The Impact of Dementia on Patients Admitted with Acute Respiratory Failure: An Insight from the National Inpatient Sample

The Impact of Dementia on Patients Admitted with Acute Respiratory Failure: An Insight from the National Inpatient Sample | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Dementia on Patients Admitted with Acute Respiratory Failure: An Insight from the National Inpatient Sample Mohamad El Labban, Ibtisam Rauf, Asim Shaikh, Gbemisola Olorode, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3673207/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute respiratory failure is one of the most common causes of hospitalizations in the US. By 2030, the number of Americans with dementia is expected to reach nearly 9 million and 12 million in 2040. Dementia increases the risk of respiratory illnesses, including pneumonia. This study delves into the intricate interplay between dementia and acute respiratory failure. Methods Our retrospective study analyzed adult patients with acute respiratory failure and secondary diagnosis of dementia using ICD-10 codes in the National Inpatient Sample (NIS) Database from 2017 to 2020. An analysis was conducted on various demographic factors such as age, race, and gender. The study's primary endpoint was mortality, with mechanical ventilation, tracheostomy, and length of stay as secondary endpoints. To account for other variables that could have affected the results, we utilized a multivariate logistic regression with p < 0.05 considered significant. Results The study included 1,795,630 patients admitted with ARF, 112,175 of whom had dementia. The mean age in the dementia group was 80 years, compared to 65 years in the control group. Additionally, 62% of the dementia group were females, while the control group had 55% females. 73% of both groups were Caucasian white. Comorbidities observed in the dementia group include hypertension (81% vs. 72%), diabetes mellitus (36% vs. 35%), supraventricular tachycardia (29% vs. 20%), and sepsis (6% vs. 5%) with p-value less than 0.01. Rates and odds of mortality were higher in the dementia group (15,704 (14%) vs. 151,511 (9%), p-value < 0.01, aOR 1.08, p-value < 0.01). Patients with dementia had lower rates of in-hospital mechanical ventilation, but higher adjusted odds (27% vs. 28%; p < 0.01; aOR + 1.2, p < 0.01). Patients with dementia had lower rates and adjusted odds of undergoing a tracheostomy during their stay (762 (0.7) vs. 16,834 ( 1 ), p-value < 0.01, aOR 0.91 p-value 0.33).. Patients with dementia had a longer length of stay (LOS) than those without, with a mean difference of + 0.3 days and p-value < 0.01. Conclusions Clinicians should be aware that dementia was found to be an independent risk factor for mortality in patients admitted with acute respiratory failure. Acute respiratory failure. Dementia. National Inpatient Sample. Length of stay. Determinants Epidemiologic Introduction Dementia is a complex condition that affects cognitive functioning and daily life. It is caused by various underlying conditions, such as Alzheimer's disease, vascular dementia, Lewy body dementia, and frontotemporal dementia, with Alzheimer's being the most common ( 1 ). In the United States, the CDC estimated that 5.1 million adults aged 65 and older had dementia in 2014, projected to increase to 14 million by 2060 ( 1 ). Dementia is influenced by many risk factors, such as age, family history, chronic medical conditions, smoking, racial disparities, and traumatic brain injuries ( 1 ). Dementia also imposes a substantial global disease burden, causing 1.62 million deaths in 2019, with women being more affected than men ( 2 ). It was the seventh leading cause of death overall and the fourth among individuals aged 70 and older in 2019 ( 2 ). Furthermore, dementia increases the risk for respiratory illnesses, including pneumonia, sleep apnea, and recurrent shortness of breath. Additionally, chronic respiratory failure increases the likelihood of developing dementia, with studies showing an association between poor pulmonary function and increased incidence of dementia. As these two conditions exhibit a dual relationship that can affect the incidence and severity of one another, patients suffering from one of these may develop the other and face high healthcare costs if they develop respiratory failure ( 3 ). Therefore, owing to the high global and national incidence rates of dementia and respiratory failure, we conducted this retrospective population-based cohort study to comprehensively investigate the demographics, risk factors, and prevalence of dementia and respiratory failure, economic and the intricate relationship between these two conditions. Methods Design and description of the database We conducted a retrospective cohort study using the National Inpatient Sample (NIS) from 2017 to 2020. The NIS is part of the Healthcare Cost and Utilization Project (HCUP) and is sponsored by the Agency for Healthcare Research and Quality (AHRQ) ( 4 ). The NIS is the largest inpatient hospital discharge database in the United States. It has abundant data such as reasons for hospitalization, inpatient procedures, mortality, length of stay, and epidemiological specifics (age, sex, insurance status, etc.). Data user agreement Dr. El-Labban (first author) completed the data user agreement with HCUP-AHRQ. The HCUP datasets are publicly available and hence are considered exempt from full or expedited institutional review boards (IRB) review (Federal Regulations 45 CFR 46.101 (b). Selection of cases and outcome variables examined In the NIS dataset, the principal diagnosis is the main ICD-10-CM (International Classification of Disease, 10th edition, clinical modification) code of admission to the hospital and is linked to inpatient status. It does not have to be the reason for admission to the hospital. The secondary diagnosis is a medical condition the patient has on the problem list that could have happened before or during that admission. All procedure codes detected via NIS are linked to the admission. In our study, “acute respiratory failure (ARF)” was selected as the principal diagnosis according to the ICD-10 codes. Our inclusion criteria included adult patients (age 18 years or older) presenting with a non-elective/urgent admission under a principal diagnosis of ARF in the years 2017 to 2020. We excluded patients younger than 18 years and elective admissions. ICD-10 codes were also used to identify secondary diagnoses that included dementia, diabetes mellitus, essential hypertension, supraventricular tachycardia, obesity, and sepsis. Codes defining dementia had the following clinical conditions: vascular dementia, Alzheimer’s disease, and unspecified dementia (supplemental table 1 ). Patients’ co-morbidities were also described through the Charlson Comorbidity Index. Outcomes, including mortality, mechanical ventilation, tracheostomy, and length of stay, were generated from the NIS dataset. Statistical Analysis Statistical analyses were conducted using STATA BE Version 17.0. All statistical tests were two-sided, and a P-value of < 0.05 was considered statistically significant. Chi-square analysis was used to describe the difference in patients’ characteristics and secondary diagnoses according to the presence and absence of dementia. The impact of dementia on outcomes was described using Chi-square analysis to compare outcomes’ rates and multivariable regression models to describe the isolated impact of dementia on the odds of the outcomes. Results Table 1 shows the demographic characteristics of patients with acute respiratory failure, indicating that 94% of patients did not have dementia, while 6% had dementia. The median age in the non-dementia group was 65, compared to 80 in the dementia group. Both groups had a higher percentage of female patients who were predominantly Caucasian. Patients were categorized based on Charlson Comorbidity Index scores, ranging from 0 (no comorbidities) to > 3 (higher mortality risk). Patients in the dementia group had a higher comorbidity score. Medicare was the most prevalent insurance coverage for all patients. The primary and secondary outcomes are outlined in Table 2 . Regarding the primary outcome, inpatient mortality was higher in the dementia group (14% vs. 9%). Mechanical ventilation was noted in 28% of patients without dementia and 27% of patients with dementia. Inpatient tracheostomy occurred in 1% of patients without dementia and 0.7% of patients with dementia. Table 1 Demographic and Clinical Characteristics of the Study Population Characteristics Without Dementia With Dementia p-value Acute Respiratory Failure, no (%) 1683455 (94) 112,175 (6) Age (y) 65 80 Female, no (%) 925,900 (55) 69,548 (62) < 0.01 Race, no (%) < 0.01 White 1,228,922 (73) 81,887 (73) Black 269,352 (16) 15,704 (14) Hispanic 117,841 (7) 8,974 (8) Asian or Pacific Islander 33,669 (2) 2,243 (2) Native American 10,100 (0.6) 448 (0.4) Other 33,669 (2) 2,243 (2) Charlson Comorbidity Index score, no. (%) =3 808,058 (48) 80,766 (72) Insurance type, no. (%) < 0.01 Medicare 1,111,080 (66) 100,957 (90) Medicaid 252,518 (15) 3,926 (3.5) Private Insurance 252,518 (15) 6,730 (6) Self-pay 50,503 (3) 560 (0.5) Comorbidities, no. (%) Sepsis 84,172 (5) 6,730 (6) < 0.01 Obesity 454,532 (27) 16,826 (15) < 0.01 DMII 589,209 (35) 40,383 (36) 0.012 HTN 1,212,088 (72) 90,861 (81) < 0.01 SVT 336,691 (20) 32,530 (29) < 0.01 DMII: Diabetes Mellitus Type II, HTN: Hypertension, SVT: Supraventricular tachycardia Table 2 – Primary and Secondary Outcomes In-hospital mortality rates and odds Total Without Dementia With Dementia p-value Adjusted Odds Ratio p-value no (%) 169,415 151,511 (9) 15,704(14) < 0.01 1.08 < 0.01 In-hospital mechanical ventilation rates and odds Total Without Dementia With Dementia p-value Adjusted Odds Ratio p-value no (%) 497,410 471,367 (28) 30,287 (27) < 0.01 1.2 < 0.01 In-hospital tracheostomy rates and odds Total Without Dementia With Dementia p-value Adjusted Odds Ratio p-value no (%) 18,895 16,834 (1) 762 (0.7) < 0.01 0.91 0.33 Length of stay means and adjusted means Mean Without Dementia With Dementia - Adjusted Means p-value days 6.2 6.2 6.5 - + 0.3 < 0.01 Discussion The primary findings of our retrospective study were that patients with dementia had a higher Charleson Comorbidity Index score, indicating a higher severity of illness and that they had a 5% higher risk of mortality. Previous findings lend strength to ours, with studies showing that dementia is a predictor of mortality in patients with respiratory illnesses and ARF and that clinicians need to be aware of the greater than doubled risk of death (Hazard Ratio = 2.44) when managing such patients ( 5 – 7 ). However, it should be addressed that most studies evaluating outcomes and dementia in respiratory failure specifically focus on COVID-19 or chronic obstructive pulmonary disease (COPD). Therefore, this is the first study evaluating the risk of developing worsening outcomes in patients with ARF, regardless of etiology. Moreover, the increased risk of mortality seen in dementia patients can be explained by the fact that, amongst dementia patients, certain respiratory illnesses are more commonly found, such as bronchopneumonia and pulmonary embolism ( 8 – 10 ). Furthermore, the quality of end-of-life care for dementia patients varies widely ( 11 , 12 ). Healthcare transitions near the end of life for dementia patients have increased slightly in the past decade. These transitions include more hospice care and longer stays in critical care settings ( 13 ). We also found that dementia patients have extended hospital stay durations in ARF patients. Despite being contrary to the literature, with one study showing that 200 general patient admissions in the United Kingdom with dementia had a lesser length of hospital stay, our results are statistically rather than clinically significant (6.5 vs. 6.3 days)( 14 ). It is also important to understand that the literature focuses primarily on the development of dementia in ARF patients and less on the role dementia might play in affecting outcomes in ARF patients. Therefore, while there are numerous data on the increased risk of dementia and cognitive decline in ARF patients, with worsening outcomes as well, the literature does not abundantly discuss the impact of dementia on ARF ( 15 ). We found that the high prevalence of dementia in ARF patients was more common in female and Caucasian patients. Our findings are consistent with previous studies that have projected the global dementia trends and their impact on health systems. According to the Lancet Public Health ( 16 ), by 2050, there will be 152.8 million cases of dementia worldwide, with women having a higher prevalence than men. These studies have also suggested that age and sex are major risk factors for developing dementia, possibly due to different biological mechanisms underlying Alzheimer's disease ( 17 , 18 ). However, it should be noted that the gender differences observed in dementia rates vary across geographies ( 19 ). Yet, in the United States and Europe, women develop dementia at higher rates than men ( 20 ). Possible reasons for this finding include the fact that women have a higher life expectancy, and since dementia incidence increases with age, it increases the complete lifetime risk for dementia in women ( 21 ). Some female reproductive-specific factors have also been identified to increase the risk for certain types of dementia, specifically Alzheimer’s dementia, such as menopausal age > 55 and fertility duration > 40 ( 22 ). This study identified hypertension, diabetes mellitus type 2, obesity, and supraventricular tachycardia as the most common comorbid conditions associated with ARF in these patients. The literature also supports this finding. One study found the presence of hypertension and cardiac dysfunction to be the most common present comorbidities in ARF ( 23 ), primarily because these are known to increase the risk of developing ARF. Additionally, this is also a concerning finding. The presence of obesity and arterial hypertension significantly increases the risk of mortality, specifically those with acute respiratory distress syndrome. We also found lower rates of IMV among patients with dementia. This finding starkly differs from the literature, with several studies showing that the use of IMV in hospitalized dementia patients has increased considerably in the United States ( 24 ), Canada ( 24 ), and Spain ( 25 ). This trend is especially pronounced among those with advanced dementia, who may need IMV for conditions like pneumonia or septicemia more than twice as often ( 24 ). However, IMV is expensive and labor-intensive, increasing the risk of hospital mortality with age ( 26 ). Our retrospective study has some limitations that should be acknowledged. The sample size of patients with dementia (6%) may affect the statistical power of our results, and the data may be inaccurate or incomplete due to the quality of certain existing medical records. Moreover, our observational study makes it difficult to establish causation between dementia and acute respiratory failure, and the variables we did not include in this analysis could influence the associations we observed. Nevertheless, our study adds valuable insights to this field and suggests the need for more research to confirm and extend our findings. Conclusion The study showed a worrying increase in dementia cases by 2050, mainly affecting women. It also showed racial disparities in the care received by dementia patients, calling for fair healthcare provision. It revealed the immediate causes of death among dementia patients may often be the underlying condition. It also examined the complexities of healthcare coverage and end-of-life care for dementia patients, highlighting the changing nature of such care. These insights emphasize the complexity of dementia care and the need for customized and fair healthcare strategies. Abbreviations NIS: National Inpatient Sample HCUP: Healthcare Cost and Utilization Project AHRQ: Agency for Healthcare Research and Quality IRB: Institutional Review Boards ARF: Acute Respiratory Failure ICD-10-CM: International Classification of Disease, 10th edition, clinical modification [ICD-10-CM] COPD: Chronic Obstructive Pulmonary Disease IMV: Invasive Mechanical Ventilation Declarations Ethics approval and consent to participate: Experiments on humans and/or the use of human tissue samples: N/A The institutional and/or licensing committee approving the experiments: N/A. The HCUP datasets are publicly available and hence are considered exempt from full or expedited institutional review boards (IRB) review (Federal Regulations 45 CFR 46.101 (b). Informed consent was obtained from all subjects and/or their legal guardian(s): N/A (this study does not contain any patient identifiers) Consent for publication: N/A Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The dataset used contains deidentified data. Competing interests: None The abstract of this research was presented at CHEST 2023 and published in the CHEST Journal (DOI: https://doi.org/10.1016/j.chest.2023.07.1065). Funding: None Diversity The authors of this study are diverse in their gender, nationality, practice locality, practice type, religion, and race. Authorship contributorship Conceptualization: Mohamad El Labban Data Curation: Mohamad El Labban Formal Analysis: Mohamad El Labban Funding acquisition: N/A Investigation: Mohamad El Labban Methodology: Mohamad El Labban Project Administration: Mohamad El Labban, Syed Khan Resources: Mohamad El Labban Software: Mohamad El Labban Supervision: Mohamad El Labban, Syed Khan Writing original draft: Mohamad El Labban, Ibtisam Rauf, Asim Shaikh, Gbemisola Olorode, Anwar Khedr, Muhammad Khuzzaim Khan, Rida Asim Editing: Mohamad El Labban, Syed Khan Acknowledgments : None References Gale SA, Acar D, Daffner KR. Dement Am J Med. 2018;131(10):1161–9. Global mortality from dementia. Application of a new method and results from the Global Burden of Disease Study 2019. Alzheimers Dement (N Y). 2021;7(1):e12200. Khandelwal N, May P, Downey LM, Engelberg RA, Curtis JR. 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Rates of Mechanical Ventilation for Patients With Dementia in Ontario: A Population-Based Cohort Study. Anesth Analg. 2019;129(4):e122–e5. Bouza C, Martínez-Alés G, López-Cuadrado T. Effect of dementia on the incidence, short-term outcomes, and resource utilization of invasive mechanical ventilation in the elderly: a nationwide population-based study. Crit Care. 2019;23(1):291. Wunsch H, Linde-Zwirble WT, Angus DC, Hartman ME, Milbrandt EB, Kahn JM. The epidemiology of mechanical ventilation use in the United States. Crit Care Med. 2010;38(10):1947–53. Additional Declarations No competing interests reported. Supplementary Files supplementaltable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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It is caused by various underlying conditions, such as Alzheimer's disease, vascular dementia, Lewy body dementia, and frontotemporal dementia, with Alzheimer's being the most common (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In the United States, the CDC estimated that 5.1\u0026nbsp;million adults aged 65 and older had dementia in 2014, projected to increase to 14\u0026nbsp;million by 2060 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Dementia is influenced by many risk factors, such as age, family history, chronic medical conditions, smoking, racial disparities, and traumatic brain injuries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Dementia also imposes a substantial global disease burden, causing 1.62\u0026nbsp;million deaths in 2019, with women being more affected than men (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It was the seventh leading cause of death overall and the fourth among individuals aged 70 and older in 2019 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, dementia increases the risk for respiratory illnesses, including pneumonia, sleep apnea, and recurrent shortness of breath. Additionally, chronic respiratory failure increases the likelihood of developing dementia, with studies showing an association between poor pulmonary function and increased incidence of dementia. As these two conditions exhibit a dual relationship that can affect the incidence and severity of one another, patients suffering from one of these may develop the other and face high healthcare costs if they develop respiratory failure (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, owing to the high global and national incidence rates of dementia and respiratory failure, we conducted this retrospective population-based cohort study to comprehensively investigate the demographics, risk factors, and prevalence of dementia and respiratory failure, economic and the intricate relationship between these two conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and description of the database\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the National Inpatient Sample (NIS) from 2017 to 2020. The NIS is part of the Healthcare Cost and Utilization Project (HCUP) and is sponsored by the Agency for Healthcare Research and Quality (AHRQ) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The NIS is the largest inpatient hospital discharge database in the United States. It has abundant data such as reasons for hospitalization, inpatient procedures, mortality, length of stay, and epidemiological specifics (age, sex, insurance status, etc.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData user agreement\u003c/h2\u003e \u003cp\u003eDr. El-Labban (first author) completed the data user agreement with HCUP-AHRQ. The HCUP datasets are publicly available and hence are considered exempt from full or expedited institutional review boards (IRB) review (Federal Regulations 45 CFR 46.101 (b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of cases and outcome variables examined\u003c/h2\u003e \u003cp\u003eIn the NIS dataset, the principal diagnosis is the main ICD-10-CM (International Classification of Disease, 10th edition, clinical modification) code of admission to the hospital and is linked to inpatient status. It does not have to be the reason for admission to the hospital. The secondary diagnosis is a medical condition the patient has on the problem list that could have happened before or during that admission. All procedure codes detected via NIS are linked to the admission. In our study, \u0026ldquo;acute respiratory failure (ARF)\u0026rdquo; was selected as the principal diagnosis according to the ICD-10 codes. Our inclusion criteria included adult patients (age 18 years or older) presenting with a non-elective/urgent admission under a principal diagnosis of ARF in the years 2017 to 2020. We excluded patients younger than 18 years and elective admissions. ICD-10 codes were also used to identify secondary diagnoses that included dementia, diabetes mellitus, essential hypertension, supraventricular tachycardia, obesity, and sepsis. Codes defining dementia had the following clinical conditions: vascular dementia, Alzheimer\u0026rsquo;s disease, and unspecified dementia (supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients\u0026rsquo; co-morbidities were also described through the Charlson Comorbidity Index. Outcomes, including mortality, mechanical ventilation, tracheostomy, and length of stay, were generated from the NIS dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using STATA BE Version 17.0. All statistical tests were two-sided, and a P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Chi-square analysis was used to describe the difference in patients\u0026rsquo; characteristics and secondary diagnoses according to the presence and absence of dementia. The impact of dementia on outcomes was described using Chi-square analysis to compare outcomes\u0026rsquo; rates and multivariable regression models to describe the isolated impact of dementia on the odds of the outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of patients with acute respiratory failure, indicating that 94% of patients did not have dementia, while 6% had dementia. The median age in the non-dementia group was 65, compared to 80 in the dementia group. Both groups had a higher percentage of female patients who were predominantly Caucasian. Patients were categorized based on Charlson Comorbidity Index scores, ranging from 0 (no comorbidities) to \u0026gt;\u0026thinsp;3 (higher mortality risk). Patients in the dementia group had a higher comorbidity score. Medicare was the most prevalent insurance coverage for all patients. The primary and secondary outcomes are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Regarding the primary outcome, inpatient mortality was higher in the dementia group (14% vs. 9%). Mechanical ventilation was noted in 28% of patients without dementia and 27% of patients with dementia. Inpatient tracheostomy occurred in 1% of patients without dementia and 0.7% of patients with dementia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics of the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Dementia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith Dementia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Respiratory Failure, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1683455 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112,175 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e925,900 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,548 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,228,922 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81,887 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269,352 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,704 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117,841 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,974 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,669 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,243 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,100 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,669 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,243 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index score, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101,007 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420,863 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,852 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353,525 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,556 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e808,058 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80,766 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance type, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,111,080 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,957 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252,518 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,926 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252,518 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,730 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,503 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e560 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84,172 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,730 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e454,532 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,826 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e589,209 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,383 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,212,088 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90,861 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336,691 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,530 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDMII: Diabetes Mellitus Type II, HTN: Hypertension, SVT: Supraventricular tachycardia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Primary and Secondary Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIn-hospital mortality rates and odds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151,511 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,704(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital mechanical ventilation rates and odds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497,410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471,367 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,287 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital tracheostomy rates and odds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,834 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e762 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay means and adjusted means\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith Dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Means\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary findings of our retrospective study were that patients with dementia had a higher Charleson Comorbidity Index score, indicating a higher severity of illness and that they had a 5% higher risk of mortality. Previous findings lend strength to ours, with studies showing that dementia is a predictor of mortality in patients with respiratory illnesses and ARF and that clinicians need to be aware of the greater than doubled risk of death (Hazard Ratio\u0026thinsp;=\u0026thinsp;2.44) when managing such patients (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, it should be addressed that most studies evaluating outcomes and dementia in respiratory failure specifically focus on COVID-19 or chronic obstructive pulmonary disease (COPD). Therefore, this is the first study evaluating the risk of developing worsening outcomes in patients with ARF, regardless of etiology. Moreover, the increased risk of mortality seen in dementia patients can be explained by the fact that, amongst dementia patients, certain respiratory illnesses are more commonly found, such as bronchopneumonia and pulmonary embolism (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, the quality of end-of-life care for dementia patients varies widely (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Healthcare transitions near the end of life for dementia patients have increased slightly in the past decade. These transitions include more hospice care and longer stays in critical care settings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also found that dementia patients have extended hospital stay durations in ARF patients. Despite being contrary to the literature, with one study showing that 200 general patient admissions in the United Kingdom with dementia had a lesser length of hospital stay, our results are statistically rather than clinically significant (6.5 vs. 6.3 days)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It is also important to understand that the literature focuses primarily on the development of dementia in ARF patients and less on the role dementia might play in affecting outcomes in ARF patients. Therefore, while there are numerous data on the increased risk of dementia and cognitive decline in ARF patients, with worsening outcomes as well, the literature does not abundantly discuss the impact of dementia on ARF (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that the high prevalence of dementia in ARF patients was more common in female and Caucasian patients. Our findings are consistent with previous studies that have projected the global dementia trends and their impact on health systems. According to the Lancet Public Health (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), by 2050, there will be 152.8\u0026nbsp;million cases of dementia worldwide, with women having a higher prevalence than men. These studies have also suggested that age and sex are major risk factors for developing dementia, possibly due to different biological mechanisms underlying Alzheimer's disease (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, it should be noted that the gender differences observed in dementia rates vary across geographies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Yet, in the United States and Europe, women develop dementia at higher rates than men (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Possible reasons for this finding include the fact that women have a higher life expectancy, and since dementia incidence increases with age, it increases the complete lifetime risk for dementia in women (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Some female reproductive-specific factors have also been identified to increase the risk for certain types of dementia, specifically Alzheimer\u0026rsquo;s dementia, such as menopausal age\u0026thinsp;\u0026gt;\u0026thinsp;55 and fertility duration\u0026thinsp;\u0026gt;\u0026thinsp;40 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study identified hypertension, diabetes mellitus type 2, obesity, and supraventricular tachycardia as the most common comorbid conditions associated with ARF in these patients. The literature also supports this finding. One study found the presence of hypertension and cardiac dysfunction to be the most common present comorbidities in ARF (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), primarily because these are known to increase the risk of developing ARF. Additionally, this is also a concerning finding. The presence of obesity and arterial hypertension significantly increases the risk of mortality, specifically those with acute respiratory distress syndrome.\u003c/p\u003e \u003cp\u003eWe also found lower rates of IMV among patients with dementia. This finding starkly differs from the literature, with several studies showing that the use of IMV in hospitalized dementia patients has increased considerably in the United States (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), Canada (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and Spain (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This trend is especially pronounced among those with advanced dementia, who may need IMV for conditions like pneumonia or septicemia more than twice as often (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, IMV is expensive and labor-intensive, increasing the risk of hospital mortality with age (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur retrospective study has some limitations that should be acknowledged. The sample size of patients with dementia (6%) may affect the statistical power of our results, and the data may be inaccurate or incomplete due to the quality of certain existing medical records. Moreover, our observational study makes it difficult to establish causation between dementia and acute respiratory failure, and the variables we did not include in this analysis could influence the associations we observed. Nevertheless, our study adds valuable insights to this field and suggests the need for more research to confirm and extend our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study showed a worrying increase in dementia cases by 2050, mainly affecting women. It also showed racial disparities in the care received by dementia patients, calling for fair healthcare provision. It revealed the immediate causes of death among dementia patients may often be the underlying condition. It also examined the complexities of healthcare coverage and end-of-life care for dementia patients, highlighting the changing nature of such care. These insights emphasize the complexity of dementia care and the need for customized and fair healthcare strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNIS: National Inpatient Sample\u003c/p\u003e\n\u003cp\u003eHCUP:\u0026nbsp;Healthcare Cost and Utilization Project\u003c/p\u003e\n\u003cp\u003eAHRQ: Agency for Healthcare Research and Quality\u003c/p\u003e\n\u003cp\u003eIRB: Institutional Review Boards\u003c/p\u003e\n\u003cp\u003eARF: Acute Respiratory Failure\u003c/p\u003e\n\u003cp\u003eICD-10-CM: International Classification of Disease, 10th edition, clinical modification [ICD-10-CM]\u003c/p\u003e\n\u003cp\u003eCOPD:\u0026nbsp;Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eIMV: Invasive Mechanical Ventilation\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003col\u003e\n \u003cli\u003eEthics approval and consent to participate:\u0026nbsp;\u003col\u003e\n \u003cli\u003eExperiments on humans and/or the use of human tissue samples: N/A\u003c/li\u003e\n \u003cli\u003eThe institutional and/or licensing committee approving the experiments: N/A.\u0026nbsp;The HCUP datasets are publicly available and hence are considered exempt from full or expedited institutional review boards (IRB) review (Federal Regulations 45 CFR 46.101 (b).\u003c/li\u003e\n \u003cli\u003eInformed consent was obtained from all subjects and/or their legal guardian(s): N/A (this study does not contain any patient identifiers)\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003eConsent for publication: N/A\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The dataset used contains deidentified data.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCompeting interests: None\u003c/li\u003e\n \u003cli\u003eThe abstract of this research was presented at CHEST 2023 and published in the CHEST Journal (DOI: https://doi.org/10.1016/j.chest.2023.07.1065). \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFunding: None\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eDiversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this study are diverse in their gender, nationality, practice locality, practice type, religion, and race.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contributorship\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eConceptualization: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eData Curation: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFormal Analysis: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFunding acquisition: N/A\u003c/li\u003e\n \u003cli\u003eInvestigation: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMethodology: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProject Administration: Mohamad El Labban, Syed Khan\u003c/li\u003e\n \u003cli\u003eResources: Mohamad El Labban \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSoftware: Mohamad El Labban\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSupervision: Mohamad El Labban, Syed Khan\u003c/li\u003e\n \u003cli\u003eWriting original draft: Mohamad El Labban,\u0026nbsp;Ibtisam Rauf,\u0026nbsp;Asim Shaikh,\u0026nbsp;Gbemisola Olorode,\u0026nbsp;Anwar Khedr, Muhammad Khuzzaim Khan, Rida Asim\u003c/li\u003e\n \u003cli\u003eEditing: Mohamad El Labban, Syed Khan\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: None\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGale SA, Acar D, Daffner KR. Dement Am J Med. 2018;131(10):1161\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal mortality from dementia. Application of a new method and results from the Global Burden of Disease Study 2019. Alzheimers Dement (N Y). 2021;7(1):e12200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandelwal N, May P, Downey LM, Engelberg RA, Curtis JR. Advance Identification of Patients With Chronic Conditions and Acute Respiratory Failure at Greatest Risk for High-Intensity, Costly Care. J Pain Symptom Manage. 2022;63(4):618\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuality HCaUPHAfHRa. HCUP National Inpatient Sample (NIS) 2012 [.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao KM, Lin TC, Li CY, Yang YK. Dementia Increases Severe Sepsis and Mortality in Hospitalized Patients With Chronic Obstructive Pulmonary Disease. Med (Baltim). 2015;94(23):e967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHariyanto TI, Putri C, Situmeang RFV, Kurniawan A. Dementia is a predictor for mortality outcome from coronavirus disease 2019 (COVID-19) infection. Eur Arch Psychiatry Clin Neurosci. 2021;271(2):393\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Sanz MT, C\u0026aacute;nive-G\u0026oacute;mez JC, Sen\u0026iacute;n-Rial L, Aboal-Vi\u0026ntilde;as J, Barreiro-Garc\u0026iacute;a A, L\u0026oacute;pez-Val E, et al. One-year and long-term mortality in patients hospitalized for chronic obstructive pulmonary disease. J Thorac Dis. 2017;9(3):636\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeene J, Hope T, Fairburn CG, Jacoby R. Death and dementia. Int J Geriatr Psychiatry. 2001;16(10):969\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTodd S, Barr S, Passmore AP. Cause of death in Alzheimer's disease: a cohort study. QJM. 2013;106(8):747\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunnstr\u0026ouml;m HR, Englund EM. Cause of death in patients with dementia disorders. Eur J Neurol. 2009;16(4):488\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeno JM, Mitchell SL, Skinner J, Kuo S, Fisher E, Intrator O, et al. Churning: the association between health care transitions and feeding tube insertion for nursing home residents with advanced cognitive impairment. J Palliat Med. 2009;12(4):359\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGozalo P, Teno JM, Mitchell SL, Skinner J, Bynum J, Tyler D, et al. End-of-life transitions among nursing home residents with cognitive issues. N Engl J Med. 2011;365(13):1212\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeno JM, Gozalo P, Khandelwal N, Curtis JR, Meltzer D, Engelberg R, et al. Association of Increasing Use of Mechanical Ventilation Among Nursing Home Residents With Advanced Dementia and Intensive Care Unit Beds. JAMA Intern Med. 2016;176(12):1809\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanatinia R, Burns A, Crome P, Gordon F, Hood C, Lee W, et al. Factors associated with shorter length of admission among people with dementia in England and Wales: retrospective cohort study. BMJ Open. 2021;11(10):e047255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai CC, Ho CH, Chen CM, Chiang SR, Chao CM, Liu WL, et al. Long-term risk of dementia after acute respiratory failure requiring intensive care unit admission. PLoS ONE. 2017;12(7):e0180914.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstimation of the global prevalence of dementia. in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105\u0026ndash;e25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHohman TJ, Dumitrescu L, Barnes LL, Thambisetty M, Beecham G, Kunkle B, et al. Sex-Specific Association of Apolipoprotein E With Cerebrospinal Fluid Levels of Tau. JAMA Neurol. 2018;75(8):989\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWimo A, Guerchet M, Ali GC, Wu YT, Prina AM, Winblad B, et al. The worldwide costs of dementia 2015 and comparisons with 2010. Alzheimers Dement. 2017;13(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMielke MM, Aggarwal NT, Vila-Castelar C, Agarwal P, Arenaza-Urquijo EM, Brett B, et al. Consideration of sex and gender in Alzheimer's disease and related disorders from a global perspective. Alzheimers Dement. 2022;18(12):2707\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeam CR, Kaneshiro C, Jang JY, Reynolds CA, Pedersen NL, Gatz M. Differences Between Women and Men in Incidence Rates of Dementia and Alzheimer's Disease. J Alzheimers Dis. 2018;64(4):1077\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMielke MM. Sex and Gender Differences in Alzheimer's Disease Dementia. Psychiatr Times. 2018;35(11):14\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo JE, Shin DW, Han K, Kim D, Won HS, Lee J, et al. Female reproductive factors and the risk of dementia: a nationwide cohort study. Eur J Neurol. 2020;27(8):1448\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdler D, P\u0026eacute;pin JL, Dupuis-Lozeron E, Espa-Cervena K, Merlet-Violet R, Muller H, et al. Comorbidities and Subgroups of Patients Surviving Severe Acute Hypercapnic Respiratory Failure in the Intensive Care Unit. Am J Respir Crit Care Med. 2017;196(2):200\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorjaille CZ, Hill AD, Pinto R, Fowler RA, Scales DC, Wunsch H. Rates of Mechanical Ventilation for Patients With Dementia in Ontario: A Population-Based Cohort Study. Anesth Analg. 2019;129(4):e122\u0026ndash;e5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouza C, Mart\u0026iacute;nez-Al\u0026eacute;s G, L\u0026oacute;pez-Cuadrado T. Effect of dementia on the incidence, short-term outcomes, and resource utilization of invasive mechanical ventilation in the elderly: a nationwide population-based study. Crit Care. 2019;23(1):291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWunsch H, Linde-Zwirble WT, Angus DC, Hartman ME, Milbrandt EB, Kahn JM. The epidemiology of mechanical ventilation use in the United States. Crit Care Med. 2010;38(10):1947\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory failure. Dementia. National Inpatient Sample. Length of stay. Determinants, Epidemiologic","lastPublishedDoi":"10.21203/rs.3.rs-3673207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3673207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute respiratory failure is one of the most common causes of hospitalizations in the US. By 2030, the number of Americans with dementia is expected to reach nearly 9\u0026nbsp;million and 12\u0026nbsp;million in 2040. Dementia increases the risk of respiratory illnesses, including pneumonia. This study delves into the intricate interplay between dementia and acute respiratory failure.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur retrospective study analyzed adult patients with acute respiratory failure and secondary diagnosis of dementia using ICD-10 codes in the National Inpatient Sample (NIS) Database from 2017 to 2020. An analysis was conducted on various demographic factors such as age, race, and gender. The study's primary endpoint was mortality, with mechanical ventilation, tracheostomy, and length of stay as secondary endpoints. To account for other variables that could have affected the results, we utilized a multivariate logistic regression with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 1,795,630 patients admitted with ARF, 112,175 of whom had dementia. The mean age in the dementia group was 80 years, compared to 65 years in the control group. Additionally, 62% of the dementia group were females, while the control group had 55% females. 73% of both groups were Caucasian white. Comorbidities observed in the dementia group include hypertension (81% vs. 72%), diabetes mellitus (36% vs. 35%), supraventricular tachycardia (29% vs. 20%), and sepsis (6% vs. 5%) with p-value less than 0.01. Rates and odds of mortality were higher in the dementia group (15,704 (14%) vs. 151,511 (9%), p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, aOR 1.08, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Patients with dementia had lower rates of in-hospital mechanical ventilation, but higher adjusted odds (27% vs. 28%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; aOR\u0026thinsp;+\u0026thinsp;1.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Patients with dementia had lower rates and adjusted odds of undergoing a tracheostomy during their stay (762 (0.7) vs. 16,834 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, aOR 0.91 p-value 0.33).. Patients with dementia had a longer length of stay (LOS) than those without, with a mean difference of +\u0026thinsp;0.3 days and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eClinicians should be aware that dementia was found to be an independent risk factor for mortality in patients admitted with acute respiratory failure.\u003c/p\u003e","manuscriptTitle":"The Impact of Dementia on Patients Admitted with Acute Respiratory Failure: An Insight from the National Inpatient Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-04 19:41:41","doi":"10.21203/rs.3.rs-3673207/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44093f7c-399f-4529-8b8e-7db431e042a2","owner":[],"postedDate":"December 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-28T08:31:56+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-04 19:41:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3673207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3673207","identity":"rs-3673207","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}