Effects of Malnutrition on In-Hospital and Discharge Outcomes Among Young Adults Hospitalized with Gastrointestinal Cancers: National Estimates from the United States

Effects of Malnutrition on In-Hospital and Discharge Outcomes Among Young Adults Hospitalized with Gastrointestinal Cancers: National Estimates from the United States | 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 Effects of Malnutrition on In-Hospital and Discharge Outcomes Among Young Adults Hospitalized with Gastrointestinal Cancers: National Estimates from the United States Manas Pustake, Lakshmi Kattamuri, Shubhangi Deoker, Kunal Sharma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899778/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose Protein–energy malnutrition (PEM) is common in gastrointestinal (GI) cancers and may worsen inpatient outcomes. Contemporary national data describing the impact of PEM among young adults with GI malignancies are limited. Methods We conducted a retrospective cohort study using HCUP NIS data from 2018 to 2021. We identified hospitalizations of adults aged 18 to 39 years with GI cancers using ICD 10 CM codes C15 to C26. We defined PEM by diagnosis codes recorded during the index admission. Primary outcomes were in hospital mortality and discharge disposition. Secondary outcomes were LOS and total hospital charges. We used survey weighted multivariable logistic and linear regression to estimate adjusted associations, accounting for age, sex, race or ethnicity, payer, income quartile, admission type, calendar year, and age adjusted CCI. Results Among 58,910 weighted hospitalizations of young adults with gastrointestinal cancers, 11,915 (20.2%) had protein–energy malnutrition (PEM). Compared with those without PEM, patients with PEM had a higher burden of advanced disease and acute illness, including a greater prevalence of metastatic disease (71.8% vs 53.1%), and experienced worse unadjusted outcomes, including higher in-hospital mortality (8.4% vs 3.2%), longer length of stay (10.43 vs 5.63 days), and higher total hospital charges ( $ 133,790 vs $ 83,702). In adjusted analyses, PEM was independently associated with increased odds of in-hospital mortality (aOR 2.13, 95% CI 1.72–2.56; p < 0.001) and higher odds of non-home discharge (aOR 1.67, 95% CI 1.47–1.89; p < 0.001). PEM was also associated with substantially greater resource utilization, including an adjusted increase of 4.49 hospital days (β + 4.492; SE 0.248; p < 0.001) and $ 53,513 higher total hospital charges (β + $ 53,512.6; SE $ 5,527.6; p < 0.001). Conclusion PEM affected one in five hospitalizations among young adults with GI cancers and independently increased mortality, non-home discharge, LOS, and hospital charges. These findings support routine inpatient nutritional assessment and early intervention in this high risk population. protein energy malnutrition gastrointestinal cancers young adults in hospital mortality discharge disposition length of stay hospital charges National Inpatient Sample health care utilization cancer outcomes INTRODUCTION Protein–energy malnutrition (PEM) remains a pervasive yet underrecognized comorbidity among patients with malignancy, particularly those with gastrointestinal (GI) cancers, where tumor-related obstruction, treatment-related toxicities, systemic inflammation, and cancer cachexia converge to accelerate nutritional decline. [ 1 – 5 ] Malnutrition in oncology has been consistently associated with impaired immune function, increased susceptibility to infection, poor tolerance of systemic therapy, delayed wound healing, and excess mortality. [ 6 ] Despite its well-established biological and clinical relevance, PEM is frequently underdiagnosed and undertreated in routine practice, especially among younger adults who are often perceived as physiologically resilient. In the context of GI malignancies which carry a high risk of cachexia and metabolic derangements, the implications of malnutrition may be especially profound, affecting both short-term inpatient outcomes and longer-term treatment trajectories. [ 1 , 7 , 8 ] Although prior studies have documented associations between malnutrition and adverse outcomes, much of the existing literature is derived from single-center cohorts, disease-specific registries, or studies focused predominantly on older Medicare populations. [ 9 , 10 ] Consequently, contemporary, nationally representative data describing the burden and impact of PEM among young adults with GI cancers in the United States remain limited. [ 1 , 9 , 10 ] Young adults (18–39 years) represent a distinct oncologic subgroup with unique tumor biology, treatment patterns, insurance structures, and socioeconomic vulnerabilities. [ 11 ] National-scale analyses are essential to capture geographic, institutional, and payer heterogeneity, as well as to generate externally generalizable estimates that reflect real-world inpatient care across diverse hospital settings. Generating national estimates provide epidemiologic insight into the prevalence, resource utilization, and in-hospital outcomes. Such analyses inform not only clinical practice but also health policy and resource allocation by quantifying the attributable burden of malnutrition on mortality, length of stay, discharge disposition, and hospital charges at the population level. In this era of value-based care and escalating oncology expenditures, nationally representative data are necessary to justify systematic inpatient nutritional risk screening, early intervention strategies, and integration of nutrition-directed therapies as potential targets to improve outcomes and reduce avoidable healthcare utilization across the United States. We aimed to estimate the hospitalization and discharge outcomes associated and factors influencing them among young adults with PEM hospitalized with GI malignancies. METHODS Study Design and Data Source: We conducted a retrospective cohort study using the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS), an all-payer, nationally representative database of inpatient hospitalizations in the United States. The analytic period spanned January 1, 2018 through December 31, 2021. [ 12 ] The NIS employs a stratified, single-stage cluster sampling design that samples hospital discharges and provides discharge-level survey weights to generate national estimates. Consistent with HCUP analytic recommendations, all analyses accounted for the complex sampling design, including discharge weights, hospital clustering, and stratification, to obtain unbiased point estimates and valid standard errors for nationally representative inference. Study Population: Hospitalizations of young adults aged 18–39 years were eligible for inclusion. We identified gastrointestinal (GI) cancer hospitalizations using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) malignant neoplasm diagnosis codes C15–C26, corresponding to cancers of the esophagus through other/ill-defined digestive organs. Hospitalizations were categorized by the presence versus absence of PEM, defined using ICD-10-CM diagnosis codes indicative of PEM recorded during the index hospitalization. Demographic and socioeconomic covariates included age, sex, race/ethnicity, primary expected payer, and patient ZIP-code–linked income quartile. Clinical and hospitalization characteristics included elective versus non-elective admission status, calendar year, and comorbidity burden measured using the age-adjusted Charlson Comorbidity Index (CCI). Baseline characteristics were summarized by PEM status using survey-weighted descriptive statistics to provide national estimates. Outcomes: The primary outcomes were (1) in-hospital mortality and (2) discharge disposition. Discharge disposition was operationalized as discharge to home/home health versus non-home discharge. Secondary outcomes were inpatient resource utilization measures, including (3) length of stay (LOS; days) and (4) inflation adjusted total hospital charges (USD) as reported in the dataset. Outcomes were assessed during the index hospitalization only and were compared between hospitalizations with and without PEM. Statistical Analysis: Survey-weighted analyses were performed to generate nationally representative estimates. Categorical variables were summarized as weighted proportions and continuous variables as weighted means with standard deviations. Multivariable survey-weighted logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for in-hospital mortality and discharge disposition. Multivariable survey-weighted linear regression models were used to estimate adjusted differences (β coefficients) for LOS and total hospital charges. Each multivariable model included PEM status as the primary exposure and adjusted a priori for age, sex, race/ethnicity, primary expected payer, ZIP-income quartile, elective admission status, calendar year, and age-adjusted CCI. For multi-level categorical predictors (race/ethnicity, payer, income quartile), statistical significance was assessed using overall model-effect tests (Wald F tests) in addition to category-specific estimates. Two-sided p-values < 0.05 were considered statistically significant. All analyses incorporated NIS discharge weights and accounted for the database’s stratified cluster sampling design. Reporting Guidelines This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational cohort studies. The study design, participant selection, variable definitions, statistical methodology, handling of confounding, and presentation of results were structured to align with STROBE recommendations. Specifically, we clearly defined eligibility criteria, exposure and outcome measures, covariates, and statistical modeling strategies; accounted for the complex survey design of the National Inpatient Sample; reported effect estimates with measures of precision; and distinguished between unadjusted and adjusted analyses. Ethical Considerations The NIS contains de-identified, publicly available data; therefore, this study was considered non–human subjects research and exempt from institutional review board oversight in accordance with applicable federal regulations and institutional policies. RESUTLS Baseline Characteristics: Among 58,910 weighted hospitalizations of young adults with gastrointestinal malignancies, 11,915 (20.2%) had PEM. Patients with PEM were similar in age to those without PEM but were more frequently male and more likely to be insured by Medicaid. The PEM cohort demonstrated a substantially greater burden of advanced disease and acute illness, including higher prevalence of metastatic solid tumors (71.8% vs 53.1%), moderate-to-severe liver disease (7.1% vs 5.3%), chronic kidney disease (3.9% vs 3.2%), severe sepsis (9.8% vs 3.7%), septic shock (6.3% vs 2.1%), acute kidney injury (20.0% vs 10.1%), and need for mechanical ventilation (5.3% vs 2.3%). Correspondingly, unadjusted clinical outcomes were markedly worse among PEM admissions, with higher in-hospital mortality (8.4% vs 3.2%), longer length of stay (10.43 vs 5.63 days), and greater total hospital charges ( $ 133,790 vs $ 83,702). Table 1 Baseline characteristics of young adults (18–39 years) hospitalized with gastrointestinal cancers, stratified by protein–energy malnutrition status, National Inpatient Sample 2018–2021 Characteristic No PEM (n = 46,995) PEM (n = 11,915) Age, years, mean (SD) 33.63 (4.77) 33.20 (5.01) Sex Male 24,525 (52.2%) 6,690 (56.1%) Female 22,455 (47.8%) 5,225 (43.9%) Primary expected payer Medicare 3,015 (6.4%) 755 (6.3%) Medicaid 15,235 (32.4%) 4,325 (36.3%) Private insurance 23,990 (51.1%) 5,605 (47.1%) Self-pay 2,855 (6.1%) 735 (6.2%) No charge 250 (0.5%) 70 (0.6%) Other payer 1,610 (3.4%) 420 (3.5%) Race / ethnicity White 24,080 (52.6%) 5,505 (47.2%) Black 7,580 (16.6%) 2,360 (20.2%) Hispanic 8,980 (19.6%) 2,300 (19.7%) Asian or Pacific Islander 2,610 (5.7%) 815 (7.0%) Native American 385 (0.8%) 100 (0.9%) Other race 2,140 (4.7%) 575 (4.9%) Myocardial infarction 330 (0.7%) 95 (0.8%) Congestive heart failure 880 (1.9%) 285 (2.4%) Peripheral vascular disease 940 (2.0%) 295 (2.5%) Cerebrovascular disease 405 (0.9%) 110 (0.9%) Dementia 25 (0.1%) 0 (0.0%) Chronic pulmonary disease 3,960 (8.4%) 815 (6.8%) Rheumatic disease 350 (0.7%) 105 (0.9%) Peptic ulcer disease 1,025 (2.2%) 390 (3.3%) Mild liver disease 2,910 (6.2%) 740 (6.2%) Diabetes mellitus without chronic complications 2,875 (6.1%) 610 (5.1%) Diabetes mellitus with chronic complications 615 (1.3%) 130 (1.1%) Hemiplegia or paraplegia 305 (0.6%) 60 (0.5%) Chronic kidney disease 1,520 (3.2%) 465 (3.9%) Moderate or severe liver disease 2,480 (5.3%) 850 (7.1%) Metastatic solid tumor 24,940 (53.1%) 8,555 (71.8%) Acquired immunodeficiency syndrome (AIDS/HIV) 1,110 (2.4%) 320 (2.7%) Severe sepsis (ICD-10-CM R65.20 or R65.21) 1,755 (3.7%) 1,170 (9.8%) Septic shock (ICD-10-CM R65.21) 990 (2.1%) 745 (6.3%) Mechanical ventilation 1,075 (2.3%) 630 (5.3%) Continuous renal replacement therapy 75 (0.2%) 70 (0.6%) Vasopressor use 560 (1.2%) 320 (2.7%) Acute kidney injury 4,750 (10.1%) 2,385 (20.0%) In-hospital mortality 1,505 (3.2%) 1,000 (8.4%) Length of stay, days, mean (SD) 5.63 (6.71) 10.43 (11.17) Total hospital charges, USD, mean (SD) 83,702 (127,139) 133,790 (247,094) In Hospital Mortality In survey-weighted multivariable logistic regression, PEM was independently associated with more than a twofold increase in the adjusted odds of in-hospital death (aOR 2.13, 95% CI 1.72–2.56; p < 0.001). Higher comorbidity burden was also strongly associated with mortality (per-point age-adjusted Charlson Comorbidity Index aOR 1.21, 95% CI 1.17–1.25; p < 0.001), whereas elective admission was associated with significantly lower odds of death compared with non-elective admission (aOR 0.34, 95% CI 0.24–0.49; p < 0.001). Primary payer demonstrated selective associations, with Medicaid and private insurance linked to lower adjusted mortality relative to other payers. Age, sex, race/ethnicity, income quartile, and calendar year were not independently associated with mortality in the fully adjusted model. (Table 2 ) Table 2 Survey-weighted logistic regression for in-hospital mortality Covariate (reference) aOR for in-hospital death 95% CI p-value Protein–energy malnutrition: Yes (ref: No) 2.13 1.72–2.56 < 0.001 Elective admission: Elective (ref: Non-elective) 0.34 0.24–0.49 < 0.001 Charlson Comorbidity Index, age-adjusted (per 1-point increase) 1.21 1.17–1.25 < 0.001 Sex: Male (ref: Female) 1.15 0.94–1.40 0.169 Age (per 1-year increase) 1.01 0.99–1.03 0.476 Primary payer (ref: Other payer) Medicare (ref: Other) 0.64 0.37–1.09 0.099 Medicaid (ref: Other) 0.59 0.38–0.92 0.020 Private insurance (ref: Other) 0.54 0.34–0.84 0.007 Self-pay (ref: Other) 0.74 0.44–1.27 0.282 No charge (ref: Other) ~ 0.0 ~ 0.0 < 0.001 Race/ethnicity (ref: Other race) NS overall — 0.323 ZIP income quartile (ref: Quartile 4 highest income) NS overall — 0.594 Calendar year (ref: 2021) NS overall — 0.965 Discharge Outcomes: In survey-weighted multivariable logistic regression modeling non-home discharge (vs discharge to home/home health), the presence of PEM was independently associated with significantly higher odds of non-home discharge (aOR 1.67, 95% CI 1.47–1.89; p < 0.001). Greater comorbidity burden was also associated with increased odds of non-home disposition (per-point age-adjusted Charlson Comorbidity Index aOR 1.09, 95% CI 1.07–1.12; p < 0.001), as was male sex (aOR 1.27, 95% CI 1.12–1.43; p < 0.001). Elective admission was strongly associated with lower odds of non-home discharge (aOR 0.25, 95% CI 0.20–0.31; p < 0.001). Among socioeconomic variables, private insurance and self-pay status were associated with reduced odds of non-home discharge, whereas race/ethnicity and calendar year were not independently associated in the adjusted model. (Table 3 ) Table 3 Survey-weighted logistic regression for discharge disposition (Non-home discharge vs Home/Home Health) Covariate (reference) aOR 95% CI p-value Presence of PEM: Yes (ref: No) 1.67 1.47–1.89 < 0.001 Elective: Elective (ref: Non-elective) 0.25 0.20–0.31 < 0.001 Charlson Comorbidity Index, age-adjusted (per 1-point increase) 1.09 1.07–1.12 < 0.001 Sex: Male (ref: Female) 1.27 1.12–1.43 < 0.001 Age (per 1-year increase) 1.00 0.99–1.02 0.669 Primary payer (ref: Other payer) Medicare (ref: Other) 1.02 0.71–1.46 0.930 Medicaid (ref: Other) 0.76 0.55–1.05 0.094 Private insurance (ref: Other) 0.56 0.41–0.77 < 0.001 Self-pay (ref: Other) 0.68 0.47–1.00 0.049 No charge (ref: Other) 0.53 0.21–1.33 0.174 Race/ethnicity (ref: Other race) White (ref: Other) 1.18 0.88–1.60 0.273 Black (ref: Other) 1.38 1.00–1.90 0.050 Hispanic (ref: Other) 1.22 0.89–1.68 0.217 Asian/Pacific Islander (ref: Other) 1.28 0.89–1.84 0.176 Native American (ref: Other) 0.81 0.36–1.81 0.603 ZIP income quartile (ref: Quartile 4 highest income) Quartile 1 (lowest) 1.17 0.97–1.40 0.103 Quartile 2 1.21 1.00–1.45 0.047 Quartile 3 1.12 0.93–1.35 0.222 Calendar year (ref: 2021) 2018 0.96 0.80–1.14 0.619 2019 0.95 0.80–1.14 0.584 2020 0.84 0.71–1.01 0.061 Hospitalization Charges and Length of Hospitalization: In survey-weighted multivariable linear regression, PEM was independently associated with a substantial increase in total hospital charges (+ $ 53,512.6; SE 5,527.6; p < 0.001). Elective admissions were associated with higher charges relative to non-elective admissions (+ $ 28,081.5; SE 3,670.9; p < 0.001), while greater comorbidity burden and later calendar year were modestly associated with incremental increases in charges. Increasing age was associated with slightly lower charges, and sex was not independently significant. Race/ethnicity and ZIP-income quartile demonstrated significant overall model effects, with most racial/ethnic groups and lower income quartiles exhibiting lower adjusted charges compared with their respective reference categories. (Table 4 ) Table 4 Survey-weighted multivariable linear regression for total hospital charges (USD) Covariate (reference) Adjusted difference in charges, β (USD) SE Model-effect p-value* Protein–energy malnutrition: Yes (ref: No) + 53,512.6 5,527.6 < 0.001 Elective admission: Elective (ref: Non-elective) + 28,081.5 3,670.9 < 0.001 Age (per 1-year increase) −796.1 381.9 0.037 Calendar year (per 1-year increase) + 4,975.1 1,702.5 0.003 Charlson Comorbidity Index, age-adjusted (per 1-point increase) + 1,263.7 474.3 0.008 Sex: Male (ref: Female) + 4,939.3 3,261.1 0.130 Race/ethnicity (ref: Other race) < 0.001 White −46,916.3 11,725.6 Black −41,313.0 12,226.2 Hispanic −27,920.7 11,992.7 Asian/Pacific Islander −35,933.9 12,356.8 Native American −61,054.9 14,017.4 ZIP-income quartile (ref: Quartile 4 highest) 0.005 Quartile 1 (lowest) −16,897.8 5,074.8 Quartile 2 −14,269.4 4,577.6 Quartile 3 −9,738.6 5,558.1 Primary payer (ref: Other payer) 0.122 Medicare −17,181.9 12,452.9 Medicaid −7,876.4 11,986.5 Private insurance −9,301.0 12,123.5 Self-pay −12,710.5 13,512.8 No charge −27,717.7 15,824.8 *For multi-level factors (race, payer, income quartile), the p-value shown is the overall Wald F test for that variable from “Tests of Model Effects.” In adjusted survey-weighted linear regression, PEM was independently associated with a significantly longer length of stay (+ 4.49 days; SE 0.248; p < 0.001). Higher comorbidity burden was also associated with incremental increases in LOS (β + 0.129 days per Charlson point; p < 0.001), and male sex was modestly associated with longer hospitalization. Age, calendar year, and elective status were not independently associated with LOS. Race/ethnicity demonstrated a significant overall effect, whereas income quartile did not. (Table 5 ) Table 5 Survey-weighted multivariable linear regression for length of stay (days) Covariate (reference) Adjusted difference in LOS, β (days) SE Model-effect p-value* Protein–energy malnutrition: Yes (ref: No) + 4.492 0.248 < 0.001 Charlson Comorbidity Index, age-adjusted (per 1-point increase) + 0.129 0.027 < 0.001 Sex: Male (ref: Female) + 0.334 0.151 0.027 Age (per 1-year increase) −0.023 0.017 0.156 Calendar year (per 1-year increase) −0.069 0.067 0.307 Elective admission: Non-elective (ref: Elective) + 0.179 0.164 0.274 Race/ethnicity (ref: Other race) < 0.001 White −1.354 0.429 Black −0.513 0.462 Hispanic −0.789 0.459 Asian/Pacific Islander −1.049 0.507 Native American −1.409 0.857 ZIP-income quartile (ref: Quartile 4 highest) 0.906 Quartile 1 (lowest) −0.144 0.235 Quartile 2 −0.106 0.217 Quartile 3 −0.143 0.208 Primary payer (ref: Other payer) < 0.001 Medicare −0.578 0.526 Medicaid + 0.138 0.468 Private insurance −0.706 0.460 Self-pay + 0.115 0.554 No charge −0.428 0.789 DISCUSSION This national analysis demonstrates that PEM is common among young adults hospitalized with GI cancers and is strongly associated with adverse inpatient outcomes. The association between PEM and mortality is consistent with established evidence linking malnutrition to impaired immune function, increased infection risk, and reduced physiologic reserve in patients with cancer [ 13 ]. The magnitude of effect persisted after adjustment for CCI and admission type, which suggests that PEM captures risk beyond measured comorbidity alone. [ 14 ] The marked differences in sepsis, organ failure, and mechanical ventilation observed in the PEM cohort further support the relationship between poor nutritional status and acute clinical deterioration. [ 15 ] Clinicians should therefore recognize PEM as a high risk condition during hospitalization for GI malignancy. PEM also exerted a strong effect on health care utilization. An adjusted increase of more than four hospital days and over fifty thousand dollars in charges per admission represents a substantial resource burden at the national level. Longer LOS and non-home discharge often reflect greater functional decline and care complexity. In an era of rising oncology expenditures, these data underscore the importance of systematic nutritional assessment and management as part of routine inpatient cancer care. National guidelines recommend early nutrition screening and intervention in oncology practice [ 16 ]. Our findings provide population level evidence that reinforces these recommendations in young adults with GI cancers. These findings quantify the national inpatient burden of PEM in this population in the United States. These data support system level strategies that mandate routine nutritional screening, standardized documentation, and early nutrition intervention during hospitalization. Policymakers and health systems should recognize PEM as a measurable and actionable target to improve outcomes and reduce avoidable resource use in oncology care. This study has limitations inherent to administrative data. The NIS relies on diagnosis codes and lacks detailed clinical measures of nutritional status, cancer stage, treatment intensity, and laboratory values. However, documented confirmed PEM in hospitalized patients reflects clinically recognized malnutrition and identifies a population with substantial disease burden. The observational design precludes causal inference. Nonetheless, the strength and consistency of associations across mortality, discharge disposition, LOS, and charges support the validity of the findings. Residual confounding may persist despite adjustment. However, we accounted for major demographic, socioeconomic, and comorbidity factors, including age adjusted CCI and admission type, which reduces measurable confounding. The database captures hospitalizations rather than unique patients and does not include post discharge outcomes. However, hospitalization level analysis is appropriate for evaluating inpatient outcomes. The large nationally representative sample enhances precision and generalizability across hospital settings in the United States. Conclusion PEM affects a substantial proportion of young adults hospitalized with GI cancers and independently increases mortality, non home discharge, LOS, and hospital charges. These findings support routine inpatient nutritional screening and timely intervention as core components of comprehensive oncologic care in this high risk population. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study used deidentified publicly available data and was exempt from institutional review board review. Consent to participate For deidentified administrative data: Not applicable Consent to publish Not applicable. Funding No funding received Author Contribution Manas Pustake: Conceptualization, study design, data interpretation, drafting of the manuscript, critical revision for important intellectual content. Lakshmi Kattamuri: Study design, data analysis, interpretation of results, critical revision of the manuscript. Shubhangi Deoker: Literature review, data interpretation, drafting of sections of the manuscript, critical revision. Kunal Sharma: Statistical analysis, methodology, validation of results, critical revision of the manuscript. Abhizith Deoker: Conceptualization, supervision, oversight of the study, critical revision of the manuscript, final approval of the version to be published. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgments Nil Data Availability Data are available from HCUP subject to data use agreements. References Jin J, Zhu X, Deng Z, Zhang P, Xiao Y, Han H, Li Y, Li H. 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Impact of protein-energy malnutrition on outcomes of patients with diffuse large B cell lymphoma admitted for inpatient chemotherapy. Proc (Bayl Univ Med Cent). 2023;36(4):439–42. PMID: 37334087; PMCID: PMC10269417. Adejumo AC, Akanbi O, Pani L. Protein Energy Malnutrition Is Associated with Worse Outcomes in Sepsis-A Nationwide Analysis. J Acad Nutr Diet. 2019;119(12):2069–84. 10.1016/j.jand.2019.04.019 . Epub 2019 Jul 8. PMID: 31296426. Kelly L, Datta M, Arthur A, Strang M, Hui K. From Revision to Practice: Key Changes in the Revised 2025 Scope and Standards of Practice for Registered Dietitian Nutritionists in Oncology Nutrition. J Acad Nutr Dietetics. 2025;125(12):1907–10. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 05 Apr, 2026 Editor assigned by journal 21 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 17 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8899778","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618917151,"identity":"799f356d-183d-4d95-817e-1a1327bf9723","order_by":0,"name":"Manas Pustake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYJADZgaGCiAlAeMQBGwgVWcMJEjUwthGhBbd9rMHP/5guJM4f37zY4OP8/7UGdxuf/iBocI6sQGHFrMzecnSPAzPEjccYzNOnLnNQMLgzhljCYYz6bi1HMgxkGZgOJy4gY3B+DAvSMuNHDagCw/j1nL+jfHPH0At89vYPx/+OwekJf0ZA+M/PFpu5JhJ8AC1NBzjMU5mbABpSTBjYGzAp+WNmTWPwTPjDcdyig17jhlLzryRYyyRcCzdGLfDcoxv/qi4Izu/+fhmiR81cvx8N9IffvhQYy2LSwsEGBxAE0jAqxwM0LWMglEwCkbBKEACAA0RXKMh9tVoAAAAAElFTkSuQmCC","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":true,"prefix":"","firstName":"Manas","middleName":"","lastName":"Pustake","suffix":""},{"id":618917152,"identity":"512ec5b5-0781-4c7e-9d49-3f020d8be917","order_by":1,"name":"Lakshmi Kattamuri","email":"","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":false,"prefix":"","firstName":"Lakshmi","middleName":"","lastName":"Kattamuri","suffix":""},{"id":618917153,"identity":"5b44c1cd-b2bc-4af6-8f63-76d409875e00","order_by":2,"name":"Shubhangi Deoker","email":"","orcid":"","institution":"Byramjee Jeejeebhoy Government Medical College and Sassoon General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shubhangi","middleName":"","lastName":"Deoker","suffix":""},{"id":618917154,"identity":"1c94cba9-d572-4ffd-b09b-d790931e6e97","order_by":3,"name":"Kunal Sharma","email":"","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":false,"prefix":"","firstName":"Kunal","middleName":"","lastName":"Sharma","suffix":""},{"id":618917155,"identity":"de4b5ade-6649-462b-9658-0a21a2cbf934","order_by":4,"name":"Abhizith Deoker","email":"","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":false,"prefix":"","firstName":"Abhizith","middleName":"","lastName":"Deoker","suffix":""}],"badges":[],"createdAt":"2026-02-17 09:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8899778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8899778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106646631,"identity":"3140e9e5-d443-4b6d-99f6-959ff1623548","added_by":"auto","created_at":"2026-04-10 20:27:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1831542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8899778/v1/1ce8c7aa-8b66-465b-b997-f40f56b055c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Malnutrition on In-Hospital and Discharge Outcomes Among Young Adults Hospitalized with Gastrointestinal Cancers: National Estimates from the United States","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eProtein\u0026ndash;energy malnutrition (PEM) remains a pervasive yet underrecognized comorbidity among patients with malignancy, particularly those with gastrointestinal (GI) cancers, where tumor-related obstruction, treatment-related toxicities, systemic inflammation, and cancer cachexia converge to accelerate nutritional decline. [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Malnutrition in oncology has been consistently associated with impaired immune function, increased susceptibility to infection, poor tolerance of systemic therapy, delayed wound healing, and excess mortality. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Despite its well-established biological and clinical relevance, PEM is frequently underdiagnosed and undertreated in routine practice, especially among younger adults who are often perceived as physiologically resilient. In the context of GI malignancies which carry a high risk of cachexia and metabolic derangements, the implications of malnutrition may be especially profound, affecting both short-term inpatient outcomes and longer-term treatment trajectories. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlthough prior studies have documented associations between malnutrition and adverse outcomes, much of the existing literature is derived from single-center cohorts, disease-specific registries, or studies focused predominantly on older Medicare populations. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Consequently, contemporary, nationally representative data describing the burden and impact of PEM among young adults with GI cancers in the United States remain limited. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Young adults (18\u0026ndash;39 years) represent a distinct oncologic subgroup with unique tumor biology, treatment patterns, insurance structures, and socioeconomic vulnerabilities. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] National-scale analyses are essential to capture geographic, institutional, and payer heterogeneity, as well as to generate externally generalizable estimates that reflect real-world inpatient care across diverse hospital settings.\u003c/p\u003e \u003cp\u003eGenerating national estimates provide epidemiologic insight into the prevalence, resource utilization, and in-hospital outcomes. Such analyses inform not only clinical practice but also health policy and resource allocation by quantifying the attributable burden of malnutrition on mortality, length of stay, discharge disposition, and hospital charges at the population level. In this era of value-based care and escalating oncology expenditures, nationally representative data are necessary to justify systematic inpatient nutritional risk screening, early intervention strategies, and integration of nutrition-directed therapies as potential targets to improve outcomes and reduce avoidable healthcare utilization across the United States. We aimed to estimate the hospitalization and discharge outcomes associated and factors influencing them among young adults with PEM hospitalized with GI malignancies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source:\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS), an all-payer, nationally representative database of inpatient hospitalizations in the United States. The analytic period spanned January 1, 2018 through December 31, 2021. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] The NIS employs a stratified, single-stage cluster sampling design that samples hospital discharges and provides discharge-level survey weights to generate national estimates. Consistent with HCUP analytic recommendations, all analyses accounted for the complex sampling design, including discharge weights, hospital clustering, and stratification, to obtain unbiased point estimates and valid standard errors for nationally representative inference.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population:\u003c/h3\u003e\n\u003cp\u003eHospitalizations of young adults aged 18\u0026ndash;39 years were eligible for inclusion. We identified gastrointestinal (GI) cancer hospitalizations using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) malignant neoplasm diagnosis codes C15\u0026ndash;C26, corresponding to cancers of the esophagus through other/ill-defined digestive organs. Hospitalizations were categorized by the presence versus absence of PEM, defined using ICD-10-CM diagnosis codes indicative of PEM recorded during the index hospitalization. Demographic and socioeconomic covariates included age, sex, race/ethnicity, primary expected payer, and patient ZIP-code\u0026ndash;linked income quartile. Clinical and hospitalization characteristics included elective versus non-elective admission status, calendar year, and comorbidity burden measured using the age-adjusted Charlson Comorbidity Index (CCI). Baseline characteristics were summarized by PEM status using survey-weighted descriptive statistics to provide national estimates.\u003c/p\u003e\n\u003ch3\u003eOutcomes:\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were (1) in-hospital mortality and (2) discharge disposition. Discharge disposition was operationalized as discharge to home/home health versus non-home discharge. Secondary outcomes were inpatient resource utilization measures, including (3) length of stay (LOS; days) and (4) inflation adjusted total hospital charges (USD) as reported in the dataset. Outcomes were assessed during the index hospitalization only and were compared between hospitalizations with and without PEM.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eSurvey-weighted analyses were performed to generate nationally representative estimates. Categorical variables were summarized as weighted proportions and continuous variables as weighted means with standard deviations. Multivariable survey-weighted logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for in-hospital mortality and discharge disposition. Multivariable survey-weighted linear regression models were used to estimate adjusted differences (β coefficients) for LOS and total hospital charges. Each multivariable model included PEM status as the primary exposure and adjusted a priori for age, sex, race/ethnicity, primary expected payer, ZIP-income quartile, elective admission status, calendar year, and age-adjusted CCI. For multi-level categorical predictors (race/ethnicity, payer, income quartile), statistical significance was assessed using overall model-effect tests (Wald F tests) in addition to category-specific estimates. Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. All analyses incorporated NIS discharge weights and accounted for the database\u0026rsquo;s stratified cluster sampling design.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReporting Guidelines\u003c/h3\u003e\n\u003cp\u003eThis study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational cohort studies. The study design, participant selection, variable definitions, statistical methodology, handling of confounding, and presentation of results were structured to align with STROBE recommendations. Specifically, we clearly defined eligibility criteria, exposure and outcome measures, covariates, and statistical modeling strategies; accounted for the complex survey design of the National Inpatient Sample; reported effect estimates with measures of precision; and distinguished between unadjusted and adjusted analyses.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eThe NIS contains de-identified, publicly available data; therefore, this study was considered non\u0026ndash;human subjects research and exempt from institutional review board oversight in accordance with applicable federal regulations and institutional policies.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESUTLS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics:\u003c/h2\u003e \u003cp\u003eAmong 58,910 weighted hospitalizations of young adults with gastrointestinal malignancies, 11,915 (20.2%) had PEM. Patients with PEM were similar in age to those without PEM but were more frequently male and more likely to be insured by Medicaid. The PEM cohort demonstrated a substantially greater burden of advanced disease and acute illness, including higher prevalence of metastatic solid tumors (71.8% vs 53.1%), moderate-to-severe liver disease (7.1% vs 5.3%), chronic kidney disease (3.9% vs 3.2%), severe sepsis (9.8% vs 3.7%), septic shock (6.3% vs 2.1%), acute kidney injury (20.0% vs 10.1%), and need for mechanical ventilation (5.3% vs 2.3%). Correspondingly, unadjusted clinical outcomes were markedly worse among PEM admissions, with higher in-hospital mortality (8.4% vs 3.2%), longer length of stay (10.43 vs 5.63 days), and greater total hospital charges (\u003cspan\u003e$\u003c/span\u003e133,790 vs \u003cspan\u003e$\u003c/span\u003e83,702).\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\u003eBaseline characteristics of young adults (18\u0026ndash;39 years) hospitalized with gastrointestinal cancers, stratified by protein\u0026ndash;energy malnutrition status, National Inpatient Sample 2018\u0026ndash;2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo PEM (n\u0026thinsp;=\u0026thinsp;46,995)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePEM (n\u0026thinsp;=\u0026thinsp;11,915)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.63 (4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.20 (5.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,525 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,690 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,455 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,225 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary expected payer\u003c/b\u003e\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 \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\u003e3,015 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e755 (6.3%)\u003c/p\u003e \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\u003e15,235 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,325 (36.3%)\u003c/p\u003e \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\u003e23,990 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,605 (47.1%)\u003c/p\u003e \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\u003e2,855 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e735 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther payer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,610 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace / ethnicity\u003c/b\u003e\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 \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\u003e24,080 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,505 (47.2%)\u003c/p\u003e \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\u003e7,580 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,360 (20.2%)\u003c/p\u003e \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\u003e8,980 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,300 (19.7%)\u003c/p\u003e \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\u003e2,610 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815 (7.0%)\u003c/p\u003e \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\u003e385 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,140 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e575 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyocardial infarction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive heart failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeripheral vascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e940 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerebrovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDementia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic pulmonary disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,960 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRheumatic disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeptic ulcer disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,025 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMild liver disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,910 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus without chronic complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,875 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e610 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus with chronic complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e615 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemiplegia or paraplegia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic kidney disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,520 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate or severe liver disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,480 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e850 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastatic solid tumor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,940 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,555 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcquired immunodeficiency syndrome (AIDS/HIV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,110 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere sepsis (ICD-10-CM R65.20 or R65.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,755 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,170 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeptic shock (ICD-10-CM R65.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e990 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e745 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMechanical ventilation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,075 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e630 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContinuous renal replacement therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasopressor use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcute kidney injury\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,750 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,385 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,505 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,000 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay, days, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.63 (6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.43 (11.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal hospital charges, USD, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83,702 (127,139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133,790 (247,094)\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 \u003cstrong\u003eIn Hospital Mortality\u003c/strong\u003e \u003cp\u003eIn survey-weighted multivariable logistic regression, PEM was independently associated with more than a twofold increase in the adjusted odds of in-hospital death (aOR 2.13, 95% CI 1.72\u0026ndash;2.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher comorbidity burden was also strongly associated with mortality (per-point age-adjusted Charlson Comorbidity Index aOR 1.21, 95% CI 1.17\u0026ndash;1.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas elective admission was associated with significantly lower odds of death compared with non-elective admission (aOR 0.34, 95% CI 0.24\u0026ndash;0.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Primary payer demonstrated selective associations, with Medicaid and private insurance linked to lower adjusted mortality relative to other payers. Age, sex, race/ethnicity, income quartile, and calendar year were not independently associated with mortality in the fully adjusted model. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\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\u003eSurvey-weighted logistic regression for in-hospital mortality\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate (reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR for in-hospital death\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\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\u003e\u003cb\u003eProtein\u0026ndash;energy malnutrition: Yes (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.72\u0026ndash;2.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElective admission: Elective (ref: Non-elective)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.24\u0026ndash;0.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index, age-adjusted (per 1-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Male (ref: Female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary payer (ref: Other payer)\u003c/b\u003e\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\u003eMedicare (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo charge (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (ref: Other race)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS overall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZIP income quartile (ref: Quartile 4 highest income)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS overall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalendar year (ref: 2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS overall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDischarge Outcomes:\u003c/h2\u003e \u003cp\u003eIn survey-weighted multivariable logistic regression modeling non-home discharge (vs discharge to home/home health), the presence of PEM was independently associated with significantly higher odds of non-home discharge (aOR 1.67, 95% CI 1.47\u0026ndash;1.89; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Greater comorbidity burden was also associated with increased odds of non-home disposition (per-point age-adjusted Charlson Comorbidity Index aOR 1.09, 95% CI 1.07\u0026ndash;1.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was male sex (aOR 1.27, 95% CI 1.12\u0026ndash;1.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Elective admission was strongly associated with lower odds of non-home discharge (aOR 0.25, 95% CI 0.20\u0026ndash;0.31; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among socioeconomic variables, private insurance and self-pay status were associated with reduced odds of non-home discharge, whereas race/ethnicity and calendar year were not independently associated in the adjusted model. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey-weighted logistic regression for discharge disposition (Non-home discharge vs Home/Home Health)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate (reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\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\u003e\u003cb\u003ePresence of PEM: Yes (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u0026ndash;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElective: Elective (ref: Non-elective)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index, age-adjusted (per 1-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Male (ref: Female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026ndash;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary payer (ref: Other payer)\u003c/b\u003e\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\u003eMedicare (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo charge (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (ref: Other race)\u003c/b\u003e\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\u003eWhite (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Pacific Islander (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative American (ref: Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZIP income quartile (ref: Quartile 4 highest income)\u003c/b\u003e\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\u003eQuartile 1 (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalendar year (ref: 2021)\u003c/b\u003e\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\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHospitalization Charges and Length of Hospitalization:\u003c/h2\u003e \u003cp\u003eIn survey-weighted multivariable linear regression, PEM was independently associated with a substantial increase in total hospital charges (+\u003cspan\u003e$\u003c/span\u003e53,512.6; SE 5,527.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Elective admissions were associated with higher charges relative to non-elective admissions (+\u003cspan\u003e$\u003c/span\u003e28,081.5; SE 3,670.9; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while greater comorbidity burden and later calendar year were modestly associated with incremental increases in charges. Increasing age was associated with slightly lower charges, and sex was not independently significant. Race/ethnicity and ZIP-income quartile demonstrated significant overall model effects, with most racial/ethnic groups and lower income quartiles exhibiting lower adjusted charges compared with their respective reference categories. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey-weighted multivariable linear regression for total hospital charges (USD)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate (reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted difference in charges, β (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel-effect p-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u0026ndash;energy malnutrition: Yes (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;53,512.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,527.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElective admission: Elective (ref: Non-elective)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;28,081.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,670.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;796.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e381.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalendar year (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;4,975.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,702.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index, age-adjusted (per 1-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1,263.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e474.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Male (ref: Female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;4,939.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,261.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (ref: Other race)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;46,916.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,725.6\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\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;41,313.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,226.2\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\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27,920.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,992.7\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\u003eAsian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;35,933.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,356.8\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\u003eNative American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;61,054.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,017.4\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\u003e\u003cb\u003eZIP-income quartile (ref: Quartile 4 highest)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;16,897.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,074.8\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\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;14,269.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,577.6\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\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;9,738.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,558.1\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\u003cb\u003ePrimary payer (ref: Other payer)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;17,181.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,452.9\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;7,876.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,986.5\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;9,301.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,123.5\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\u003eSelf-pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;12,710.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,512.8\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\u003eNo charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27,717.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,824.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*For multi-level factors (race, payer, income quartile), the p-value shown is the \u003cb\u003eoverall Wald F test\u003c/b\u003e for that variable from \u0026ldquo;Tests of Model Effects.\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn adjusted survey-weighted linear regression, PEM was independently associated with a significantly longer length of stay (+\u0026thinsp;4.49 days; SE 0.248; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher comorbidity burden was also associated with incremental increases in LOS (β\u0026thinsp;+\u0026thinsp;0.129 days per Charlson point; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and male sex was modestly associated with longer hospitalization. Age, calendar year, and elective status were not independently associated with LOS. Race/ethnicity demonstrated a significant overall effect, whereas income quartile did not. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey-weighted multivariable linear regression for length of stay (days)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate (reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted difference in LOS, β (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel-effect p-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u0026ndash;energy malnutrition: Yes (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;4.492\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index, age-adjusted (per 1-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.129\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Male (ref: Female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.334\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalendar year (per 1-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElective admission: Non-elective (ref: Elective)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (ref: Other race)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.429\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.462\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.459\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/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.507\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\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\u003cb\u003eZIP-income quartile (ref: Quartile 4 highest)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.235\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\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.217\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\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208\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\u003cb\u003ePrimary payer (ref: Other payer)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.526\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.460\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.554\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\u003eNo charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis national analysis demonstrates that PEM is common among young adults hospitalized with GI cancers and is strongly associated with adverse inpatient outcomes.\u003c/p\u003e \u003cp\u003eThe association between PEM and mortality is consistent with established evidence linking malnutrition to impaired immune function, increased infection risk, and reduced physiologic reserve in patients with cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The magnitude of effect persisted after adjustment for CCI and admission type, which suggests that PEM captures risk beyond measured comorbidity alone. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] The marked differences in sepsis, organ failure, and mechanical ventilation observed in the PEM cohort further support the relationship between poor nutritional status and acute clinical deterioration. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Clinicians should therefore recognize PEM as a high risk condition during hospitalization for GI malignancy.\u003c/p\u003e \u003cp\u003ePEM also exerted a strong effect on health care utilization. An adjusted increase of more than four hospital days and over fifty thousand dollars in charges per admission represents a substantial resource burden at the national level. Longer LOS and non-home discharge often reflect greater functional decline and care complexity. In an era of rising oncology expenditures, these data underscore the importance of systematic nutritional assessment and management as part of routine inpatient cancer care. National guidelines recommend early nutrition screening and intervention in oncology practice [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our findings provide population level evidence that reinforces these recommendations in young adults with GI cancers.\u003c/p\u003e \u003cp\u003eThese findings quantify the national inpatient burden of PEM in this population in the United States. These data support system level strategies that mandate routine nutritional screening, standardized documentation, and early nutrition intervention during hospitalization. Policymakers and health systems should recognize PEM as a measurable and actionable target to improve outcomes and reduce avoidable resource use in oncology care.\u003c/p\u003e \u003cp\u003eThis study has limitations inherent to administrative data. The NIS relies on diagnosis codes and lacks detailed clinical measures of nutritional status, cancer stage, treatment intensity, and laboratory values. However, documented confirmed PEM in hospitalized patients reflects clinically recognized malnutrition and identifies a population with substantial disease burden. The observational design precludes causal inference. Nonetheless, the strength and consistency of associations across mortality, discharge disposition, LOS, and charges support the validity of the findings. Residual confounding may persist despite adjustment. However, we accounted for major demographic, socioeconomic, and comorbidity factors, including age adjusted CCI and admission type, which reduces measurable confounding. The database captures hospitalizations rather than unique patients and does not include post discharge outcomes. However, hospitalization level analysis is appropriate for evaluating inpatient outcomes. The large nationally representative sample enhances precision and generalizability across hospital settings in the United States.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePEM affects a substantial proportion of young adults hospitalized with GI cancers and independently increases mortality, non home discharge, LOS, and hospital charges. These findings support routine inpatient nutritional screening and timely intervention as core components of comprehensive oncologic care in this high risk population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThis study used deidentified publicly available data and was exempt from institutional review board review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor deidentified administrative data: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding received\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eManas Pustake: Conceptualization, study design, data interpretation, drafting of the manuscript, critical revision for important intellectual content. Lakshmi Kattamuri: Study design, data analysis, interpretation of results, critical revision of the manuscript. Shubhangi Deoker: Literature review, data interpretation, drafting of sections of the manuscript, critical revision. Kunal Sharma: Statistical analysis, methodology, validation of results, critical revision of the manuscript. Abhizith Deoker: Conceptualization, supervision, oversight of the study, critical revision of the manuscript, final approval of the version to be published. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eNil\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available from HCUP subject to data use agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJin J, Zhu X, Deng Z, Zhang P, Xiao Y, Han H, Li Y, Li H. Protein-energy malnutrition and worse outcomes after major cancer surgery: A nationwide analysis. Front Oncol. 2023;13:970187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2023.970187\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.970187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36733308; PMCID: PMC9886875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanardhanan S, Subramanian T. Identification of Malnutrition Risk Factors in Gastrointestinal Cancer: A Multicentric Cross-Sectional Study. Asian Pac J Cancer Prev. 2025;26(10):3815\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31557/APJCP.2025.26.10.3815\u003c/span\u003e\u003cspan address=\"10.31557/APJCP.2025.26.10.3815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 41148624; PMCID: PMC12906779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng HL, Lu J, Li P, Xie JW, Wang JB, Lin JX, Chen QY, Cao LL, Lin M, Tu R, Huang CM. Effects of preoperative malnutrition on short-and long-term outcomes of patients with gastric cancer: can we do better? Ann Surg Oncol. 2017;24(11):3376\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeneghan HM, Zaborowski A, Fanning M, McHugh A, Doyle S, Moore J, Ravi N, Reynolds JV. Prospective study of malabsorption and malnutrition after esophageal and gastric cancer surgery. Ann Surg. 2015;262(5):803\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyu SW, Kim IH. Comparison of different nutritional assessments in detecting malnutrition among gastric cancer patients. World J Gastroenterol. 2010;16(26):3310\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v16.i26.3310\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v16.i26.3310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 20614488; PMCID: PMC2900724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBossi P, De Luca R, Ciani O, D'Angelo E, Caccialanza R. Malnutrition management in oncology: An expert view on controversial issues and future perspectives. Front Oncol. 2022;12:910770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2022.910770\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.910770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36276153; PMCID: PMC9579941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSauer AC. Malnutrition in patients with cancer: An often overlooked and undertreated problem.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIqbal R, Kimball H, Gaddam M, Peshin S, Sinha S, Quadri K. The impact of protein-energy malnutrition on clinical outcomes in hospitalized gastric cancer patients: A population-based analysis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng D, Zong K, Yang H, Huang Z, Mou T, Jiang P, Wu Z. Malnutrition diagnosed by the Global Leadership Initiative on Malnutrition criteria predicting survival and clinical outcomes of patients with cancer: A systematic review and meta-analysis. Front Nutr. 2022;9:1053165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2022.1053165\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.1053165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullock AF, Greenley SL, McKenzie GA, Paton LW, Johnson MJ. Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis and meta-analysis. Eur J Clin Nutr. 2020;74(11):1519\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSender L, Zabokrtsky KB. Adolescent and young adult patients with cancer: a milieu of unique features. Nat Rev Clin Oncol. 2015;12(8):465\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2015.92\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2015.92\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2015 May 26. PMID: 26011488.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealthcare Cost and Utilization Project (HCUP). National Inpatient Sample (NIS), 2018\u0026ndash;2021. Agency for Healthcare Research and Quality, Rockville, MD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.hcup-us.ahrq.gov/db/nation/nis/nisdbdocumentation.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Quan Y, Wang X, Wei X, Shen X, Li X, Liang T. Global epidemiological characteristics of malnutrition in cancer patients: a comprehensive meta-analysis and systematic review. BMC Cancer. 2025;25(1):1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12885-025-14558-2\u003c/span\u003e\u003cspan address=\"10.1186/s12885-025-14558-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40684092; PMCID: PMC12275410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeenadayalan V, Olafimihan A, Ganesan V, Kumi D, Zia M. Impact of protein-energy malnutrition on outcomes of patients with diffuse large B cell lymphoma admitted for inpatient chemotherapy. Proc (Bayl Univ Med Cent). 2023;36(4):439\u0026ndash;42. PMID: 37334087; PMCID: PMC10269417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdejumo AC, Akanbi O, Pani L. Protein Energy Malnutrition Is Associated with Worse Outcomes in Sepsis-A Nationwide Analysis. J Acad Nutr Diet. 2019;119(12):2069\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jand.2019.04.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jand.2019.04.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 Jul 8. PMID: 31296426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly L, Datta M, Arthur A, Strang M, Hui K. From Revision to Practice: Key Changes in the Revised 2025 Scope and Standards of Practice for Registered Dietitian Nutritionists in Oncology Nutrition. J Acad Nutr Dietetics. 2025;125(12):1907\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"protein energy malnutrition, gastrointestinal cancers, young adults, in hospital mortality, discharge disposition, length of stay, hospital charges, National Inpatient Sample, health care utilization, cancer outcomes","lastPublishedDoi":"10.21203/rs.3.rs-8899778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8899778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;energy malnutrition (PEM) is common in gastrointestinal (GI) cancers and may worsen inpatient outcomes. Contemporary national data describing the impact of PEM among young adults with GI malignancies are limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using HCUP NIS data from 2018 to 2021. We identified hospitalizations of adults aged 18 to 39 years with GI cancers using ICD 10 CM codes C15 to C26. We defined PEM by diagnosis codes recorded during the index admission. Primary outcomes were in hospital mortality and discharge disposition. Secondary outcomes were LOS and total hospital charges. We used survey weighted multivariable logistic and linear regression to estimate adjusted associations, accounting for age, sex, race or ethnicity, payer, income quartile, admission type, calendar year, and age adjusted CCI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 58,910 weighted hospitalizations of young adults with gastrointestinal cancers, 11,915 (20.2%) had protein\u0026ndash;energy malnutrition (PEM). Compared with those without PEM, patients with PEM had a higher burden of advanced disease and acute illness, including a greater prevalence of metastatic disease (71.8% vs 53.1%), and experienced worse unadjusted outcomes, including higher in-hospital mortality (8.4% vs 3.2%), longer length of stay (10.43 vs 5.63 days), and higher total hospital charges (\u003cspan\u003e$\u003c/span\u003e133,790 vs \u003cspan\u003e$\u003c/span\u003e83,702). In adjusted analyses, PEM was independently associated with increased odds of in-hospital mortality (aOR 2.13, 95% CI 1.72\u0026ndash;2.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher odds of non-home discharge (aOR 1.67, 95% CI 1.47\u0026ndash;1.89; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PEM was also associated with substantially greater resource utilization, including an adjusted increase of 4.49 hospital days (β\u0026thinsp;+\u0026thinsp;4.492; SE 0.248; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cspan\u003e$\u003c/span\u003e53,513 higher total hospital charges (β +\u003cspan\u003e$\u003c/span\u003e53,512.6; SE \u003cspan\u003e$\u003c/span\u003e5,527.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePEM affected one in five hospitalizations among young adults with GI cancers and independently increased mortality, non-home discharge, LOS, and hospital charges. These findings support routine inpatient nutritional assessment and early intervention in this high risk population.\u003c/p\u003e","manuscriptTitle":"Effects of Malnutrition on In-Hospital and Discharge Outcomes Among Young Adults Hospitalized with Gastrointestinal Cancers: National Estimates from the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 20:27:24","doi":"10.21203/rs.3.rs-8899778/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"108497015715540554862951173644146352597","date":"2026-05-18T00:49:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62811867211586524514601968331663126214","date":"2026-04-07T11:46:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-05T21:40:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-22T00:04:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T15:16:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Gastrointestinal Cancer","date":"2026-02-17T09:45:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c0e5540f-5b72-4730-889e-9419f7ab939c","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"108497015715540554862951173644146352597","date":"2026-05-18T00:49:21+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T20:27:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 20:27:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8899778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8899778","identity":"rs-8899778","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}