{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T03:43:14Z","timestamp":1789962194050,"version":"4.0.1"},"reference-count":34,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"1","license":[{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHEOR"],"abstract":"<jats:p>Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY\u00ae Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown.\n\nObjectives: The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting.\n\nMethods: A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY\u00ae Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice.\n\nResults: In Sweden, the total cost per patient amounts to \u20ac16 436 and \u20ac16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of \u20ac76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of \u20ac1009 per ICU patient. Stochastic scenario analysis showed that NAVOY\u00ae Sepsis was a dominant treatment option in most scenarios and well below an established threshold of \u20ac20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year.\n\nConclusions: A sepsis prediction algorithm such as NAVOY\u00ae Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.<\/jats:p>","DOI":"10.36469\/jheor.2022.33951","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T13:10:08Z","timestamp":1650978608000},"page":"101-110","source":"Crossref","is-referenced-by-count":27,"title":["The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden"],"prefix":"10.36469","volume":"9","member":"22273","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","unstructured":"1. Rhee C, Dantes R, Epstein L, et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA. 2017;318(13):1241-1249. doi:10.1001\/jama.2017.13836","DOI":"10.1001\/jama.2017.13836"},{"key":"ref1","doi-asserted-by":"publisher","unstructured":"2. Sakr Y, Jaschinski U, Wittebole X, et al. Sepsis in intensive care unit patients: worldwide data from the Intensive Care Over Nations Audit. Open Forum Infect Dis. 2018;5(12):ofy313. doi:10.1093\/ofid\/ofy313","DOI":"10.1093\/ofid\/ofy313"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"3. Fleischmann C, Scherag A, Adhikari NKJ, et al. Assessment of global incidence and mortality of hospital-treated sepsis: current estimates and limitations. Am J Respir Crit Care Med. 2016;193(3):259-272. doi:10.1164\/rccm.201504-0781oc","DOI":"10.1164\/rccm.201504-0781OC"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"4. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-1062. doi:10.1016\/s0140-6736(20)30566-3","DOI":"10.1016\/S0140-6736(20)30566-3"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"5. Kumar A, Roberts D, Wood KE, et al. 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):1589-1596. doi:10.1097\/01.CCM.0000217961.75225.E9","DOI":"10.1097\/01.CCM.0000217961.75225.E9"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"6. Mok K, Christian MD, Nelson S, Burry L. Time to administration of antibiotics among inpatients with severe sepsis or septic shock. Can J Hosp Pharm. 2014;67(3):213-219. doi:10.4212\/cjhp.v67i3.1358","DOI":"10.4212\/cjhp.v67i3.1358"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"7. Husab\u00f8 G, Nilsen RM, Flaatten H, et al. Early diagnosis of sepsis in emergency departments, time to treatment, and association with mortality: an observational study. PLoS ONE. 2020;15(1):e0227652. doi:10.1371\/journal.pone.0227652","DOI":"10.1371\/journal.pone.0227652"},{"key":"ref7","doi-asserted-by":"publisher","unstructured":"8. Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235-2244. doi:10.1056\/nejmoa1703058","DOI":"10.1056\/NEJMoa1703058"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"9. Arefian H, Heublein S, Scherag A, et al. Hospital-related cost of sepsis: a systematic review. J Infect. 2017;74(2):107-117. doi:10.1016\/j.jinf.2016.11.006","DOI":"10.1016\/j.jinf.2016.11.006"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"10. Ferrer R, Martin-Loeches I, Phillips G, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med. 2014;42(8):1749-1755. doi:10.1097\/ccm.0000000000000330","DOI":"10.1097\/CCM.0000000000000330"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"11. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi:10.1001\/jama.2016.0287","DOI":"10.1001\/jama.2016.0287"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"12. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14(1):R15. doi:10.1186\/cc8872","DOI":"10.1186\/cc8872"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"13. Cho SY, Choi JH. Biomarkers of sepsis. Infect Chemother. 2014;46(1):1-12. doi:10.3947\/ic.2014.46.1.1","DOI":"10.3947\/ic.2014.46.1.1"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"14. Pierrakos C, Velissaris D, Bisdorff M, Marshall JC, Vincent JL. Biomarkers of sepsis: time for a reappraisal. Crit Care. 2020;24(1):287. doi:10.1186\/s13054-020-02993-5","DOI":"10.1186\/s13054-020-02993-5"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"15. Persson I, \u00d6stling A, Arlbrandt M, S\u00f6derberg J, Becedas D. A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY Sepsis): proof-of-concept study. JMIR Form Res. 2021;5(9):e28000. doi:10.2196\/28000","DOI":"10.2196\/28000"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"16. Bray A, Kampouraki E, Winter A, Jesuthasan A, Messer B, Graziadio S. High variability in sepsis guidelines in UK: why does it matter? Int Environ Res Public Health. 2020;17(6):2026. doi:10.3390\/ijerph17062026","DOI":"10.3390\/ijerph17062026"},{"key":"ref16","doi-asserted-by":"publisher","unstructured":"17. van Vught LA, Klein Klouwenberg PMC, Spitoni C, et al. Incidence, risk factors, and attributable mortality of secondary infections in the intensive care unit after admission for sepsis. JAMA. 2016;315(14):1469-1479. doi:10.1001\/jama.2016.2691","DOI":"10.1001\/jama.2016.2691"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"18. Karlsson S, Varpula M, Ruokonen E, et al. Incidence, treatment, and outcome of severe sepsis in ICU-treated adults in Finland: the Finnsepsis study. Intensive Care Med. 2007;33(3):435-443. doi:10.1007\/s00134-006-0504-z","DOI":"10.1007\/s00134-006-0504-z"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"19. Lengquist M, Lundberg OHM, Sp\u00e5ngfors M, et al. Sepsis is underreported in Swedish intensive care units: a retrospective observational multicentre study. Acta Anaesthesiol Scand. 2020;64(8):1167-1176. doi:10.1111\/aas.13647","DOI":"10.1111\/aas.13647"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"20. Zilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. 30-day readmission, antibiotics costs and costs of delay to adequate treatment of Enterobacteriaceae UTI, pneumonia, and sepsis: a retrospective cohort study. Antimicrob Resist Infect Control. 2017;6(1):124. doi:10.1186\/s13756-017-0286-9","DOI":"10.1186\/s13756-017-0286-9"},{"key":"ref20","unstructured":"21. The Swedish Intensive Care Registry. Yearly Report 2018. Accessed February 1, 2021. https:\/\/www.icuregswe.org\/globalassets\/arsrapporter\/analyserande_arsrapport_2018.pdf"},{"key":"ref21","unstructured":"22. York Health Economics Consortium. The Cost of Sepsis Care in the UK. Final Report 2017. Whitewater Charitable Trust; 2017. Accessed February 1, 2021. http:\/\/allcatsrgrey.org.uk\/wp\/wpfb-file\/yhec-sepsis-report-17-02-17-final-pdf\/"},{"key":"ref22","doi-asserted-by":"publisher","unstructured":"23. Linder A, Guh D, Boyd JH, Walley KR, Anis AH, Russell JA. Long-term (10-year) mortality of younger previously healthy patients with severe sepsis\/septic shock is worse than that of patients with nonseptic critical illness and of the general population. Crit Care Med. 2014;42(10):2211-2218. doi:10.1097\/ccm.0000000000000503","DOI":"10.1097\/CCM.0000000000000503"},{"key":"ref23","unstructured":"24. Royal College of Physicians. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. In: Updated Report of a Working Party. Royal College of Physicians; 2017."},{"key":"ref24","unstructured":"25. NHS England. National Early Warning Score (NEWS). Accessed February 1, 2021. https:\/\/www.england.nhs.uk\/ourwork\/clinical-policy\/sepsis\/nationalearlywarningscore\/"},{"key":"ref25","unstructured":"26. Avtal\/priser-S\u00f6dra sjukv\u00e5rdsregionen. S\u00f6dra sjukv\u00e5rdsregionen (Swedish Southern Healthcare Region). Published 2021. Accessed February 1, 2021. https:\/\/sodrasjukvardsregionen.se\/verksamhet\/avtal-priser\/"},{"key":"ref26","doi-asserted-by":"publisher","unstructured":"27. Cuthbertson BH, Elders A, Hall S, et al. Mortality and quality of life in the five years after severe sepsis. Crit Care. 2013;17(2):R70. doi:10.1186\/cc12616","DOI":"10.1186\/cc12616"},{"key":"ref27","doi-asserted-by":"publisher","unstructured":"28. Husereau D, Drummond M, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. BMJ. 2013;346:f1049. doi:10.1136\/bmj.f1049","DOI":"10.1136\/bmj.f1049"},{"key":"ref28","doi-asserted-by":"publisher","unstructured":"29. Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform. 2016;4(3):e28. doi:10.2196\/medinform.5909","DOI":"10.2196\/medinform.5909"},{"key":"ref29","doi-asserted-by":"publisher","unstructured":"30. Mellhammar L, Linder A, Tverring J, et al. NEWS2 is superior to qSOFA in detecting sepsis with organ dysfunction in the emergency department. J Clin Med. 2019;8(8):1128. doi:10.3390\/jcm8081128","DOI":"10.3390\/jcm8081128"},{"key":"ref30","unstructured":"31. Statistik \u00f6ver v\u00e4gtrafikolyckor. Transportstyrelsen (Swedish Transport Agency). Published 2021. Accessed February 1, 2021. https:\/\/www.transportstyrelsen.se\/sv\/vagtrafik\/statistik\/olycksstatistik\/statistik-over-vagtrafikolyckor\/"},{"key":"ref31","unstructured":"32. Analysmetod och samh\u00e4llsekonomiska kalkylv\u00e4rden f\u00f6r transportsektorn: ASEK 7.0. Transportstyrelsen (Swedish Transport Agency). Published 2020. Accessed February 1, 2021. https:\/\/www.trafikverket.se\/contentassets\/4b1c1005597d47bda386d81dd3444b24\/asek-7.0--2020\/09_trafiksakerhet_a7_201204.pdf"},{"key":"ref32","unstructured":"33. Persson U, Olofsson S. Ett QALY \u00e4r v\u00e4rt mer \u00e4n tv\u00e5 miljoner kronor. L\u00e4kartidningen. 2018;115:E96F."},{"key":"ref33","doi-asserted-by":"publisher","unstructured":"34. Higgins AM, Brooker JE, Mackie M, Cooper DJ, Harris AH. Health economic evaluations of sepsis interventions in critically ill adult patients: a systematic review. J Intensive Care. 2020;8:5. doi:10.1186\/s40560-019-0412-2","DOI":"10.1186\/s40560-019-0412-2"}],"container-title":["Journal of Health Economics and Outcomes Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jheor.org\/article\/33951-the-potential-cost-and-cost-effectiveness-impact-of-using-a-machine-learning-algorithm-for-early-detection-of-sepsis-in-intensive-care-units-in-sweden","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T12:36:07Z","timestamp":1665491767000},"score":1,"resource":{"primary":{"URL":"https:\/\/jheor.org\/article\/33951-the-potential-cost-and-cost-effectiveness-impact-of-using-a-machine-learning-algorithm-for-early-detection-of-sepsis-in-intensive-care-units-in-sweden"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,26]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1,3]]}},"URL":"https:\/\/doi.org\/10.36469\/jheor.2022.33951","archive":["Portico","Portico","Portico"],"relation":{},"ISSN":["2327-2236"],"issn-type":[{"value":"2327-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,26]]},"article-number":"33951"}}