Abstract
The prevalence and incidence of diabetes mellitus (DM) are increasing worldwide. We aim to assess mortality and socio-economic outcomes among patients hospitalized for stroke and diabetes in the US and evaluate their recent trends. We examined: in-hospital mortality, length of stay (LoS), and overall hospital charges in diabetic patients over 18 years old who were hospitalized with a stroke from 2005 to 2014, included in the National Inpatient Sample. In those patients, the mean (SD) age slightly decreased from 70 (13) years to 69 (13) years (p-trend < 0.001). Interestingly, although incident cases of stroke amongst DM patients increased from 17.4 to 20.0 /100,000 US adults (p-trend < 0.001), age-adjusted mortality for those with hemorrhagic strokes decreased from 24.3% to 19.6%, and also decreased from 3.23% to 2.48% for those with ischemic strokes (p-trend < 0.01 for both), but remained unchanged in TIAs patients. As expected, the average total charges per hospital stay almost doubled over the ten-year period, increasing from 15 970 to 31 018 USD/stay (adjusted for inflation). Nonetheless, median (IQR) LoS slightly decreased from 4 (2–6) to 3 (2–6) days (p-trend < 0.001). In total, our data show that, from 2005 to 2014, the incidence of stroke among the diabetes patient population are gradually increasing, in-hospital mortality is steadily decreasing, along with average LoS. Admission costs were up almost twofold during the same period.
Subject terms: Endocrinology, Health care, Neurology
Introduction
Stroke is the leading cause of long term disability1, and the fifth most common cause of mortality in the United States2. In 2016, the global lifetime risk of stroke for adults > = 25 years was 24.9%; a relative increase of almost 9% since 19903.
Over the last decade, the prevalence of diabetes mellitus (DM) has continued to rise worldwide due to the aging of the population and a pandemic surge in obesity and sedentary lifestyles4. Patients with DM have an increased risk of developing large- and medium vessel acute atherosclerotic disease, in addition to microangiopathy5. This accelerated atherosclerosis makes hyperglycemia an important risk factor for ischemic stroke, resulting in an almost twofold increased incidence in DM patients6. This risk is also notably higher in women7–9. Further, diabetes also increases the risk of hemorrhagic stroke10, which is majored by the concomitant presence of hypertension and uncontrolled hyperglycemia11.
Cardiovascular disease remains the leading cause of death worldwide despite the implementation of more efficacious primary and secondary prevention measures12. Nevertheless, recent temporal trend analysis suggest that CVD-related mortality is decreasing gradually13,14, in particular from stroke and coronary artery disease14. In stroke patients, it is estimated that mortality decreased by over 55% in a decade15.
Despite the steady increase in diabetes’ incidence and prevalence, age-adjusted and cause-specific mortality and hospitalization for some cardiovascular complications in DM patients has also been on the decline16. For instance, in-hospital mortality in diabetic patients hospitalized for myocardial infarction was reduced by almost 4%17.However, it is not known whether the presence of diabetes in stroke patients follows the same trend. We therefore assessed mortality and socio-economic outcomes among patients hospitalized for stroke and diabetes in the US and examined their national trend.
Methods
Database
Data were extracted from the National Inpatient Sample (NIS) database between 2005 and 2014. NIS is the largest publicly available hospital discharge database in the United States, initiated in 1998 as part of the Healthcare Cost and Utilization Project (HCUP), and funded by the Agency of Healthcare Research and Quality (AHRQ)18. Approximately 7 million hospitalizations are recorded every year from over 1000 hospitals, which represent 20% of all inpatient admissions to non-federal hospitals in the US. The database contains de-identified information related to diagnosis, procedures, in-patient outcomes, patient demographics, cost and disposition status19. The study was waived from institutional review board because participants were de-identified in the NIS.
Diagnosis and outcomes
All diagnoses and outcomes were identified using the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). The primary diagnosis was stroke, which was further divided into 3 groups: Hemorrhagic stroke (ICD-9 codes 430.0, 431.0, 432.0), ischemic stroke (ICD-9 codes 434.01, 434.11, 434.91, 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.01, 434.11, 434.91, 436), and transient ischemic attacks (TIAs) (ICD-9 codes 435.0, 435.1, 435.2, 435.3, 435.8, 435.9), all of which have been utilized in previous studies19–21. DM as a secondary diagnosis was identified using the diagnostic code (250.x). The primary outcome for this study was in-hospital mortality. Secondary outcomes included hospitalization for incident cases of stroke in patients with concomitant diabetes, length of stay (LoS), hospital charges and patient disposition. All patients less than 18 years of age, or with missing age, gender, in-hospital mortality status, length of stay, and total charges were excluded.
Statistical analysis
Data weighting was used to allow for representative nationwide population estimates as recommended by the AHRQ18. Patient-level discharge trend weights consisted of applying the DISCWT variable prior to 2012 and the TRENDWT variable from 2012 to 2014. Patient demographics are presented using frequency distributions and percentages for categorical variable and means (standard deviation) or median (interquartile range) for continuous variables, depending on the distribution. Hospitalizations for incident cases of stroke patients with diabetes are presented per 100,000 individuals using the US population size that is annually reported by the US census bureau (https://www.census.gov). In-hospital mortality is given as age-adjusted and gender-stratified. Spearman correlation was used to assess temporal changes in outcomes. Multivariable logistic regression analysis was conducted to assess the association of in-hospital mortality with clinical characteristics. The model included age, gender, past medical history, cardiovascular risk factors, hospital characteristics and Charlson’s score, a categorized prognostic index that includes 22 different comorbidities22. Total hospital charges were adjusted for inflation according to the US bureau of labor statistics (https://data.bls.gov/cgi-bin/cpicalc.pl). Data were analyzed using SPSS (IBM, version 26). A p value < 0.05 was considered statistically significant.
Ethical approval
The study went through an administrative review only since it did not meet the definition of research involving human subjects given the nature of de-identified data per our institutional review board (IRB), determination letter number 18-00017.
Results
Demographics and clinical characteristics of stroke patients with diabetes
A total of 1,378,910 (weighted n = 6,822,816) patients between 2005 and 2014 were hospitalized with the primary diagnosis of stroke after exclusion of patients with missing data (Fig. 1). Diabetes as a secondary diagnosis was present in 31.7% of all stroke patients. After weighting, our study sample consisted of 2,165,165 stroke patients with diabetes: 66.1% with ischemic stroke, 24.6% hemorrhagic stroke and 9.3% with TIAs.
Figure 1.
Flow chart of the study.
The prevalence of diabetes among all stroke patients gradually increased from 28.1% to 35.5% between 2005 and 2014 (p-trend < 0.001) (Supplementary Table 1). Over the 10-year period, the mean (SD) age of stroke patients with diabetes slightly decreased from 70 (13) to 69 (13) years (p-trend < 0.001) (Table 1). Although there were more females than males overall, the proportion of males increased with time (p < 0.001).
Table 1.
Baseline demographics of diabetic patients admitted for stroke between 2005 to 2014.
| Years | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | P-trend |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Total cases | 37,828 (28.2) | 39,740 (28.8) | 39,853 (30.1) | 43,593 (30.7) | 42,084 (31.4) | 44,892 (31.8) | 48,799 (32.8) | 46,416 (33.9) | 46,531 (34.6) | 47,779 (35.5) | |
| Weighted cases | 184,759 (8.5) | 194,441 (9.0) | 196,702 (9.1) | 213,372 (9.9) | 213,045 (9.8) | 224,892 (10.4) | 234,325 (10.8) | 232,080 (10.7) | 232,655 (10.7) | 238,895 (11.0) | |
| Stroke/100 000 | 17.4 | 18.1 | 17.9 | 19.4 | 18.6 | 19.6 | 21.1 | 19.8 | 19.6 | 20 | < 0.001 |
| Age | |||||||||||
| Mean (SD) | 70 (13) | 69 (13) | 69 (13) | 69 (13) | 70 (13) | 69 (13) | 70 (13) | 69 (13) | 69 (13) | 69 (13) | < 0.001 |
| < 55 | 24,860 (13.5) | 27,063 (13.9) | 28,086 (14.3) | 30,192 (14.1) | 29,112 (13.7) | 32,582 (14.5) | 32,819 (14.0) | 32,055 (13.8) | 32,945 (14.2) | 32,105 (13.4) | 0.002 |
| 55–64 | 35,749 (19.5) | 40,001 (20.6) | 40,461 (20.6) | 44,231 (20.7) | 44,470 (20.9) | 48,193 (21.4) | 49,971 (21.3) | 50,480 (21.8) | 49,690 (21.4) | 51,865 (21.7) | < 0.001 |
| 65—74 | 47,875 (25.9) | 49,720 (25.6) | 49,606 (25.2) | 54,260 (25.4) | 55,363 (26.0) | 58,057 (25.8) | 60,331 (25.7) | 61,210 (26.4) | 63,015 (27.1) | 65,415 (27.4) | < 0.001 |
| 75–84 | 54,120 (29.2) | 54,284 (27.8) | 54,438 (27.6) | 58,179 (27.3) | 57,554 (27.0) | 57,310 (25.5) | 60,827 (26.0) | 58,440 (25.2) | 57,035 (24.5) | 59,415 (24.9) | 0.083 |
| > 84 | 22,153 (11.9) | 23,373 (12.0) | 24,111 (12.2) | 26,511 (12.4) | 26,545 (12.5) | 28,749 (12.8) | 30,377 (13.0) | 29,895 (12.9) | 29,970 (12.9) | 30,095 (12.6) | < 0.001 |
| Gender | |||||||||||
| Male | 85,500 (46.3) | 90,094 (46.3) | 91,738 (46.6) | 100,670 (47.2) | 100,595 (47.2) | 107,976 (48.0) | 112,536 (48.0) | 112,560 (48.5) | 114,575 (49.2) | 118,930 (49.8) | < 0.001 |
| Race | |||||||||||
| White | 89,419 (66.1) | 90,685 (62.0) | 88,112 (60.6) | 110,729 (64.0) | 114,668 (62.5) | 121,782 (60.6) | 131,347 (61.6) | 138,430 (62.4) | 138,775 (62.6) | 143,125 (62.5) | < 0.001 |
| Black | 22,978 (17.0) | 29,094 (19.9) | 30,832 (21.2) | 34,001 (19.7) | 34,639 (18.9) | 45,287 (22.5) | 45,893 (21.5) | 45,000 (20.3) | 44,845 (20.2) | 46,480 (20.3) | < 0.001 |
| Hispanic | 15,600 (11.5) | 17,899 (12.2) | 16,581 (11.4) | 16,500 (9.5) | 20,723 (11.3) | 21,481 (10.7) | 22,598 (10.6) | 23,060 (10.4) | 23,750 (10.7) | 24,080 (10.5) | < 0.001 |
| Asian | 3272 (2.4) | 3832 (2.6) | 4453 (3.1) | 4774 (2.8) | 5697 (3.1) | 5804 (2.90) | 5545 (2.6) | 6220 (2.8) | 6600 (3.0) | 7075 (3.1) | < 0.001 |
| Native American | 678 (0.5) | 1163 (0.8) | 1235 (0.8) | 1109 (0.6) | 1100 (0.6) | 1548 (0.80) | 1336 (0.6) | 1550 (0.7) | 1290 (0.6) | 1430 (0.6) | 0.014 |
| Other | 3310 (2.4%) | 3653 (2.5) | 4136 (2.8) | 5797 (3.4) | 6553 (3.6) | 4955 (2.5) | 6471 (3.0) | 7605 (3.4) | 6515 (2.9) | 6990 (3.1) | 0.001 |
| Income | |||||||||||
| Low | 57,068 (31.6) | 63,842 (33.6) | 65,386 (34.1) | 68,238 (32.6) | 66,225 (31.9) | 73,584 (33.5) | 75,792 (33.0) | 78,750 (34.7) | 76,015 (33.4) | 78,445 (33.5) | < 0.001 |
| Low-Mid | 48,172 (26.7) | 49,256 (26.0) | 50,370 (26.3) | 58,992 (28.2) | 56,653 (27.3) | 58,047 (26.5) | 57,984 (25.2) | 58,270 (25.7) | 61,365 (26.9) | 66,620 (28.5) | < 0.001 |
| High-Mid | 42,328 (23.4) | 42,027 (22.1) | 42,517 (22.2) | 45,332 (21.7) | 47,725 (23.0) | 50,040 (22.8) | 56,596 (24.6) | 51,205 (22.5) | 52,390 (23.0) | 51,050 (21.8) | 0.01 |
| High | 33,039 (18.3) | 34,630 (18.2) | 33,482 (17.5) | 36,614 (17.5) | 37,160 (17.9) | 37,775 (17.2) | 39,532 (17.2) | 38,900 (17.1) | 37,935 (16.7) | 38,035 (16.2) | 0.015 |
| PEP | |||||||||||
| Medicare | 122,660 (68.6) | 129,939 (66.9) | 128,614 (65.5) | 137,865 (64.7) | 138,398 (65.1) | 144,626 (64.5) | 154,260 (66.0) | 154,330 (66.6) | 154,195 (66.4) | 157,925 (66.2) | < 0.001 |
| Medicaid | 13,179 (7.1) | 15,285 (7.9) | 14,957 (7.6) | 16,410 (7.7) | 17,166 (8.1) | 19,740 (8.8) | 19,511 (8.4) | 19,730 (8.5) | 19,200 (8.3) | 23,430 (9.8) | < 0.001 |
| Private Insurance | 33,921 (18.4) | 36,241 (18.7) | 38,889 (19.8) | 43,765 (20.5) | 42,120 (19.8) | 42,492 (18.9) | 42,286 (18.1) | 39,270 (17.0) | 39,845 (17.1) | 41,755 (17.5) | 0.162 |
| Self-Pay | 7000 (3.8) | 7662 (3.9) | 8380 (4.3) | 9179 (4.3) | 9532 (4.5) | 11,260 (5.0) | 10,877 (4.7) | 11,705 (5.1) | 11,795 (5.1) | 9515 (4.0) | 0.006 |
| No charge | 860 (0.5) | 799 (0.4) | 950 (0.5) | 963 (0.5) | 940 (0.4) | 1091 (0.5) | 1280 (0.5) | 880 (0.4) | 1320 (0.6) | 915 (0.4) | 0.186 |
| Other | 3019 (1.6) | 4271 (2.2) | 4620 (2.4) | 4818 (2.3) | 4509 (2.1) | 5159 (2.3) | 5397 (2.3) | 5640 (2.4) | 6000 (2.6) | 5010 (2.1) | 0.011 |
| Comorbidities | |||||||||||
| Obesity | 14,540 (7.9) | 16,638 (8.6) | 19,559 (9.9) | 24,646 (11.6) | 26,634 (12.5) | 29,745 (13.2) | 33,157 (14.2) | 36,230 (15.6) | 39,830 (17.1) | 41,230 (17.3) | < 0.001 |
| HTN | 148,944 (80.6) | 161,541 (83.1) | 167,260 (85.0) | 183,545 (86.0) | 184,997 (86.8) | 197,332 (87.7) | 208,538 (89.0) | 208,415 (89.8) | 210,000 (90.3) | 216,850 (90.8) | < 0.001 |
| Smoking | 23,554 (12.7) | 27,623 (14.2) | 30,767 (15.6) | 36,919 (17.3) | 42,131 (19.8) | 48,863 (21.7) | 55,481 (23.7) | 59,115 (25.5) | 62,795 (27.0) | 73,055 (30.6) | < 0.001 |
| Dyslipidemia | 71,253 (38.6) | 83,253 (42.8) | 91,336 (46.4) | 105,232 (49.3) | 115,263 (54.1) | 127,951 (56.9) | 139,990 (59.7) | 145,475 (62.7) | 149,415 (64.2) | 157,545 (65.9) | < 0.001 |
| Past Medical History | |||||||||||
| Peripheral Vascular Disease | 14,653 (7.9) | 16,134 (8.3) | 17,438 (8.9) | 19,707 (9.2) | 20,637 (9.7) | 21,160 (9.4) | 23,813 (10.2) | 23,025 (9.9) | 23,125 (9.9) | 24,445 (10.2) | < 0.001 |
| Valvular Heart Disease | 10,130 (5.5) | 11,480 (5.9) | 15,986 (8.1) | 16,464 (7.7) | 16,255 (7.6) | 16,864 (7.5) | 18,817 (8.0) | 198,445 (7.9) | 19,155 (8.2) | 20,300 (8.5) | < 0.001 |
| Renal Failure | 17,187 (9.3) | 27,012 (13.9) | 29,889 (15.2) | 34,339 (16.1) | 37,586 (17.6) | 40,474 (18.0) | 45,924 (19.6) | 45,125 (19.4) | 47,125 (20.3) | 49,685 (20.8) | < 0.001 |
| Coronary Artery Disease | 51,290 (27.8) | 54,330 (27.9) | 56,765 (28.9) | 63,584 (29.8) | 66,714 (31.3) | 68,447 (30.4) | 73,408 (31.3) | 73,445 (31.6) | 72,325 (31.1) | 73,540 (30.8) | < 0.001 |
| Hospital Bedsize | |||||||||||
| Small | 20,041 (10.8) | 234,630 (12.7) | 21,856 (11.1) | 22,842 (10.7) | 21,544 (10.3) | 23,391 (10.5) | 24,669 (10.7) | 26,365 (11.4) | 25,785 (11.1) | 36,630 (15.3) | 0.022 |
| Medium | 45,632 (24.7) | 48,325 (24.9) | 49,796 (25.4) | 48,231 (22.6) | 50,971 (24.4) | 48,310 (21.8) | 53,589 (23.2) | 59,995 (25.9) | 60,615 (26.1) | 70,790 (29.6) | 0.001 |
| Large | 119,085 (64.5) | 120,948 (62.4) | 124,654 (63.5) | 141,947 (66.6) | 136,273 (65.3) | 150,280 (67.7) | 153,083 (66.2) | 145,720 (62.8) | 146,255 (62.9) | 131,475 (55.0) | 0.067 |
| Hospital Location | |||||||||||
| Rural | 29,339 (15.9) | 28,114 (14.5) | 27,391 (14.0) | 27,670 (13.0) | 25,822 (12.4) | 27,612 (12.4) | 279,641 (11.9) | 24,755 (10.7) | 24,130 (10.4) | 20,410 (8.5) | 0.006 |
| Urban | 155,420 (84.1) | 165,789 (85.5) | 168,915 (86.0) | 185,350 (87.0) | 182,965 (87.6) | 194,369 (87.6) | 203,701 (88.1) | 207,325 (89.3) | 208,525 (89.6) | 218,485 (91.5) | < 0.001 |
| Hospital Region | |||||||||||
| Northeast | 34,989 (18.9) | 38,003 (19.5) | 36,318 (18.5) | 37,847 (17.7) | 39,865 (18.7) | 43,024 (19.1) | 42,985 (18.3) | 42,135 (18.2) | 41,195 (17.7) | 43,645 (18.3) | 0.001 |
| Midwest | 43,273 (23.4) | 44,979 (23.1) | 45,205 (23.0) | 47,646 (22.3) | 46,098 (21.6) | 49,268 (21.9) | 53,278 (22.7) | 48,900 (21.1) | 49,155 (21.1) | 49,430 (20.7) | 0.028 |
| South | 79,081 (42.8) | 81,118 (41.7) | 83,880 (42.6) | 97,022 (45.5) | 90,785 (42.6) | 96,734 (43.0) | 99,210 (42.3) | 103,495 (44.6) | 104,175 (44.8) | 106,330 (44.5) | < 0.001 |
| West | 27,416 (14.8) | 30,342 (15.6) | 31,299 (15.9) | 30,857 (14.5) | 36,297 (17.0) | 35,866 (15.9) | 38,851 (16.6) | 37,550 (16.2) | 38,130 (16.4) | 39,490 (16.5) | 0.001 |
| Charlson’s Score | |||||||||||
| 0 | 3603 (2.0) | 3839 (2.0) | 4465 (2.3) | 4082 (1.9) | 3512 (1.6) | 3687 (1.6) | 3710 (1.6) | 3535 (1.5) | 3690 (1.6) | 4240 (1.8) | 0.888 |
| 1 | 77,745 (42.1) | 83,215 (42.8) | 80,346 (40.8) | 76,032 (35.6) | 73,410 (34.5) | 75,854 (33.7) | 76,461 (32.6) | 73,940 (31.9) | 73,515 (31.6) | 73,650 (30.8) | 0.012 |
| 2 | 45,207 (24.5) | 47,798 (24.6) | 48,171 (24.5) | 54,167 (25.4) | 52,430 (24.6) | 53,939 (24.0) | 56,258 (24.0) | 55,200 (23.8) | 54,170 (23.3) | 53,870 (22.5) | 0.004 |
| > = 3 | 58,203 (31.5) | 59,589 (30.6) | 63,721 (32.4) | 79,092 (37.1) | 83,693 (39.3) | 91,412 (40.6) | 97,896 (41.8) | 99,405 (42.8) | 101,280 (43.5) | 107,135 (44.8) | < 0.001 |
Cardiovascular outcomes
Hospitalizations for incident cases of stroke and diabetes followed a distinct trend. For both hemorrhagic and ischemic stroke groups, incident cases significantly increased from 1.5 to 1.8 / 100,000 US adults (p-trend < 0.001) and 11.3 to 14.3 / 100,000 US adults, respectively (p-trend < 0.001 for both). However, incident cases of TIAs had a significant decrease over the same 10-year period, dropping from 4.6 to 3.8 / 100,000 US adults (p-trend < 0.001) (Fig. 2).
Figure 2.
Trends in hospitalizations per 100,000 US adults for incident cases.
The age-ajusted in-hospital mortality amongst patients admitted for hemorrhagic stroke showed a decrease by almost 5%, from 24.3% to 19.6% (p-trend < 0.001). Similarly, the gender stratified age-adjusted in-hospital mortality decreased in both males and females, from 25.3% to 19.1% (p-trend = 0.004) and 23.4% to 20.9% (p = 0.007), respectively (Fig. 3).
Figure 3.
In hospital mortality among patients with diabetes admitted for hemorrhagic stroke.
Age-adjusted in-hospital mortality decreased from 3.23% to 2.48% in patients with ischemic stroke (p-trend < 0.001) (Fig. 4). Further, age-adjusted mortality significantly decreased in males from 3.15% to 2.25% (p-trend = 0.025) and in females, dropping from 3.31% to 2.73% (p-trend = 0.002).
Figure 4.
In hospital mortality among patients with diabetes admitted for ischemic stroke.
In-hospital mortality in patients hospitalized for TIAs was low (crude mortality 0.1%, adjusted mortality 0.073% in 2005) and remained almost unchanged during the course of follow-up (crude mortality 0.2%, adjusted mortality 0.098% in 2014). No clear pattern for gender distribution was found.
Furthermore, we examined other predictors of mortality among the 3 stroke sub-groups. As expected, older patients had a higher risk of stroke. For instance, the risk of TIAs was 5-times higher in patients > 85 years old compared to patients < 55 years old. Women were more predisposed to hemorrhagic stroke. Interestingly, African Americans and Latinos with diabetes had a 20% average lower risk of developing a stroke, compared to their Caucasian counterparts. A history of cardiovascular diseases such as renal failure, CAD, peripheral vascular disease and a Charlson index > 3 also contributed to an increased stroke risk. Unexpectedly, DM patients with cardiovascular risk factors such as obesity, hypertension, smoking and dyslipidemia had significantly lower odds of in-hospital mortality compared to diabetic patients without these comorbidities (Table 2).
Table 2.
Predictors of mortality in patients with diabetes admitted for stroke per stroke type.
| Hemorrhagic Stroke | Ischemic Stroke | TIAs | ||||
|---|---|---|---|---|---|---|
| Adjusted OR (95% CI) | p-value | Adjusted OR (95% CI) | p-value | Adjusted OR (95% CI) | p-value | |
| Age | ||||||
| < 55 (ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| 55–64 | 1.12 (1.08–1.16) | < 0.001 | 1.15 (1.10–1.20) | < 0.001 | 0.89 (0.57–1.37) | 0.59 |
| 65–74 | 1.14 (1.09–1.19) | < 0.001 | 1.76 (1.69–1.84) | < 0.001 | 1.88 (1.25–2.83) | 0.003 |
| 75–84 | 1.33 (1.28–1.39) | < 0.001 | 2.55 (2.44–2.66) | < 0.001 | 3.31 (2.22–4.95) | < 0.001 |
| > 85 | 1.35 (1.29–1.42) | < 0.001 | 3.70 (3.54–3.87) | < 0.001 | 5.01 (3.32–7.57) | < 0.001 |
| Gender | ||||||
| Male (Ref*) | Ref | Ref | Ref | Ref | Ref | Ref |
| Female | 1.05 (1.02–1.07) | < 0.001 | 1.00 (0.99–1.02) | 0.75 | 0.86 (0.74–1.01) | 0.064 |
| Race | ||||||
| White (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Black | 0.87 (0.84–0.90) | < 0.001 | 0.75 (0.73–0.77) | < 0.001 | 0.83 (0.66–1.02) | 0.081 |
| Hispanic | 0.81 (0.78–0.84) | < 0.001 | 0.88 (0.85–0.91) | < 0.001 | 0.63 (0.47–0.86) | 0.003 |
| Asian | 0.96 (0.91–1.01) | 0.15 | 0.90 (0.86–0.96) | < 0.001 | 0.61 (0.32–1.17) | 0.13 |
| Native American | 0.89 (0.77–1.02) | 0.1 | 1.00 (0.89–1.12) | 0.97 | 1.21 (0.5–2.94) | 0.67 |
| Other | 1.08 (1.02–1.14) | 0.007 | 1.11 (1.05–1.17) | < 0.001 | 0.55 (0.29–1.03) | 0.061 |
| Income | ||||||
| Low (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Low-Mid | 1.03 (0.97–1.10) | 0.27 | 0.99 (0.96–1.01) | 0.21 | 0.81 (0.66–0.99) | 0.044 |
| High-Mid | 0.94 (0.89–1.00) | 0.09 | 0.95 (0.93–0.98) | < 0.001 | 0.54 (0.42–0.68) | < 0.001 |
| High | 1.06 (0.99–1.13) | 0.11 | 0.96 (0.93–0.99) | 0.003 | 1.08 (0.88–1.33) | 0.45 |
| Primary Expected Payer | ||||||
| Medicare (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Medicaid | 0.87 (0.83–0.91) | < 0.001 | 1.41 (1.35–1.47) | < 0.001 | 1.93 (1.24–2.69) | 0.002 |
| Private Insurance | 0.86 (0.83–0.89) | < 0.001 | 1.21 (1.18–1.25) | < 0.001 | 0.95 (0.70–1.27) | 0.71 |
| Self-Pay | 1.20 (1.13–1.27) | < 0.001 | 1.59 (1.50–1.67) | < 0.001 | 1.29 (0.67–2.49) | 0.44 |
| No Charge | 0.62 (0.51–0.74) | < 0.001 | 1.19 (1.01–1.40) | 0.039 | 0.00 (0.00–0.00) | 0.99 |
| Other | 1.15 (1.07–1.23) | < 0.001 | 2.37 (2.25–2.50) | < 0.001 | 0.75 (0.30–1.85) | 0.53 |
| Obesity | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.84 (0.81–0.87) | < 0.001 | 0.88 (0.86–0.91) | < 0.001 | 0.85 (0.64–1.13) | 0.27 |
| Hypertension | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.87 (0.84–0.90) | < 0.001 | 0.70 (0.69–0.72) | < 0.001 | 0.81 (0.66–0.99) | 0.04 |
| Smoking | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.78 (0.76–0.81) | < 0.001 | 0.73 (0.71–0.75) | < 0.001 | 0.72 (0.57–0.91) | 0.006 |
| Dyslipidemia | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.61 (0.60–0.63) | < 0.001 | 0.52 (0.51–0.53) | < 0.001 | 0.52 (0.45–0.61) | < 0.001 |
| Peripheral vascular disease | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | – | – | 1.09 (1.06–1.13) | < 0.001 | 1.19 (0.95–1.48) | 0.12 |
| Valvular Heart Disease | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.80 (0.76–0.84) | < 0.001 | 0.86 (0.84–0.89) | < 0.001 | 0.99 (0.76–1.30) | 0.95 |
| Renal Failure | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 1.28 (1.24–1.32) | < 0.001 | 1.38 (1.35–1.41) | < 0.001 | 2.10 (1.78–2.48) | < 0.001 |
| CAD | ||||||
| No (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 1.27 (1.24–1.31) | < 0.001 | 1.05 (1.03–1.07) | < 0.001 | 0.94 (0.80–1.10) | 0.42 |
| Hospital Bedsize | ||||||
| Small (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Medium | – | – | 1.08 (1.05–1.12) | < 0.001 | 1.73 (1.32–2.28) | < 0.001 |
| Large | – | – | 1.23 (1.19–1.26) | < 0.001 | 1.35 (1.04–1.74) | 0.025 |
| Hospital Location | ||||||
| Rural (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Urban | 0.80 (0.76–0.84) | < 0.001 | 1.03 (1.00–1.05) | 0.099 | – | – |
| Hospital Region | ||||||
| Northeast (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| Midwest | 0.91 (0.87–0.94) | < 0.001 | 0.88 (0.85–0.90) | < 0.001 | 0.67 (0.52–0.86) | 0.002 |
| South | 0.95 (0.92–0.98) | < 0.001 | 0.90 (0.88–0.92) | < 0.001 | 0.89 (0.74–1.07) | 0.22 |
| West | 0.98 (0.95–1.02) | 0.39 | 0.96 (0.93–0.99) | 0.005 | 0.84 (0.65–1.08) | 0.18 |
| Charlson's Score | ||||||
| 0 (Ref) | Ref | Ref | Ref | Ref | Ref | Ref |
| 1 | 1.93 (1.74–2.14) | < 0.001 | 0.91 (0.84–0.99) | 0.034 | 0.88 (0.47–1.66) | 0.69 |
| 2 | 1.84 (1.66–2.04) | < 0.001 | 1.12 (1.03–1.22) | 0.009 | 2.06 (0.56–2.00) | 0.87 |
| > = 3 | 1.34 (1.21–1.49) | < 0.001 | 1.65 (1.51–1.79) | < 0.001 | 2.11 (1.12–3.97) | 0.02 |
Socio-economic outcomes
After adjusting for inflation, the total charges per hospital stay had a general two-fold increasing trend in all stroke groups. Median (IQR) hospital charges increased from USD 26684 (12701-59919) to USD 51538 (25345-120105) in the hemorrhagic stroke group, USD 17886 (1174-29482) to USD 32723 (20290-55128) in the ischemic stroke group, and USD 12644 (7975-19469) to USD 22993 (15331-34692) in the TIAs group (p-trend < 0.001 for all) (Fig. 5).
Figure 5.
Median (IQR) total hospital charges (USD/stay—adjusted for inflation) of patients with diabetes and hemorrhagic stroke, ischemic stroke, and TIAs.
Between 2005 and 2014, in hospital LoS had a slight but statistically significant decrease. In all stroke patients with diabetes, median (IQR) LoS decreased from 4 (2–6) to 3 (2–6) days (p-trend < 0.001). However, the trend was not homogenous within the 3 groups: LoS moderately increased from 5 (2–10) to 5 (2–11) days (p-trend = 0.032) in the hemorrhagic stroke group but decreased in the ischemic stroke group from 4 (3–7) in 2005 to 4 (2–6) days in 2014; p-trend < 0.001 and in the TIAs group from 3 (2–4) days in 2005 to 2 (1–3) days in 2014; p-trend < 0.001. (Supplementary Table 2).
In patients with hemorrhagic stroke, there was an upward trend of discharge to a long-term facility by almost 5% from 2005 till 2010 (Supplementary table 3). Interestingly, disposition to a long-term facility slightly but significantly decreased from 44.9% in 2010 to 44.5% in 2014, which was counteracted by a non-significant increase in short term facilities and a steady significant one to home discharge. In TIAs, the vast majority of patients were discharged home (data not shown).
Discussion
Our retrospective analysis over a 10-year period provides a general outlook on mortality and socio-economic trends in DM patients admitted for stroke in the US. We have shown that the prevalence of diabetes as well as hospitalizations for incident cases of stroke patients with diabetes have increased while age-adjusted mortality has continued to decline. These changes occurred in the setting of doubling in healthcare costs despite adjustment for inflation.
There has been an overall decrease in the stroke incidence and mortality amongst the general population, which seems applicable to patients with diabetes as shown in our study. This decrease has been observed worldwide in both high and low-income countries23. In the US, while the age-adjusted incidence of stroke is decreasing, its prevalence continues to rise24. This is the result on an aging of population that drives higher the lifetime risk of developing a stroke, coupled with an overall decrease in age-adjusted stroke-related mortality3,25,26.
In the Tromso study, up to 60% of the temporal reduction in stroke incidence over a 17-year follow up period was attributed to the modification of cardiovascular risk factors27. With advancements in diagnostic and therapeutic strategies for stroke, as well as the implementation of primary and secondary prevention measures, further decreases in incidence and mortality are expected, which may be counterbalanced by an increased stroke prevalence in the general population.
Pre-diabetes and diabetes are major risk factors for stroke5. The risk is almost doubled in both genders with women experiencing a higher risk and worse prognosis8. Nevertheless, a decrease in cause-specific mortality of patients with cardiovascular disease has been documented during the last two decades16. Using the same NIS database, we have recently reported a steady decline in age-adjusted mortality in heart failure and diabetes between 2005 and 201428. A similar observation was reported in diabetic patients hospitalized for acute myocardial infarction during the same period of time17. Our results appear concordant with several other studies reporting a decrease in mortality in patients with diabetes and cardiovascular disease in the US.
The classification of TIAs is challenging. TIAs were initially described using a “time-based” definition as focal neurological signs or symptoms that resolve in less than 24 h29. This was consequently followed by a shift towards a tissue-based definition, which describes a TIAs as a transient neurological event without clinical or radiographic evidence of an acute infarction29. Studies have suggested that redefining TIAs will account for better prognosis in both TIAs and ischemic strokes, and reduce the annual incidence of TIAs30,31. This correlates with the trend analysis of our study that illustrates a statistically significant decrease in TIAs-related hospitalizations over the chosen 10-year time period. Furthermore, patients presenting to the hospital with a suspected TIA tend to get managed and discharged directly from the emergency room as stroke management guidelines evolve over time. This means that a large number of TIAs patients presenting to the hospital do not get admitted as inpatients, thus further justifying the decreasing trend in TIAs cases. Similarly, many patients fail to report to a health care facility after experiencing transient neurological symptoms, further contributing to the decreasing trend32.
The overall decrease in stroke-related in-hospital mortality, reported in several studies in the general patient population, comes at a price of an increase in the economic burden of stroke25. Cardiovascular disease, all pathologies included, is the leading cause of medical expenditure in the US, according to the latest heart disease and stroke statistics2. Stroke-related healthcare costs were estimated to be around 45.5 billion USD, and appear to follow a yearly ascending trend2. The cost of diabetes with its risk factors and cardiovascular complications was estimated to be 237 billion USD in 2017, which represents a 26% increase over 5 years33.
Our results are aligned with several studies that assessed the economic burden of diabetes on cardiovascular complications. The presence of CVD in patients with diabetes has been shown to significantly increase care costs34. In a systematic review, Einarso et al. estimated that the presence of stroke in patients with DM resulted in a threefold increase in costs as compared to patients with diabetes without Stroke35. We reported an almost two-fold increase in the total cost per hospital stay over the defined 10-year period despite a slight decrease in LoS among stroke patients. This is congruent with the forecasted threefold increase in costs between 2010 and 2030 in the US36.
In our study, an unexpected relationship emerged between several atherosclerotic risk factors and in-hospital mortality, including smoking, hypertension, dyslipidemia and obesity. Ahmed et al. also reported in a recent analysis of the NIS that smokers and obese patients had a lower in-hospital mortality in myocardial infarction patients with diabetes17. These findings may be explained by several factors. First, there has been an increase in the screening and documentation of these comorbidities, especially as they pertain to patients with cardiovascular and cerebrovascular disease. It is reasonable to assume that increased identification of these modifiable risk factors has resulted in more targeted risk-factor modifications, thus influencing outcomes. Moreover, primary and secondary prevention strategies, including medications and lifestyle modifications have been more aggressively advocated and implemented. Lastly, pharmacological agents used in risk-factor modification, such as statins, ACE-inhibitors and ARBs, may convey benefit through multiple mechanisms, such as cardioprotective and anti-inflammatory effects. These effect-combinations may further reduce mortality due to stroke and other cardiovascular diseases.
One of the limitations of our study is the absence of hospital-level data in the NIS database, including patients’ medications at the time of admission or during their hospital stay. Other important parameters, such as HBA1C, duration of DM and blood pressure, were also not available. Additionally, it was not possible to differentiate type 1 and type 2 diabetes since most patients in the NIS were simply labelled as diabetics. These factors act as potentially major confounding variables in assessing predictors of stroke-related in-hospital mortality.
A second limitation involves TIAs patients given the relatively subjective nature of the diagnosis and the constantly evolving definition, which may result in a level of reporting error of primary diagnoses as TIAs versus ischemic stroke or other diagnosis. On the other hand, this study provides trend analysis and outcome-data that has been collected under strict and specific standards across different hospitals in the US. The data were collected over 10 years and is fairly representative of the US population after weighting, thus providing a very large sample for analysis.
In total, despite the increasing prevalence of diabetes and its associated cardiovascular risk factors and complications over the past decade, in-hospital mortality for stroke patients with diabetes is on the decline. This has occurred in parallel with a doubling in stroke-related hospitalization costs. In the future, we expected that widespread risk factor screening, coupled with aggressive primary and secondary prevention measures, will continue to reduce the burden of stroke, thus decreasing the burden of stroke-related disability in diabetic patients.
Supplementary Information
Acknowledgements
Dr Abi Khalil’s lab is funded by a grant from the Qatar National Research Funds under its National Priorities Research Program award number NPRP10-0207-170385. All of the above-mentioned sources did not have a role in the study’s concept, analysis and writing of the manuscript.
Author contributions
C.A.K. conceived the study concept and design. A.T. acquired data and performed statistical analyses with SD. A.T., J.A.S., S.A., S.A.J. and C.A.K. analyzed and interpreted data. A.T. wrote the first draft and conducted the literature search. All authors contributed to critical revision of the manuscript. C.A.K. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.
Funding
Open access funding provided by the Qatar National Library.
Data availability
The NIS is a publicly available database.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-021-87320-w.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The NIS is a publicly available database.