The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Identifying Patients With Sepsis on the Hospital Wards
Open accessAbstract
Sepsis contributes to up to half of all deaths in hospitalized patients, and early interventions, such as appropriate antibiotics, have been shown to improve outcomes. Most research has focused on early identification and treatment of patients with sepsis in the ED and the ICU; however, many patients acquire sepsis on the general wards. The goal of this review is to discuss recent advances in the detection of sepsis in patients on the hospital wards. We discuss data highlighting the benefits and limitations of the systemic inflammatory response syndrome (SIRS) criteria for screening patients with sepsis, such as its low specificity, as well as newly described scoring systems, including the proposed role of the quick sepsis-related organ failure assessment (qSOFA) score. Challenges specific to detecting sepsis on the wards are discussed, and future directions that use big data approaches and automated alert systems are highlighted.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“There is wide variation in diagnostic accuracy of these tools with most having poor predictive values, although the use of some was associated with improvements in care processes [28–31].”
From Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021 (2021) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
Bone, R. C., BALK, R. A., Cerra, F. B., Dellinger, R. P., Fein, A. M., Knaus, W. A., Schein, R. M. H., & Sibbald, W. J. (1992) · CHEST Journal · doi:10.1378/chest.101.6.1644 · Cited by 13,317
Early goal-directed therapy in the treatment of severe sepsis and septic shock
Rivers, E., Nguyen, B., Havstad, S., Ressler, J., Muzzin, A., Knoblich, B., Peterson, E., & Tomlanovich, M. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010307 · Cited by 10,743
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
Angus, D. C., Linde-Zwirble, W. T., Lidicker, J., Clermont, G., Carcillo, J., & Pinsky, M. R. (2001) · Critical Care Medicine · doi:10.1097/00003246-200107000-00002 · Cited by 8,503
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Intensive Care Medicine · doi:10.1007/s00134-012-2769-8 · Cited by 7,371
Dellinger, R. P., Levy, M. M., Carlet, J. M., Bion, J., Parker, M. M., Jaeschke, R., Reinhart, K., Angus, D. C., Brun‐Buisson, C., Beale, R. J., et al. (2007) · Intensive Care Medicine · doi:10.1007/s00134-007-0934-2 · Cited by 6,789
Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000217961.75225.e9 · Cited by 6,109
2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference
Levy, M. M., Fink, M. P., Marshall, J. C., Abraham, E. H., Angus, D. C., Cook, D., Cohen, J., Opal, S. M., Vincent, J., & Ramsay, G. (2003) · Critical Care Medicine · doi:10.1097/01.ccm.0000050454.01978.3b · Cited by 5,976
Seymour, C. W., Liu, V. X., Iwashyna, T. J., Brunkhorst, F. M., Rea, T. D., Scherag, A., Rubenfeld, G., Kahn, J. M., Shankar-Hari, M., Singer, M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0288 · Cited by 3,939
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–120 of 120
Optimization of personnel response in an eICU environment
Ferguson, P. D. (2018) · DSpace@MIT (Massachusetts Institute of Technology)
Sepsis Screening Tool Increased the Usage of Sepsis Order Set
Liengsawangwong, R. (2019) · doi:10.21007/chp.hiim.0066
A Pilot Study of the Evaluation of Using Antibiotics in Sepsis Patients in Palu, Indonesia
Mukaddas, A., Utami, T. R., & Rumi, A. (2020) · Open Access Macedonian Journal of Medical Sciences · doi:10.3889/oamjms.2020.4879
Bell, C., Hazell, P., & Slade, R. H. (1977)
Singh, M. K., Singh, D., Ansari, A. A., Kumar, Y., Kumar, A., & Sharma, S. (2021) · INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH · doi:10.36106/ijsr/5802166
ABORDAGEM LINEAR DO PACIENTE SÉPTICO: REVISÃO DE LITERATURA
Toni, L. P., Viana, J. F. M., Filho, C. R. G. D. S., Carvalho, L. F. D. Q., Mendes, V. P. D. S., LIMA, R. K. S. D., de Queiroz, J. V. J., Montenegro, B. V., Damasceno, A. B., Cavalcanti, B. B., et al. (2022) · Editora Pasteur eBooks · doi:10.29327/554269.3-29
Evaluation of antibiotic escalation in response to nurse-driven inpatient sepsis screen
Furukawa, D., Dieringer, T. D., Wong, M. D., Tong, J. T., Cader, I. A., Wisk, L. E., Han, M. A., Gupta, S., Kerbel, R. B., Uslan, D. Z., et al. (2021) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2021.232
Tian, T., Xu, X., & Yang, J. (2021) · Discussion of Clinical Cases · doi:10.5430/dcc.v7n4p15
Development and Validation of a Machine Learning Model for Early Detection of Untreated Infection
Buell, K. G., Carey, K. A., Dussault, N. E., Parker, W. F., Dumanian, J., Bhavani, S. V., Gilbert, E. R., Winslow, C. J., Shah, N. S., Afshar, M., et al. (2024) · Critical Care Explorations · doi:10.1097/cce.0000000000001165 · Cited by 1
Self-citationKang, J., & Choi, H. (2024) · Journal of Korean Critical Care Nursing · doi:10.34250/jkccn.2024.17.3.14
Predictors of sepsis in hospitalized COVID-19 patients
Gabada, W. R., Yousef, A. M., Ali, R. E., & Tohlob, M. A. (2024) · Egyptian Journal of Bronchology · doi:10.1186/s43168-024-00351-5
Revolutionizing Healthcare Diagnostics: Precision and Timeliness Through AI and ML Integration
Mahalingam, T. V., Gokul, J., Likesh, K. R., Divakar, G. V., & Vijayan, B. R. (2024) · International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC) · doi:10.1109/icmacc62921.2024.10894118
Neural Network based Medical Self-Diagnostic System using Artificial Intelligence
Saravanakumar, S., Divya, N. S., Rao, K. S., Anthoniraj, S., Rao, P. S., & Purushothaman, D. (2024) · International Conference on Computational Intelligence for Security, Communication and Sustainable Development (CISCSD) · doi:10.1109/ciscsd63381.2024.00026
Burdick, H., Pino, E., Gabel-Comeau, D., Gu, C., Roberts, J., Le, S., Slote, J., Saber, N., Pellegrini, E., Green‐Saxena, A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13152208
Timing matters: a machine learning–driven comparison of community and hospital-onset sepsis
Verma, R., Elhance, A., Marsh, T., Baudier, R. L., Sherry, S. P., Reddy, R. C., SHER, N., Adibuzzaman, M., Mohan, V., & Khan, A. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03353-z
Impact of manual sepsis screening in hospitalized adult patients: A systematic review
Hechtman, R. K., Garamani, N., Anabtawi, Y., Rivas, A. G., Antonowicz, B. R., Barker, A., Garcia, T., Mulcahy, R., Posa, P. J., Sjoding, M. W., et al. (2026) · Journal of Hospital Medicine · doi:10.1002/jhm.70284
Borta, S. M., Popețiu, R. O., Rus, L. A., Vîlcea, A., Maghera, C., Padurean, R., Nica, D. V., & Crişan, A. S. (2026) · Diagnostics · doi:10.3390/diagnostics16111623
Liao, J., Xiang, Q., Chen, X., Wu, L., Chen, H., Yao, Z., Wu, H., & Lai, J. (2026) · PLoS ONE · doi:10.1371/journal.pone.0348676
The Efficacy of Antibacterial Therapy in Patients with Sepsis and Septic Shock
Shapoval, С. D. (2026) · The Ukrainian Journal of Clinical Surgery · doi:10.26779/2786-832x.2026.3.72
Automating chart review for identification of infected patients using natural language processing
Khan, A. Z., Buell, K. G., Karway, G., Carey, K., Dussault, N., Dumanian, J., Bhavani, S., Gilbert, E. R., Winslow, C., Shah, N. S., et al. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag163
Self-citation
Metadata
- Authors
- Bhattacharjee, P., Edelson, D. P., & Churpek, M. M.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Churpek, Matthew M.
- Venue
- Chest
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 151
- Issue
- 4
- Pages
- 898-907
- ISSN
- 0012-3692 (ISSN-L)
- PMC
- PMC5472513
- OpenAlex
- W2465673526
- Cited by
- 120
- References
- 57
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 12 · 2023: 11 · 2022: 9 · 2021: 15 · 2020: 17 · 2019: 19 · 2018: 11 · 2017: 11
- FWCI
- 1.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHEST Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Heart Association
- Agency for Healthcare Research and Quality (T32HS000078)
- National Heart, Lung, and Blood Institute (K08HL121080)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Life Sciences › Immunology and Microbiology › Immunology › Inflammation biomarkers and pathways
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- sepsis, sepsis detection, hospital wards, systemic inflammatory response syndrome, SIRS criteria, qSOFA score, sepsis screening, scoring system, automated alert system, big data
- MeSH terms
- Diagnosis, Differential, Hospitalization, Humans, Hospital Mortality, Systemic Inflammatory Response Syndrome, Sepsis, Decision Support Systems, Clinical, Organ Dysfunction Scores
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review