Abstract
Background
Delirium is a common complication during acute care hospitalizations in older adults. A substantial percentage of admissions are for ambulatory care-sensitive conditions (ACSCs) or potentially avoidable hospitalizations—conditions that might be treated early in the outpatient setting to prevent hospitalization and hospital complications.
Methods
This retrospective cross-sectional study examined rates of delirium among older adults hospitalized for ACSCs. Participants were 39 933 older adults ≥65 years of age admitted from January 1, 2015 to December 31, 2019 to general inpatient units and ICUs of a large Southeastern academic medical center. Delirium was defined as a score ≥ 2 on the Nursing Delirium Screening Scale or positive on the Confusion Assessment Method for the Intensive Care Unit during admission, and ACSCs were identified from the primary admission diagnosis using standardized definitions. Generalized linear mixed models were used to examine the association between ACSCs and delirium, compared with admissions for non-ACSC diagnoses, adjusting for covariates and repeated observations for individuals with multiple admissions.
Results
Delirium occurred in 15.6% of admissions for older adults. Rates were lower for ACSC admissions versus admissions for other conditions (13.9% vs 15.8%, p < .001). Older age and higher comorbidity were significant predictors of the development of delirium.
Conclusions
Rates of delirium among older adults hospitalized for ACSCs were lower than rates for non-ACSC hospitalization but still substantial. Optimizing the treatment of ACSCs in the outpatient setting is an important goal not only for reducing hospitalizations but also for reducing risks for hospital-associated complications such as delirium.
Keywords: Ambulatory care-sensitive conditions, Delirium, Older adults, Potentially avoidable hospitalizations
Background
Delirium, an acute confusional state, is present in 10%–15% of hospitalized older adults at the time of admission, with an additional 10%–40% developing incident delirium during their hospital stay (1). Once considered transient cognitive dysfunction, delirium is now recognized to persist after hospital discharge in over 50% of patients and is associated with increased adverse outcomes, such as increased morbidity/morality, higher rates of institutionalization, elevated health care costs, and accelerated cognitive and functional decline (2–7). Because the older adult population is projected to grow by 69% to 95 million people by 2060, the long-term consequences of delirium will affect an even greater number of adults aged 65 and older (8).
Despite the high prevalence of delirium, few effective treatments exist. Pharmacologic treatments, such as antipsychotics or sedatives, have not been shown to decrease the incidence of delirium and are often reserved for cases of severe agitation (9). The American Geriatrics Society clinical practice guidelines for delirium management in older adults recommend multicomponent nonpharmacologic interventions performed by an interdisciplinary team, predominantly focusing on primary prevention as efficacy has only been demonstrated in preventing rather than treating delirium (10–12). However, only about 40% of delirium cases in hospitalized older adults can be prevented with current interventions (11–13). Thus, a considerable need remains to explore additional methods of preventing delirium.
Ambulatory care-sensitive conditions (ACSCs) are common medical conditions that can be treated by ambulatory primary care, which provides a potential intervenable opportunity to prevent hospital admission (14). ACSCs encompass acute care of older adults across several conditions, such as asthma, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), diabetes, hypertension, pneumonia, and urinary tract infection (15). Through early identification and management of ACSCs, hospitalizations may be avoided with intervention in the outpatient care setting, thus potentially decreasing the incidence of delirium associated with hospitalization. The main objective of this study is to examine the rate of delirium among hospitalized older adults attributable to ACSCs.
Method
Study Design and Oversight
This study was a retrospective cross-sectional analysis of inpatient hospital admissions to examine rates of delirium for admissions for ACSCs versus admissions for other conditions. All study procedures were exempted by the local Institutional Review Board.
Study Site and Patient Population
Participants aged 65 and older with an inpatient hospitalization were identified from the electronic health records (EHR) of the University of Alabama at Birmingham (UAB) Hospital, a large academic medical center in the Southeastern United States, using the Informatics for Integrating Biology and the Bedside (i2b2) platform (16). Using this data query, we extracted additional identifiable information on participants from the UAB Electronic Data Warehouse for the period from January 1, 2015 to December 31, 2019.
Inclusion criteria of this study were (1) age ≥65 years; (2) admission to one of the medical or surgical units on inpatient status during the study period; (3) at least 1 assessment of delirium during hospitalization. We deliberately kept our inclusion criteria broad to allow maximal generalization to the hospitalized older adult population.
Outcome
The primary outcome of this study was the presence of delirium based on screening instruments administered as part of care in the general hospital units and the ICU. Assessments on the general hospital units utilized the Nursing Delirium Screening Scale (NuDESC) (17), a reliable and validated 5-item screener for hospital-based assessment of delirium, at any time during the hospitalization. The NuDESC is collected once per nursing shift on all older adult inpatients as part of our UAB Virtual Acute Care for Elders (ACE) quality improvement program (18). The NuDESC has demonstrated comparability with the widely used Confusion Assessment Method (CAM) (19) in hospitalized geriatric patients including surgical populations (20,21). The screener uses nurses’ ratings of patient behavior across 5 core areas: disorientation, inappropriate behavior, inappropriate communication, illusions or hallucinations, and psychomotor retardation. Each domain was rated on a 2-point scale (absent, mild, and severe) for a possible total of 10 points. Scores ≥ 2 were considered a positive screen for delirium. We have shown completion rates of 89%–96% for the NuDESC among our units implementing Virtual ACE (18).
Assessments in the ICU utilized the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) (22), an adaptation of the CAM algorithm (19) for use by ICU nurses. It has been validated in 2 different samples and has high sensitivity (95%–100%) and specificity (93%–100%) as compared to an independent Diagnostic and Statistical Manual, Fourth Edition, diagnosis (22,23). Individuals who were positive on both items 1 and 2 and either items 3 or 4 of the CAM-ICU were defined as positive for delirium. As the CAM-ICU was not implemented in our ICUs until 2018, we also conducted a sensitivity analysis excluding individuals admitted to ICUs from 2015 to 2017 whose delirium status during their ICU stay could not be determined.
In addition to the primary outcome of any delirium during hospitalization, we specifically examined prevalent delirium, defined as the presence of delirium within 1 day of admission, and incident delirium, defined as the absence of delirium on the first day but the presence of delirium at least once after the first day, as secondary outcomes (24). Admissions in which delirium status on the first day could not be determined due to missing NuDESC scores were excluded from the analysis of prevalent and incident delirium, but not from the analysis of any delirium. Prevalent delirium is assumed to reflect delirium present before hospital admission, while incident delirium is assumed to reflect delirium occurring after admission. These analyses reflect potential differences in the effects of ACSCs and covariates based on the timing of delirium, which would have implications for the timing of preventive interventions. We also conducted sensitivity analyses using a cutoff of ≥1 on the NuDESC, which provides greater sensitivity with a slight loss of specificity (25). A systematic review has shown a sensitivity of 72%–82% (except for 1 study with 32%) and specificity of 81%–92% of the NuDESC in hospitalized older adults with a cutoff of 2, and sensitivity of 80%–96% and specificity of 69%–79% with a cutoff of 1 (21).
A subset of medical-surgical units received the intensive Virtual ACE intervention that included 3 to 4 1-h case-based education modules over 8 weeks for unit nurses and other unit staff. Training for nurses included material covering the administration and interpretation of the NuDESC, as well as nurse-driven care for delirium prevention and management. The UAB Hospital Geriatric Quality Improvement team subsequently utilized lessons learned in developing the Virtual ACE curriculum to operationalize NuDESC training into nurse education for current and newly hired nurses throughout UAB Hospital. As part of our development of Virtual ACE, a trained research assistant conducted independent assessments of patients using the NuDESC on the first 2 units (1 medical and 1 surgical) implementing this quality improvement program. We have compared the NuDESC ratings of the research assistant and unit nurses after Virtual ACE training, using Gwet’s agreement statistic (26) in the R package rel due to the large number of ties. Across n = 817 patients assessed after Virtual ACE training, Gwet’s statistic was 0.821 (95% CI: 0.798 to 0.844) for the NUDESC score, indicating good agreement, and was 0.914 (95% CI: 0.885 to 0.943) for classification as delirium positive using the NUDESC, indicating excellent agreement.
For the CAM-ICU, nurse leaders received training on its implementation from the developers of the instrument when it was first implemented at UAB in 2017. Subsequently, nurse leaders provided information on the CAM-ICU to nurses as part of their orientation process, and information was added to the UAB EHR to assist nurses with the completion of the CAM-ICU. Ongoing monitoring of CAM-ICU results from 3 ICUs is also performed to identify process improvement strategies.
Predictor
Each hospitalization was classified as being due to an ACSC (ACSC +) or not (ACSC−) based on the primary discharge diagnosis, using the published ICD-10 codes from Purdy and colleagues (Supplementary Table 1) (27). Additional analyses examined each of the 17 separate conditions classified as ACSCs (Supplementary Table 1). Sensitivity analyses also examined the potential effects of the definition of ACSCs by using alternative definitions based on the published ICD-10 codes of Freund (Supplementary Table 2) (28), Sundmacher (Supplementary Table 3) (29), and Naumann (Supplementary Table 4) (30).
Covariates
Demographics
Demographic characteristics of age in years, gender, and race were based on self-reported data recorded in the EHR.
Comorbidity
As comorbidity is a recognized risk factor for delirium (31), we included the Elixhauser comorbidity index (32) as a predictor in our models. The Elixhauser comorbidity index was calculated using admission ICD-10 diagnostic codes, based on the algorithm of Quan and colleagues (33). The Elixhauser specifically excludes dementia from its codes, allowing us to examine the effects of dementia separately. The Elixhauser score is a count of the number of comorbidities and ranges from 0 to 30, with higher scores representing greater comorbidity. As this range of scores is much larger than the range of other predictors, which can cause problems with numerical computations in model fitting (34), the Elixhauser score was scaled by dividing by 10 for all statistical analyses.
Dementia
All-cause dementia was determined using admission ICD-10 diagnostic codes, based on the codes of Goodman and colleagues, (35) and classified as present or absent.
Service type
We classified the types of providers (medical or surgical) based on the specialty of the treating physician. We classified the type of service as medical or surgical only based on the type of provider caring for the patient on admission. We conducted stratified analyses based on service type to examine potential differences between medical and surgical patients in the association of ACSCs and covariates with the development of delirium.
Intensive care unit
We classified ICUs based on the unit location (Medical ICU, Surgical ICU, Neurological ICU, Coronary Care Unit, Cardiothoracic ICU, Heart Transplant ICU, and Trauma/Burn ICU) within the EHR.
Statistical Analysis
We used generalized mixed effects models (GLMMs; covariance pattern models) (36) to examine whether rates of delirium were different between older adults hospitalized for an ACSC versus other conditions, adjusting for covariates and accounting for correlation among repeated hospitalizations for the same participant. GLMMs included fixed effects for the predictor of interest (ACSC admission) representing the overall effect and a random subject effect (random intercept) representing individual variation from the overall effect. The mixed effects model was used as it accounts for the correlation between observations for subjects with repeated hospitalizations and better controls for Type I errors in the presence of missing data (37). We also conducted sensitivity analyses that included admission functional status, admission cognitive function, and length of stay as covariates. Additional analyses examined the specific effect of each ACSC category on rates of delirium compared with admissions for non-ACSC admissions, as well as the specific effects within each service type, within general hospital units, and within ICUs. Analyses were conducted using R (38), version 4.2.1, and the lmer package (39), version 1.1–23.
Results
Across the 5-year study period, there were a total of 84 648 hospitalizations among 48 758 participants, with 64 020 hospitalizations among 39 933 participants meeting inclusion/ exclusion criteria (Figure 1). There was a median of 1 hospitalization per participant (range 1–37, interquartile range 1–2). Participant characteristics for all hospitalizations are shown in Table 1, while participant characteristics at first hospitalization are shown in Supplementary Table 5. Participants admitted for an ACSC were slightly older, more likely to be minority and female, had slightly more comorbidities, and had a greater prevalence of all-cause dementia. Approximately 9.8% of all admissions were for an ACSC. The majority of ACSC admissions (86.4%) were to medical services, while non-ACSC admissions showed a less dramatic split between medical and surgical services. Rates of delirium were lower for admissions for an ACSC versus admissions for other conditions (13.9% vs 15.8%, p < .001). Characteristics of admissions by delirium type (prevalent vs incident) are shown in Table 2 for admissions for which delirium type could be determined.
Figure 1.
Flow diagram for participant selection. The number of hospitalizations does not equal the number of participants due to multiple hospitalizations for some participants during the study period.
Table 1.
Participant Characteristics by ACSC Status
| Participant characteristic | ACSC admission | Other admission | p Value | N |
|---|---|---|---|---|
| N = 6 248 | N = 57 772 | |||
| Age (y) | 76.1 (8.77) | 75.2 (7.91) | <.001 | 64 020 |
| Race | <.001 | 64 020 | ||
| White | 3 722 (59.6%) | 40 628 (70.3%) | ||
| Black or African American | 2 267 (36.3%) | 15 090 (26.1%) | ||
| Other | 259 (4.15%) | 2 054 (3.56%) | ||
| Gender: female | 3 458 (55.3%) | 29 418 (50.9%) | <.001 | 64 020 |
| Admission status: | .030 | 64 020 | ||
| Inpatient | 6 120 (98.0%) | 56 325 (97.5%) | ||
| Observation | 128 (2.05%) | 1 447 (2.50%) | ||
| Service type: | <.001 | 64 020 | ||
| Medical | 5 655 (90.5%) | 33 477 (57.9%) | ||
| Surgical | 593 (9.49%) | 24 294 (42.1%) | ||
| Missing | 0 (0.00%) | 1 (0.00%) | ||
| Length of stay (D) | 5.55 (6.36) | 6.00 (8.96) | <.001 | 64 020 |
| ICU: yes | 1 162 (18.6%) | 16 012 (27.7%) | <.001 | 64 020 |
| Elixhauser score | 5.16 (2.22) | 4.40 (2.38) | <.001 | 64 020 |
| Baseline SIS | 5.12 (1.68) | 5.20 (1.66) | <.001 | 64 020 |
| Baseline Katz | 9.14 (3.86) | 9.11 (4.10) | .584 | 64 020 |
| Dementia: yes | 682 (10.9%) | 5 621 (9.73%) | .003 | 64 020 |
| Any delirium: yes | 867 (13.9%) | 9 132 (15.8%) | <.001 | 64 020 |
| Any ward delirium: present | 861 (15.8%) | 8 863 (17.5%) | .003 | 56 196 |
| Any ICU delirium: present | 31 (0.58%) | 798 (1.72%) | <.001 | 51 762 |
Notes: ACSC = ambulatory care-sensitive condition; SIS = six item screener.
Values shown are for 33 117 unique participants; participants with multiple hospitalizations contributed data at each hospitalization.
Table 2.
Participant Characteristics by Delirium Type
| Participant characteristic | Prevalent delirium | Incident delirium | ||||||
|---|---|---|---|---|---|---|---|---|
| Not present | Present | p Value | n | Not present | Present | p Value | n | |
| n = 44 769 | n = 5 338 | n = 38 084 | n = 2 946 | |||||
| Age (y) | 74.9 (7.65) | 79.9 (9.23) | <.001 | 50 107 | 74.8 (7.65) | 76.8 (8.46) | <.001 | 41 030 |
| Race: | <.001 | 50 107 | .048 | 41 030 | ||||
| White | 31 528 (70.4%) | 3 301 (61.8%) | 26 389 (69.3%) | 2 001 (67.9%) | ||||
| Black or African American | 11 688 (26.1%) | 1 835 (34.4%) | 10 332 (27.1%) | 854 (29.0%) | ||||
| Other | 1 553 (3.47%) | 202 (3.78%) | 1 363 (3.58%) | 91 (3.09%) | ||||
| Gender: female | 22 399 (50.0%) | 3 053 (57.2%) | <.001 | 50 107 | 19 021 (49.9%) | 1 453 (49.3%) | .527 | 41 030 |
| Service type: | <.001 | 50 107 | <.001 | 41 030 | ||||
| Medical | 25 768 (57.6%) | 25 768 (57.6%) | 22 221 (58.3%) | 22 221 (58.3%) | ||||
| Surgical | 19 000 (42.4%) | 961 (18.0%) | 15 862 (41.7%) | 1 042 (35.4%) | ||||
| Missing | 1 (0.00%) | 0 (0.00%) | 1 (0.00%) | 0 (0.00%) | ||||
| Length of stay (D) | 5.57 (6.60) | 6.83 (7.82) | <.001 | 50 107 | 6.30 (6.08) | 14.7 (14.4) | <.001 | 41 030 |
| Elixhauser score | 4.40 (2.36) | 5.14 (2.29) | <.001 | 50 107 | 4.56 (2.33) | 6.16 (2.60) | <.001 | 41 030 |
| Baseline SIS | 5.55 (1.10) | 2.36 (2.36) | .000 | 50 107 | 5.49 (1.22) | 4.72 (1.87) | <.001 | 41 030 |
| Baseline Katz | 9.82 (3.48) | 4.36 (4.31) | .000 | 50 107 | 9.48 (3.74) | 7.11 (4.49) | <.001 | 41 030 |
| Dementia: yes | 2 707 (6.05%) | 2 412 (45.2%) | .000 | 50 107 | 2 103 (5.52%) | 579 (19.7%) | <.001 | 41 030 |
| ACSC: other admission | 40 380 (90.2%) | 4 823 (90.4%) | .735 | 50 107 | 34 150 (89.7%) | 2 705 (91.8%) | <.001 | 41 030 |
Notes: ACSC = ambulatory care-sensitive condition; SIS = six item screener.
Any delirium was defined as delirium occurring at any time during hospitalization. Prevalent delirium was defined as delirium occurring within 1 day of admission, and incident delirium was defined as the absence of delirium in the first day but presence of delirium at least once after the first day.
Results of the GLMM analysis are shown in Table 3. The odds of any delirium were lower for admissions for ACSCs versus admissions for other conditions (odds ratio [OR] = 0.60, 95% CI: 0.54 to 0.67). Higher comorbidity on the Elixhauser index had the largest odds for any delirium episode (OR 37.47, 95% CI: 31.85 to 44.09, for every 10 additional comorbidities), followed closely by all-cause dementia (OR 15.10, 95% CI: 13.62 to 16.73). The odds of delirium also increased substantially with older age, with an odds of 1.32 (95% CI: 1.22 to 1.42) for participants aged 75–84, an odds of 2.22 (95% CI: 2.00 to 2.45) for participants aged 85–94, and an odds of 4.59 (95% CI: 3.71 to 5.66) for participants age 95 and above, relative to the reference age of 65–74. Sensitivity analyses using alternative definitions of ACSCs did not change the results of the analysis (data not shown). Analyses including admission functional status, admission cognitive function, and length of stay as covariates showed similar odds of any delirium for ACSCs (OR 0.71, 95% CI: 0.64 to 0.79), with substantially reduced but still significant odds of delirium associated with Elixhauser comorbidity and all-cause dementia (Supplementary Table 6).
Table 3.
Predictors of Any, Prevalent, and Incident Delirium
| Age (65–74) | Reference | Reference | Reference |
| Age (75–84) | 1.32 [1.22, 1.42] | 1.44 [1.31, 1.58] | 1.22 [1.09, 1.35] |
| Age (85–94) | 2.22 [2.00, 2.45] | 2.66 [2.37, 2.98] | 1.42 [1.23, 1.63] |
| Age (95+) | 4.59 [3.71, 5.66] | 5.10 [4.08, 6.37] | 2.39 [1.78, 3.22] |
| Race, White | Reference | Reference | Reference |
| Race, Black or African American | 1.29 [1.19, 1.39] | 1.48 [1.36, 1.62] | 0.98 [0.89, 1.09] |
| Race, other | 1.21 [1.01, 1.45] | 1.36 [1.10, 1.67] | 0.90 [0.69, 1.16] |
| Gender, female | 0.95 [0.89, 1.02] | 1.08 [1.00, 1.17] | 0.89 [0.81, 0.97] |
| Elixhauser score | 37.47 [31.85, 44.09] | 6.82 [5.74, 8.10] | 29.81 [23.98, 37.04] |
| Dementia | 15.10 [13.62, 16.73] | 15.32 [13.73, 17.09] | 4.11 [3.58, 4.70] |
| ACSC (Purdy) | 0.60 [0.54, 0.67] | 0.74 [0.65, 0.84] | 0.59 [0.51, 0.69] |
| Observations | 64 020 | 60 823 | 49 317 |
| Subjects | 39 933 | 38 200 | 32 151 |
| Conditional R2 | 0.56 | 0.51 | 0.42 |
| Marginal R2 | 0.24 | 0.19 | 0.15 |
| AIC | 45 310.52 | 29 948.11 | 20 214.02 |
Notes: ACSC = ambulatory care-sensitive condition; AIC = Akaike’s information criterion; SIS = six item screener.
Any delirium was defined as delirium occurring at any time during hospitalization. Prevalent delirium was defined as delirium occurring within 1 day of admission, and incident delirium was defined as absence of delirium in the first day but presence of delirium at least once after the first day. Generalized linear mixed models were used to examine the association of ACSCs with each type of delirium, adjusting for covariates and for correlations among multiple hospitalizations for each participant.
ACSC Categories
The rates of each of the 17 ACSC categories from the Purdy definition by delirium status are shown in Supplementary Table 7. Similar to the national population of hospitalized older adults (40), the most common ACSC conditions were CHF, diabetes, and pneumonia among admissions with and without delirium; epilepsy was also a common ACSC condition among admissions with delirium.
Results of the GLMM analysis for each ACSC category, relative to admissions for non-ACSC conditions, are shown in Supplementary Tables 8–25. These showed that the odds of delirium were significantly lower for admissions for asthma (OR 0.34, 95% CI: 0.12 to 0.97), cellulitis (OR 0.48, 95% CI: 0.32 to 0.70), CHF (OR 0.33, 95% CI: 0.27 to 0.40), COPD (OR 0.41, 95% CI: 0.21 to 0.80), gastroenteritis (OR 0.43, 95% CI: 0.27 to 0.68), hypertension (OR 0.19, 95% CI: 0.04 to 0.88), and anemia (OR 0.06, 95% CI: 0.01 to 0.23); while the odds of delirium were significantly higher for admissions for epilepsy (OR 2.18, 95% CI: 1.58 to 3.02). For angina, dental, diabetes, ear, nose, and throat (ENT), pneumonia, nutrition, vaccine, pelvic inflammatory disease (PID), ulcer, and pyelonephritis, the odds of delirium were not significantly different from the odds of delirium for non-ACSC admissions, though for several conditions (particularly angina), the estimates were not very precise, as evidenced by the wide confidence intervals for the estimates.
Prevalent and Incident Delirium
The associations of prevalent delirium and incident delirium with ACSCs were similar to the association with any delirium (OR = 0.74, 95% CI: 0.65 to 0.84 and OR = 0.59, 95% CI: 0.51 to 0.69, respectively; Table 3). The odds of incident delirium due to age were lower than the odds of any delirium but still significant, but the odds of prevalent delirium were higher. The association between incident delirium and all-cause dementia was substantially lower than the association with any delirium, while for comorbidity, the association with prevalent delirium was substantially reduced.
NuDESC Score ≥1
Analyses using a NuDESC cutoff score of 1 showed similar results to analyses using a cutoff of 2 (Supplementary Table 26). However, the association of any delirium with comorbidity was lower, while the association with age was greater.
Admission Service Type
Analyses of admissions to medical services only were generally consistent with analyses of all participants (Supplementary Table 27). The association with comorbidity was reduced but still significant, as was the association with age. Admissions to medical services for ACSCs did show reduced odds of developing delirium relative to admissions to medical services for other conditions (OR 0.56, 95% CI: 0.50 to 0.63).
Analyses of admissions to surgical services were generally consistent with the analyses of all participants (Supplementary Table 28), but the association of ACSCs with delirium was no longer statistically significant (OR 0.80, 95% CI: 0.56 to 1.13). The association with age was higher, as was the association with Elixhauser comorbidity score. Thus, there was no detectable difference in the odds of developing delirium with admissions for ACSCs versus admissions for other conditions in these populations. Figure 2 shows the associations of ACSCs with delirium overall and by service type.
Figure 2.
Adjusted odds of any delirium for ACSC versus non-ACSC admissions by admission service type. Odds ratios show the odds of developing any delirium (defined as delirium occurring at any time during hospitalization), stratified by service type on admission, and adjusted for covariates and for correlations among multiple hospitalizations for each participant.
Intensive Care Units
Analyses of admissions to general medical/ surgical units only are shown in Supplementary Table 29. The association of ACSCs with delirium was similar to the primary analysis (OR 0.68, 95% CI: 0.60 to 0.78). However, the association of delirium with age and with dementia was higher, while the association of delirium with comorbidity was lower.
Analyses of admissions to ICUs also showed an association of ACSCs with delirium that was similar to the primary analysis (OR 0.53, 95% CI: 0.33 to 0.85; Supplementary Table 30). The associations of delirium with age, comorbidity, and dementia were all substantially lower.
A sensitivity analysis excluding individuals admitted to ICUs from 2015 to 2017, prior to implementation of the CAM-ICU to determine delirium status in the ICU, is shown in Supplementary Table 31. The association of ACSCs with delirium remained similar to other analyses (OR 0.63, 95% CI: 0.56 to 0.72). The association of delirium with age and dementia was higher, while the association of delirium with comorbidity was somewhat lower, compared with the primary analysis.
Conclusion
This study represents the first analysis of the contribution of inpatient admissions for ACSCs to delirium. Consistent with previous studies (1), the rate of delirium across all hospitalizations was 15.6%, with 8.7% of delirium occurring in admissions for ACSCs. We also found that 9.8% of admissions were for ACSCs, with 13.9% of admissions for ACSCs experiencing delirium during their hospital stay. The risk for delirium was 40% lower for admissions due to ACSCs compared with admissions for other conditions after adjusting for covariates. However, the risk of delirium varied considerably for individual ACSCs. The risk of delirium was lower compared with non-ACSC admissions for asthma, cellulitis, CHF, COPD, gastroenteritis, hypertension, and anemia, but higher for epilepsy. Rates of delirium were not significantly different from non-ACSC admissions for angina, dental, diabetes, ENT, pneumonia, nutrition, vaccine, PID, ulcer, and pyelonephritis. Collectively, these findings indicate that hospitalizations for ACSCs are not benign events but are associated with potentially significant consequences for the individual patient and for the health care of older adults through the development of delirium.
Stratified analyses by service type (medical only or surgical only) showed that the lower odds of delirium for ACSC admissions were largely driven by admissions to medical services. There was no statistically significant difference in the odds of delirium for ACSC versus non-ACSC admissions to surgical services.
This study adds to a growing literature on the critical need for prevention strategies to reduce the burden of delirium in older adults. Antipsychotics had long been considered the standard treatment for delirium but have largely fallen out of favor as an increasing number of studies have not shown benefits over standard treatment (9,41). Multicomponent nonpharmacological interventions have demonstrated efficacy in the prevention of delirium but not in the treatment of established delirium (10–12), leading to a shift in focus on prevention over treatment in clinical practice. However, only about 40% of delirium cases in hospitalized older adults can be prevented (11–13); thus, there is an urgent need for novel preventive strategies, including new methods to reduce the risk factors for the development of delirium.
Meta-analyses have identified multiple risk factors for delirium in general medical wards (31), intensive care units (42), and surgical units (43). In these analyses, prominent risk factors include illness severity as well as visual impairment, urinary catheterization, low albumin level, and length of stay. Illness severity among admissions for ACSCs may be lower than illness severity among admissions for other conditions but is still substantial (44). Thus, although ACSCs are conditions that are potentially treatable in the outpatient setting early in their course, admissions for ACSCs may be sufficiently advanced so that the risk of delirium is comparable to the risk for other conditions. In addition, during the course of their hospitalization, individuals admitted for ACSCs may acquire risk factors for delirium, such as adverse drug reactions or urinary catheterization (45). Shifting care for ACSCs from the inpatient to the outpatient setting would presumably avoid the development of delirium, making ACSCs a potentially intervenable risk factor in prevention of delirium. Although previous studies have analyzed health care costs associated with hospitalizations for ACSCs and potential savings with prevention and early treatment (46), our analysis emphasizes the nonmonetary human impact of ACSC hospitalizations and the additional potential benefits of treatment of ACSCs in the outpatient setting. Our analysis would also support the use of programs such as Hospital at Home as alternatives for treating ACSCs (47), although it would need to be determined if such programs also lower rates of delirium.
A particular strength of our study is the use of EHR data to include all hospital admissions over a multiyear period, increasing the sample size and generalizability of our findings. However, some limitations must be acknowledged. Foremost, we are unable to demonstrate direct causation between the ACSC that necessitated admission and the development of delirium, only an association. Our analysis also only focused on events occurring after admission for ACSCs, and not on factors such as social determinants of health (SDoH) that may have led to the admission. Thus, it is not conclusive that interventions to improve the management of ACSCs in the outpatient setting would necessarily lead to corresponding reductions in the rates of delirium among hospitalized older adults, though more detailed studies are clearly warranted to address this question. Second, some consequences of delirium may also be viewed as risk factors for delirium. This is particularly the case for length of stay, where the development of delirium may also prolong hospitalization, leading to longer LOS, but prolonged hospitalization may also result in longer periods for potential development of delirium risk factors (such as adverse drug events). However, including these variables as predictors of delirium potentially introduce collider bias (48), or bias arising from adjusting for variables that actually occur after the event of interest, and so must be viewed with caution. Although this does not invalidate the results of our analysis, our models capture only a part of the complex relationships in delirium. Estimates of the odds of delirium associated with ACSCs are similar when these covariates are removed from our models, suggesting the risk of delirium due to ACSCs is not substantially affected by these factors, but the effect of other risk factors (such as dementia and other comorbidities) were substantially changed. Third, our study examined only 1 academic medical center in the Southeastern United States, and the percentage of minorities other than African Americans was low. Although we expect our findings to be generalizable to other groups and settings, further studies are needed to confirm this. Fourth, the implementation of the CAM-ICU occurred later than the implementation of the NuDESC for delirium screening, resulting in an early period of incomplete data for hospitalizations with ICU stays. We also did not have information on the severity of the delirium or of the ACSC condition, only presence or absence. Fifth, although the ICD-10 codes for dementia in our study are based on published lists, the sensitivity and specificity of these codes have not been formally evaluated but would appear to be high (22). Finally, our definition of delirium for the general medical and surgical units is based on the NuDESC score, rather than the gold standard Confusion Assessment Method (CAM) (19). However, the NuDESC has demonstrated excellent performance relative to the CAM (85.7% sensitivity and 86.8% specificity) (17) and compares favorably to other bedside assessments for delirium (49).
In summary, our analysis demonstrated that rates of delirium among older adults hospitalized for ACSCs were lower than rates of delirium during hospitalization for other conditions but were still substantial, with the rates varying considerably across specific ACSCs. This result indicates that treatment of ACSCs in the outpatient setting could be an important component of efforts to reduce delirium. Given the lack of efficacy with pharmacological treatments in treating delirium once it is established, further research into delirium prevention is clearly warranted to reduce the burden of delirium in the older adult population.
Supplementary Material
Funding
This work was supported by NIH/NIA grants R21 AG057982 and R01 AG060993 (R.E.K.), AHRQ grant T32 HS013852 (R.M.S.), the McKnight Brain Research Foundation (R.C.M.), a UAB School of Medicine AMC21 pilot grant (C.J.B.), and the UAB Integrative Center for Aging Research (A.D.M.).
Contributor Information
Caroline Whittington, Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Rachel M Skains, Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Yue Zhang, Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
John D Osborne, Division of General Internal Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Tobias O’Leary, Division of General Internal Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Hyun B Freeman, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Roy C Martin, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Jasmine K Vickers, Department of Nursing Research and Scholarship, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Kellie L Flood, Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Alayne D Markland, Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA; Birmingham/Atlanta Geriatric Research, Education, and Clinical Center (GRECC), Department of Veterans Affairs, Birmingham, Alabama, USA.
Thomas W Buford, Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA; Birmingham/Atlanta Geriatric Research, Education, and Clinical Center (GRECC), Department of Veterans Affairs, Birmingham, Alabama, USA.
Cynthia J Brown, Department of Medicine, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA.
Richard E Kennedy, Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Lewis A Lipsitz, (Medical Sciences Section).
Conflict of Interest
None.
Author Contributions
C.W. to the analysis and interpretation of data, and drafting/revising the manuscript for content. R.M.S. contributed to the analysis and interpretation of data, and drafting/revising the manuscript for content. Y.Z. contributed to the analysis and interpretation of data and drafting/revising the manuscript for content. J.D.O. contributed to the acquisition and analysis of data, and drafting/revising the manuscript for content. T.O. contributed to the acquisition and analysis of data, and drafting/revising the manuscript for content. H.B.F. contributed to drafting/revising the manuscript for content. R.C.M. contributed to drafting/revising the manuscript for content. J.K.V. contributed to the acquisition of data, and drafting/revising the manuscript for content. K.L.F. contributed to the acquisition of data, and drafting/revising the manuscript for content. A.D.M. contributed to drafting/revising the manuscript for content. T.W.B. contributed to drafting/revising the manuscript for content. C.J.B. contributed to the obtaining of funding, the study concept and design, the analysis and interpretation of data, and drafting/revising the manuscript for content. R.E.K. contributed to the obtaining of funding, the study concept and design, the analysis and interpretation of data, and drafting/revising the manuscript for content.
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