Department of Respiratory Medicine Intensive Care Unit, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China

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June Zhang,

Corresponding Author

June Zhang

[email protected]

orcid.org/0000-0002-6599-702X

School of Nursing, Sun Yat-Sen University, Guangzhou, China

Correspondence:

June Zhang ([email protected])

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Hongtao Cheng,

Hongtao Cheng

orcid.org/0000-0003-3739-9097

School of Nursing, Sun Yat-Sen University, Guangzhou, China

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Lu Shao,

Lu Shao

orcid.org/0000-0002-4973-2291

School of Nursing, Sun Yat-Sen University, Guangzhou, China

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Huijun Wu,

Huijun Wu

School of Nursing, Sun Yat-Sen University, Guangzhou, China

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Boya Mi,

Boya Mi

School of Nursing, Sun Yat-Sen University, Guangzhou, China

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Qiugui Li,

Qiugui Li

Department of Respiratory Medicine Intensive Care Unit, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China

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June Zhang,

Corresponding Author

June Zhang

[email protected]

orcid.org/0000-0002-6599-702X

School of Nursing, Sun Yat-Sen University, Guangzhou, China

Correspondence:

June Zhang ([email protected])

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First published: 01 June 2025

https://doi.org/10.1111/nicc.70078

Funding: This work was supported by the National Social Science Foundation of China (21BSH120).

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ABSTRACT

Background

Sepsis is a critical global health issue, particularly affecting older adults. Despite advances in acute sepsis management, the long-term outcomes for survivors, particularly those transitioning to skilled nursing facilities (SNFs), remain poorly characterized.

Aim

To evaluate the prognostic value of the age-adjusted Charlson Comorbidity Index (ACCI) in predicting 6-month mortality among older adult sepsis survivors discharged to SNFs.

Study Design

An observational cohort study of older adult sepsis patients in the intensive care unit (ICU) of a tertiary academic medical centre in Boston from 2008 to 2019 was performed. Patients were stratified into low (≤ 5), intermediate (6, 7) and high (≥ 8) ACCI score groups. Using the Cox proportional hazards model, we determined the association between ACCI scores and 6-month mortality, calculating hazard ratios (HR) and 95% confidence intervals (CIs). The predictive performance was assessed using ROC curve analysis and compared with Sequential Organ Failure Assessment (SOFA) scores.

Results

The study included 3713 sepsis survivors aged 65 and older discharged to SNFs. The median age was around 80 years, and 52.6% of the participants were female. The analysis revealed that each one-point increase in the ACCI was associated with an 18% higher risk of mortality within 6 months (HR 1.18; 95% CI 1.14–1.22; p < 0.001). Furthermore, compared with individuals with low ACCI scores, those in the moderate ACCI score group had a HR of 1.55 (95% CI: 1.26–1.91, p < 0.001), and those in the high ACCI score group had a HR of 2.43 (95% CI: 1.96–3.03, p < 0.001). ACCI demonstrated superior predictive performance compared with SOFA scores (area under the curve [AUC] 0.65 vs. 0.53, p < 0.001).

Conclusions

ACCI serves as an independent predictor of 6-month mortality in older adult sepsis survivors discharged to SNFs.

Relevance to Clinical Practice

Critical care nurses can use ACCI as a risk stratification tool to identify high-risk older sepsis survivors, inform discharge planning and improve interprofessional communication for tailored post-acute care interventions in SNFs.

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Data Availability Statement

The data that support the findings of this study are available in PhysioNet at https://physionet.org/content/mimiciv/2.0/. These data were derived from the following resources available in the public domain: PhysioNet, https://physionet.org/content/mimiciv/2.0/.

Supporting Information

Filename

Description

nicc70078-sup-0001-supinfo.docxWord 2007 document , 535.8 KB

Table S1. Methods for calculating method of the Charlson comorbidity index and the age-adjusted Charlson comorbidity index.

Table S2. Association between age-adjusted Charlson comorbidity index and 6-month mortality.

Table S3. Association between age-adjusted Charlson comorbidity index groups and 6-month mortality after propensity score matching.

Table S4. Sensitivity analysis: association between age-adjusted Charlson comorbidity index groups and 6-month mortality in special populations.

Table S5. Performance metrics of multivariable models with and without ACCI to predict 6-month mortality in older sepsis survivors admitted to skilled nursing facilities.

Figure S1. Flowchart of the criteria for inclusion and exclusion of patients in the study.

Figure S2. Variable importance in predicting 6-month mortality: random survival forest analysis.

Figure S3. Distribution of ACCI in older sepsis survivors admitted to skilled nursing facilities.

Figure S4. Analysis of restricted cubic spline.

Figure S5. Receiver operating characteristic curves for ACCI and SOFA scores.

nicc70078-sup-0002-supinfo.pdfPDF document, 101.2 KB

Data S1. Supporting Information.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

References

1M. Singer, C. S. Deutschman, C. W. Seymour, et al., “The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),” Journal of the American Medical Association 315, no. 8 (2016): 801–810.
10.1001/jama.2016.0287
CAS PubMed Web of Science® Google Scholar
2K. E. Rudd, S. C. Johnson, K. M. Agesa, et al., “Global, Regional, and National Sepsis Incidence and Mortality, 1990-2017: Analysis for the Global Burden of Disease Study,” Lancet 395, no. 10219 (2020): 200–211.
10.1016/S0140-6736(19)32989-7
PubMed Web of Science® Google Scholar
3M. Ibarz, L. Haas, A. Ceccato, and A. Artigas, “The Critically Ill Older Patient With Sepsis: A Narrative Review,” Annals of Intensive Care 14, no. 1 (2024): 6.
10.1186/s13613-023-01233-7
PubMed Web of Science® Google Scholar
4D. C. Angus, W. T. Linde-Zwirble, J. Lidicker, G. Clermont, J. Carcillo, and M. R. Pinsky, “Epidemiology of Severe Sepsis in the United States: Analysis of Incidence, Outcome, and Associated Costs of Care,” Critical Care Medicine 29, no. 7 (2001): 1303–1310.
10.1097/00003246-200107000-00002
CAS PubMed Web of Science® Google Scholar
5V. Y. Dombrovskiy, A. A. Martin, J. Sunderram, and H. L. Paz, “Rapid Increase in Hospitalization and Mortality Rates for Severe Sepsis in the United States: A Trend Analysis From 1993 to 2003,” Critical Care Medicine 35, no. 5 (2007): 1244–1250.
10.1097/01.CCM.0000261890.41311.E9
PubMed Web of Science® Google Scholar
6G. S. Martin, D. M. Mannino, and M. Moss, “The Effect of Age on the Development and Outcome of Adult Sepsis,” Critical Care Medicine 34, no. 1 (2006): 15–21.
10.1097/01.CCM.0000194535.82812.BA
PubMed Web of Science® Google Scholar
7H. Vallet, B. Guidet, A. Boumendil, et al., “The Impact of Age-Related Syndromes on ICU Process and Outcomes in Very Old Patients,” Annals of Intensive Care 13, no. 1 (2023): 68.
10.1186/s13613-023-01160-7
PubMed Web of Science® Google Scholar
8N. Meyer, M. O. Harhay, D. S. Small, et al., “Temporal Trends in Incidence, Sepsis-Related Mortality, and Hospital-Based Acute Care After Sepsis,” Critical Care Medicine 46, no. 3 (2018): 354–360.
10.1097/CCM.0000000000002872
PubMed Web of Science® Google Scholar
9C. Fleischmann-Struzek, L. Mellhammar, N. Rose, et al., “Incidence and Mortality of Hospital- and ICU-Treated Sepsis: Results From an Updated and Expanded Systematic Review and Meta-Analysis,” Intensive Care Medicine 46, no. 8 (2020): 1552–1562.
10.1007/s00134-020-06151-x
CAS PubMed Web of Science® Google Scholar
10D. F. Gaieski, J. M. Edwards, M. J. Kallan, and B. G. Carr, “Benchmarking the Incidence and Mortality of Severe Sepsis in the United States,” Critical Care Medicine 41, no. 5 (2013): 1167–1174.
10.1097/CCM.0b013e31827c09f8
PubMed Web of Science® Google Scholar
11B. D. Winters, M. Eberlein, J. Leung, D. M. Needham, P. J. Pronovost, and J. E. Sevransky, “Long-Term Mortality and Quality of Life in Sepsis: A Systematic Review,” Critical Care Medicine 38, no. 5 (2010): 1276–1283.
10.1097/CCM.0b013e3181d8cc1d
PubMed Web of Science® Google Scholar
12W. J. Ehlenbach, C. L. Hough, P. K. Crane, et al., “Association Between Acute Care and Critical Illness Hospitalization and Cognitive Function in Older Adults,” Journal of the American Medical Association 303, no. 8 (2010): 763–770.
10.1001/jama.2010.167
CAS PubMed Web of Science® Google Scholar
13P. P. Pandharipande, T. D. Girard, J. C. Jackson, et al., “Long-Term Cognitive Impairment After Critical Illness,” New England Journal of Medicine 369, no. 14 (2013): 1306–1316.
10.1056/NEJMoa1301372
CAS PubMed Web of Science® Google Scholar
14T. J. Iwashyna, E. W. Ely, D. M. Smith, and K. M. Langa, “Long-Term Cognitive Impairment and Functional Disability Among Survivors of Severe Sepsis,” Journal of the American Medical Association 304, no. 16 (2010): 1787–1794.
10.1001/jama.2010.1553
CAS PubMed Web of Science® Google Scholar
15H. C. Prescott, J. J. Osterholzer, K. M. Langa, D. C. Angus, and T. J. Iwashyna, “Late Mortality After Sepsis: Propensity Matched Cohort Study,” BMJ (Clinical Research Ed) 353 (2016): i2375.
PubMed Google Scholar
16G. Kumar, N. Kumar, A. Taneja, et al., “Nationwide Trends of Severe Sepsis in the 21st Century (2000-2007),” Chest 140, no. 5 (2011): 1223–1231.
10.1378/chest.11-0352
PubMed Web of Science® Google Scholar
17W. Tian, “An All-Payer View of Hospital Discharge to Postacute Care, 2013,” (2006).
Google Scholar
18M. Toles, C. Colon-Emeric, J. Asafu-Adjei, E. Moreton, and L. C. Hanson, “Transitional Care of Older Adults in Skilled Nursing Facilities: A Systematic Review,” Geriatric Nursing 37, no. 4 (2016): 296–301.
10.1016/j.gerinurse.2016.04.012
PubMed Web of Science® Google Scholar
19M. E. Charlson, P. Pompei, K. L. Ales, and C. R. MacKenzie, “A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation,” Journal of Chronic Diseases 40, no. 5 (1987): 373–383.
10.1016/0021-9681(87)90171-8
CAS PubMed Google Scholar
20M. E. Charlson, D. Carrozzino, J. Guidi, and C. Patierno, “Charlson Comorbidity Index: A Critical Review of Clinimetric Properties,” Psychotherapy and Psychosomatics 91, no. 1 (2022): 8–35.
10.1159/000521288
PubMed Web of Science® Google Scholar
21M. Charlson, T. P. Szatrowski, J. Peterson, and J. Gold, “Validation of a Combined Comorbidity Index,” Journal of Clinical Epidemiology 47, no. 11 (1994): 1245–1251.
10.1016/0895-4356(94)90129-5
CAS PubMed Web of Science® Google Scholar
22D. L. Zhang, Y. X. Cong, Y. Zhuang, X. Xu, and B. F. Zhang, “Age-Adjusted Charlson Comorbidity Index Predicts Postoperative Mortality in Elderly Patients With Hip Fracture: A Prospective Cohort,” Frontiers in Medicine (Lausanne) 10 (2023): 1066145.
10.3389/fmed.2023.1066145
PubMed Web of Science® Google Scholar
23Q. Huang, T. Luo, J. Yang, et al., “Association Between the Age-Adjusted Charlson Comorbidity Index and Complications After Kidney Transplantation: A Retrospective Observational Cohort Study,” BMC Nephrology 25, no. 1 (2024): 457.
10.1186/s12882-024-03888-1
PubMed Web of Science® Google Scholar
24Y. Koseki, M. Hikage, K. Fujiya, et al., “Utility of a Modified Age-Adjusted Charlson Comorbidity Index in Predicting Cause-Specific Survival Among Patients With Gastric Cancer,” European Journal of Surgical Oncology 47, no. 8 (2021): 2010–2015.
10.1016/j.ejso.2021.01.026
PubMed Web of Science® Google Scholar
25X. Shi, R. Li, X. Shi, Y. Yan, and A. Gong, “The Impact of the Age-Adjusted Charlson Comorbidity Index as a Prognostic Factor in Patients With Early Gastric Cancer After Endoscopic Submucosal Dissection,” Scandinavian Journal of Gastroenterology 60, no. 2 (2025): 136–142.
10.1080/00365521.2024.2449072
CAS PubMed Web of Science® Google Scholar
26B. M. de Oliveira, I. M. de Sousa, A. L. Miranda, et al., “Age-Adjusted Charlson Comorbidity Index and Its Association With Body Composition and Overall Survival in Patients With Colorectal Cancer,” Supportive Care in Cancer 32, no. 8 (2024): 517.
10.1007/s00520-024-08730-w
PubMed Google Scholar
27Y. Takada, H. Kawashima, E. Ohno, et al., “The Impact of the Age-Adjusted Charlson Comorbidity Index as a Prognostic Factor for Endoscopic Papillectomy in Ampullary Tumors,” Journal of Gastroenterology 57, no. 3 (2022): 199–207.
10.1007/s00535-022-01853-z
PubMed Web of Science® Google Scholar
28N. Zhang, Q. Lin, H. Jiang, and H. Zhu, “Age-Adjusted Charlson Comorbidity Index as Effective Predictor for In-Hospital Mortality of Patients With Cardiac Arrest: A Retrospective Study,” BMC Emergency Medicine 23, no. 1 (2023): 7.
10.1186/s12873-022-00769-4
PubMed Web of Science® Google Scholar
29A. C. Lemay, A. Anzueto, M. I. Restrepo, and E. M. Mortensen, “Predictors of Long-Term Mortality After Severe Sepsis in the Elderly,” American Journal of the Medical Sciences 347, no. 4 (2014): 282–288.
10.1097/MAJ.0b013e318295a147
PubMed Web of Science® Google Scholar
30Y. Yang, K. S. Yang, Y. M. Hsann, V. Lim, and B. C. Ong, “The Effect of Comorbidity and Age on Hospital Mortality and Length of Stay in Patients With Sepsis,” Journal of Critical Care 25, no. 3 (2010): 398–405.
10.1016/j.jcrc.2009.09.001
PubMed Web of Science® Google Scholar
31H. C. Prescott and D. C. Angus, “Enhancing Recovery From Sepsis: A Review,” Journal of the American Medical Association 319, no. 1 (2018): 62–75.
10.1001/jama.2017.17687
PubMed Web of Science® Google Scholar
32E. von Elm, D. G. Altman, M. Egger, S. J. Pocock, P. C. Gotzsche, and J. P. Vandenbroucke, “The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies,” Lancet 370, no. 9596 (2007): 1453–1457.
10.1016/S0140-6736(07)61602-X
PubMed Web of Science® Google Scholar
33Y. Xu, X. Zheng, Y. Li, et al., “Exploring Patient Medication Adherence and Data Mining Methods in Clinical Big Data: A Contemporary Review,” Journal of Evidence-Based Medicine 16, no. 3 (2023): 342–375.
10.1111/jebm.12548
PubMed Web of Science® Google Scholar
34A. Johnson, L. Bulgarelli, L. Shen, et al., “MIMIC-IV, a Freely Accessible Electronic Health Record Dataset,” Scientific Data 10, no. 1 (2023): 1.
10.1038/s41597-022-01899-x
CAS PubMed Web of Science® Google Scholar
35E. Scosyrev and E. Glimm, “Power Analysis for Multivariable Cox Regression Models,” Statistics in Medicine 38, no. 1 (2019): 88–99.
10.1002/sim.7964
PubMed Web of Science® Google Scholar
36H. Cheng, Y. Ling, Q. Li, et al., “Association Between Modified Frailty Index and Postoperative Delirium in Patients After Cardiac Surgery: A Cohort Study of 2080 Older Adults,” CNS Neuroscience & Therapeutics 30, no. 6 (2024): e14762.
10.1111/cns.14762
PubMed Web of Science® Google Scholar
37R. B. Lindell, A. Nishisaki, S. L. Weiss, D. M. Traynor, and J. C. Fitzgerald, “Risk of Mortality in Immunocompromised Children With Severe Sepsis and Septic Shock,” Critical Care Medicine 48, no. 7 (2020): 1026–1033.
10.1097/CCM.0000000000004329
CAS PubMed Web of Science® Google Scholar
38T. K. Oh and I. A. Song, “Weekend Admission and Mortality Among Patients With Sepsis: A Nationwide Cohort Study in South Korea,” Acta Anaesthesiologica Scandinavica 65, no. 5 (2021): 639–647.
10.1111/aas.13786
PubMed Web of Science® Google Scholar
39S. H. Baig, D. A. Oxman, and E. J. Yoo, “Weekend Admission Does Not Confer an Increased Risk of Mortality in Septic Shock,” Journal of Intensive Care Medicine 37, no. 6 (2022): 810–816.
10.1177/08850666211038549
PubMed Web of Science® Google Scholar
40P. C. Austin, I. R. White, D. S. Lee, and S. van Buuren, “Missing Data in Clinical Research: A Tutorial on Multiple Imputation,” Canadian Journal of Cardiology 37, no. 9 (2021): 1322–1331.
10.1016/j.cjca.2020.11.010
PubMed Web of Science® Google Scholar
41H. Lin, Y. B. Xi, Z. C. Yang, et al., “Optimizing Prediction of in-Hospital Mortality in Elderly Patients With Acute Myocardial Infarction: A Nomogram Approach Using the Age-Adjusted Charlson Comorbidity Index Score,” Journal of the American Heart Association 13, no. 14 (2024): e32589.
10.1161/JAHA.123.032589
Web of Science® Google Scholar
42S. J. Rigatti, “Random Forest,” Journal of Insurance Medicine 47, no. 1 (2017): 31–39.
10.17849/insm-47-01-31-39.1
PubMed Google Scholar
43T. J. Vander Weele and P. Ding, “Sensitivity Analysis in Observational Research: Introducing the E-Value,” Annals of Internal Medicine 167, no. 4 (2017): 268–274.
10.7326/M16-2607
PubMed Web of Science® Google Scholar
44N. Asai, W. Ohashi, D. Sakanashi, et al., “Combination of Sequential Organ Failure Assessment (SOFA) Score and Charlson Comorbidity Index (CCI) Could Predict the Severity and Prognosis of Candidemia More Accurately Than the Acute Physiology, Age, Chronic Health Evaluation II (APACHE II) Score,” BMC Infectious Diseases 21, no. 1 (2021): 77.
10.1186/s12879-020-05719-8
CAS PubMed Web of Science® Google Scholar
45R. Jouffroy, P. A. Parfait, B. Gilbert, et al., “Relationship Between Prehospital Modified Charlson Comorbidity Index and Septic Shock 30-Day Mortality,” American Journal of Emergency Medicine 60 (2022): 128–133.
10.1016/j.ajem.2022.08.003
PubMed Web of Science® Google Scholar
46Y. Huang, Y. Gao, S. Quan, et al., “Development and Internal-External Validation of the ACCI-Sofa Model for Predicting In-Hospital Mortality of Patients With SEPSIS-3 in the ICU: A Multicenter Retrospective Cohort Study,” Shock 61, no. 3 (2024): 367–374.
10.1097/SHK.0000000000002311
CAS PubMed Web of Science® Google Scholar
47M. J. Yousefzadeh, R. R. Flores, Y. Zhu, et al., “An Aged Immune System Drives Senescence and Ageing of Solid Organs,” Nature 594, no. 7861 (2021): 100–105.
10.1038/s41586-021-03547-7
CAS PubMed Web of Science® Google Scholar
48S. Borgoni, K. S. Kudryashova, K. Burka, and J. P. de Magalhaes, “Targeting Immune Dysfunction in Aging,” Ageing Research Reviews 70 (2021): 101410.
10.1016/j.arr.2021.101410
CAS PubMed Web of Science® Google Scholar
49M. Cesari, R. Calvani, and E. Marzetti, “Frailty in Older Persons,” Clinics in Geriatric Medicine 33, no. 3 (2017): 293–303.
10.1016/j.cger.2017.02.002
PubMed Web of Science® Google Scholar
50Z. Mostel, A. Perl, M. Marck, et al., “Post-Sepsis Syndrome - An Evolving Entity That Afflicts Survivors of Sepsis,” Molecular Medicine 26, no. 1 (2019): 6.
10.1186/s10020-019-0132-z
PubMed Web of Science® Google Scholar
51E. C. van der Slikke, A. Y. An, R. Hancock, and H. R. Bouma, “Exploring the Pathophysiology of Post-Sepsis Syndrome to Identify Therapeutic Opportunities,” eBioMedicine 61 (2020): 103044.
10.1016/j.ebiom.2020.103044
PubMed Google Scholar
52N. M. Orr, R. S. Boxer, M. A. Dolansky, L. A. Allen, and D. E. Forman, “Skilled Nursing Facility Care for Patients With Heart Failure: Can we Make It “Heart Failure Ready?”,” Journal of Cardiac Failure 22, no. 12 (2016): 1004–1014.
10.1016/j.cardfail.2016.10.009
PubMed Google Scholar
53E. C. van der Slikke, L. Beumeler, M. Holmqvist, A. Linder, R. T. Mankowski, and H. R. Bouma, “Understanding Post-Sepsis Syndrome: How Can Clinicians Help?,” Infection and Drug Resistance 16 (2023): 6493–6511.
10.2147/IDR.S390947
PubMed Google Scholar
54A. F. Rousseau, H. C. Prescott, S. J. Brett, et al., “Long-Term Outcomes After Critical Illness: Recent Insights,” Critical Care 25, no. 1 (2021): 108.
10.1186/s13054-021-03535-3
PubMed Web of Science® Google Scholar
55S. Leviner, “Post-Sepsis Syndrome,” Critical Care Nursing Quarterly 44, no. 2 (2021): 182–186.
10.1097/CNQ.0000000000000352
PubMed Web of Science® Google Scholar
56E. Ko, S. Lowie, and P. Ni, “Confidence in Carrying out Palliative Care Among Intensive Care Nurses,” Nursing in Critical Care 28, no. 1 (2023): 13–20.
10.1111/nicc.12735
PubMed Web of Science® Google Scholar
57P. Arasen, “Rehabilitation and Sustainability: Is There a Link for the ICU Nurse and the Multidisciplinary Team?,” Nursing in Critical Care 30, no. 1 (2025): 83–85.
10.1111/nicc.13149
PubMed Google Scholar
58E. R. Gabbard, D. Klein, K. Vollman, T. B. Chamblee, L. M. Soltis, and M. Zellinger, “Clinical Nurse Specialist: A Critical Member of the ICU Team,” Critical Care Medicine 49, no. 6 (2021): e634–e641.
10.1097/CCM.0000000000005004
PubMed Web of Science® Google Scholar

Volume30, Issue4

July 2025

e70078

Older Adult Sepsis Survivors Discharged to Skilled Nursing Facilities: Age-Adjusted Charlson Comorbidity Index as a Predictor of 6-Month Mortality

ABSTRACT

Background

Aim

Study Design

Results

Conclusions

Relevance to Clinical Practice

Conflicts of Interest

Open Research

Data Availability Statement

Supporting Information

References

References

Information

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Older Adult Sepsis Survivors Discharged to Skilled Nursing Facilities: Age-Adjusted Charlson Comorbidity Index as a Predictor of 6-Month Mortality

ABSTRACT

Background

Aim

Study Design

Results

Conclusions

Relevance to Clinical Practice

Conflicts of Interest

Open Research

Data Availability Statement

Supporting Information

References

References

Related

Information