[Skip to Navigation]
Sign In
Figure.  Attributable Costs Due to Incident Delirium and Delirium Severity (Adjusted Mean Differences, 95% Confidence Intervals)
Attributable Costs Due to Incident Delirium and Delirium Severity (Adjusted Mean Differences, 95% Confidence Intervals)

Attributable costs (adjusted mean differences) are presented for incident delirium (A) (delirium vs no delirium) and for delirium severity (B) (severe vs none-mild severity groups) through 1 year after surgery. The excess cumulative costs attributable to incident delirium are $44 291 (95% CI, $34 554-$56 673). The excess cumulative costs attributable to severe delirium are $56 474 (95% CI, $40 927-$77 440). The shaded area represents 95% CI.

Table 1.  Baseline Characteristics of Patients
Baseline Characteristics of Patients
Table 2.  Adjusted Health Care Costs per Patient Associated With Incident Deliriuma
Adjusted Health Care Costs per Patient Associated With Incident Deliriuma
Table 3.  Adjusted Health Care Costs per Patient Associated With Delirium Severitya
Adjusted Health Care Costs per Patient Associated With Delirium Severitya
1.
Inouye  SK, Westendorp  RG, Saczynski  JS.  Delirium in elderly people.   Lancet. 2014;383(9920):911-922. doi:10.1016/S0140-6736(13)60688-1 PubMedGoogle ScholarCrossref
2.
Oh  ES, Fong  TG, Hshieh  TT, Inouye  SK.  Delirium in older persons: advances in diagnosis and treatment.   JAMA. 2017;318(12):1161-1174. doi:10.1001/jama.2017.12067 PubMedGoogle ScholarCrossref
3.
Hshieh  TT, Saczynski  J, Gou  RY,  et al; SAGES Study Group.  Trajectory of functional recovery after postoperative delirium in elective surgery.   Ann Surg. 2017;265(4):647-653. doi:10.1097/SLA.0000000000001952 PubMedGoogle ScholarCrossref
4.
Hshieh  TT, Yang  T, Gartaganis  SL, Yue  J, Inouye  SK. Hospital elder life program: systematic review and meta-analysis of effectiveness.  Am J Geriatr Psychiatry. 2018;26(10):1015-1033. doi:10.1016/j.jagp.2018.06.007PubMedCrossref
5.
Hshieh  TT, Yue  J, Oh  E,  et al.  Effectiveness of multicomponent nonpharmacological delirium interventions: a meta-analysis.   JAMA Intern Med. 2015;175(4):512-520. doi:10.1001/jamainternmed.2014.7779 PubMedGoogle ScholarCrossref
6.
Vasunilashorn  SM, Fong  TG, Albuquerque  A,  et al.  Delirium severity post-surgery and its relationship with long-term cognitive decline in a cohort of patients without dementia.   J Alzheimers Dis. 2018;61(1):347-358. doi:10.3233/JAD-170288 PubMedGoogle ScholarCrossref
7.
Morandi  A, Davis  D, Fick  DM,  et al.  Delirium superimposed on dementia strongly predicts worse outcomes in older rehabilitation inpatients.   J Am Med Dir Assoc. 2014;15(5):349-354. doi:10.1016/j.jamda.2013.12.084 PubMedGoogle ScholarCrossref
8.
Fong  TG, Davis  D, Growdon  ME, Albuquerque  A, Inouye  SK.  The interface between delirium and dementia in elderly adults.   Lancet Neurol. 2015;14(8):823-832. doi:10.1016/S1474-4422(15)00101-5 PubMedGoogle ScholarCrossref
9.
Hshieh  TT, Fong  TG, Schmitt  EM,  et al; BASIL Study Group.  Does Alzheimer’s disease and related dementias modify delirium severity and hospital outcomes?   J Am Geriatr Soc. 2020;68(8):1722-1730. doi:10.1111/jgs.16420 PubMedGoogle ScholarCrossref
10.
Inouye  SK, Marcantonio  ER, Kosar  CM,  et al.  The short-term and long-term relationship between delirium and cognitive trajectory in older surgical patients.   Alzheimers Dement. 2016;12(7):766-775. doi:10.1016/j.jalz.2016.03.005 PubMedGoogle ScholarCrossref
11.
Leslie  DL, Inouye  SK.  The importance of delirium: economic and societal costs.   J Am Geriatr Soc. 2011;59(suppl 2):S241-S243. doi:10.1111/j.1532-5415.2011.03671.x PubMedGoogle ScholarCrossref
12.
Leslie  DL, Marcantonio  ER, Zhang  Y, Leo-Summers  L, Inouye  SK.  One-year health care costs associated with delirium in the elderly population.   Arch Intern Med. 2008;168(1):27-32. doi:10.1001/archinternmed.2007.4 PubMedGoogle ScholarCrossref
13.
Vasilevskis  EE, Meltzer  D, Schnipper  J,  et al.  Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non-hospitalists.   J Gen Intern Med. 2008;23(9):1399-1406. doi:10.1007/s11606-008-0680-3 PubMedGoogle ScholarCrossref
14.
Brown  CH  IV, Laflam  A, Max  L,  et al.  The impact of delirium after cardiac surgical procedures on postoperative resource use.   Ann Thorac Surg. 2016;101(5):1663-1669. doi:10.1016/j.athoracsur.2015.12.074 PubMedGoogle ScholarCrossref
15.
Franco  K, Litaker  D, Locala  J, Bronson  D.  The cost of delirium in the surgical patient.   Psychosomatics. 2001;42(1):68-73. doi:10.1176/appi.psy.42.1.68 PubMedGoogle ScholarCrossref
16.
Caplan  GA, Teodorczuk  A, Streatfeild  J, Agar  MR.  The financial and social costs of delirium.   Eur Geriatr Med. 2020;11(1):105-112. doi:10.1007/s41999-019-00257-2 PubMedGoogle ScholarCrossref
17.
Schmitt  EM, Marcantonio  ER, Alsop  DC,  et al. Novel risk markers and long-term outcomes of delirium: the Successful Aging after Elective Surgery (SAGES) study design and methods.  J Am Med Dir Assoc. 2012;13(9):818.e1-818.e10. doi:10.1016/j.jamda.2012.08.004PubMedCrossref
18.
Schmitt  EM, Saczynski  JS, Kosar  CM,  et al; SAGES Study Group.  The Successful Aging after Elective Surgery (SAGES) study: cohort description and data quality procedures.   J Am Geriatr Soc. 2015;63(12):2463-2471. doi:10.1111/jgs.13793 PubMedGoogle ScholarCrossref
19.
Teng  ELCH, Chui  HC.  The Modified Mini-Mental State (3MS) examination.   J Clin Psychiatry. 1987;48(8):314-318.PubMedGoogle Scholar
20.
Katz  S, Ford  AB, Moskowitz  RW, Jackson  BA, Jaffe  MW.  Studies of illness in the aged. the index of ADL: a standardized measure of biological and psychosocial function.   JAMA. 1963;185:914-919. doi:10.1001/jama.1963.03060120024016 PubMedGoogle ScholarCrossref
21.
Lawton  MP, Brody  EM.  Assessment of older people: self-maintaining and instrumental activities of daily living.   Gerontologist. 1969;9(3):179-186. doi:10.1093/geront/9.3_Part_1.179 PubMedGoogle ScholarCrossref
22.
Jorm  AF.  A short form of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): development and cross-validation.   Psychol Med. 1994;24(1):145-153. doi:10.1017/S003329170002691X PubMedGoogle ScholarCrossref
23.
Jorm  AF.  The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): a review.   Int Psychogeriatr. 2004;16(3):275-293. doi:10.1017/S1041610204000390 PubMedGoogle ScholarCrossref
24.
Research Data Assistance Center. About us. Accessed May 17, 2020. https://www.resdac.org/about-resdac
25.
Simon  SE, Bergmann  MA, Jones  RN, Murphy  KM, Orav  EJ, Marcantonio  ER.  Reliability of a structured assessment for nonclinicians to detect delirium among new admissions to postacute care.   J Am Med Dir Assoc. 2006;7(7):412-415. doi:10.1016/j.jamda.2006.02.006 PubMedGoogle ScholarCrossref
26.
Albert  MS, Levkoff  SE, Reilly  C,  et al.  The delirium symptom interview: an interview for the detection of delirium symptoms in hospitalized patients.   J Geriatr Psychiatry Neurol. 1992;5(1):14-21. doi:10.1177/002383099200500103 PubMedGoogle ScholarCrossref
27.
Wong  CL, Holroyd-Leduc  J, Simel  DL, Straus  SE.  Does this patient have delirium?: value of bedside instruments.   JAMA. 2010;304(7):779-786. doi:10.1001/jama.2010.1182 PubMedGoogle ScholarCrossref
28.
Wei  LA, Fearing  MA, Sternberg  EJ, Inouye  SK.  The Confusion Assessment Method: a systematic review of current usage.   J Am Geriatr Soc. 2008;56(5):823-830. doi:10.1111/j.1532-5415.2008.01674.x PubMedGoogle ScholarCrossref
29.
Inouye  SK, Leo-Summers  L, Zhang  Y, Bogardus  ST  Jr, Leslie  DL, Agostini  JV.  A chart-based method for identification of delirium: validation compared with interviewer ratings using the confusion assessment method.   J Am Geriatr Soc. 2005;53(2):312-318. doi:10.1111/j.1532-5415.2005.53120.x PubMedGoogle ScholarCrossref
30.
Saczynski  JS, Kosar  CM, Xu  G,  et al.  A tale of two methods: chart and interview methods for identifying delirium.   J Am Geriatr Soc. 2014;62(3):518-524. doi:10.1111/jgs.12684 PubMedGoogle ScholarCrossref
31.
Gross  AL, Jones  RN, Fong  TG, Tommet  D, Inouye  SK.  Calibration and validation of an innovative approach for estimating general cognitive performance.   Neuroepidemiology. 2014;42(3):144-153. doi:10.1159/000357647 PubMedGoogle ScholarCrossref
32.
Inouye  SK, Kosar  CM, Tommet  D,  et al.  The CAM-S: development and validation of a new scoring system for delirium severity in 2 cohorts.   Ann Intern Med. 2014;160(8):526-533. doi:10.7326/M13-1927 PubMedGoogle ScholarCrossref
33.
US Bureau of Labor Statistics. Consumer price index databases. Accessed November 13, 2019. https://www.bls.gov/cpi/data.htm
34.
Manning  WG, Basu  A, Mullahy  J.  Generalized modeling approaches to risk adjustment of skewed outcomes data.   J Health Econ. 2005;24(3):465-488. doi:10.1016/j.jhealeco.2004.09.011 PubMedGoogle ScholarCrossref
35.
Deb  P, Norton  EC.  Modeling health care expenditures and use.   Annu Rev Public Health. 2018;39:489-505. doi:10.1146/annurev-publhealth-040617-013517 PubMedGoogle ScholarCrossref
36.
Mayhew  D, Mendonca  V, Murthy  BVS.  A review of ASA physical status - historical perspectives and modern developments.   Anaesthesia. 2019;74(3):373-379. doi:10.1111/anae.14569 PubMedGoogle ScholarCrossref
37.
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.   J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8 PubMedGoogle ScholarCrossref
38.
United States Census Bureau. US and world population clock. Accessed May 18, 2020. https://www.census.gov/popclock/
39.
United States Census Bureau. Facts for features: older Americans month: May 2017. Accessed May 18, 2020. https://www.census.gov/newsroom/facts-for-features/2017/cb17-ff08.html
40.
Sun  R, Karaca  Z, Wong  H. Statistical brief #235: trends in hospital inpatient stays by age and payer, 2000-2015. Accessed May 18, 2020. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb235-Inpatient-Stays-Age-Payer-Trends.jsp
41.
Vasunilashorn  SM, Marcantonio  ER, Gou  Y,  et al.  Quantifying the severity of a delirium episode throughout hospitalization: the combined importance of intensity and duration.   J Gen Intern Med. 2016;31(10):1164-1171. doi:10.1007/s11606-016-3671-9 PubMedGoogle ScholarCrossref
42.
Gu  Q, Koenig  L, Mather  RC  III, Tongue  J.  Surgery for hip fracture yields societal benefits that exceed the direct medical costs.   Clin Orthop Relat Res. 2014;472(11):3536-3546. doi:10.1007/s11999-014-3820-6 PubMedGoogle ScholarCrossref
43.
Burns  ER, Stevens  JA, Lee  R.  The direct costs of fatal and non-fatal falls among older adults—United States.   J Safety Res. 2016;58:99-103. doi:10.1016/j.jsr.2016.05.001 PubMedGoogle ScholarCrossref
44.
American Diabetes Association. Economic costs of diabetes in the US in 2017.  Diabetes Care. 2018;41(5):917-928.doi:10.2337/dci18-0007PubMedCrossref
45.
Benjamin  EJ, Virani  SS, Callaway  CW,  et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics-2018 update: a report from the American Heart Association.   Circulation. 2018;137(12):e67-e492. doi:10.1161/CIR.0000000000000558 PubMedGoogle ScholarCrossref
1 Comment for this article
Reference correction
Tammy Hshieh, MD, MPH | Brigham and Women's Hospital
Please note that Reference #13 is erroneous. Correct citation:
Vasilevskis EE, Chandrasekhar R, Holtze CH, et al. The Cost of ICU Delirium and Coma in the Intensive Care Unit Patient. Med Care. Published online August 31, 2018. doi:10.1097/MLR.0000000000000975.
CONFLICT OF INTEREST: None Reported
Original Investigation
February 24, 2021

One-Year Medicare Costs Associated With Delirium in Older Patients Undergoing Major Elective Surgery

Author Affiliations
  • 1Aging Brain Center, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts
  • 2Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
  • 3Harvard Medical School, Boston, Massachusetts
  • 4Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 5Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
  • 6Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, Rhode Island
  • 7Department of Neurology, Warren Alpert Medical School, Brown University, Providence, Rhode Island
  • 8Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 9Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 10Department of Orthopedic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
  • 11Department of Orthopedic Surgery, Brigham and Women’s Faulkner Hospital, Boston, Massachusetts
  • 12Center for Applied Studies in Health Economics, Department of Public Health Sciences, Pennsylvania State College of Medicine, Hershey, Pennsylvania
  • 13Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
JAMA Surg. 2021;156(5):462-470. doi:10.1001/jamasurg.2020.7260
Key Points

Question  What Medicare costs are attributable to delirium after elective surgery each year?

Findings  In this cohort study of 497 older adults undergoing major elective surgery, the cumulative costs attributable to delirium were $44 291 per patient over 1 year. The adjusted mean cumulative costs attributable to severe delirium were $56 474 per patient over 1 year.

Meaning  Results suggest that incident delirium and severe delirium after elective surgery are associated with a high cost to the health care system, with substantial public health implications that warrant renewed efforts to bolster prevention, early detection, and management of delirium.

Abstract

Importance  Delirium is a common, serious, and potentially preventable problem for older adults, associated with adverse outcomes. Coupled with its preventable nature, these adverse sequelae make delirium a significant public health concern; understanding its economic costs is important for policy makers and health care leaders to prioritize care.

Objective  To evaluate current 1-year health care costs attributable to postoperative delirium in older patients undergoing elective surgery.

Design, Setting, and Participants  This prospective cohort study included 497 patients from the Successful Aging after Elective Surgery (SAGES) study, an ongoing cohort study of older adults undergoing major elective surgery. Patients were enrolled from June 18, 2010, to August 8, 2013. Eligible patients were 70 years or older, English-speaking, able to communicate verbally, and scheduled to undergo major surgery at 1 of 2 Harvard-affiliated hospitals with an anticipated length of stay of at least 3 days. Eligible surgical procedures included total hip or knee replacement; lumbar, cervical, or sacral laminectomy; lower extremity arterial bypass surgery; open abdominal aortic aneurysm repair; and open or laparoscopic colectomy. Data were analyzed from October 15, 2019, to September 15, 2020.

Exposures  Major elective surgery and hospitalization.

Main Outcomes and Measures  Cumulative and period-specific costs (index hospitalization, 30-day, 90-day, and 1-year follow-up) were examined using Medicare claims and extensive clinical data. Total inflation-adjusted health care costs were determined using data from Medicare administrative claims files for the 2010 to 2014 period. Delirium was rated using the Confusion Assessment Method. We also examined whether increasing delirium severity was associated with higher cumulative and period-specific costs. Delirium severity was measured with the Confusion Assessment Method–Severity long form. Regression models were used to determine costs associated with delirium after adjusting for patient demographic and clinical characteristics.

Results  Of the 566 patients who were eligible for the study, a total of 497 patients (mean [SD] age, 76.8 [5.1] years; 281 women [57%]; 461 White participants [93%]) were enrolled after exclusion criteria were applied. During the index hospitalization, 122 patients (25%) developed postoperative delirium, whereas 375 (75%) did not. Patients with delirium had significantly higher unadjusted health care costs than patients without delirium (mean [SD] cost, $146 358 [$140 469] vs $94 609 [$80 648]). After adjusting for relevant confounders, the cumulative health care costs attributable to delirium were $44 291 (95% CI, $34 554-$56 673) per patient per year, with the majority of costs coming from the first 90 days: index hospitalization ($20 327), subsequent rehospitalizations ($27 797), and postacute rehabilitation stays ($2803). Health care costs increased directly and significantly with level of delirium severity (none-mild, $83 534; moderate, $99 756; severe, $140 008), suggesting an exposure-response relationship. The adjusted mean cumulative costs attributable to severe delirium were $56 474 (95% CI, $40 927-$77 440) per patient per year. Extrapolating nationally, the health care costs attributable to postoperative delirium were estimated at $32.9 billion (95% CI, $25.7 billion-$42.2 billion) per year.

Conclusions and Relevance  These findings suggest that the economic outcomes of delirium and severe delirium after elective surgery are substantial, rivaling costs associated with cardiovascular disease and diabetes. These results highlight the need for policy imperatives to address delirium as a large-scale public health issue.

Introduction

Delirium is a common, serious clinical problem for older adults, often complicating major surgery.1 Delirium has been associated with poor hospital outcomes, including prolonged hospital stay, functional and cognitive decline, institutionalization, and death.2,3 With robust evidence documenting the effectiveness of prevention by multicomponent nonpharmacologic strategies,4,5 incident delirium is currently used as a quality marker for hospital care. Accumulating evidence also links postoperative delirium to adverse long-term outcomes, including accelerated cognitive decline and dementia.6,7 With these many adverse sequelae, delirium has emerged as a significant public health concern.8-10

There is a substantial gap in knowledge about the current costs of delirium for our health care system. Such knowledge is essential to guide policy makers and health care leaders in decision-making and prioritization surrounding delirium care.11 The most widely cited cost estimates for delirium of $16 303 to $64 421 per patient per year were derived from a controlled clinical trial of a delirium prevention model using Medicare claims data from 1995 to 1999.12 Three more recent prospective studies have examined health care costs associated with delirium. One study of 479 patients in the intensive care unit (ICU) estimated cumulative 30-day costs attributable to ICU delirium at $17 838 per patient; however, this likely represents an underestimate given the substantial rate of early mortality in these patients.13 A second study of 66 patients who had cardiac surgery analyzed the differences in median length of hospital stay, leading to a nonsignificant $10 000 adjusted cost difference between patients with and without delirium.14 A third prospective study in 500 patients who had elective surgery found differences of approximately $2000 per patient in health care costs related to delirium.15 All other prior studies examining costs of delirium have involved retrospective analyses or simulation studies.16

Updated cost estimates are urgently needed to assist leaders of health care organizations to motivate investing scarce resources into programs to address delirium. Accurate cost estimates are needed by funders and health systems to develop incentives and strategies to improve processes and quality of care for older adults and to track improvements over time. Thus, we undertook this study to examine 1-year cost estimates attributable to postoperative delirium derived from an ongoing prospective cohort of older patients having elective surgery, using Medicare claims and clinical data. Our specific aims were to evaluate total health care costs associated with incident postoperative delirium during the index hospitalization and at 30-day, 90-day, and 1-year follow-up in a prospective cohort of 497 patients undergoing elective major surgery, with careful adjustment for relevant confounders. We examined both cumulative and period-specific costs and hypothesized that incident delirium and increasing delirium severity would be associated with higher costs.

Methods
Study Sample

The Successful Aging after Elective Surgery (SAGES) study is an ongoing prospective cohort study of older adults undergoing major elective surgery. The study design and methods have been described previously.17,18 In brief, eligible participants were 70 years or older, English-speaking, able to communicate verbally, and scheduled to undergo major surgery at 1 of 2 Harvard-affiliated hospitals with an anticipated length of stay of at least 3 days. Eligible surgical procedures were total hip or knee replacement; lumbar, cervical, or sacral laminectomy; lower extremity arterial bypass surgery; open abdominal aortic aneurysm repair; and open or laparoscopic colectomy. Exclusion criteria included evidence of dementia, delirium, prior hospitalization within 3 months, legal blindness, severe deafness, terminal condition, history of schizophrenia or psychosis, and history of alcohol abuse or withdrawal. A total of 566 patients met eligibility criteria and were enrolled between June 18, 2010, and August 8, 2013. Six patients were excluded after enrollment for suspected dementia after neuropsychological testing and clinical adjudication. An additional 63 (11%) were excluded from the present analyses due to missing data: 33 (6%) because their Social Security numbers were unavailable to provide linkage to the Medicare database, and 30 (5%) because cost data were not available owing to enrollment in a Medicare managed care plan. Thus, the final sample was 497 participants (eFigure 1 in the Supplement). Written informed consent for study participation was obtained from all participants according to procedures approved by the institutional review boards of the 2 Harvard hospitals and Hebrew SeniorLife, the study coordinating center in Boston, Massachusetts. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Data Source

Participants were interviewed at baseline in their homes within 30 days (median [interquartile range], 9 [5-17] days) before their scheduled surgery for detailed demographic characteristics, cognitive functioning using the Modified Mini-Mental State Examination,19 and assessments of basic and instrumental activities of daily living20,21 before surgery. During hospitalization, participants were interviewed daily with cognitive and delirium assessments from the first postoperative day through discharge. A family caregiver was interviewed at baseline about the participant’s functional status using the proxy Informant Questionnaire on Cognitive Decline in the Elderly,22,23 a validated assessment for dementia based on proxy-reported cognitive changes over 10 years. Medical record review for the index hospitalization was conducted by a trained research physician to extract information on medical diagnoses, comorbidities, and surgical type.

Our total cost calculations were derived from Medicare Part A and B administrative claims, including Medicare Provider Analysis and Review (MEDPAR), Outpatient (fee-for-service), and Home Health Agency (fee-for-service) files, acquired for the 2010 to 2014 period. MEDPAR data detail inpatient hospital and skilled postacute rehabilitation stays covered by Medicare. Outpatient data comprise fee-for-service claims billed by institutional outpatient health care workers. Home Health Agency data include services covered by Medicare Home Health Agency health care workers. We derived further costs from the Cost and Utilization segment of the Master Beneficiary Summary File, which included summarized annual payments for services such as professional fees, hospice, and durable medical equipment.24 These latter costs were added separately in sensitivity analyses (eMethods in the Supplement).

Assessment of Delirium and Delirium Severity

Daily delirium assessment included brief cognitive testing,17,25 a Delirium Symptom Interview,26 and interviews with family and nurses. Delirium was rated using the Confusion Assessment Method (CAM),27 a standardized approach with high sensitivity (94%-100%) and specificity (90%-95%).28,29 Combined with a validated chart review method,30,31 patients were classified as having developed delirium if either CAM or chart criteria were met on a given day. Delirium severity was measured with the CAM-Severity (CAM-S) score long form (scored 0-19; 19 = most severe).32 The highest value of CAM-S during hospitalization, peak CAM-S, was used as the measure of delirium severity for these analyses, categorized into 3 severity groups: none-mild (0-2 points), moderate (3-7 points), and severe (8-19 points), based on prior work.6

Outcome Measures

Costs were measured from the perspective of the health care system. Cumulative total health care costs were calculated for the index hospitalization and for 30 days, 90 days, and 1 year after discharge. Total costs incurred during the index hospitalization were determined from MEDPAR data. The 30-day, 90-day, and 1-year costs were derived from MEDPAR (inpatient and postacute rehabilitation stays), outpatient, and home health services files. Period-specific costs were computed for intervals between discharge and 30 days, 30 days and 90 days, and 90 days and 1 year. Cumulative and period-specific costs were calculated for individual health care cost categories, including inpatient, postacute rehabilitation facility, outpatient, and home health care.12 Costs were calculated using Medicare reimbursed amounts rather than charges, because reimbursed amounts represent the actual payments received by hospitals and clinicians and are considered better measures of transaction prices than billed charges.12 All costs are reported in 2019 US dollars, adjusting for inflation using the medical care component of the Consumer Price Index.33

Statistical Analysis

Statistical analyses were conducted from October 15, 2019, to September 15, 2020. For baseline characteristics, standard statistics were used to compare delirium and nondelirium groups, including analysis of variance test for continuous variables and χ2 test for categorical variables. We also used standard statistics to compare the delirium and nondelirium groups on hospital factors, including length of stay, and rates and duration of ICU stay. For the cost analyses, we compared unadjusted mean total costs between delirium and nondelirium groups using the Wilcoxon test. For the 3 delirium severity groups, we compared unadjusted mean costs using the Kruskal-Wallis test. We then used generalized linear models to examine our cost data, which displayed substantial skewness in their distributions with error terms exhibiting heteroscedasticity.34 To account for skewness, adjusted mean total costs were estimated with generalized linear models assuming γ-distributed costs and a log link function.35 All covariables were mean-centered. Model estimated cumulative adjusted mean total costs from index hospitalization through discharge were first computed cumulatively, then similarly computed for each time period. The excess cumulative costs attributable to incident delirium and delirium severity were calculated as mean cost differences from the adjusted models with 95% CIs derived from standard error estimates.

With complete data collection and follow-up, there were no missing data on any of the outcomes or covariables used in the models. We used SAS, version 9.4 (SAS Institute) for processing the Medicare data files and Stata, version 15 (StataCorp LLC) for all statistical analyses. All P values were 2 sided, and P < .05 was used to indicate statistical significance. Detailed description of the selection of covariables, 2-part models for analysis health care cost categories, and details of sensitivity analyses are provided in the eMethods in the Supplement.

Results

Of the 566 patients who were eligible for the study, a total of 497 patients (mean [SD] age, 76.8 [5.1] years; 281 women [57%]; 461 White participants [93%]) were enrolled after exclusion criteria were applied. Baseline characteristics are presented in Table 1. We compared the excluded sample (n = 63) to the final sample (n = 497) and found them to be similar (eTable 1 in the Supplement). Among the 497 SAGES participants, delirium developed in 122 (25%) during hospitalization. The patients who developed delirium were significantly more medically complex (55 of 122 [45%] vs 95 of 375 [25%] with a Charlson score37 ≥2; P < .001), were more cognitively impaired at baseline (12 of 122 [10%] vs 15 of 375 [4%] with proxy Informant Questionnaire for Cognitive Decline in the Elderly score >3.5; P = .01), and were higher health care users in the prior 6 months (mean [SD] total costs, $8880 [$25 168] vs $3959 [$10 181]; P = .002). Only 1 patient with and 6 patients without delirium died during 1-year follow-up, with no significant differences in survival rate or time.

Adjusted cumulative health care costs per patient were significantly higher for those who developed incident delirium compared with those who did not at all time points (mean costs at 30 days after discharge, $99 676 [95% CI, $85 577-$116 097] vs $71 599 [95% CI, $62 223-$82 387]; at 90 days after discharge, $108 978 [95% CI, $90 596-$131 091] vs $74 644 [95% CI, $63 096-$88 306]; and at 1 year after discharge, $130 816 [95% CI, $105 676-$161 937] vs $86 525 [95% CI, $71 122-$105 264]) (Table 2). At 1-year follow-up, patients who developed delirium accumulated an adjusted mean $44 291 (P < .001) more health care costs compared with those without delirium. Period-specific costs were also significantly higher in all periods for patients with delirium vs those without (mean cost for discharge to 30 days, $13 526 [95% CI, $8767-$20 870] vs $7600 [95% CI, $5007-$11 537]; for 30-90 days, $7351 [95% CI, $2262-$23 887] vs $2467 [95% CI, $904-$6737]; and for 90 days to 1 year, $22 636 [95% CI, $11 240-$45 589] vs $11 780 [95% CI, $6032-$23 005]). The adjusted mean differences were highest for the index hospitalization costs (adjusted mean difference = $20 327; P < .001) and relatively lower for the discharge to 30-day costs ($5926; P = .001), 30- to 90-day costs ($4884; P = .01), and 90-day to 1-year costs ($10 856; P = .01). Thus, patients with delirium consistently cost more to the health care system with significantly higher costs in all time periods. The unadjusted analyses for costs of incident delirium appear in eTable 2 in the Supplement.

When stratified by delirium severity, mean adjusted health care costs increased directly with degree of severity (Table 3) from none to mild delirium (peak CAM-S score 0-2) to moderate delirium (peak CAM-S score 3-7) and to severe delirium (peak CAM-S score 8-19). Cumulative total costs demonstrated significant increases across the severity groups, with the largest cost differences at 1 year following surgery (adjusted mean costs for groups with none to mild delirium were $83 534 [95% CI, $67 300-$103 683]; for moderate delirium, $99 756 [95% CI, $81 683-$121 829]; and for severe delirium, $140 008 [95% CI, $108 227-$181 123]; P < .001). For period-specific costs, significant increasing mean costs were demonstrated across delirium severity groupings for index hospitalization (none-mild, $62 608 [95% CI, $54 003-$72 584]; moderate, $66 255 [95% CI, $57 746-$76 017]; and severe, $90 700 [95% CI, $75 772-$108 570]; P < .001). Although values increased across delirium severity groupings for other time periods, results were generally not statistically significant. The unadjusted analyses for costs of delirium stratified by severity appear in eTable 3 in the Supplement.

Attributable costs (adjusted mean differences) are presented in the Figure for incident delirium and for delirium severity (severe vs none-mild severity groups) for 1 year after surgery. The excess cumulative costs attributable to delirium were $44 291 (95% CI, $34 554-$56 673) for incident delirium and $56 474 (95% CI, $40 927-$77 440) for severe delirium over 1 year. Extrapolating nationally, the health care costs attributable to postoperative delirium were estimated at $32.9 billion (95% CI, $25.7 billion-$42.2 billion) per year.38-40

We examined categories of health care costs to determine their relative contributions to the increased total health care costs in patients with delirium (eTable 4 in the Supplement). For patients who developed delirium, adjusted mean differences in cumulative costs at 1 year were highest for inpatient costs ($36 404; P < .001) with approximately half of these costs due to the initial index hospitalization, where patients with delirium had longer lengths of stay (mean [SD] days, 6.9 [5.6] vs 4.8 [2.2]; P < .001), higher rates of ICU stay (19 of 122 [16%] vs 15 of 375 [4%]; P < .001), and longer ICU duration (1.3 [5.5] vs 0.1 [0.9] days; P < .001) compared with patients without delirium. The next highest cost category at 1 year after discharge was for postacute rehabilitation facility stays (mean difference, $4986; P = .02). Adjusted mean differences in outpatient and home health care costs were small and not statistically significant. During 1-year follow-up, patients in the delirium group (n = 122) compared with the nondelirium group (n = 375) had significantly more rehospitalizations (65 of 122 [53%] vs 116 of 375 [31%]; P < .001) and postacute rehabilitation stays (64 of 122 [52%] vs 135 of 375 [36%]; P = .001). Moreover, in the period from 90 days to 1 year, the rates of rehospitalization (34 of 122 [28%] vs 67 of 375 [18%]; P = .02) and postacute rehabilitation stays (15 of 122 [12%] vs 18 of 375 [5%]; P = .04) remained significantly higher in the delirium group. To adjust for survival time, we performed sensitivity analysis using Cox regression models (eTable 5 in the Supplement). We also conducted sensitivity analyses adding in prorated Part B hospice and durable medical equipment costs (eTable 6 in the Supplement). The results and conclusions remained unchanged across these sensitivity analyses. The details of the generalized linear regression models for all analyses appear in eTable 7 in the Supplement (incident delirium), eTable 8 in the Supplement (delirium severity), and eTables 9 to 12 in the Supplement (individual models for cost categories).

Discussion

Results of this study suggest that delirium after elective surgery was associated with significant increases in cumulative health care costs from hospital admission through 1 year follow-up. The adjusted cumulative costs attributable to delirium originated from the index hospitalization, subsequent rehospitalizations, and postacute rehabilitation stays. Increases in health care costs were directly and significantly associated with delirium severity, supporting an exposure-response relationship. Because the presence and severity of delirium have been directly associated with adverse clinical outcomes in our prior work,32,41 it is not surprising that they may be contributing to increased health care costs.

Following Leslie et al,12 and assuming that incident delirium complicates hospital stays for 25% of the 3 million older adults hospitalized for elective surgery each year in the US, our results suggest that total direct 1-year health care costs attributable to postoperative delirium were approximately $32.9 billion (95% CI, $25.7-$42.2 billion) per year extrapolating nationally (eFigure 2 in the Supplement).38-40 By comparison, estimates of US health care costs for other conditions range from $6 billion for hip fracture42 to $34 billion for nonfatal falls,43 $248 billion for diabetes,44 and $357 to $363 billion for cardiovascular disease.45 Thus, postoperative delirium appears to be a major contributor to health care costs, rivaling other well-recognized conditions for which more resources are routinely directed. Given the documented effectiveness of delirium prevention strategies,5 some of these costs may be avoidable.

Because delirium is associated with many adverse short-term and long-term outcomes, the substantial contributions to health care costs are not surprising. However, the exact mechanisms leading to these increased costs warrant further investigation. Our results suggest that delirium was associated with an increase in the acuity and duration of hospitalization, resulting in increased costs from greater daily expenditures, ICU stays, longer lengths of stay, and higher rates of rehospitalization and institutionalization.

Strengths and Limitations

This study has a number of noteworthy strengths, including rigorous longitudinal follow-up over 1 year with detailed clinical information and comprehensive service use and cost data. We used standardized, validated delirium assessments daily throughout hospitalization. The study examined costs associated with delirium across a wide range of services (inpatient, emergency department, outpatient, postacute rehabilitation stay, home health care, etc) over a full year after hospital discharge. With complete follow-up, there were no missing data on any key study outcomes or covariables.

This study has some limitations. First, our costs reflected those incurred by an older elective surgical population with hospitalization and may not reflect the entire older surgical population. Second, medications and polypharmacy are well-known risk factors for delirium in the hospital, but we did not have medication cost data. Exploring mediating factors leading to the association of delirium with high costs will be an important area for future research. Third, cost estimates were derived from 2 medical centers in a single city, and thus the results may not generalize to other sites. This particular caveat may be less of a concern, because our findings were based on mean differences in costs between patients with and without delirium rather than absolute costs. Similarly, our cohort was made up of mostly White patients who were English-speaking, well-educated, and residing close to the urban Boston area. As such, additional studies are needed in the future with more heterogeneous health care systems and diverse participants to further corroborate our findings. Comparable with other studies based on Medicare data, approximately 11% of our cohort participated in Medicare managed care plans, and their costs were not available. The group of excluded patients was similar in baseline characteristics to the included group. In addition, the cost estimates include direct health care costs only and do not consider important indirect costs which may be substantial, such as out-of-pocket expenses, job or productivity losses for the patient or family members, informal caregiving costs, caregiver burden, and reduced quality of life. This study was a prospective cohort and not a randomized clinical trial; thus, we were unable to calculate the cost savings per case of delirium prevented. We examined an elective surgery cohort that does not include patients with coronavirus disease 2019 or those receiving emergency surgical, general medical, or palliative care, all of whom would have had higher illness severity and more costly care. Our elective surgery population also underwent careful evaluation and documentation of delirium, which is not routinely done across all hospitals and thus likely underestimates the true costs of delirium and severe delirium actually present throughout the US. For these reasons, our estimates are likely to be conservative, and the overall health care costs per year attributable to delirium are likely to be higher than reflected in our results.

Conclusions

This cohort study found that the economic outcomes of delirium and severe delirium after elective surgery were substantial, rivaling costs associated with those of cardiovascular disease and diabetes. These results suggest the need for policy imperatives to address delirium as a large-scale public health issue. It is our hope that these results on the high cost of postoperative delirium will drive much-needed attention to address this problem and, ultimately, to reduce its long-term clinical and economic implications. We hope to encourage health care leaders and policy makers to invest resources to address this critically important area, especially in this era of bundled health insurance payments. Future research is needed to determine whether delirium prevention programs, such as the cost-effective Hospital Elder Life Program,4 would be successful in reducing these delirium-related costs on a wide scale. Given that the condition is increasing in magnitude with the aging population worldwide, renewed efforts to prevent, detect, and manage delirium are urgently needed.

Back to top
Article Information

Accepted for Publication: December 9, 2020.

Published Online: February 24, 2021. doi:10.1001/jamasurg.2020.7260

Corresponding Authors: Tammy T. Hshieh, MD, MPH, Division of Aging, Department of Medicine, Brigham and Women’s Hospital, 1620 Tremont St, 3rd Floor, One Brigham Circle, Boston, MA 02120 (thshieh@bwh.harvard.edu).

Author Contributions: Ms Gou and Dr Hshieh had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Ms Gou and Dr Hshieh contributed equally as co–first authors. Drs Leslie and Inouye contributed equally as co–senior authors.

Concept and design: Gou, Hshieh, Marcantonio, Jones, Travison, Earp, Inouye.

Acquisition, analysis, or interpretation of data: Gou, Hshieh, Marcantonio, Cooper, Jones, Travison, Fong, Abdeen, Lange, Schmitt, Leslie, Inouye.

Drafting of the manuscript: Gou, Hshieh, Jones, Fong, Inouye.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Gou, Hshieh, Leslie, Inouye.

Obtained funding: Marcantonio, Travison, Inouye.

Administrative, technical, or material support: Gou, Marcantonio, Abdeen, Lange, Schmitt, Inouye.

Supervision: Hshieh, Marcantonio, Travison, Earp, Schmitt, Inouye.

Conflict of Interest Disclosures: Dr Lange reported receiving royalties/honorarium from OnPoint Knee for serving on their scientific advisory board and serving as committee member for the American Association of Hip and Knee Surgeons outside the submitted work. Dr Earp reported receiving stock ownership from Johnson and Johnson and stock ownership from Pfizer outside the submitted work. Dr Leslie reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported in part by grants from the National Institute on Aging grant nos. P01AG031720 (SKI), R24AG054259 (SKI), R01AG044518 (SKI/RNJ). Dr Marcantonio’s time was supported in part by grant nos. K24AG035075 (ERM) and R01AG030618; Dr Fong’s time in part by R21AG057955 (TGF). Dr Inouye holds the Milton and Shirley F. Levy Family Chair at Hebrew SeniorLife/Harvard Medical School.

Role of the Funder/Sponsor: The funders provided funding support for the design and conduct of the study and collection and management of the data. They did not contribute to analysis and interpretation of data or the preparation, review, or approval of the manuscript.

SAGES Study Group: (Presented in alphabetical order; individuals listed may be part of multiple groups, but are listed only once under major activity, listed in parentheses). Overall Principal Investigator: Hebrew SeniorLife, Beth Israel Deaconess Medical Center, Harvard Medical School: Sharon K. Inouye, MD, MPH (Overall PI, Administrative Core, Project 1). Project and Core Leaders: Beth Israel Deaconess Medical Center, Harvard Medical School: David Alsop, PhD (Project 3) and Bradford Dickerson, MD (Project 3); Brown University: Richard Jones, ScD (Data Core, Project 4); Hebrew SeniorLife, Harvard Medical School: Thomas Travison, PhD (Data Core); Beth Israel Deaconess Medical Center, Harvard Medical School: Edward R. Marcantonio, MD, SM (Overall Co-PI, Epidemiology Core, Project 2); Harvard Medical School, Beth Israel Deaconess Medical Center: Towia Libermann, PhD (Project 2), Alvaro Pascual-Leone, MD, PhD (Project 5), and Mouhsin Shafi, MD, PhD (Project 5). Executive Committee: Massachusetts General Hospital: Steven Arnold, MD; Brigham and Women’s Hospital: Michele Cavallari, MD; Harvard Medical School, Hebrew SeniorLife, Beth Israel Deaconess Medical Center: Tamara Fong, MD, PhD; Hebrew SeniorLife: Eva M. Schmitt, PhD (Overall Project Director); Harvard Medical School, Beth Israel Deaconess Medical Center: Emiliano Santarnecchi, PhD; Harvard Medical School, Massachusetts General Hospital: Alexandra Touroutoglou, PhD. Other Coinvestigators: Harvard Medical School, Beth Israel Deaconess Medical Center: Simon T. Dillon, PhD; Brigham and Women’s Hospital, Harvard Medical School: Charles Guttmann, MD; Brigham and Women’s Hospital: Tammy Hshieh, MD; University of Connecticut Health Center: George Kuchel, MD, FRCP; Harvard Medical School, Beth Israel Deaconess Medical Center: Long Ngo, PhD, and Daniel Press, MD; Beth Israel Deaconess Medical Center: Sarinnapha Vasunilashorn, PhD. Clinical Consensus Panel: Harvard Medical School, Beth Israel Deaconess Medical Center: Franchesca Arias, PhD; Massachusetts General Hospital: Eyal Kimchi, MD; Cambridge Health Alliance: Jason Strauss, MD; Beth Israel Deaconess Medical Center: Bonnie Wong, PhD. Surgical and Anesthesia Leaders: Harvard Medical School, Beth Israel Deaconess Medical Center: Ayesha Abdeen, MD, and Douglas Ayres, MD; Harvard Medical School, BWFH: Brandon Earp, MD; Harvard Medical School, Brigham and Women’s Hospital: Michael Belkin, MD; Harvard Medical School, Beth Israel Deaconess Medical Center: Mark Callery, MD, and Lisa Kunze, MD; Harvard Medical School, Brigham and Women’s Hospital: Jeffrey Lange, MD; Harvard Medical School, Beth Israel Deaconess Medical Center: Frank Pomposelli, MD; Harvard Medical School, Brigham and Women’s Hospital: John Wright, MD; Harvard Medical School, Beth Israel Deaconess Medical Center: Marc Schermerhorn, MD; Harvard Medical School, BWFH: David Shaff, MD; Harvard Medical School, Brigham and Women’s Hospital: Kamen Vlassakov, MD. Epidemiology Core: Hebrew SeniorLife: Tatiana Abrantes, BS; Beth Israel Deaconess Medical Center: Brett Armstrong, MA; Hebrew SeniorLife: Sylvia Bertrand, BS; Beth Israel Deaconess Medical Center: Rejoice Dhliwayo, BS; Hebrew SeniorLife: Madeline D’Aquila, BS, Sarah Garteganis, LMSW, MPH, and Kristen Erickson, MA; Beth Israel Deaconess Medical Center: Meaghan Fox, MA, Jacqueline Gallagher, MA, and Ben Helfand, MSc; Hebrew SeniorLife: Yonah Joffe, BA, Jacqueline Nee, BA, and Madeleine Martine, BA; Beth Israel Deaconess Medical Center: Abigail Overstreet, MA, and Kerry Palihnich, BA; Hebrew SeniorLife: Sarah Rastegar, MA and Meghan Shanahan, MA; Beth Israel Deaconess Medical Center: Louis Shaevel, MA; Hebrew SeniorLife: Guoquan Xu, MD, PhD. Data Management and Statistical Analysis Core: Hebrew SeniorLife: Fan Chen, MS, and Yun Gou, MA; John Hopkins School of Medicine: Alden Gross, PhD, MHS; Hebrew SeniorLife: Ilean Isaza, PhD; Brown University: Douglas Tommet, MPH. Fiscal Management Committee: Hebrew SeniorLife: Sabrina Carretie, MPH, Ted Gruen, and Katherine Tasker, BA (Chair).

Additional Information: This work is dedicated to the memory of Joshua Bryan Inouye Helfand.

References
1.
Inouye  SK, Westendorp  RG, Saczynski  JS.  Delirium in elderly people.   Lancet. 2014;383(9920):911-922. doi:10.1016/S0140-6736(13)60688-1 PubMedGoogle ScholarCrossref
2.
Oh  ES, Fong  TG, Hshieh  TT, Inouye  SK.  Delirium in older persons: advances in diagnosis and treatment.   JAMA. 2017;318(12):1161-1174. doi:10.1001/jama.2017.12067 PubMedGoogle ScholarCrossref
3.
Hshieh  TT, Saczynski  J, Gou  RY,  et al; SAGES Study Group.  Trajectory of functional recovery after postoperative delirium in elective surgery.   Ann Surg. 2017;265(4):647-653. doi:10.1097/SLA.0000000000001952 PubMedGoogle ScholarCrossref
4.
Hshieh  TT, Yang  T, Gartaganis  SL, Yue  J, Inouye  SK. Hospital elder life program: systematic review and meta-analysis of effectiveness.  Am J Geriatr Psychiatry. 2018;26(10):1015-1033. doi:10.1016/j.jagp.2018.06.007PubMedCrossref
5.
Hshieh  TT, Yue  J, Oh  E,  et al.  Effectiveness of multicomponent nonpharmacological delirium interventions: a meta-analysis.   JAMA Intern Med. 2015;175(4):512-520. doi:10.1001/jamainternmed.2014.7779 PubMedGoogle ScholarCrossref
6.
Vasunilashorn  SM, Fong  TG, Albuquerque  A,  et al.  Delirium severity post-surgery and its relationship with long-term cognitive decline in a cohort of patients without dementia.   J Alzheimers Dis. 2018;61(1):347-358. doi:10.3233/JAD-170288 PubMedGoogle ScholarCrossref
7.
Morandi  A, Davis  D, Fick  DM,  et al.  Delirium superimposed on dementia strongly predicts worse outcomes in older rehabilitation inpatients.   J Am Med Dir Assoc. 2014;15(5):349-354. doi:10.1016/j.jamda.2013.12.084 PubMedGoogle ScholarCrossref
8.
Fong  TG, Davis  D, Growdon  ME, Albuquerque  A, Inouye  SK.  The interface between delirium and dementia in elderly adults.   Lancet Neurol. 2015;14(8):823-832. doi:10.1016/S1474-4422(15)00101-5 PubMedGoogle ScholarCrossref
9.
Hshieh  TT, Fong  TG, Schmitt  EM,  et al; BASIL Study Group.  Does Alzheimer’s disease and related dementias modify delirium severity and hospital outcomes?   J Am Geriatr Soc. 2020;68(8):1722-1730. doi:10.1111/jgs.16420 PubMedGoogle ScholarCrossref
10.
Inouye  SK, Marcantonio  ER, Kosar  CM,  et al.  The short-term and long-term relationship between delirium and cognitive trajectory in older surgical patients.   Alzheimers Dement. 2016;12(7):766-775. doi:10.1016/j.jalz.2016.03.005 PubMedGoogle ScholarCrossref
11.
Leslie  DL, Inouye  SK.  The importance of delirium: economic and societal costs.   J Am Geriatr Soc. 2011;59(suppl 2):S241-S243. doi:10.1111/j.1532-5415.2011.03671.x PubMedGoogle ScholarCrossref
12.
Leslie  DL, Marcantonio  ER, Zhang  Y, Leo-Summers  L, Inouye  SK.  One-year health care costs associated with delirium in the elderly population.   Arch Intern Med. 2008;168(1):27-32. doi:10.1001/archinternmed.2007.4 PubMedGoogle ScholarCrossref
13.
Vasilevskis  EE, Meltzer  D, Schnipper  J,  et al.  Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non-hospitalists.   J Gen Intern Med. 2008;23(9):1399-1406. doi:10.1007/s11606-008-0680-3 PubMedGoogle ScholarCrossref
14.
Brown  CH  IV, Laflam  A, Max  L,  et al.  The impact of delirium after cardiac surgical procedures on postoperative resource use.   Ann Thorac Surg. 2016;101(5):1663-1669. doi:10.1016/j.athoracsur.2015.12.074 PubMedGoogle ScholarCrossref
15.
Franco  K, Litaker  D, Locala  J, Bronson  D.  The cost of delirium in the surgical patient.   Psychosomatics. 2001;42(1):68-73. doi:10.1176/appi.psy.42.1.68 PubMedGoogle ScholarCrossref
16.
Caplan  GA, Teodorczuk  A, Streatfeild  J, Agar  MR.  The financial and social costs of delirium.   Eur Geriatr Med. 2020;11(1):105-112. doi:10.1007/s41999-019-00257-2 PubMedGoogle ScholarCrossref
17.
Schmitt  EM, Marcantonio  ER, Alsop  DC,  et al. Novel risk markers and long-term outcomes of delirium: the Successful Aging after Elective Surgery (SAGES) study design and methods.  J Am Med Dir Assoc. 2012;13(9):818.e1-818.e10. doi:10.1016/j.jamda.2012.08.004PubMedCrossref
18.
Schmitt  EM, Saczynski  JS, Kosar  CM,  et al; SAGES Study Group.  The Successful Aging after Elective Surgery (SAGES) study: cohort description and data quality procedures.   J Am Geriatr Soc. 2015;63(12):2463-2471. doi:10.1111/jgs.13793 PubMedGoogle ScholarCrossref
19.
Teng  ELCH, Chui  HC.  The Modified Mini-Mental State (3MS) examination.   J Clin Psychiatry. 1987;48(8):314-318.PubMedGoogle Scholar
20.
Katz  S, Ford  AB, Moskowitz  RW, Jackson  BA, Jaffe  MW.  Studies of illness in the aged. the index of ADL: a standardized measure of biological and psychosocial function.   JAMA. 1963;185:914-919. doi:10.1001/jama.1963.03060120024016 PubMedGoogle ScholarCrossref
21.
Lawton  MP, Brody  EM.  Assessment of older people: self-maintaining and instrumental activities of daily living.   Gerontologist. 1969;9(3):179-186. doi:10.1093/geront/9.3_Part_1.179 PubMedGoogle ScholarCrossref
22.
Jorm  AF.  A short form of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): development and cross-validation.   Psychol Med. 1994;24(1):145-153. doi:10.1017/S003329170002691X PubMedGoogle ScholarCrossref
23.
Jorm  AF.  The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): a review.   Int Psychogeriatr. 2004;16(3):275-293. doi:10.1017/S1041610204000390 PubMedGoogle ScholarCrossref
24.
Research Data Assistance Center. About us. Accessed May 17, 2020. https://www.resdac.org/about-resdac
25.
Simon  SE, Bergmann  MA, Jones  RN, Murphy  KM, Orav  EJ, Marcantonio  ER.  Reliability of a structured assessment for nonclinicians to detect delirium among new admissions to postacute care.   J Am Med Dir Assoc. 2006;7(7):412-415. doi:10.1016/j.jamda.2006.02.006 PubMedGoogle ScholarCrossref
26.
Albert  MS, Levkoff  SE, Reilly  C,  et al.  The delirium symptom interview: an interview for the detection of delirium symptoms in hospitalized patients.   J Geriatr Psychiatry Neurol. 1992;5(1):14-21. doi:10.1177/002383099200500103 PubMedGoogle ScholarCrossref
27.
Wong  CL, Holroyd-Leduc  J, Simel  DL, Straus  SE.  Does this patient have delirium?: value of bedside instruments.   JAMA. 2010;304(7):779-786. doi:10.1001/jama.2010.1182 PubMedGoogle ScholarCrossref
28.
Wei  LA, Fearing  MA, Sternberg  EJ, Inouye  SK.  The Confusion Assessment Method: a systematic review of current usage.   J Am Geriatr Soc. 2008;56(5):823-830. doi:10.1111/j.1532-5415.2008.01674.x PubMedGoogle ScholarCrossref
29.
Inouye  SK, Leo-Summers  L, Zhang  Y, Bogardus  ST  Jr, Leslie  DL, Agostini  JV.  A chart-based method for identification of delirium: validation compared with interviewer ratings using the confusion assessment method.   J Am Geriatr Soc. 2005;53(2):312-318. doi:10.1111/j.1532-5415.2005.53120.x PubMedGoogle ScholarCrossref
30.
Saczynski  JS, Kosar  CM, Xu  G,  et al.  A tale of two methods: chart and interview methods for identifying delirium.   J Am Geriatr Soc. 2014;62(3):518-524. doi:10.1111/jgs.12684 PubMedGoogle ScholarCrossref
31.
Gross  AL, Jones  RN, Fong  TG, Tommet  D, Inouye  SK.  Calibration and validation of an innovative approach for estimating general cognitive performance.   Neuroepidemiology. 2014;42(3):144-153. doi:10.1159/000357647 PubMedGoogle ScholarCrossref
32.
Inouye  SK, Kosar  CM, Tommet  D,  et al.  The CAM-S: development and validation of a new scoring system for delirium severity in 2 cohorts.   Ann Intern Med. 2014;160(8):526-533. doi:10.7326/M13-1927 PubMedGoogle ScholarCrossref
33.
US Bureau of Labor Statistics. Consumer price index databases. Accessed November 13, 2019. https://www.bls.gov/cpi/data.htm
34.
Manning  WG, Basu  A, Mullahy  J.  Generalized modeling approaches to risk adjustment of skewed outcomes data.   J Health Econ. 2005;24(3):465-488. doi:10.1016/j.jhealeco.2004.09.011 PubMedGoogle ScholarCrossref
35.
Deb  P, Norton  EC.  Modeling health care expenditures and use.   Annu Rev Public Health. 2018;39:489-505. doi:10.1146/annurev-publhealth-040617-013517 PubMedGoogle ScholarCrossref
36.
Mayhew  D, Mendonca  V, Murthy  BVS.  A review of ASA physical status - historical perspectives and modern developments.   Anaesthesia. 2019;74(3):373-379. doi:10.1111/anae.14569 PubMedGoogle ScholarCrossref
37.
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.   J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8 PubMedGoogle ScholarCrossref
38.
United States Census Bureau. US and world population clock. Accessed May 18, 2020. https://www.census.gov/popclock/
39.
United States Census Bureau. Facts for features: older Americans month: May 2017. Accessed May 18, 2020. https://www.census.gov/newsroom/facts-for-features/2017/cb17-ff08.html
40.
Sun  R, Karaca  Z, Wong  H. Statistical brief #235: trends in hospital inpatient stays by age and payer, 2000-2015. Accessed May 18, 2020. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb235-Inpatient-Stays-Age-Payer-Trends.jsp
41.
Vasunilashorn  SM, Marcantonio  ER, Gou  Y,  et al.  Quantifying the severity of a delirium episode throughout hospitalization: the combined importance of intensity and duration.   J Gen Intern Med. 2016;31(10):1164-1171. doi:10.1007/s11606-016-3671-9 PubMedGoogle ScholarCrossref
42.
Gu  Q, Koenig  L, Mather  RC  III, Tongue  J.  Surgery for hip fracture yields societal benefits that exceed the direct medical costs.   Clin Orthop Relat Res. 2014;472(11):3536-3546. doi:10.1007/s11999-014-3820-6 PubMedGoogle ScholarCrossref
43.
Burns  ER, Stevens  JA, Lee  R.  The direct costs of fatal and non-fatal falls among older adults—United States.   J Safety Res. 2016;58:99-103. doi:10.1016/j.jsr.2016.05.001 PubMedGoogle ScholarCrossref
44.
American Diabetes Association. Economic costs of diabetes in the US in 2017.  Diabetes Care. 2018;41(5):917-928.doi:10.2337/dci18-0007PubMedCrossref
45.
Benjamin  EJ, Virani  SS, Callaway  CW,  et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics-2018 update: a report from the American Heart Association.   Circulation. 2018;137(12):e67-e492. doi:10.1161/CIR.0000000000000558 PubMedGoogle ScholarCrossref
×