Identifying Patients With Sepsis on the Hospital Wards

The third international consensus definitions for sepsis and septic shock (Sepsis-3).

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Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis

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Early goal-directed therapy in the treatment of severe sepsis and septic shock

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Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock

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Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care

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Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012

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Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008

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Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock

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2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference

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Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

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Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021.

Evans, L., Rhodes, A., Alhazzani, W., Antonelli, M., Coopersmith, C. M., French, C., Machado, F. R., Mcintyre, L., Ostermann, M., Prescott, H. C., et al. (2021) · Critical Care Medicine · doi:10.1097/ccm.0000000000005337 · Cited by 2,935

Sepsis associated acute kidney injury

Poston, J. T., & Koyner, J. L. (2019) · BMJ · doi:10.1136/bmj.k4891 · Cited by 917

Prognostic Accuracy of the Quick Sequential Organ Failure Assessment for Mortality in Patients With Suspected Infection: A Systematic Review and Meta-analysis

Fernando, S. M., Tran, A., Taljaard, M., Cheng, W., Rochwerg, B., Seely, A. J. E., & Perry, J. J. (2018) · Annals of Internal Medicine · doi:10.7326/m17-2820 · Cited by 281

Prediction of sepsis patients using machine learning approach: a meta-analysis

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Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., El–Sappagh, S., Islam, M. S., & Islam, S. M. R. (2020) · IEEE Access · doi:10.1109/access.2020.3042273 · Cited by 289

Sepsis and septic shock: Guideline-based management

Dugar, S. P., Choudhary, C., & Duggal, A. (2020) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.87a.18143 · Cited by 248

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

Egi, M., Ogura, H., Yatabe, T., Atagi, K., Inoue, S., Iba, T., Kakihana, Y., Kawasaki, T., Kushimoto, S., Kuroda, Y., et al. (2021) · Journal of Intensive Care · doi:10.1186/s40560-021-00555-7 · Cited by 232

Performance of the quick Sequential (sepsis-related) Organ Failure Assessment score as a prognostic tool in infected patients outside the intensive care unit: a systematic review and meta-analysis

Song, J., Sin, C., Park, H. K., Shim, S. R., & Lee, J. H. (2018) · Critical Care · doi:10.1186/s13054-018-1952-x · Cited by 234

Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system

Henry, K. E., Kornfield, R., Sridharan, A., Linton, R. C., Groh, C., Wang, T., Wu, A., Mutlu, B., & Saria, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00597-7 · Cited by 207

Integrating molecular pathogenesis and clinical translation in sepsis-induced acute respiratory distress syndrome

Englert, J. A., Bobba, C. M., & Baron, R. M. (2019) · JCI Insight · doi:10.1172/jci.insight.124061 · Cited by 210

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Barton, C., Chettipally, U., Zhou, Y., Jiang, Z., Lynn-Palevsky, A., Le, S., Calvert, J., & Das, R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.027 · Cited by 175

Monocyte Distribution Width: A Novel Indicator of Sepsis-2 and Sepsis-3 in High-Risk Emergency Department Patients*

Crouser, E. D., Parrillo, J. E., Seymour, C. W., Angus, D. C., Bicking, K., Esguerra, V. G., Peck‐Palmer, O. M., Magari, R. T., JULIAN, M. W., Kleven, J. M., et al. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003799 · Cited by 169

Clinician perception of a machine learning–based early warning system designed to predict severe sepsis and septic shock

Ginestra, J. C., Giannini, H. M., Schweickert, W. D., Meadows, L., Lynch, M. J., Pavan, K., Chivers, C. J., Draugelis, M., Donnelly, P. J., Fuchs, B. D., et al. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003803 · Cited by 159

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Yan, M. Y., Gustad, L. T., & Nytrø, Ø. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab236 · Cited by 119

Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021

Evans, L., Rhodes, A., Alhazzani, W., Antonelli, M., Coopersmith, C. M., French, C., Machado, F. R., Mcintyre, L., Ostermann, M., Prescott, H. C., et al. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06506-y · Cited by 5,082

Machine learning for early detection of sepsis: an internal and temporal validation study

Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa006 · Cited by 109

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J‐SSCG 2020)

Egi, M., Ogura, H., Yatabe, T., Atagi, K., Inoue, S., Iba, T., Kakihana, Y., Kawasaki, T., Kushimoto, S., Kuroda, Y., et al. (2021) · Acute Medicine & Surgery · doi:10.1002/ams2.659 · Cited by 88

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

Burdick, H., Pino, E., Gabel-Comeau, D., McCoy, A., Gu, C., Roberts, J., Le, S., Slote, J., Pellegrini, E., Green-Saxena, A., et al. (2020) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100109 · Cited by 80

The intensive care medicine research agenda on septic shock

Perner, A., Gordon, A. C., Angus, D. C., Lamontagne, F., Machado, F. R., Russell, J. A., Timsit, J., Marshall, J. C., Myburgh, J., Shankar‐Hari, M., et al. (2017) · Intensive Care Medicine · doi:10.1007/s00134-017-4821-1 · Cited by 78

The new sepsis consensus definitions (Sepsis-3): the good, the not-so-bad, and the actually-quite-pretty

Singer, M. (2016) · Intensive Care Medicine · doi:10.1007/s00134-016-4600-4 · Cited by 70

Current understanding of tumor lysis syndrome

Rahmani, B., Patel, S., Seyam, O., Gandhi, J., Reid, I., Smith, N. L., & Khan, S. A. (2019) · Hematological Oncology · doi:10.1002/hon.2668 · Cited by 71

Critical Illness Weakness, Polyneuropathy and Myopathy: Diagnosis, treatment, and long-term outcomes

Latronico, N., Rasulo, F. A., Eikermann, M., & Piva, S. (2023) · Critical Care · doi:10.1186/s13054-023-04676-3 · Cited by 67

Sepsis-3 Septic Shock Criteria and Associated Mortality Among Infected Hospitalized Patients Assessed by a Rapid Response Team

Fernando, S. M., Reardon, P. M., Rochwerg, B. N., Shapiro, N. I., Yealy, D. M., SEELY, A. J. E., Perry, J. J., Barnaby, D. P., Murphy, K., Tanuseputro, P., et al. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.05.004 · Cited by 64

Quick sequential organ failure assessment compared to systemic inflammatory response syndrome for predicting sepsis in emergency department

Park, H., Kim, W. Y., Kim, M. C., Jung, W., & Ko, B. S. (2017) · Journal of Critical Care · doi:10.1016/j.jcrc.2017.06.020 · Cited by 63

Oscillometric versus invasive blood pressure measurement in patients with shock: a prospective observational study in the emergency department

Meidert, A. S., Dolch, M. E., Mühlbauer, K., Zwißler, B., Klein, M., Briegel, J., & Czerner, S. F. A. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-020-00482-2 · Cited by 55

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

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) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01284-x · Cited by 53

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review

Hassan, N., Slight, R., Weiand, D., Vellinga, A., Morgan, G., Aboushareb, F., & Slight, S. P. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104457 · Cited by 42

Usefulness of qSOFA and SIRS scores for detection of incipient sepsis in general ward patients: A prospective cohort study

Luo, J., Jiang, W., Weng, L., Peng, J., Hu, X., Wang, C.-Y., Liu, G.-Y., Huang, H., & Du, B. (2019) · Journal of Critical Care · doi:10.1016/j.jcrc.2019.01.012 · Cited by 41

Prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) among hospitalized patients assessed by a rapid response team

Fernando, S. M., Fox‐Robichaud, A., Rochwerg, B. N., Cardinal, P., SEELY, A. J. E., Perry, J. J., McIsaac, D. I., Tran, A., Skitch, S., Tam, B., et al. (2019) · Critical Care · doi:10.1186/s13054-019-2355-3 · Cited by 40

A comparison of pre ICU admission SIRS, EWS and q SOFA scores for predicting mortality and length of stay in ICU

Siddiqui, S., Chua, M., Venkatesan, K., & Choo, R. W. M. (2017) · Journal of Critical Care · doi:10.1016/j.jcrc.2017.05.017 · Cited by 38

Detection of sepsis in patient blood samples using CD64 expression in a microfluidic cell separation device

Zhang, Y., Li, W., Zhou, Y., Johnson, A., Venable, A., Hassan, A. E., Griswold, J., & Pappas, D. (2017) · The Analyst · doi:10.1039/c7an01471f · Cited by 36

Application of the Sepsis-3 Consensus Criteria in a Geriatric Acute Care Unit: A Prospective Study

Bastoni, D., Ticinesi, A., Lauretani, F., Calamai, S., Catalano, M. L., Catania, P., Cecchia, M., Cerundolo, N., Galluzzo, C., Giovini, M., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030359 · Cited by 35

Predicting Infections Using Computational Intelligence – A Systematic Review

Baldominos, A., Puello, A., Oğul, H., Aşuroğlu, T., & Colomo‐Palacios, R. (2020) · IEEE Access · doi:10.1109/access.2020.2973006 · Cited by 33

Sepsis in Internal Medicine wards: current knowledge, uncertainties and new approaches for management optimization

Zaccone, V., Tosoni, A., Passaro, G., Vallone, C., Impagnatiello, M., Puma, D. D. L., Cosmo, S. D., Landolfi, R., Mirijello, A., & on behalf of the Internal Medicine Sepsis Study Group (2017) · Annals of Medicine · doi:10.1080/07853890.2017.1332776 · Cited by 35

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

Ackermann, K., Baker, J., Green, M., Fullick, M., Varinli, H., Westbrook, J. I., & Li, L. (2021) · Journal of Medical Internet Research · doi:10.2196/31083 · Cited by 37

Multiparameter Affinity Microchip for Early Sepsis Diagnosis Based on CD64 and CD69 Expression and Cell Capture

Zhang, Y., Zhou, Y., Li, W., Lyons, V., Johnson, A., Venable, A., Griswold, J., & Pappas, D. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b05305 · Cited by 28

Diagnostic and Prognostic Utility Compared Among Different Sepsis Scoring Systems in Adult Patients With Sepsis in Thailand: A Prospective Cohort Study

Pairattanakorn, P., Angkasekwinai, N., Sirijatuphat, R., Wangchinda, W., Tancharoen, L., & Thamlikitkul, V. (2020) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofaa573 · Cited by 27

Early Diagnosis of Sepsis: The Role of Biomarkers and Rapid Microbiological Tests

Plata-Menchaca, E. P., Ruiz‐Rodríguez, J. C., & Ferrer, R. (2024) · Seminars in Respiratory and Critical Care Medicine · doi:10.1055/s-0044-1787270 · Cited by 26

Heart Rate in Patients with SARS-CoV-2 Infection: Prevalence of High Values at Discharge and Relationship with Disease Severity

Maloberti, A., Ughi, N., Bernasconi, D. P., Rebora, P., Cartella, I., Grasso, E., Lenoci, D., Gaudio, F. D., Algeri, M., Scarpellini, S., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10235590 · Cited by 24

Critical Transitions in Intensive Care Units: A Sepsis Case Study

Ghalati, P. F., Samal, S. S., Bhat, J. S., Deisz, R., Marx, G., & Schuppert, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-49006-2 · Cited by 25

Machine learning-based clinical decision support for infection risk prediction

Feng, T., Noren, D. P., Kulkarni, C., Mariani, S., Zhao, C. Y., Ghosh, E., Swearingen, D., Frassica, J. J., McFarlane, D. C., & Conroy, B. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1213411 · Cited by 23

qSOFA, Cue Confusion

Singer, M., & Shankar‐Hari, M. (2018) · Annals of Internal Medicine · doi:10.7326/m17-3415 · Cited by 22

Comparison of automated sepsis identification methods and electronic health record-based sepsis phenotyping: improving case identification accuracy by accounting for confounding comorbid conditions.

Henry, K. E., Hager, D. N., Osborn, T. M., Wu, A. W., & Saria, S. (2019) · Critical Care Explorations · doi:10.1097/cce.0000000000000053 · Cited by 22

Interleukin-1β Protection Against Experimental Sepsis in Mice

Guo, H., Shi, F., Zhou, Q., Liu, Q.-Y., Wang, Y.-X., Yang, S. T., Wu, Z., Shi, Y., Zhang, L., Xu, K.-Z., et al. (2020) · Inflammation · doi:10.1007/s10753-020-01341-7 · Cited by 21

Rapid Response System Improves Sepsis Bundle Compliances and Survival in Hospital Wards for 10 Years

Choi, S., Son, J., Huh, J. W., Lim, C.-M., Oh, D. K., & Hong, S. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10184244 · Cited by 21

Research in Review: Advancing Critical Care Practice

Bridges, E. J., McNeill, M. M., & Munro, N. (2016) · American Journal of Critical Care · doi:10.4037/ajcc2017609 · Cited by 27

Evaluation of a novel prognostic score based on thrombosis and inflammation in patients with sepsis: a retrospective cohort study

Li, D., Zhou, Y., Yu, J., Yu, H., Xia, Y., Zhang, L., Wu, W. K. K., Zeng, Z., Yao, R., & Cao, Y. (2018) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2017-0863 · Cited by 19

Sepsis triggers and tools to support early identification in healthcare settings: An integrative review.

Kumar, A., Abbenbroek, B., Delaney, A., Hammond, N., Grattan, S., & Finfer, S. (2023) · Australian Critical Care · doi:10.1016/j.aucc.2023.01.001 · Cited by 17

Clinical Approach to the Patient in Critical State Following Immunotherapy and/or Stem Cell Transplantation: Guideline for the On-Call Physician

Constantinescu, C. S., Bodolea, C., Paşca, S., Teodorescu, P., Dima, D. M., Rus, I., Tat, T., Achimaș-Cădariu, P., Tănase, A., Tomuleasa, C., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8060884 · Cited by 16

Predicting Severe Sepsis Using Text from the Electronic Health RecordPreprint

Culliton, P., Levinson, M., Ehresman, A., Wherry, J., Steingrub, J. S., & Gallant, S. I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11536 · Cited by 19

Sepsis-Associated Acute Kidney Injury

Wang, D., Sun, T., & Liu, Z. (2023) · Intensive Care Research · doi:10.1007/s44231-023-00049-0 · Cited by 17

Quick Sequential Organ Failure Assessment: Modifications for Identifying Maternal Morbidity and Mortality in Obstetrical Patients

Cagino, S. G., Burke, A. A., Letner, D. R., Leizer, J. M., & Zelig, C. M. (2021) · American Journal of Perinatology · doi:10.1055/s-0041-1735624 · Cited by 15

Validation of Sepsis-3 using survival analysis and clinical evaluation of quick SOFA, SIRS, and burn-specific SIRS for sepsis in burn patients with suspected infection

Yoon, J., Kym, D., Hur, J., Cho, Y. S., Chun, W., & Yoon, D. (2023) · PLoS ONE · doi:10.1371/journal.pone.0276597 · Cited by 12

Predictive value of IL-8 for mortality risk in elderly sepsis patients of emergency department

Zhang, X., Wei, B., Zhang, Y., Li, Y., Long, Y., Wang, J., & Guo, S. (2024) · Cytokine · doi:10.1016/j.cyto.2024.156774 · Cited by 11

Validation the performance of New York Sepsis Severity Score compared with Sepsis Severity Score in predicting hospital mortality among sepsis patients

Sathaporn, N., & Khwannimit, B. (2019) · Journal of Critical Care · doi:10.1016/j.jcrc.2019.06.017 · Cited by 11

Probabilistic modeling approach for interpretable inference and prediction with data for sepsis diagnosis

Yao, S., Yang, J., Xu, D., & Dark, P. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115333 · Cited by 11

Vaspin Alleviates Sepsis-Induced Cardiac Injury and Cardiac Inflammation by Inhibiting Kallikrein 7 in Mice

Yin, N., Pan, F., Qiu, L., Yang, Z., Xiong, R., Shi, L., Shi, Y., Wu, N., Wu, K., Li, Q., et al. (2022) · Mediators of Inflammation · doi:10.1155/2022/1149582 · Cited by 10

Combined CD25, CD64, and CD69 Biomarker in 3D-Printed Multizone Millifluidic Device for Sepsis Detection in Clinical Samples

Yang, Y., Griffin, K., Li, X., Sharp, E., Young, L., Garcia, L., Griswold, J., & Pappas, D. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c01797 · Cited by 10

Use and Evaluation of Computerized Clinical Decision Support Systems for Early Detection of Sepsis in Hospitals: Protocol for a Scoping Review

Li, L., Ackermann, K., Baker, J., & Westbrook, J. I. (2020) · JMIR Research Protocols · doi:10.2196/24899 · Cited by 10

[Consensus document for sepsis code implementation and development in the Community of Madrid].

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Information Technology and Acute Kidney Injury: Alerts, Alarms, Bells, and Whistles

Wilson, F. P. (2017) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2017.05.007 · Cited by 8

Prognostic Accuracy of VqSOFA for Predicting 28-day Mortality in Patients with Suspected Sepsis in the Emergency Department

Suttapanit, K., Wisan, M., Sanguanwit, P., & Prachanukool, T. (2021) · Shock · doi:10.1097/shk.0000000000001754 · Cited by 9

Are you Ernest Shackleton, the polar explorer? Refining the criteria for delirium and brain dysfunction in sepsis

Rasulo, F. A., Bellelli, G., Ely, E. W., Morandi, A., Pandharipande, P. P., & Latronico, N. (2017) · Journal of Intensive Care · doi:10.1186/s40560-017-0218-z · Cited by 8

Evaluation of the Recommended 30 cc/kg Fluid Dose for Patients With Septic Shock and Hypoperfusion With Lactate Greater Than 4 mmol/L

Yohannes, S., Serafim, L. P., Slavinsky, V., O’Connor, T., Cabrera, M., Chin, M. K., & Pratt, A. K. (2023) · Critical Care Explorations · doi:10.1097/cce.0000000000000932 · Cited by 7

Blood long non‐coding RNA intersectin 1–2 is highly expressed and links with increased Th17 cells, inflammation, multiple organ dysfunction, and mortality risk in sepsis patients

Huang, Q., Wang, Y., & He, F. (2022) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.24330 · Cited by 7

A new evidence-based optimal control in healthcare delivery: A better clinical treatment management for septic patients

Chen, Y., Bi, K., Wu, C.-H. J., & Ben‐Arieh, D. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106010 · Cited by 7

Validation of quick sequential organ failure assessment score for poor outcome prediction among emergency department patients with suspected infection

Sohn, Y., Jang, H. Y., Park, S., Lee, Y. J., Cho, Y. S., Park, J., Chung, H., & Kim, S.-I. (2019) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.18.070 · Cited by 6

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: Scoping Review

Ackermann, K., Baker, J., Festa, M. S., McMullan, B. J., Westbrook, J. I., & Li, L. (2022) · JMIR Medical Informatics · doi:10.2196/35061 · Cited by 6

Monocyte Distribution Width (MDW) as an Early Investigational Marker for the Diagnosis of Sepsis in an Emergency Department of a Tertiary Care Hospital in North India

Singla, N., Jandial, A., Sharma, N., Sharma, N., Bhatia, M. S., & Behera, A. (2022) · Cureus · doi:10.7759/cureus.30302 · Cited by 6

SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis

Chen, Q., Li, R., Lin, C., Lai, C., Huang, Y., Lu, W., & Li, L. (2022) · Applied Clinical Informatics · doi:10.1055/a-1990-3037 · Cited by 8

Erkenntnistheorie auf der Intensivstation – Welchen Zweck erfüllt eine Definition?

Dickmann, P., Scherag, A., Coldewey, S. M., Sponholz, C., Brunkhorst, F. M., & Bauer, M. W. (2017) · Der Anaesthesist · doi:10.1007/s00101-017-0315-3 · Cited by 6

Non-stenting treatment versus endoscopic stent placement in staple line leaks after laparoscopic sleeve gastrectomy

Kiriakopoulos, A., Kounatidis, N., Menenakos, I., Kostrova, M., Zografos, K., & Menenakos, E. (2022) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-022-02498-5 · Cited by 6

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction

Yin, C., Fu, S., Yao, B., Pham, T.-H., Cao, W., Wang, D., Caterino, J. M., & Zhang, P. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709402 · Cited by 11

Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments

Thiboud, P.-E., François, Q., Faure, C., Chaufferin, G., Arribe, B., & Ettahar, N. (2025) · Diagnostics · doi:10.3390/diagnostics15030302 · Cited by 5

Comparison of different prognostic scores for risk stratification in septic patients arriving to the Emergency Department

Caramello, V., Beux, V., Salve, A. V. D., Macciotta, A., Ricceri, F., & Boccuzzi, A. (2020) · Italian Journal of Medicine · doi:10.4081/itjm.2020.1232 · Cited by 5

Sublingual microcirculation and internal environment changes as early indications of sepsis: A prospective observational study

Lu, Y., Yang, J., Li, P., Teng, F., & Guo, S. (2023) · Microcirculation · doi:10.1111/micc.12801 · Cited by 5

Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Alnsour, Y., Hadidi, R., & Singh, N. (2019) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.2019070104 · Cited by 5

Adult Code Sepsis: A Narrative Review of its Implementation and Impact

Giglio, A., Aranda, M., Ferre, A., & Borges-Sa, M. (2024) · Journal of Intensive Care Medicine · doi:10.1177/08850666241293034 · Cited by 4

Prognostic Accuracy of the Quick Sequential Organ Failure Assessment for Outcomes Among Patients with Trauma in the Emergency Department: A Comparison with the Modified Early Warning Score, Revised Trauma Score, and Injury Severity Score

Kang, M. W., Ko, S. Y., Song, S. W., Kim, W. J., Kang, Y. J., Kang, K. W., Park, H. S., Park, C. B., Kang, J. H., Bang, J. H., et al. (2020) · Journal of Trauma and Injury · doi:10.20408/jti.2020.0048 · Cited by 4

Evaluation of computer‐based training and high‐fidelity simulation to improve early recognition of sepsis on the adult general ward

Marsack, W., Gagne, J. C. D., Reid, J., Pakieser-Reed, K., Francisco, M. A., & Kang, H. S. (2023) · Nursing Open · doi:10.1002/nop2.1718 · Cited by 4

Controversies Regarding the New Definition of Sepsis

Jeon, J. H., & Park, D. W. (2017) · Korean Journal of Medicine · doi:10.3904/kjm.2017.92.4.342 · Cited by 4

Comparison of Quick Sequential Organ Failure Assessment and Modified Systemic Inflammatory Response Syndrome Criteria in a Lower Middle Income Setting.

Beane, A., Silva, A. P. D., Munasinghe, S. B., Silva, N. D., Arachchige, S. J., Athapattu, P. L., Sigera, C., Miskin, M. F., Liyanagama, P. M., Rathnayake, R. M. D., et al. (2017) · PubMed Central · doi:10.6705/j.jacme.2017.0704.002 · Cited by 4

Fluid Resuscitation and Initial Management in Patients Presenting with Sepsis in the General Ward

Chang, S. W., Choi, J., Oh, J. Y., Lee, Y. S., Min, K. H., Hur, G. Y., Lee, S. Y., Shim, J. J., & Sim, J. K. (2025) · Life · doi:10.3390/life15010124 · Cited by 3

The U-Shaped Relationship Between Serum Bicarbonate Levels and 28-Day Mortality in Patients With Sepsis: A Retrospective Cohort Study of MIMIC-IV Database

Liang, M., Guan, F., Sun, L., Xi, W., Jia, H.-L., Xu, Y., Jin, M., Chen, X., Huang, D., & Qiu, Z. (2025) · Dose-Response · doi:10.1177/15593258251374417 · Cited by 3

Comparison of the quick Sepsis‐related Organ Failure Assessment and adult sepsis pathway in predicting adverse outcomes among adult patients in general wards: a retrospective observational cohort study

Li, L., Rathnayake, K., Green, M., Shetty, A., Fullick, M., Walter, S. R., Middleton‐Rennie, C., Meller, M., Braithwaite, J., Lander, H., et al. (2020) · Internal Medicine Journal · doi:10.1111/imj.14746 · Cited by 3

SEPRES: Sepsis prediction via the clinical data integration system in the ICUPreprint

Chen, Q., Li, R., Lin, C., Lai, C., Huang, Y., Lu, W., & Li, L. (2022) · medRxiv · doi:10.1101/2022.07.06.22277188 · Cited by 3

Bacteria and host: what does this mean for sepsis bottleneck?

Alharbi, A. S., Sanyi, R. H. H., & Azhar, E. I. (2024) · World Journal of Emergency Medicine · doi:10.5847/wjem.j.1920-8642.2025.001 · Cited by 2

SPINK1 is a Potential Diagnostic and Prognostic Biomarker for Sepsis

Chen, D., Shi, Z., Gao, X., Yang, Y., Lei, X., & Hu, Y. (2024) · Infection and Drug Resistance · doi:10.2147/idr.s440117 · Cited by 2

Identification of Risk of Early Decompensation and Predictors of ICU Admission in Patients Triggering Code Sepsis

Anwar, M. S., Bhide, P. P., Mohan, G., Khan, A. A., Gaddameedi, S. R., Nawaz, F. K., Nasr, H., Khan, M. A., Khalid, F., & Du, D. T. (2025) · Cureus · doi:10.7759/cureus.77026 · Cited by 2

Unveiling key predictors of sepsis mortality in intermediate care units: a decision tree study

Turcato, G., Zaboli, A., Filippi, L., Cipriano, A., Parodi, M., Sibilio, S., Ferretto, P., Milazzo, D., Marchetti, M., Ghiadoni, L., et al. (2025) · Current Medical Research and Opinion · doi:10.1080/03007995.2025.2466730 · Cited by 2

Information Significance of the qSOFA Scale for Current Clinical Medicine. Literature Review

Руднов, В. А., & Astafyeva, M. (2018) · Annals of critical care · doi:10.21320/1818-474x-2018-4-30-37 · Cited by 2

Development of a Standardized System to Detect and Treat Early Patient Deterioration

Ashbeck, R., Stellpflug, C., Ihrke, E., Marsh, S., Fraune, M., Brummel, A., & Holst, C. (2020) · Journal of Nursing Care Quality · doi:10.1097/ncq.0000000000000484 · Cited by 2

Early identification and initial treatment of sepsis by emergency nurses

Bezerra, N. K. D. S., da Silva, P. S., Maciel, J. D. C., de Carvalho, F. C. A., & Caldart, R. V. (2023) · Revista de Enfermagem da UFPI · doi:10.26694/reufpi.v11i1.2809 · Cited by 2

Reducing the Effect of Critical Illness by Continuing to Think beyond the Intensive Care Unit

Admon, A. J. (2019) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201910-753ed · Cited by 2

Timing of prophylactic antibiotics administration and suspected systemic infection after percutaneous biliary intervention

Im, H., Oh, S., Lim, L., Lee, H., Kwon, J., & Ryu, H. G. (2023) · Journal of Hepato-Biliary-Pancreatic Sciences · doi:10.1002/jhbp.1366 · Cited by 1

ANTIBACTERIAL THERAPY IN SEPSIS PATIENTS

Shapoval, С. D. (2024) · Art of Medicine · doi:10.21802/artm.2024.3.31.263 · Cited by 1

The Protective Effect of Kentut Leaf Extract (Paederia foetida L.) on Gastric Histopathology in Escherichia coli-Infected Sepsis Mice Model

Savitri, L., & Kasimo, E. R. (2024) · Biology Medicine & Natural Product Chemistry · doi:10.14421/biomedich.2024.131.227-233 · Cited by 1

Effect of Timely Lactate Measurement on In-hospital Mortality among Adults with Hypotension and Hyperlactatemia: An Observational Study on Two CohortsPreprint

Chen, H., Bao, X., Xu, Y., Guo, Y., Zhou, M., Zhao, C., Wei, Y., & Jin, J. (2020) · doi:10.21203/rs.3.rs-60524/v1 · Cited by 1

Sepsis: The Road Ahead

Xie, J., & Coopersmith, C. M. (2018) · Springer eBooks · doi:10.1007/978-3-319-73506-1_16

Identifying Patients at Risk for Sepsis

Bridges, E. J., & Greco, S. A. (2018) · Critical Care Nurse · doi:10.4037/ccn2018245

Scoring systems for early detection of sepsis on the ward

Karlsen, E. E., Rønsåsbjørg, N. A., Skrede, S., & Mosevoll, K. A. (2023) · Tidsskrift for Den norske legeforening · doi:10.4045/tidsskr.21.0905 · Cited by 7

Usefulness of qSOFA and SIRS scores for detection of incipient sepsis in general ward patients: A prospective cohort study

Luo, J., Jiang, W., Weng, L., Peng, J., Hu, X., Wang, C.-Y., Liu, G.-Y., Huang, H., & Du, B. (2019) · Journal of Critical Care · doi:10.1016/j.jcrc.2019.01.012 · Cited by 41

Screening for sepsis: SIRS or qSOFA? A literature review

Feist, B. (2019) · Emergency Nurse · doi:10.7748/en.2019.e1939 · Cited by 8

Scoring systems for the characterization of sepsis and associated outcomes

McLymont, N., & Glover, G. W. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.12.53 · Cited by 68

Quick Sequential Organ Failure Assessment Versus Systemic Inflammatory Response System in Early Detection of Sepsis in Emergency Department

Embaby, K. M., Attia, S. M., El-Saeed, A. S., & Ismael, H. K. (2021) · The Egyptian Journal of Hospital Medicine · doi:10.21608/ejhm.2021.154600 · Cited by 1