened variables, we constructed Nomogram to predict the likelihood of depression in COPD patients. The point total is calculated from the sum of the points assigned to each variable. The higher the point, the higher depression prevalence rate in COPD,Combined with this model, the risk of depression is relatively good(Fig. 3).

3.4.Validation of the Predicative Value via the Testing Set.

ROC curves of training set and test set are shown in Fig. 3. The AUC value of the prediction model was 0.774 in the training set and 0.713 in the test set which indicates average and good performance(Fig. 4).

3.5. the calibration of the model in the training set and the testing set

Calibration curve had be drawn as evaluating the calibration of the model. The calibration curve accounted for the relationship between the depression risk predicted by the model and the depression risk observed in the training cohort. The calibration curve of the nomogram for predicting depression risk in COPD patients made clear that relative good agreement both the training and validation datasets (Figs. 5A and B). To summarize the above verification results, the nomogram of the model has relative good predictive ability.

3.6.DCA of the prediction model in the training set and the testing set

DCA was used to evaluate the clinical efficacy of the prediction model. The DCA curve is plotted using the predicted depression probabilities of the model and validation group and the actual occurrence of depression probabilities. The DCA curves of the two groups are revealed in Fig. 6A and B.The prediction probability threshold in the training and validation was between 12.5%-60% and 12%-30%, and validation sets was between 0-2.5% and 0–2%.The smaller the threshold, the better the net benefit.

This study collected data on depression in 494 COPD patients to assess the factors associated with the onset of depression in COPD patients and to establish a predictive model based on these predictors. The data showed that Age, hypertension, CRP and MONO levels were predictors of depression in COPD patients,Which is a supplement to Tingyu Tang’s report^[12] inclusion of predictors in the model.In addition, a predictive model of depression occurrence in COPD patients was established based on these predictors, and the AUC value of the predictive model was 0.774 in the training set and 0.713 in the testing set, showing relative good predictive performance.

Some studies have shown that age is associated with the incidence of depression in COPD patients. Importantly, we found that depression range from age group:Significant levels of depression and anxiety were more prevalent in patients under 60 years of age, regardless of the clinical severity of COPD. The apparent reduction in depression and anxiety as people age may be due to a series of factors, Firstly older Participants may have difficulties and Secondly they will considered that physical disabilities and functional limitations are uncommon after retirement. But younger patients may find it difficult to accept lifestyle changes, leading to a higher risk of depression^[13].Previous studies have demonstrated elevated systemic inflammatory markers in COPD patients, including C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α)^[14]–[15],Recent literature has shown that adults with major depression are associated with elevated IL-1ß, IL-6, and CRP. A recent study of a sample of older adults showed that elevated levels of the inflammatory biomarker C-reactive protein are partly responsible for depressive symptoms and COPD^[16].Higher CRP were associated with an increased risk of depression in patients with COPD^[17]. For patients with COPD, Regular blood routine tests should be performed, CRP levels should be closely monitored, and patients at high risk of depression should be identified in time.Studies have shown that COPD combined with other conditions, including high blood pressure, has a significantly higher risk of depression^[18]. In addition, depressive mood may be associated with blood pressure medications^[19],Which our research is consistent.Although Monocytes were included in the Nomogram model.But Monocytes have low Odds Ratio in predictive models and are of little clinical significance.

The current study evaluated predictors of depression in COPD patients and developed a predictive model based on these predictors in a training set. Validation of the prediction model is performed in the testing set. The AUC values of the model showed good predictive ability in both the training set and the testing set. The calibration curve of the model also shows that the prediction model has a good consistency between the predicted probability and the actual probability, indicating that our prediction model has a good prediction performance. In addition, a nomogram based on our model makes it easy for clinicians to calculate scores directly and quickly estimate the likelihood of depression in COPD patients. The nomogram may provide clinicians with the tools to intervene in a timely manner to prevent cardiovascular disease in patients at high risk for cardiovascular disease.

The study has several considerable limitations. First of all, we don't have pulmonary function data. GOLD grades C and D are predictive variables for depression. Second, the main population did not distinguish between COPD patients with acute exacerbation or stable COPD. The underlying mechanism of depression during acute exacerbations may be different from that stable periods, and therefore the predictors in the model may be of difference. In addition, our study was cross-sectional and further prospective studies are needed to explore the risk of depression in patients with COPD. Third, This data comes from NHANES.Multi-center prospective studies should be conducted to verify the results externally.Last and most,More relevant medical information that it may contribute to depression, such as other comorbidities, multiple medications, living alone, unemployment, childhood trauma, history of mental illness, etc. and can be incorporated into the predictive model. Further research was be explored.

This study indicated that simple nomogram can be used to calculate depression risk in COPD patients and can be used for individualized clinical management of COPD patients where depression may occur. Further studies need to be confirmed with larger scale investigation and placed on more risk factors .

Author Contributions

Data collection,Dan.Du.; Investigation,Dan.Du., Ding.Chao.Wei and Xian.Ming Zhang.; Methodology,Dan.Du and Xian.Ming.Zhang.;Supervision,Yuan.Ya.Dong.;Visualization, Chao.Wei.Ding.; Writing—original manuscript, Dan.Du.; All authors have read and agreed to the published version of the manuscript.

Funding

This Study was supported by the Hebei Province Health innovation Project ( 21377701D).

Conflicts of interest

The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.

Ethics Approval and Consent to Participate

The study protocol was approved by National Center for Health Statistics,and all participants had written informed consent.

Availability of data and materials

No appliance

Venkatesan P. GOLD report: 2022 update. Lancet Respir Med 2022; 10(2):e20. doi: 10.1016/S2213-2600(1021)00561-00560. Epub 02021 Dec 00520.
Phan T, Carter O, Waterer G, Chung LP, Hawkins M, Rudd C, et al. Determinants for concomitant anxiety and depression in people living with chronic obstructive pulmonary disease. J Psychosom Res 2019; 120:60-65.(doi):10.1016/j.jpsychores.2019.1003.1004. Epub 2019 Mar 1018.
Qu S, Zhu J. A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients. J Healthc Eng 2022; 2022:6394290.(doi):10.1155/2022/6394290. eCollection 6392022.
Negewo NA, Gibson PG, McDonald VM. COPD and its comorbidities: Impact, measurement and mechanisms. Respirology 2015; 20(8):1160-1171. doi: 1110.1111/resp.12642. Epub 12015 Sep 12616.
Barrueco-Otero E, Refoyo Matellán B, Martín Puente J, Viñado Mañes C, León Subias E, Olivera Pueyo J, et al. [Prevalence of Depressive Symptoms, Predictive Factors, and Diagnosis of Suspicion of Depression in Patients with COPD]. Aten Primaria 2022; 54(3):102236. doi: 102210.101016/j.aprim.102021.102236. Epub 102022 Feb 102237.
Panagioti M, Scott C, Blakemore A, Coventry PA. Overview of the prevalence, impact, and management of depression and anxiety in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2014; 9:1289-306.(doi):10.2147/COPD.S72073. eCollection 72014.
Ng TP, Niti M, Tan WC, Cao Z, Ong KC, Eng P. Depressive symptoms and chronic obstructive pulmonary disease: effect on mortality, hospital readmission, symptom burden, functional status, and quality of life. Arch Intern Med 2007; 167(1):60-67. doi: 10.1001/archinte.1167.1001.1060.
Guo Q, Wu M, Li H, Ouyang H, Sun R, Wang J, et al. Development and validation of a prognostic nomogram for myocardial infarction patients in intensive care units: a retrospective cohort study. BMJ Open 2020; 10(12):e040291. doi: 040210.041136/bmjopen-042020-040291.
Costantini L, Pasquarella C, Odone A, Colucci ME, Costanza A, Serafini G, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. J Affect Disord 2021; 279:473-483.(doi):10.1016/j.jad.2020.1009.1131. Epub 2020 Oct 1016.
Wang N, Guo Z, Gong X, Kang S, Cui Z, Yuan Y. A Nomogram for Predicting the Risk of Pulmonary Hypertension for Patients with Chronic Obstructive Pulmonary Disease. Int J Gen Med 2022; 15:5751-5762.(doi):10.2147/IJGM.S363035. eCollection 362022.
Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010; 33(1):1-22.
Tang T, Li Z, Lu X, Du J. Development and validation of a risk prediction model for anxiety or depression among patients with chronic obstructive pulmonary disease between 2018 and 2020. Ann Med 2022; 54(1):2181-2190. doi: 2110.1080/07853890.07852022.02105394.
Cleland JA, Lee AJ, Hall S. Associations of depression and anxiety with gender, age, health-related quality of life and symptoms in primary care COPD patients. Fam Pract 2007; 24(3):217-223. doi: 210.1093/fampra/cmm1009. Epub 2007 May 1015.
Strollo HC, Nouraie SM, Hoth KF, Riley CM, Karoleski C, Zhang Y, et al. Association of Systemic Inflammation with Depressive Symptoms in Individuals with COPD. Int J Chron Obstruct Pulmon Dis 2021; 16:2515-2522.(doi):10.2147/COPD.S322144. eCollection 322021.
Chen YW, Leung JM, Sin DD. A Systematic Review of Diagnostic Biomarkers of COPD Exacerbation. PLoS One 2016; 11(7):e0158843. doi: 0158810.0151371/journal.pone.0158843. eCollection 0152016.
Puteikis K, Mameniškienė R, Jurevičienė E. Neurological and Psychiatric Comorbidities in Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2021; 16:553-562.(doi):10.2147/COPD.S290363. eCollection 292021.
Xu K, Li X. Risk Factors for Depression in Patients with Chronic Obstructive Pulmonary Disease. Med Sci Monit 2018; 24:1417-1423.(doi):10.12659/msm.904969.
Tsai TY, Livneh H, Lu MC, Tsai PY, Chen PC, Sung FC. Increased risk and related factors of depression among patients with COPD: a population-based cohort study. BMC Public Health 2013; 13:976.(doi):10.1186/1471-2458-1113-1976.
Celano CM, Freudenreich O, Fernandez-Robles C, Stern TA, Caro MA, Huffman JC. Depressogenic effects of medications: a review. Dialogues Clin Neurosci 2011; 13(1):109-125. doi: 110.31887/DCNS.32011.31813.31881/ccelano.

No competing interests reported.

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Nomogram as a predictive model for depression risk in chronic obstructive pulmonary disease

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Abstract

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Introduction

Method

2.1. Study population

2.2. Depression And Candidate Predictor Variables

2.3. Statistical Analysis

Results

Discussion

Conclusion

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References

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