Heart Mind

ORIGINAL ARTICLE
Year
: 2022  |  Volume : 6  |  Issue : 2  |  Page : 62--69

Trends and outcomes of coronary artery bypass grafting in patients with major depressive disorder: A perspective from the national inpatient sample


Andrew Del Re1, Krissia M Rivera Perla2, Ghazal Aghagoli1, Krishna Bellam1, Frank W Sellke1, Afshin Ehsan1,  
1 Division of Cardiothoracic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
2 Division of Cardiothoracic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI; Department of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

Correspondence Address:
Dr. Afshin Ehsan
Medical Office Center, 2 Dudley St, Suite 360, Providence, RI 02905
USA

Abstract

Purpose: Coronary artery disease is a major cause of morbidity and mortality in the United States, representing the highest proportion of deaths due to cardiovascular disease. Treatment of coronary artery disease ranges from prevention to intervention, with the latter warranting a decision between surgical versus percutaneous revascularization. Medical optimization before coronary artery bypass grafting (CABG) is an important step in the care continuum. While the optimization of many risk factors such as smoking has been studied extensively, the inclusion of mental health conditions in preoperative health assessment is not yet standard of care. Major depressive disorder (MDD) is the most prevalent mental health disorder and has been shown to affect physiological processes that are critical in recovery after cardiac surgery. Methods: We queried the national inpatient sample from 2000 to 2017 for patients ≥18 years undergoing CABG with and without MDD. Patients who left against medical advice were excluded. Patients with a diagnosis of MDD were compared against those without. Our primary outcomes were in-hospital mortality, favorable discharge (home or home with services), and length of stay. Multivariable models were used for the various outcomes and each model adjusted for confounding variables. Results: A total of 2,988,997 met clinical criteria for inclusion including 108,782 with an MDD diagnosis. Most patients were male (n = 2,135,804, 71.46%), White (n = 2,417,216, 80.87%), and the average age was 66.3 years (standard deviation = 10.8 years). After adjustment, patients with a diagnosis of MDD were found to have lower odds of in-hospital mortality (odds ratio [OR] [95% confidence interval {CI}] 0.64 [0.56–0.73], P < 0.001) and had decreased odds of home discharge (OR = 0.66 [0.63–0.69], P < 0.001) after CABG. Overall, length of stay was similar between the groups, with MDD patients having a slightly longer length of stay (β-coefficient = 1.03 [1.03–1.04], P < 0.001). Patients with a diagnosis of MDD were also found to have lower odds of acute kidney injury (OR = 0.70 [0.61–0.81], P < 0.001), cardiogenic shock (OR = 0.75 [0.68–0.83], P < 0.001), infection (OR = 0.78 [0.69–0.89], P < 0.001), transient ischemic attack/stroke (OR = 0.75 [0.63–0.89], P = 0.001), acute liver injury (OR = 0.45 [0.34–0.61], P < 0.001), and acute limb ischemia (OR = 0.57 [0.40–0.82], P = 0.003). Conclusions: Patients with a diagnosis of MDD have decreased odds of postoperative morbidity and mortality after CABG in addition to having lower odds of home discharge. The present study suggests a need for prospective investigations on the impact of MDD diagnosis and outcomes after CABG to further understand this relationship.



How to cite this article:
Del Re A, Rivera Perla KM, Aghagoli G, Bellam K, Sellke FW, Ehsan A. Trends and outcomes of coronary artery bypass grafting in patients with major depressive disorder: A perspective from the national inpatient sample.Heart Mind 2022;6:62-69


How to cite this URL:
Del Re A, Rivera Perla KM, Aghagoli G, Bellam K, Sellke FW, Ehsan A. Trends and outcomes of coronary artery bypass grafting in patients with major depressive disorder: A perspective from the national inpatient sample. Heart Mind [serial online] 2022 [cited 2022 Dec 2 ];6:62-69
Available from: http://www.heartmindjournal.org/text.asp?2022/6/2/62/337986


Full Text



 Introduction



Cardiovascular disease is a significant cause of morbidity and mortality in the United States, representing approximately one-third of all yearly deaths, the largest of any chronic disease. In addition to imparting a significant quality of life burden to the individual, cardiovascular disease has also been attributed to national economic strain, with recent estimates suggesting a cost of approximately $214 billion to the U.S. Healthcare system per year.[1] Of the various subdivisions of cardiovascular disease, coronary artery disease remains the most lethal, representing 42.1% of all cardiovascular deaths in the United States.[2] Management of patients with coronary artery disease ranges from preventative strategies, such as lifestyle modification and medical therapy, to revascularization strategies such as percutaneous coronary intervention or coronary artery bypass grafting (CABG). CABG is generally reserved for patients with more complex coronary anatomy and severity of disease, as well as in diabetic patients with reduced ejection fraction.[3],[4],[5],[6],[7],[8] Tools to aid in clinical decision making include the EuroScore, EuroScore II, and the Society for Thoracic Surgeons risk calculator, with the final decision coming from a heart team approach that includes the expertise of the surgeon, cardiologist, and the patient's preference.[9],[10],[11] Patients who undergo CABG are afforded careful attention to their medical and surgical history to identify clinical factors that may introduce perioperative risk. Assessment of this risk includes investigation of common comorbidities such as liver, kidney, and lung disease, in addition to more cardiac-specific factors, such as the presence and degree of existing heart failure or conduction abnormalities.[12],[13],[14] While these individual parameters have been studied extensively as to their contribution to perioperative risk, recent data suggest that less traditionally associated comorbidities, such as mental illness, may also play a role in patient outcomes.[15],[16],[17],[18],[19]

In addition to coronary artery disease, mental health diagnoses represent a significant proportion of the national health burden, with approximately one in five adults in the United States having a diagnosis of an illness related to mental health.[20],[21] Among those, major depressive disorder (MDD) is the most common and is usually treated with a combination of cognitive-behavioral therapy and antidepressant medication.[22],[23] The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition defines MDD as having a history of one or more major depressive episodes and no history of mania or hypomania, with a major depressive episode comprised of at least five of the following symptoms occurring nearly every day for at least 2 consecutive weeks: depressed mood, anhedonia, insomnia/hypersomnia, significant weight change/change in appetite, psychomotor retardation/agitation, fatigue, decreased ability to concentrate, thoughts of worthlessness/guilt, and suicidal ideation/attempt, with at least one symptom being either depressed mood or anhedonia. MDD exists among other depressive disorders such as persistent depressive disorder (dysthymia), disruptive mood regulation disorder, premenstrual dysphoric disorder, substance/medication-induced depressive disorder, and depressive disorder due to another medical condition, all of which differ in severity and duration of symptoms.[24] MDD has been demonstrated to impede normal physiologic functioning, including dysfunction of the immune system (with simultaneous activation and suppression).[25],[26],[27] Likewise, studies have demonstrated that MDD has also been associated with increased serum levels of biomarkers reflective of an inflammatory state.[28],[29],[30] These physiologic implications may result in elevated risk for patients who rely on an intact immune system and the absence of prolonged inflammation for optimized recovery, such as patients undergoing CABG. In addition, many of the commonly prescribed antidepressant medications have side effects with known contributions to cardiovascular disease, such as weight gain and increased blood pressure.[31],[32],[33],[34],[35],[36] Studies have also demonstrated that depression is associated with a higher rate of a nonhome discharge after major surgical procedures.[37],[38],[39],[40] We, therefore, sought to investigate the outcomes of patients undergoing CABG with a comorbid diagnosis of MDD using a large, national database.

 Methods



Data source

We queried the National Inpatient Sample (NIS) from 2000 to 2017. The NIS, developed for the Healthcare Cost and Utilization Project (HCUP), is the largest publicly available database of inpatient admissions in the United States containing 7 million yearly admissions representing a 20% sample of all inpatient admissions in the United States.[41] The present study was deemed exempt from IRB review (IRB#1753188-3).

Study population

We conducted a cross-sectional study of patients 18 years or older who underwent CABG. Patients who left against medical advice were excluded. CABG and MDD diagnosis was ascertained through the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) procedure codes [Supplementary Table 1]. Patients with a diagnosis of MDD were compared to those without an MDD diagnosis. Patient characteristics such as age, sex, race, insurance type (Medicare, Medicaid, private insurance, and uninsured status), median household income quartile, All Patient Refined Disease Related Group (APR-DRG) severity of illness, APR-DRG risk of mortality, comorbidities (smoking status, dyslipidemia, hypertension, heart failure, prior coronary artery disease, prior myocardial infarction (MI), prior CABG, peripheral artery disease, etc.), elective admission, weekend admission, hospital location/teaching status, hospital region (Northeast, Midwest, South, and West), and hospital bed size (small, medium, and large) were collected. APR-DRG is a comorbidity measure created for HCUP and has been previously correlated to be comparable to the Charlson Comorbidity Index.[42],[43]

[INLINE:1]Outcomes

Our primary outcomes were in-hospital mortality, favorable discharge, and length of stay (LOS). Favorable discharge was denoted as discharge to home or home with services. Secondary outcomes included the following inpatient complications; acute MI, acute kidney injury (AKI), cardiogenic shock, infection, transient ischemic attack (TIA)/stroke, acute liver injury, and acute limb ischemia. The aforementioned complications were identified using ICD-9 and ICD-10 diagnosis codes.

Statistical analysis

All analyses were performed using StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. The svy command was used to apply weights and make national estimates using methodology outlined by HCUP.[44] Multivariable logistic regression models were used for binary outcomes. Careful attention was placed to adjust for confounding variables, avoid collinear variables, and avoid overfitting in each model. Model covariates were chosen a priori to avoid bias. We adjusted all models for age, median household income, race, APR-DRG risk of mortality (mortality model only), APR-DRG severity of illness (all models except mortality), elective surgery status, smoking (all except acute liver injury), coagulation defect (all except acute liver injury), chronic liver disease (all except acute limb ischemia), and heart failure (all except acute limb ischemia). In addition, all models except for acute liver failure and acute limb ischemia were also adjusted for diabetes mellitus, obesity, dyslipidemia, chronic pulmonary disease, hypertension, prior MI, prior CABG, atrial fibrillation/atrial flutter, and chronic kidney disease. We conducted gamma regression with a log-link function for LOS given the right-skewed distribution nature of this variable. We reported adjusted odds ratios (aORs) and 95% confidence limits for binary outcomes and β-coefficients for LOS. β-coefficient denotes a percent change in the outcome (ex.-coefficient = 1.09 indicates a 9% increase). Alpha level of significance was set a priori at 0.05.

 Results



Population characteristics

A total of 2,988,997 patients (mean age = 66.3 years) underwent CABG [Table 1]. Among CABG patients, 3.64% (n = 108,782) were also found to have a comorbid diagnosis of MDD. In CABG patients with a diagnosis of MDD, 42.03% were women, 5.51% were Black, and 26.37% had a median household income in the bottom 25th percentile nationally (compared to 28.04%, 6.38%, and 24.60% in those without). Medicare insurance was the most common source of insurance among patients with and without MDD [Table 1].{Table 1}

Primary outcomes

CABG patients with a comorbid MDD diagnosis experienced 1.7% decreased prevalence of mortality [Figure 1]a compared to patients without MDD (36% lower odds of in-hospital mortality (aOR = 0.64, 95% CI = [0.56–0.73], P < 0.001) [Table 2]. Among survivors, CABG patients with an MDD diagnosis had decreased home discharge (home or home with services) compared to patients without MDD (76.5% vs. 79.8%; [Figure 1]b; 34% lower odds of a favorable discharge (aOR = 0.66, 95% CI = [0.63–0.69], P < 0.001) [Table 2]. Furthermore, among survivors, a slight but significant difference (3% greater odds) in length of stay was appreciated in CABG patients with an MDD diagnosis compared to those without (β-coefficient = 1.03, 95% CI = [1.03–1.04], P < 0.001) [Table 2].{Figure 1}{Table 2}

Secondary outcomes

CABG patients with a comorbid diagnosis of MDD had significantly decreased odds of acute liver injury (aOR = 0.45, 95% CI = [0.34–0.61], P < 0.001), AKI (aOR = 0.70, 95% CI = [0.61–0.81], P < 0.001), cardiogenic shock (aOR = 0.75, 95% CI = [0.68–0.83], P < 0.001), infection (aOR = 0.78, 95% CI= [0.69–0.89], P < 0.001), acute limb ischemia (aOR = 0.57, 95% CI = [0.40–0.82], P = 0.003), and TIA/stroke (aOR = 0.75, 95% CI = [0.63–0.89], P = 0.001) [Table 2]. No significant differences were found in acute MI between groups (MDD vs. no MDD, aOR = 1.02, 95% CI = [0.99–1.06], P = 0.228) [Table 2].

 Discussion



This study is the first to investigate the relationship between the preoperative diagnosis of MDD and inpatient outcomes after CABG utilizing the NIS database. The results of this investigation revealed that CABG patients with a preoperative diagnosis of MDD experienced decreased adverse inpatient outcomes and were less likely to be discharged home. Several reports have demonstrated an association between preoperative depression and increased morbidity and mortality after major surgical procedures including cardiac surgery along with longer hospital length of stay, impaired wound healing, poor physical recovery, impaired quality of life, and increased postoperative pain and analgesic requirements.[45],[46],[47],[48],[49],[50],[51],[52],[53],[54],[55],[56],[57] In the field of cardiac surgery, research has primarily focused on the association between perioperative depression and long-term morbidity and mortality after surgery. Although seemingly contrary to the paradigm that comorbid depression increases risk for postoperative morbidity and mortality, other studies utilizing the NIS have arrived at similar conclusions. Zahner et al. investigated the impact of depression on outcomes of patients who presented to the hospital with critical limb ischemia (CLI) using the NIS. In this study, they found that those with CLI and depression had a lower risk of in-hospital mortality, lower rates of surgical revascularization and were less likely to have a MI compared to those without an MDD diagnosis.[58] Pan et al. demonstrated similar findings in their study investigating depression and complications after total knee arthroplasty.[59] Abrams et al. suggested that the presence of these seemingly contradictory findings is due to mental health diagnosis codes serving as a proxy for a lower severity hospital course, which is logically tethered to better inpatient outcomes as compared to more severe presentations.[60] Embedded in this explanation is the assumption that health-care systems and personnel would be less likely to uncover a mental health diagnosis in an acute presentation to the hospital compared to a more indolent one. This explanation may be well suited for our study, as we not only note these seemingly contrary findings, but do so alongside a prevalence of 3.64%, which is not only lower than the reported prevalence of depression among all US adults, but also lower than the prevalence of depression among adults with cardiovascular disease, which is notably higher than that of US adults.[61],[62],[63],[64],[65],[66] It is important to note, however, that the prevalence of depression reported in the literature varies, and the means by which studies have identified patients with depression is not consistent.[51]

One alternate explanation for our findings is that a diagnosis of MDD may serve as a surrogate for earlier engagement with the health-care system. Engagement within the health-care system has been described in the literature not only as one's attendance at health-care appointments, but also their knowledge, skill, and confidence in managing their own health.[67],[68],[69] Patients who are treated chronically for MDD are likely to be diagnosed and therefore prescribed treatment for comorbidities such as high blood pressure, high cholesterol, and other documented risk factors for adverse post-CABG outcomes. Strict management of the side-effects of current first-line medications for the treatment of MDD such as increased blood pressure and weight gain can further reduce cardiovascular risk relative to less compliant patients or those less engaged in the health-care system. Earlier engagement to the health-care system may therefore explain our findings whereby patients with a prior MDD diagnosis were found to have a decreased association of postoperative mortality, AKI, cardiogenic shock, infection, TIA/stroke, acute liver injury, and acute limb ischemia. This potential contributor, however, has not yet been robustly characterized in the literature investigating outcomes after surgery, though formative research has shown that there is interest in further understanding this relationship.[70]

Our results also showed that patients with a prior MDD diagnosis had 34% decreased odds of home discharge after CABG, despite controlling for age, income, insurance type, sex, race, comorbidity burden, elective admission, smoking, diabetes, and other comorbidities. This finding is consistent with other studies in the literature investigating the role of preoperative depression on nonhome discharge (NHD) in colorectal, liver transplant, orthopedic, and vascular surgery.[37],[40],[71],[72] It is presently unclear why patients with an MDD diagnosis who were found to have lower risk for morbidity and mortality after CABG would concurrently have an increased risk for NHD, though similar findings were reached in other studies investigating the impact of an MDD diagnosis of NHD after surgery.[37] One potential explanation for this finding is the effect of MDD on the ability to accomplish activities of daily living (ADLs). MDD has been demonstrated to increase risk of complex activity limitations (especially in those with coexisting physical functioning difficulties), which may interfere with the ability to complete ADLs necessary for a home discharge with or without services.[73]

It is important to note that there are limitations to this investigation. Although the NIS allows us to investigate trends across hospitals in 47 states, the NIS does not provide us with access to imaging and other detailed clinical variables afforded by single institution studies. The lack of granularity of ICD-9 coding also precluded us from conducting investigations into the impact of MDD on CABG procedures with nuanced features, such as single versus multiple arterial revascularization or differences in ejection fraction. In addition, our study relies on accurate coding of diagnoses, procedures, and outcomes, which may not be done consistently among providers and institutions.[74] Temporality of clinical events is also limited by the cross-sectional nature of the NIS, thus the attributions of clinical events relative to our categorized risk factors are reliant on the assumptions that: (1) risk factors of interest have been present for a sufficient enough time to reasonably confer risk to our outcomes of interest and (2) the outcomes of interest occurred specifically in the postoperative inpatient period. Finally, MDD is notoriously underdiagnosed and in some cases misdiagnosed (the Patient Health Questionnaire-9 scores >10, the primary diagnostic tool for MDD, has a sensitivity of 88% and a specificity of 88% for MDD).[74],[75],[76],[77],[78],[79],[80] This introduces the possibility that patients in our analyses were incorrectly categorized as having an MDD diagnosis when they truly did not have MDD or vice versa.

 Conclusions



Our study demonstrates that patients with a preoperative diagnosis of MDD had a lower association with adverse outcomes following CABG and were less likely to be discharged home after their admission. In line with other studies utilizing NIS data to investigate preoperative MDD on outcomes after surgery, our results may reflect the fact that patients with MDD diagnoses may have lower severity hospital courses and thus better outcomes. Our results may also suggest that MDD diagnosis could serve as a surrogate for earlier engagement with and stricter follow-up through the health-care system. The connection with the health-care system could help identify and address risk factors such as high blood pressure and high cholesterol associated with adverse post-CABG outcomes. Prospective investigation to better determine the effect of MDD and early engagement of the health-care system on postoperative outcomes after cardiac surgery are warranted.

Acknowledgments

The authors thank the Division of Cardiothoracic Surgery at Rhode Island Hospital for their support in this project.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Chang S, et al. Heart disease and stroke statistics – 2018 update: A report from the American Heart Association. Circulation 2018;137:e67–e492.
2Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics – 2021 update: A report from the American Heart Association. Circulation 2021;143:e254–e743.
3Park SJ, Ahn JM, Kim YH, Park DW, Yun SC, Lee JY, et al. Trial of everolimus-eluting stents or bypass surgery for coronary disease. N Engl J Med 2015;372:1204-12.
4Serruys PW, Morice MC, Kappetein AP, Colombo A, Holmes DR, Mack MJ, et al. Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N Engl J Med 2009;360:961-72.
5Head SJ, Davierwala PM, Serruys PW, Redwood SR, Colombo A, Mack MJ, et al. Coronary artery bypass grafting vs. percutaneous coronary intervention for patients with three-vessel disease: Final five-year follow-up of the SYNTAX trial. Eur Heart J 2014;35:2821-30.
6Kapur A, Hall RJ, Malik IS, Qureshi AC, Butts J, de Belder M, et al. Randomized comparison of percutaneous coronary intervention with coronary artery bypass grafting in diabetic patients. 1-year results of the CARDia (Coronary Artery Revascularization in Diabetes) trial. J Am Coll Cardiol 2010;55:432-40.
7Farkouh ME, Domanski M, Sleeper LA, Siami FS, Dangas G, Mack M, et al. Strategies for multivessel revascularization in patients with diabetes. N Engl J Med 2012;367:2375-84.
8Kamalesh M, Sharp TG, Tang XC, Shunk K, Ward HB, Walsh J, et al. Percutaneous coronary Intervention Versus coronary bypass surgery in United States veterans with diabetes. J Am Coll Cardiol 2013;61:808-16.
9Nashef SA, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, et al. EuroSCORE II. Eur J Cardiothorac Surg 2012;41:734-44.
10Roques F, Michel P, Goldstone AR, Nashef SA. The logistic EuroSCORE. Eur Heart J 2003;24:881-2.
11Shahian DM, O'Brien SM, Filardo G, Ferraris VA, Haan CK, Rich JB, et al. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: Part 1 – Coronary artery bypass grafting surgery. Ann Thorac Surg 2009;88:S2-22.
12Authors/Task Force Members; Poldermans D, Bax JJ, Boersma E, De Hert S, Eeckhout E, et al. Guidelines for pre-operative cardiac risk assessment and perioperative cardiac management in non-cardiac surgery. Eur Heart J 2009;30:2769-812.
13Waldfahrer F. Management of patients with risk factors. GMS Curr Top Otorhinolaryngol Head Neck Surg 2013;12:Doc02.
14Diaz-Fuentes G, Hashmi HR, Venkatram S. Perioperative evaluation of patients with pulmonary conditions undergoing non-cardiothoracic surgery. Health Serv Insights 2016;9:9-23.
15Bailey EA, Wirtalla C, Sharoky CE, Kelz RR. Disparities in operative outcomes in patients with comorbid mental illness. Surgery 2018;163:667-71.
16Lee DS, Marsh L, Garcia-Altieri MA, Chiu LW, Awad SS. Active mental illnesses adversely affect surgical outcomes. Am Surg 2016;82:1238-43.
17Paredes AZ, Hyer JM, Diaz A, Tsilimigras DI, Pawlik TM. The impact of mental illness on postoperative outcomes among medicare beneficiaries: A Missed opportunity to help surgical patients? Ann Surg 2020;272:419-25.
18McBride KE, Solomon MJ, Young JM, Steffens D, Lambert TJ, Glozier N, et al. Impact of serious mental illness on surgical patient outcomes. ANZ J Surg 2018;88(7-8):673-7. [doi: 10.1111/ans. 14508].
19McBride KE, Solomon MJ, Bannon PG, Glozier N, Steffens D. Surgical outcomes for people with serious mental illness are poorer than for other patients: A systematic review and meta-analysis. Med J Aust 2021;214:379-85.
20Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication – Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 2010;49:980-9.
21Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health (HHS Publication No. PEP20-07-01-001, NSDUH Series H-55). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Published 2020. Accessed September 2021. Retrieved from https://www.samhsa.gov/data/.
22Cuijpers P, van Straten A, Warmerdam L, Andersson G. Psychotherapy versus the combination of psychotherapy and pharmacotherapy in the treatment of depression: A meta-analysis. Depress Anxiety 2009;26:279-88.
23Cuijpers P, Dekker J, Hollon SD, Andersson G, et al. Adding psychotherapy to pharmacotherapy in the treatment of depressive disorders in adults: A meta-analysis. 2009;70:1219-29.
24American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.
25Blume J, Douglas SD, Evans DL. Immune suppression and immune activation in depression. Brain Behav Immun 2011;25:221-9.
26Leonard BE. The concept of depression as a dysfunction of the immune system. Curr Immunol Rev 2010;6:205-12.
27Maes M. Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression. Prog Neuropsychopharmacol Biol Psychiatry 2011;35:664-75.
28Kohler O, Krogh J, Mors O, Benros ME. Inflammation in depression and the potential for anti-inflammatory treatment. Curr Neuropharmacol 2016;14:732-42.
29Dantzer R. Cytokine, sickness behavior, and depression. Immunol Allergy Clin North Am 2009;29:247-64.
30Valkanova V, Ebmeier KP, Allan CL. CRP, IL-6 and depression: A systematic review and meta-analysis of longitudinal studies. J Affect Disord 2013;150:736-44.
31Fava M. Weight gain and antidepressants. J Clin Psychiatry 2000;61 Suppl 11:37-41.
32Breeden M, Brieler J, Salas J, Scherrer JF. Antidepressants and incident hypertension in primary care patients. J Am Board Fam Med 2018;31:22-8.
33Kıvrak Y, Güvenç TS, Akbulut N, Yağcı I, Cığşar G, Gündüz S, et al. Accelerated hypertension after venlafaxine usage. Case Rep Psychiatry 2014;2014:659715.
34Thase ME. Effects of venlafaxine on blood pressure: A meta-analysis of original data from 3744 depressed patients. J Clin Psychiatry 1998;59:502-8.
35Russo D, Morrone LF, Errichiello C, De Gregorio MG, Imbriaco M, Battaglia Y, et al. Impact of BMI on cardiovascular events, renal function, and coronary artery calcification. Blood Purif 2014;38:1-6.
36Jokinen E. Obesity and cardiovascular disease. Minerva Pediatr 2015;67:25-32.
37Ramirez JL, Zarkowsky DS, Ramirez FD, Gasper WJ, Cohen BE, Conte MS, et al. Depression predicts non-home discharge after abdominal aortic aneurysm repair. Ann Vasc Surg 2021;74:131-40.
38Ramkumar PN, Gwam C, Navarro SM, Haeberle HS, Karnuta JM, Delanois RE, et al. Discharge to the skilled nursing facility: Patient risk factors and perioperative outcomes after total knee arthroplasty. Ann Transl Med 2019;7:65.
39Paro A, Hyer JM, Pawlik T. Association of depression with in-patient and post-discharge disposition and expenditures among medicare beneficiaries undergoing resection for cancer. Ann Surg Oncol 2021;28:6525-34.
40Rogal SS, Mankaney G, Udawatta V, Chinman M, Good CB, Zickmund S, et al. Pre-transplant depression is associated with length of hospitalization, discharge disposition, and survival after liver transplantation. PLoS One 2016;11:e0165517.
41HCUP Databases. Healthcare Cost and Utilization Project (HCUP). September 2021. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/nisoverview.jsp.
42Averill RF, Goldfield N, Hughes JS, Bonazelli J, McCullough EC, Steinbeck BA, et al. All Patient Refined Diagnosis Related Groups (APR-DRGs) Methodology Overview 3M Health Information Systems; 2003. Available from: https://www. 3Mhis.com. [Last accessed on 2020 Nov 12].
43Monterde D, Cainzos-Achirica M, Cossio-Gil Y, García-Eroles L, Pérez-Sust P, Arrufat M, et al. Performance of comprehensive risk adjustment for the prediction of in-hospital events using administrative healthcare data: The queralt indices. Risk Manag Healthc Policy 2020;13:271-83.
44Producing National HCUP Estimates - Accessible Version. Healthcare Cost and Utilization Project (HCUP). December 2018. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/tech_assist/nationalestimates/508_course/508course_2018.jsp.
45Tully PJ, Baker RA, Knight JL. Anxiety and depression as risk factors for mortality after coronary artery bypass surgery. J Psychosom Res 2008;64:285-90.
46Bozic KJ, Lau E, Kurtz S, Ong K, Berry DJ. Patient-related risk factors for postoperative mortality and periprosthetic joint infection in medicare patients undergoing TKA. Clin Orthop Relat Res 2012;470:130-7.
47Smith P, Palmer S, Snyder L, Hoffman B, Ingle K, Stonerock G, et al. Depressive symptoms and post-transplant mortality: Examining the influence of perioperative outcomes. J Heart Lung Transplantat 2017;36:S208.
48Takagi H, Ando T, Umemoto T. Perioperative depression or anxiety and postoperative mortality in cardiac surgery: A systematic review and meta-analysis. Heart Vessels 2017;32:1458-68.
49Blumenthal JA, Lett HS, Babyak MA, White W, Smith PK, Mark DB, et al. Depression as a risk factor for mortality after coronary artery bypass surgery. Lancet 2003;362:604-9.
50Baker R, Andrew M, Schrader G, Knight J. Does preoperative depression predict increased late mortality in cardiac surgical patients? Ann Thorac Surg 2000;70:1789.
51Flaherty LB, Wood T, Cheng A, Khan AR. Pre-existing psychological depression confers increased risk of adverse cardiovascular outcomes following cardiac surgery: A systematic review and meta-analysis. J Thorac Cardiovasc Surg 2017;154:1578-86.e1.
52Oldham M, Hawkins K, Lin I, Deng Y, Hao Q, Scoutt LM, et al. Depression predicts delirium after coronary artery bypass graft surgery independent of cognitive impairment and cerebrovascular disease: An analysis of the neuropsychiatric outcomes after heart surgery study. Am J Geriatr Psychiatry 2019;27:476-86.
53Poole L, Kidd T, Leigh E, Ronaldson A, Jahangiri M, Steptoe A. Depression, C-reactive protein and length of post-operative hospital stay in coronary artery bypass graft surgery patients. Brain Behav Immun 2014;37:115-21.
54Connerney I, Shapiro PA, McLaughlin JS, Bagiella E, Sloan RP. Relation between depression after coronary artery bypass surgery and 12-month outcome: A prospective study. Lancet 2001;358:1766-71.
55Taenzer P, Melzack R, Jeans ME. Influence of psychological factors on postoperative pain, mood and analgesic requirements. Pain 1986;24:331-42.
56De Cosmo G, Congedo E, Lai C, Primieri P, Dottarelli A, Aceto P. Preoperative psychologic and demographic predictors of pain perception and tramadol consumption using intravenous patient-controlled analgesia. Clin J Pain 2008;24:399-405.
57Doering LV, Moser DK, Lemankiewicz W, Luper C, Khan S. Depression, healing, and recovery from coronary artery bypass surgery. Am J Crit Care 2005;14:316-24.
58Zahner GJ, Cortez A, Duralde E, Ramirez JL, Wang S, Hiramoto J, et al. Association of comorbid depression with inpatient outcomes in critical limb ischemia. Vasc Med 2020;25:25-32.
59Pan X, Wang J, Lin Z, Dai W, Shi Z. Depression and anxiety are risk factors for postoperative pain-related symptoms and complications in patients undergoing primary total knee arthroplasty in the United States. J Arthroplasty 2019;34:2337-46.
60Abrams TE, Vaughan-Sarrazin M, Rosenthal GE. Variations in the associations between psychiatric comorbidity and hospital mortality according to the method of identifying psychiatric diagnoses. J Gen Intern Med 2008;23:317-22.
61Brody DJ, Pratt LA, Hughes JP. Prevalence of depression among adults aged 20 and over: United States, 2013-2016. NCHS Data Brief 2018;303:1-8.
62NIMH: Major Depression. National Institute of Mental Health. Available from: https://www.nimh.nih.gov/health/statistics/major-depression. [Last accessed on 2021 Sep 08].
63Kurosawa H, Shimizu Y, Nishimatsu Y, Hirose S, Takano T. The relationship between mental disorders and physical severities in patients with acute myocardial infarction. Jpn Circ J 1983;47:723-8.
64Wynn A. Unwarranted emotional distress in men with ischaemic heart disease (IHD). Med J Aust 1967;2:847-51.
65Cay EL, Vetter N, Philip AE, Dugard P. Psychological status during recovery from an acute heart attack. J Psychosom Res 1972;16:425-35.
66Stern MJ, Pascale L, Ackerman A. Life adjustment postmyocardial infarction: Determining predictive variables. Arch Intern Med 1977;137:1680-5.
67Carman KL, Workman TA. Engaging patients and consumers in research evidence: Applying the conceptual model of patient and family engagement. Patient Educ Couns 2017;100:25-9.
68Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res 2005;40:1918-30.
69Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Serv Res 2004;39:1005-26.
70Yun PS, MacDonald CL, Orne J, Gutierrez-Meza D, Buentello G, Street R, et al. A novel surgical patient engagement model: A qualitative study of postoperative patients. J Surg Res 2020;248:82-9.
71Wilson JM, Farley KX, Erens GA, Bradbury TL, Guild GN. Preoperative depression is associated with increased risk following revision total joint arthroplasty. J Arthroplasty 2020;35:1048-53.
72Balentine CJ, Hermosillo-Rodriguez J, Robinson CN, Berger DH, Naik AD. Depression is associated with prolonged and complicated recovery following colorectal surgery. J Gastrointest Surg 2011;15:1712-7.
73Jonas BS, Loeb M. Mood disorders and physical functioning difficulties as predictors of complex activity limitations in young U.S. adults. Disabil Health J 2010;3:171-8.
74Chang TE, Tong X, George MG, Coleman King SM, Yin X, O'Brien S, et al. Trends and factors associated with concordance between international classification of diseases, ninth and tenth revision, clinical modification codes and stroke clinical diagnoses. Stroke 2019;50:1959-67.
75Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613. doi:10.1046/j.1525-1497.2001.016009606.x
76Levis B, Sun Y, He C, Wu Y, Krishnan A, Bhandari PM, et al. Accuracy of the PHQ-2 alone and in combination with the PHQ-9 for screening to detect major depression: Systematic review and meta-analysis. JAMA 2020;323:2290-300.
77Wu Y, Levis B, Riehm KE, Saadat N, Levis AW, Azar M, et al. Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: A systematic review and individual participant data meta-analysis. Psychol Med 2020;50:1368-80.
78Kroenke K, Spitzer RL, Williams JB, Löwe B. The Patient Health Questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. Gen Hosp Psychiatry 2010;32:345-59.
79Manea L, Gilbody S, McMillan D. A diagnostic meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) algorithm scoring method as a screen for depression. Gen Hosp Psychiatry 2015;37:67-75.
80Lee HJ, Kim SH, Lee MS. Understanding mood disorders in children. Adv Exp Med Biol 2019;1192:251-61.