The Impact of Laboratory Data Missingness on Sepsis Diagnosis Timeliness

Overview

Journal JAMIA Open

Date 2024 Sep 24

PMID 39314673

Authors

Jonathan Y Lam

Aaron Boussina

Supreeth P Shashikumar

Robert L Owens

Shamim Nemati

Christopher S Josef

Affiliations

Soon will be listed here.

Abstract

Objective: To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

Materials And Methods: In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure ( ) and time of clinical suspicion of sepsis ( ) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition.

Results: Of the patients studied, 48.7% ( = 2017) in the emergency department (ED), 30.8% ( = 209) in the wards, and 14.4% ( = 167) in the intensive care unit (ICU) had after . Patients with after had significantly higher data missingness of 1 or more of the 5 laboratory components used to determine organ failure. The mean number of missing labs was 4.23 vs 2.83 in the ED, 4.04 vs 3.38 in the wards, and 3.98 vs 3.19 in the ICU.

Discussion: Our study identified many sepsis patients with missing laboratory results vital for the identification of organ failure and the diagnosis of sepsis at or before the time of clinical suspicion of sepsis. Addressing data missingness via more timely laboratory assessment could precipitate an earlier recognition of organ failure and potentially earlier diagnosis of and treatment initiation for sepsis.

Conclusions: More prompt laboratory assessment might improve the timeliness of sepsis recognition and treatment.

References

Boussina A, Shashikumar S, Malhotra A, Owens R, El-Kareh R, Longhurst C . Impact of a deep learning sepsis prediction model on quality of care and survival. NPJ Digit Med. 2024; 7(1):14. PMC: 10805720. DOI: 10.1038/s41746-023-00986-6. View

Liu V, Fielding-Singh V, Greene J, Baker J, Iwashyna T, Bhattacharya J . The Timing of Early Antibiotics and Hospital Mortality in Sepsis. Am J Respir Crit Care Med. 2017; 196(7):856-863. PMC: 5649973. DOI: 10.1164/rccm.201609-1848OC. View

Agniel D, Kohane I, Weber G . Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ. 2018; 361:k1479. PMC: 5925441. DOI: 10.1136/bmj.k1479. View

Giannini H, Ginestra J, Chivers C, Draugelis M, Hanish A, Schweickert W . A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice. Crit Care Med. 2019; 47(11):1485-1492. PMC: 8635476. DOI: 10.1097/CCM.0000000000003891. View

Seymour C, Gesten F, Prescott H, Friedrich M, Iwashyna T, Phillips G . Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. N Engl J Med. 2017; 376(23):2235-2244. PMC: 5538258. DOI: 10.1056/NEJMoa1703058. View

Rhee C, Dantes R, Epstein L, Murphy D, Seymour C, Iwashyna T . Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014. JAMA. 2017; 318(13):1241-1249. PMC: 5710396. DOI: 10.1001/jama.2017.13836. View

Adams R, Henry K, Sridharan A, Soleimani H, Zhan A, Rawat N . Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med. 2022; 28(7):1455-1460. DOI: 10.1038/s41591-022-01894-0. View

Bone R, Balk R, Cerra F, Dellinger R, Fein A, Knaus W . Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992; 101(6):1644-55. DOI: 10.1378/chest.101.6.1644. View

Kumar A, Roberts D, Wood K, Light B, Parrillo J, Sharma S . Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006; 34(6):1589-96. DOI: 10.1097/01.CCM.0000217961.75225.E9. View

10.

Neilson H, Fortier J, Finestone P, Ogilby C, Liu R, Bridges E . Diagnostic Delays in Sepsis: Lessons Learned From a Retrospective Study of Canadian Medico-Legal Claims. Crit Care Explor. 2023; 5(2):e0841. PMC: 9894347. DOI: 10.1097/CCE.0000000000000841. View

11.

McCoy A, Das R . Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual. 2018; 6(2):e000158. PMC: 5699136. DOI: 10.1136/bmjoq-2017-000158. View

12.

Singer M, Deutschman C, Seymour C, Shankar-Hari M, Annane D, Bauer M . The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016; 315(8):801-10. PMC: 4968574. DOI: 10.1001/jama.2016.0287. View

13.

Rhee C, Brown S, Jones T, OBrien C, Pande A, Hamad Y . Variability in determining sepsis time zero and bundle compliance rates for the centers for medicare and medicaid services SEP-1 measure. Infect Control Hosp Epidemiol. 2018; 39(8):994-996. PMC: 7977505. DOI: 10.1017/ice.2018.134. View

14.

Beaulieu-Jones B, Yuan W, Brat G, Beam A, Weber G, Ruffin M . Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digit Med. 2021; 4(1):62. PMC: 8010071. DOI: 10.1038/s41746-021-00426-3. View

15.

Seymour C, Liu V, Iwashyna T, Brunkhorst F, Rea T, Scherag A . Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016; 315(8):762-74. PMC: 5433435. DOI: 10.1001/jama.2016.0288. View

16.

Gauer R, Forbes D, Boyer N . Sepsis: Diagnosis and Management. Am Fam Physician. 2020; 101(7):409-418. View

17.

Rhee C, Zhang Z, Kadri S, Murphy D, Martin G, Overton E . Sepsis Surveillance Using Adult Sepsis Events Simplified eSOFA Criteria Versus Sepsis-3 Sequential Organ Failure Assessment Criteria. Crit Care Med. 2019; 47(3):307-314. PMC: 6383796. DOI: 10.1097/CCM.0000000000003521. View

18.

Shimabukuro D, Barton C, Feldman M, Mataraso S, Das R . Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2018; 4(1):e000234. PMC: 5687546. DOI: 10.1136/bmjresp-2017-000234. View

19.

Paoli C, Reynolds M, Sinha M, Gitlin M, Crouser E . Epidemiology and Costs of Sepsis in the United States-An Analysis Based on Timing of Diagnosis and Severity Level. Crit Care Med. 2018; 46(12):1889-1897. PMC: 6250243. DOI: 10.1097/CCM.0000000000003342. View