Abstract
Background
Delirium is associated with worse outcomes in critically ill patients. In the subset of patients with myocardial infarction (MI), the impact on clinical outcomes of delirium is not as well elucidated.
Hypothesis
Delirium is associated with increased mortality in patients hospitalized for MI.
Methods
The study used data from the National Inpatient Sample 2012 to 2014, Healthcare Cost and Utilization Project. We included discharges associated with the primary diagnosis of MI using the relevant International Classification of Diseases, Ninth Revision, Clinical Modification codes. The outcome was inpatient mortality between the delirium group and propensity score–matched controls without delirium.
Results
The study included 1 330 020 weighted discharges with MI as the principal diagnosis. Within this cohort, 18 685 discharges (1.4%) had delirium. Delirium was associated with older age, lower rates of percutaneous coronary intervention, and increased comorbid conditions. The delirium group had higher mortality (10.5% vs 2.6%, P < 0.001). Propensity score–matching analysis showed increased mortality in the delirium group (10.5% vs 7.6%, relative risk: 1.39 [95% confidence interval: 1.2–1.6, P < 0.001) using nearest neighbor 1:1 matching.
Conclusions
In individuals with MI, delirium was associated with increased inpatient mortality.
Keywords: Altered Mental Status, Clinical Modification Codes, Comorbidities, Delirium, International Classification of Diseases, Myocardial Infarction, Ninth Revision
1. INTRODUCTION
Delirium is an alteration in mental status that affects orientation and awareness. Critical illness and older age are important risk factors for the development of delirium.1, 2 Studies have examined the prevalence and burden of delirium among several populations in various care settings, with most focusing on elderly patients and intensive care settings. Delirium is associated with higher short‐ and long‐term mortality and decline in functional and cognitive function.3, 4, 5 In patients with myocardial infarction (MI), delirium that occurred in up to 5.7% of patients was reported, often as an atypical presentation of myocardial infarction.6 When limited to elderly patients ≥75 years of age, the prevalence is even higher.7 Therefore, the burden of delirium will increase over time as the population ages. In this report, we examined the prevalence and impact of delirium on clinical outcomes in patients of all age groups hospitalized for MI.
2. METHODS
2.1. Data sources
The study analyzed discharge data from the National Inpatient Sample (NIS) database, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality, for the years 2012 to 2014. The NIS is the largest all‐payer database in the United States.8 Beginning with the 2012 database, the NIS included a 20% stratified sample from all discharges nationwide amounting to approximately 7 to 8 million discharges per year. It provides data that are usually included in the hospital discharge records, including, but not limited to, demographic characteristics, insurance, length of stay, principal diagnosis, procedure, and comorbidities evaluated and treated during the index hospitalization. Furthermore, the database contains a weight variable (discharge‐level weight) that allows for calculating national estimates representing up to 97% of all discharges.8
2.2. Population
Weighted results were analyzed for all individuals ≥18 years old with a principal diagnosis of MI and eligible for inclusion. Discharges with a primary diagnosis of MI were identified using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) codes (410.0, 410.1, 410.2, 410.3, 410.4, 410.5, 410.6, 410.7, 410.8, 410.9). Discharges associated with delirium were identified using the following ICD‐9‐CM codes: presenile delirium (290.11), senile delirium (290.3), vascular dementia with delirium (290.41), acute delirium (239.0), subacute delirium (293.0), metabolic encephalopathy (348.31), and altered mental status (780.97).
2.3. Outcome
The outcome was in‐hospital mortality in patients with delirium compared to patients without delirium.
2.4. Comorbid conditions and the Charlson Comorbidity Index
We identified associated comorbidities that could potentially influence the development of delirium and outcomes: acute kidney injury (AKI), treatment with percutaneous coronary intervention (PCI), acute respiratory failure, sepsis, and cardiogenic shock using relevant ICD‐9‐CM codes (see Supporting Information in the online version of this article). The Charlson Comorbidity Index was extracted from the data for each study subject and added as a numerical variable to the analysis. The Charlson index is a tool to predict mortality in large database analyses and to estimate the burden of comorbidities.9 The modified index addresses 17 comorbid conditions including MI, congestive heart failure (CHF), diabetes mellitus (DM) with or without complications, chronic kidney disease (CKD), chronic obstructive pulmonary disease, cerebrovascular accidents, dementia, peptic ulcer disease, rheumatic disease, mild and severe liver disease, acquired immune deficiency syndrome, and cancer with or without metastatic disease. Higher scores are associated with increased mortality in large database studies.9
2.5. Exclusion criteria
The study excluded patients who underwent mechanical ventilation, had coronary artery bypass graft surgery (CABG) during admission, had a cerebrovascular event (stroke or intracranial bleed) or traumatic brain injury, and had coma or other alteration to consciousness. Records with incomplete data for mortality and gender were excluded. We also excluded records with an indicator for transfer to another acute‐care facility (variable TRAN_OUT in the NIS) to reduce the chance of data duplication.
2.6. Statistical analysis
A χ2 test was used to compare categorical variables. A Mann–Whitney U and Student t test were used to compare continuous variables with skewed and normal distribution, respectively.
2.7. Multiple logistic regression
On multivariable logistic regression, age and gender were incorporated into the model. Furthermore, the model included baseline characteristics that showed significant univariate association with a P value <0.05 in backward stepwise selection method.10 The analysis reported the adjusted odds ratio (OR) with 95% confidence interval (CI). The final logistic regression model incorporated age, sex, medical therapy vs PCI, type of MI, presence of cardiogenic shock, AKI, sepsis, and the Charlson index score. We created a secondary logistic regression model by replacing the numerical Charlson score with individual comorbidities included in the index. The primary outcome was further stratified according to the 2 age groups above or below the mean age of 67 years or to whether dementia was present or absent.
2.8. Propensity score matching analysis
Propensity score matching is a tool that generates a control group with similar baseline characteristics to the delirium group. Instead of matching for demographic characteristics and several covariables, the analysis matches for a single score that reflects the baseline characteristics.11 Subsequently, the impact of the variable of interest (delirium) could be measured by direct comparison of mortality rates between cases and controls given the similarity in baseline characteristics. Therefore, using predictors of mortality and risk factors for the development of delirium, we calculated a propensity score for all subjects. Variables considered significant and included in the model were age, gender, use of PCI, type of MI, Charlson index score, CHF, DM, CKD, AKI, sepsis, acute respiratory failure, cardiogenic shock, and dementia. We included interaction terms in the model to optimize the balance in baseline characteristic (see Supporting Information in the online version of this article). Using unweighted data analysis achieved matching using the nearest neighbor algorithm with 1:1 ratio and a caliper of 0.1 as described elsewhere.12 After performing the propensity score matching, we checked the balance of the baseline characteristic between the 2 groups. A balance of >10% of the standardized difference between the 2 groups was considered significant.13 Finally, we compared mortality rates between cases and controls using the McNemar test for correlated binary proportions.12
Data analysis was performed with Stata software (StataCorp LP, College Station, TX). The study considered a P value of 0.05 as statistically significant.
This study was exempted by the Rochester Regional Health Institutional Review Board as it does not include identifying personal information, and the database used is available in the public domain.
3. RESULTS
3.1. Baseline characteristics
We included a sample of 266 004 unweighted records representing a total of 1 330 020 discharges with MI during the period 2012 to 2014. Mean age was 66.8 years (standard deviation [SD] = 14.35) and males were 61%.
Delirium was present in 3733 of the unweighted records representing total of 18 685 (1.4%) discharges. The rate of delirium increased to 2.4% in the age group 67 years or older vs 0.54% in the younger age group.
Compared to patients without delirium, patients with delirium were generally older, more often female, and had more comorbid conditions. The baseline characteristics are outlined in Table 1.
Table 1.
Baseline characteristics and inpatient mortality in patients myocardial infarction with and without delirium
| Delirium, N = 18 685 | No Delirium, N = 1 311 335) | P Value | |
|---|---|---|---|
| Age, y | 77.8 | 66.60 | <0.001 |
| 95% CI | 77.4–78.2 | 66.55–66.7 | |
| Females | 10 140 | 511 335 | <0.001 |
| % | 54.26% | 38.99% | |
| Males | 8545 | 800 000 | <0.001 |
| % | 45.73% | 61.0% | |
| PCI | 3420 | 715 960 | <0.001 |
| % | 18.30% | 54.59% | |
| Acute kidney injury | 6155 | 155 505 | <0.001 |
| % | 32.94% | 11.85% | |
| Sepsis | 845 | 10 080 | <0.001 |
| % | 4.52% | 0.77% | |
| Respiratory failure | 3340 | 69 710 | <0.001 |
| % | 17.87% | 5.30% | |
| STEMI | 3440 | 410 580 | <0.001 |
| % | 18.41% | 31.31% | |
| NSTEMI | 15 245 | 900 755 | <0.001 |
| % | 81.59% | 68.68% | |
| Heart failure | 9230 | 371 635 | <0.001 |
| % | 49.39% | 28.34% | |
| Diabetes mellitus | 7490 | 46 9455 | <0.001 |
| % | 40.08% | 35.80% | |
| Hypertension | 14 230 | 989 755 | 0.3629 |
| % | 76.16% | 75.48% | |
| Chronic kidney disease | 6885 | 260 850 | <0.001 |
| % | 36.84% | 19.89% | |
| Ventricular tachycardia | 945 | 70 510 | 0.384 |
| % | 5.06% | 5.37% | |
| Dementia | 2090 | 21 385 | <0.001 |
| % | 11.18% | 1.63% | |
| Charlson index (mean) | 3.7 | 2.65 | <0.001 |
| 95% CI | 3.63–3.77 | 2.64–2.66 | |
| Death | 1955 | 33 860 | <0.001 |
| % | 10.5% | 2.6% |
Abbreviations: CI, confidence interval; NSTEMI, non–ST‐segment elevation myocardial infarction; PCI, percutaneous coronary intervention; STEMI, ST‐segment elevation myocardial infarction.
3.2. Outcomes
Mortality rate was higher in the delirium group (10.5% vs 2.6%, P < 0.001). Multivariable logistic regression showed and adjusted OR of 1.5 (95% CI: 1.3–1.7, P < 0.001) for mortality associated with delirium. Other independent variables are shown in Table 2. A secondary logistic regression model, which replaced the numerical Charlson score with individual comorbidities, showed similar results (see Supporting Information in the online version of this article).
Table 2.
Adjusted odds ratios for mortality and independent risk factors in multivariable logistic regression
| AOR | 95% CI | P Value | |
|---|---|---|---|
| Delirium | 1.51 | 1.33–1.70 | <0.001 |
| Age, per year | 1.07 | 1.067–1.073 | <0.001 |
| Charlson index | 1.06 | 1.04–1.07 | <0.001 |
| Acute kidney injury | 1.67 | 1.57–1.77 | <0.001 |
| Medical therapy vs PCI | 5.29 | 4.90–5.71 | <0.001 |
| Sepsis | 3.50 | 3.10–4.00 | <0.001 |
| Respiratory failure | 4.26 | 4.00–4.53 | <0.001 |
| STEMI | 3.62 | 3.40–3.84 | <0.001 |
| Cardiogenic shock | 9.80 | 9.10–10.57 | <0.001 |
Abbreviations: AOR, adjusted odds ratio; CI, confidence interval; PCI, percutaneous coronary intervention; STEMI, ST‐segment elevation myocardial infarction.
In subgroup analysis, delirium was associated with increased odds of death in both age groups and in patients without dementia. In patients with dementia, delirium was not associated with increased mortality (Table 3).
Table 3.
Subgroup analysis with crude mortality rates and adjusted odds ratio for the impact of delirium according to age groups and presence or absence of dementia
| Delirium | No Delirium | AOR | 95% CI | P Value | |
|---|---|---|---|---|---|
| Young (<67 years) | 165/3405 (4.8%) | 4500/658 020 (0.68%) | 1.68 | 1.12–2.53 | 0.013 |
| Old (≥67 years) | 1790/15 280 (11.7%) | 29 360/653 315 (4.49%) | 1.47 | 1.30–1.68 | <0.001 |
| Dementia not present | 1740/16 595 (10.5%) | 31 870/1 289 950 (2.5%) | 1.57 | 1.37–1.79 | <0.001 |
| Dementia present | 215/2090 (10.3%) | 1990/21 385 (9.3%) | 0.95 | 0.66–1.36 | 0.8 |
Abbreviations: AOR, adjusted odds ratio; CI, confidence interval.
Propensity score matching resulted in 3733 matched pairs with similar baseline characteristics (Table 4). Mortality in the delirium group was 10.5% vs 7.6% in the control group (relative risk = 1.39 [95% CI: 1.2–1.6, P < 0.001]).
Table 4.
Baseline characteristics and mortality post propensity score matching
| Delirium (N = 3733) | No Delirium (N = 3733) | Standardized Differences | |
|---|---|---|---|
| Age, y, mean | 77.795 | 78.421 | −4.8 |
| Female sex | 54.22% | 55.56% | −2.7 |
| Charlson index | 3.7035 | 3.667 | 1.9 |
| Acute kidney injury | 32.95% | 33.16% | −0.5 |
| PCI | 18.32% | 17.63% | 1.6 |
| Sepsis | 4.96% | 3.88% | 6.4 |
| Respiratory failure | 17.89% | 17.06% | 2.6 |
| Diabetes mellitus | 40.10% | 40.72% | −1.3 |
| Heart failure | 49.37% | 49.29% | 0.2 |
| Dementia | 11.17% | 10.39% | 3.2 |
| CKD | 36.91% | 36.94% | −0.1 |
| Cardiogenic shock | 6.35% | 5.44% | 4.4 |
| STEMI | 18.83% | 18.40% | 1 |
| Death | 10.47% | 7.55% | 12 |
| Propensity Score | 0.03882 | 0.03882 | 0 |
Abbreviations: CKD, chronic kidney disease; PCI, percutaneous coronary intervention; STEMI, ST‐segment elevation myocardial infarction.
4. DISCUSSION
The study evaluated the impact of delirium on clinical outcomes in patients hospitalized with MI using the NIS for the years 2012 to 2014. The rate of delirium was 1.4% among all subjects. Previous reports showed variable rates of up to 5%. This is likely because studies have utilized different inclusion criteria and assessment methods.6, 7 Our study excluded patients who received mechanical ventilation or CABG, as both are known to be associated with delirium. The study also excluded patients with MI as a secondary diagnosis, as this subset of patients often presents with noncardiac acute illness.
Patients with delirium were generally older. It is known that the risk of delirium increases in older age groups. Studies on elderly patients with acute coronary syndrome reported high rates of delirium, especially in critical‐care settings. For example, in the cardiac intensive care unit (ICU), delirium in patients 85 years old and above was present in 54% of subjects.14, 15, 16
There were more comorbid conditions in the delirium group. The median Charlson index was 3 compared to 2 in the control group. The increased median score reflects the severity of illness associated with delirium. This may suggest the need to recognize acute symptoms of delirium as surrogate features for the severity of illness in MI.6 Mortality was significantly higher at 10.4% in the delirium group vs 2.6% in the nondelirium group. After adjustment for several confounders, the risk of death was still higher in the delirium group (Table 2). This association has been well reported in the general medical setting and extended to other settings of care, including the ICU and skilled nursing facilities.17, 18, 19, 20, 21, 22 Although a few studies showed no association with increased mortality, these were mostly single‐center studies with small sample sizes and examined a different subset of patients.23, 24 A meta‐analysis of studies examining mortality associated with delirium in critical care settings showed that delirium was associated with increased mortality and longer hospital stay.22 Despite the growing evidence that suggests increased mortality associated with delirium, there is still controversy about the true effect of delirium vs that due to the confounding variables.25 Multiple prospective studies examining the effect of reducing the duration of delirium have failed to show improved mortality.26
PCI was performed far less in the delirium group, with only 18.4% undergoing PCI vs 54.6% in patients without delirium. The reasons for low rates of PCI use remain unclear, and database analyses such as this study cannot address that question. However, possible explanations could be the older population with more limited goals of care, the presence of comorbidities in the delirium group such as AKI, and poor baseline functional status, which all increase the risk of procedural complications.27, 28, 29 The delay in establishing diagnosis due to atypical symptoms on presentation is another possible cause for lower rates of PCI utilization.7 Collectively, these factors may discourage active invasive interventions.
Delirium was present in 2.4% of the 67 years or older group (the mean age of the study population), but only 0.54% in the younger group. In addition, our study showed delirium was associated with increased odds of dying in both age groups. In fact, the impact of delirium was more pronounced in the young age group vs older patients. Furthermore, the effect of delirium was limited to those with no history of dementia (Table 3). Although delirium was more common in older patients, the relative impact may be stronger in younger patients who have better cognitive function. After CABG, the impact of delirium was stronger in younger patients and in those without history of stroke.30 In a study of long‐term outcomes of delirium, increased mortality was noted only in patients without dementia.19 The more prominent impact of delirium in young patients is probably a relative effect; the mortality rates in these populations are smaller to begin with, and therefore, a small increase in mortality will be proportionally higher.
To the best of our best knowledge, this is the largest study that examined the association of delirium with worse in‐hospital clinical outcomes in patients with MI. Previous studies examined the impact of delirium in limited populations, including those with cardiogenic shock, in the cardiac ICU, undergoing transcatheter aortic valve replacement, or CABG, with an emphasis on elderly populations.16, 18, 30, 31, 32 The analysis used the strength of the NIS, with large sample sizes and the ability to adjust for several confounding variables. We used 2 widely used methods, multivariable logistic regression and propensity score matching analysis, to adjust for confounding variables that affect both the outcome and the presence of the delirium. Codes for delirium due to mental disorder or medications (either drug adverse reaction or intoxication) were not included in the analysis to limit our analysis to delirium due to organic and metabolic causes, as is often seen in critically ill patients.
Our study was limited by the retrospective nature of the analysis and the possibility of selection bias. We limited the study population to those with a principal diagnosis of MI to reduce this bias. This study design allows for limitations inherent to retrospective studies, as they are unable to account for unmeasured confounders. As an administrative database, important patient‐level data were unavailable (eg, medications, angiographic and echocardiographic characteristics). Furthermore, the NIS is a discharge‐level rather than an individual‐level database, and does not account for repeated admissions. We excluded records with indicator of transfer to another hospital (TRAN_OUT) to reduce the chance of duplication. Coding practices are open to variation among institutions and medical conditions. Certain variables may be underdocumented due to lack of reimbursement value.33, 34
5. CONCLUSION
In patients hospitalized with MI, delirium was associated with higher odds of death. This effect remained significant regardless of age group.
Conflicts of interest
The authors declare no potential conflicts of interest.
Supporting information
Appendix S1. Supporting Information
Abdullah A, Eigbire G, Salama A, et al. Impact of delirium on patients hospitalized for myocardial infarction: A propensity score analysis of the National Inpatient Sample. Clin Cardiol. 2018;41:910–915. 10.1002/clc.22972
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Supplementary Materials
Appendix S1. Supporting Information