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Early warning system enables accurate mortality risk prediction for acute gastrointestinal bleeding admitted to intensive care unit

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Abstract

Acute gastrointestinal (GI) bleeding are potentially life-threatening conditions. Early risk stratification is important for triaging patients to the appropriate level of medical care and intervention. Patients admitted to intensive care unit (ICU) has a high mortality, but risk tool is scarce for these patients. This study aimed to develop and validate a risk score to improve the prognostication of death at the time of patient admission to ICU. We developed and internally validated a nomogram for mortality in patients with acute GI bleeding from the eICU Collaborative Research Database (eICU-CRD), and externally validated it in patients from the Medical Information Mart for Intensive Care III database (MIMIC-III) and Wuhan Tongji Hospital. The performance of the model was assessed by examining discrimination (C-index), calibration (calibration curves) and usefulness (decision curves). 4750 patients were included in the development cohort, with 1184 patients in the internal validation cohort, 1406 patients in the MIMIC-III validation cohort, and 342 patients in the Tongji validation cohort. The nomogram, which incorporated ten variables, showed good calibration and discrimination in the training and validation cohorts, yielded C-index ranged from 0.832 (95%CI 0.811–0.853) to 0.926 (95CI% 0.905–0.947). The nomogram-defined high-risk group had a higher mortality than the low-risk group (44.8% vs. 3.5%, P < 0.001; 41.4% vs 3.1%, P < 0.001;53.6% vs 7.5%, P < 0.001; 38.2% vs 4.2%, P < 0.001). The model performed better than the conventional Glasgow-Blatchford score, AIMS65 and the newer Oakland and Sengupta scores for mortality prediction in both the derivation and validation cohorts concerning discrimination and usefulness. Our nomogram is a reliable prognostic tool that might be useful to identify high-risk acute GI bleeding patients admitted to ICU.

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Data availability

Data pertaining to the patients’ features used for modelling are available to researchers upon reasonable request via contacting the corresponding author. The remaining data are available in the article and supplementary files.

Code availability

The code used to develop and evaluate the model is available on GitHub with R (version 3.6.2), https://github.com/jmhust/Nomogram-for-acute-gastrointestinal-bleeding.

References

  1. Barkun AN, Bardou M, Kuipers EJ, Sung J, Hunt RH, Martel M et al (2010) International consensus recommendations on the management of patients with nonvariceal upper gastrointestinal bleeding. Ann Intern Med 152:101–113

    Article  PubMed  Google Scholar 

  2. Blatchford O, Murray WR, Blatchford M (2000) A risk score to predict need for treatment for upper-gastrointestinal haemorrhage. Lancet 356:1318–1321

    Article  CAS  PubMed  Google Scholar 

  3. Saltzman JR, Tabak YP, Hyett BH, Sun X, Travis AC, Johannes RS (2011) (AMS65, upper GI bleeding) a simple risk score accurately predicts in-hospital mortality, length of stay, and cost in acute upper GI bleeding. Gastrointest Endosc 74:1215–1224

    Article  PubMed  Google Scholar 

  4. Oakland K, Jairath V, Uberoi R, Guy R, Ayaru L, Mortensen N et al (2017) Derivation and validation of a novel risk score for safe discharge after acute lower gastrointestinal bleeding: a modelling study. Lancet Gastroenterol Hepatol 2:635–643

    Article  PubMed  Google Scholar 

  5. Sengupta N, Tapper EB (2017) Derivation and internal validation of a clinical prediction tool for 30-day mortality in lower gastrointestinal bleeding. Am J Med 130:601

    Article  Google Scholar 

  6. Laursen SB, Oakland K, Laine L, Bieber V, Marmo R, Redondo-Cerezo E et al (2021) ABC score: a new risk score that accurately predicts mortality in acute upper and lower gastrointestinal bleeding: an international multicentre study. Gut 70:707–716

    Article  PubMed  Google Scholar 

  7. Barkun AN, Almadi M, Kuipers EJ, Laine L, Sung J, Tse F et al (2019) Management of nonvariceal upper gastrointestinal bleeding: guideline recommendations from the international consensus group. Ann Intern Med 171:805–822

    Article  PubMed  PubMed Central  Google Scholar 

  8. Oakland K, Chadwick G, East JE, Guy R, Humphries A, Jairath V et al (2019) Diagnosis and management of acute lower gastrointestinal bleeding: guidelines from the British Society of Gastroenterology. Gut 68:776–789

    Article  PubMed  Google Scholar 

  9. Cieniawski D, Kuźniar E, Winiarski M, Matłok M, Kostarczyk W, Pedziwiatr M (2013) Prognostic value of the rockall score in patients with acute nonvariceal bleeding from the upper gastrointestinal tract. Przegl Lek 70:1–5

    PubMed  Google Scholar 

  10. Rockall TA, Logan RF, Devlin HB, Northfield TC (1996) Risk assessment after acute upper gastrointestinal haemorrhage. Gut 38:316–321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hay JA, Lyubashevsky E, Elashoff J, Maldonado L, Weingarten SR, Ellrodt AG (1996) Upper gastrointestinal hemorrhage clinical–guideline determining the optimal hospital length of stay. Am J Med 100:313–322

    Article  CAS  PubMed  Google Scholar 

  12. Dhir V, Shah R (2019) Scoring systems for upper gastrointestinal bleeding: Which one scores better? Indian J Gastroenterol 38:95–97

    Article  PubMed  Google Scholar 

  13. Liang PS, Saltzman JR (2014) A national survey on the initial management of upper gastrointestinal bleeding. J Clin Gastroenterol 48:e93–e98

    Article  PubMed  Google Scholar 

  14. Pollard TJ, Johnson A, Raffa JD, Celi LA, Mark RG, Badawi O (2018) The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 5:180178

    Article  PubMed  PubMed Central  Google Scholar 

  15. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M et al (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M et al (2016) The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315:801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350:g7594

    Article  PubMed  Google Scholar 

  18. Harrell FJ, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in develo** models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  PubMed  Google Scholar 

  19. Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S (1997) A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 16:965–980

    Article  CAS  PubMed  Google Scholar 

  20. Vickers AJ, Cronin AM, Elkin EB, Gonen M (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak 8:53

    Article  PubMed  PubMed Central  Google Scholar 

  21. Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: A new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10:7252–7259

    Article  CAS  PubMed  Google Scholar 

  22. Blatchford O, Murray WR, Blatchford M (2000) A risk score to predict need for treatment for upper-gastrointestinal haemorrhage. Lancet 356:1318–1321

    Article  CAS  PubMed  Google Scholar 

  23. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM et al (1998) Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. working group on “sepsis-related problems” of the european society of intensive care medicine. Crit Care Med 26:1793–1800

    Article  CAS  PubMed  Google Scholar 

  24. Moreno R, Rhodes A, Piquilloud L, Hernandez G, Takala J, Gershengorn HB et al (2023) The sequential organ failure assessment (SOFA) score: has the time come for an update? Crit Care 27:15

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kollef MH, Canfield DA, Zuckerman GR (1995) Triage considerations for patients with acute gastrointestinal hemorrhage admitted to a medical intensive care unit. Crit Care Med 23:1048–1054

    Article  CAS  PubMed  Google Scholar 

  26. Tapaskar N, Jones B, Mei S, Sengupta N (2019) Comparison of clinical prediction tools and identification of risk factors for adverse outcomes in acute lower GI bleeding. Gastrointest Endosc 89:1005–1013

    Article  PubMed  Google Scholar 

  27. Deshmukh F, Merchant SS (2020) Explainable machine learning model for predicting gi bleed mortality in the intensive care unit. Am J Gastroenterol 115:1657–1668

    Article  PubMed  Google Scholar 

  28. Shung D, Laine L (2020) Machine learning prognostic models for gastrointestinal bleeding using electronic health record data. Am J Gastroenterol 115:1199

    Article  PubMed  PubMed Central  Google Scholar 

  29. Riley R, Holman C, Fletcher D (2014) Inter-rater reliability of the ASA physical status classification in a sample of anaesthetists in Western Australia. Anaesth Intensive Care 42:614–618

    Article  PubMed  Google Scholar 

  30. Laine L, Jensen DM (2012) Management of patients with ulcer bleeding. Am J Gastroenterol 107:345–361

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Gralnek IM, Dumonceau JM, Kuipers EJ, Lanas A, Sanders DS, Kurien M et al (2015) Diagnosis and management of nonvariceal upper gastrointestinal hemorrhage: European society of gastrointestinal endoscopy (ESGE) guideline. Endoscopy 47:a1–a46

    Article  PubMed  Google Scholar 

  32. Sung JJ, Chiu PW, Chan F, Lau JY, Goh KL, Ho LH et al (2018) Asia-Pacific working group consensus on non-variceal upper gastrointestinal bleeding: an update 2018. Gut 67:1757–1768

    Article  PubMed  Google Scholar 

  33. Patel R, Clancy R, Crowther E, Vannahme M, Pullyblank A (2014) A rectal bleeding algorithm can successfully reduce emergency admissions. Colorectal Dis 16:377–381

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

None.

Funding

This work was supported by the National Natural Science Foundation of China (82000479) and China Postdoctoral Science Foundation (2020M682422).

Author information

Author notes

  1. Meng Jiang and Chang-li Li contributed equally as co-first authors.

Authors and Affiliations

  1. Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang, People’s Republic of China

    Meng Jiang & **ng-chen Lin

  2. Department of FSTC Clinic, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China

    Chang-li Li

  3. Department of Traumatic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China

    Li-gang Xu

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  1. Meng Jiang
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Contributions

MJ and CLL conceived the study idea and design, drafted the manuscript. MJ obtained funding. MJ performed acquisition of data. CLL, XCL and LGX performed analysis and interpretation of data. MJ and CLL performed critical revision of the manuscript for important intellectual content and statistical analysis. MJ took administrative, technical, and material support tasks. The manuscript has been read and approved by all the authors.

Corresponding author

Correspondence to Meng Jiang.

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The authors declare that they have no conflicts of interest.

Ethical approval

Data from eICU Collaborative Research Database and Medical Information Mart for Intensive Care III database were publicly available. For data from Tongji Hospital, it was authorized by the Ethics Committee and executed, complying with the Declaration of Helsinki. The ethics committee of the Tongji Hospital approved the study (NO.: TJ-IRB20200373). Written informed consent was waived due to it is a retrospective study with no intervene.

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Jiang, M., Li, Cl., Lin, Xc. et al. Early warning system enables accurate mortality risk prediction for acute gastrointestinal bleeding admitted to intensive care unit. Intern Emerg Med 19, 511–521 (2024). https://doi.org/10.1007/s11739-023-03428-z

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