Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model

Resuscitation

Volumn 133, Issue , 2018, Pages 75-81

  Redfern, Oliver C ^a   Pimentel, Marco A F ^b   Prytherch, David ^a   Meredith, Paul ^c   Clifton, David A ^b   Tarassenko, Lionel ^b   Smith, Gary B ^d   Watkinson, Peter J ^e  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c PORTSMOUTH HOSPITALS NHS TRUST   (United Kingdom)

^d BOURNEMOUTH UNIVERSITY   (United Kingdom)

^e OXFORD UNIVERSITY HOSPITALS NHS FOUNDATION TRUST   (United Kingdom)

Author keywords

Early warning score; Laboratory test results; Physiological monitoring; Vital signs

Indexed keywords

ADOLESCENT; AGED; ARTICLE; CONTROLLED STUDY; DEATH; DECISION TREE; DETERIORATION; FEMALE; HOSPITAL MORTALITY; HOSPITALIZATION; HUMAN; INTENSIVE CARE UNIT; LABORATORY TEST; MAJOR CLINICAL STUDY; MALE; MONITORING; OUTCOME ASSESSMENT; RISK ASSESSMENT; UNIVERSITY HOSPITAL; VALIDATION PROCESS; VITAL SIGN; BLOOD EXAMINATION; CLINICAL TRIAL; MIDDLE AGED; MULTICENTER STUDY; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; STATISTICAL MODEL; VALIDATION STUDY; VERY ELDERLY;

AGED; AGED, 80 AND OVER; FEMALE; HEMATOLOGIC TESTS; HOSPITAL MORTALITY; HOSPITALIZATION; HUMANS; INTENSIVE CARE UNITS; MALE; MIDDLE AGED; MODELS, STATISTICAL; RETROSPECTIVE STUDIES; RISK ASSESSMENT; ROC CURVE; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; VITAL SIGNS;

EID: 85054838763     PISSN: 03009572     EISSN: 18731570     Source Type: Journal    
DOI: 10.1016/j.resuscitation.2018.09.021     Document Type: Article

References (27)

1. National Patient Safety Agency, Safer care for the acutely ill patient: learning from serious incidents. The fifth report from the Patient Safety Observatory, 2007, 1–48.
2. Cullinane, M., Findlay, G., Hargraves, C., Lucas, S., An acute problem? A report of the national confidential enquiry into patient outcome and death., 2005, NCEPOD, London.
3. Smith, G.B., Prytherch, D.R., Meredith, P., Schmidt, P.E., Featherstone, P.I., The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 84 (2013), 465–470.
4. Royal College of Physicians (London), National Early Warning Score (NEWS): standardising the assessment of acute illness severity in the NHS. Report of a working party. London, 2012.
5. National Institute for Health and Care Excellence, Sepsis: recognition, diagnosis and early management. NICE guideline (51). 2016.
6. National Institute for Health and Care Excellence, Acutely ill patients in hospital: recognition of and response to acute illness in adults in hospital. NICE guideline (50). 2007.
7. Wong, D., Bonnici, T., Knight, J., Morgan, L., Coombes, P., Watkinson, P., SEND: a system for electronic notification and documentation of vital sign observations. BMC Med Inform Decis Mak, 15, 2015, 68.
8. Smith, G.B., Prytherch, D.R., Schmidt, P., Featherstone, P.I., Knight, D., Clements, G., et al. Hospital-wide physiological surveillance-a new approach to the early identification and management of the sick patient. Resuscitation 71 (2006), 19–28.
9. Asadollahi, K., Hastings, I.M., Gill, G.V., Beeching, N.J., Prediction of hospital mortality from admission laboratory data and patient age: a simple model. Emerg Med Australas 23 (2011), 354–363.
10. Loekito, E., Bailey, J., Bellomo, R., Hart, G.K., Hegarty, C., Davey, P., et al. Common laboratory tests predict imminent medical emergency team calls, intensive care unit admission or death in emergency department patients. Emerg Med Australas 25 (2013), 132–139.
11. Klausen, H.H., Petersen, J., Bandholm, T., Juul-Larsen, H.G., Tavenier, J., Eugen-Olsen, J., et al. Association between routine laboratory tests and long-term mortality among acutely admitted older medical patients: a cohort study. BMC Geriatr, 17, 2017, 62.
12. Boekel, E. ten, Vroonhof, K., Huisman, A., van Kampen, C., de Kieviet, W., Clinical laboratory findings associated with in-hospital mortality. Clin Chim Acta 372 (2006), 1–13.
13. Prytherch, D.R., Sirl, J.S., Schmidt, P., Featherstone, P.I., Weaver, P.C., Smith, G.B., The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation 66 (2005), 203–207.
14. Jarvis, S.W., Kovacs, C., Badriyah, T., Briggs, J., Mohammed, M.A., Meredith, P., et al. Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions. Resuscitation 84 (2013), 1494–1499.
15. Mohammed, M.A., Rudge, G., Watson, D., Wood, G., Smith, G.B., Prytherch, D.R., et al. Index blood tests and national early warning scores within 24 hours of emergency admission can predict the risk of in-hospital mortality: a model development and validation study. PLoS One, 8, 2013.
16. Kipnis, P., Turk, B.J., Wulf, D.A., LaGuardia, J.C., Liu, V., Churpek, M.M., et al. Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU. J Biomed Inform 64 (2016), 10–19.
17. Futoma, J., Hariharan, S., Sendak, M., Brajer, N., Clement, M., Bedoya, A., et al. An improved multi-output Gaussian process RNN with Real-time validation for early sepsis detection. Proceedings of machine learning for healthcare, 2017.
18. Moons, K.G.M., Altman, D.G., Reitsma, J.B., Ioannidis, J.P.A., Macaskill, P., Steyerberg, E.W., et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 162, 2015, W1.
19. Malycha, J., Bonnici, T., Sebekova, K., Petrinic, T., Young, D., Watkinson, P., Variables associated with unplanned general adult ICU admission in hospitalised patients: protocol for a systematic review. Syst Rev, 6, 2017, 67.
20. NHS Digital Data Dictionary. (Accessed 11 April 2018, at https://www.datadictionary.nhs.uk/web_site_content/navigation/main_menu.asp).
21. Kovacs, C., Jarvis, S.W., Prytherch, D.R., Meredith, P., Schmidt, P.E., Briggs, J.S., et al. Comparison of the national early warning score in non-elective medical and surgical patients. Br J Surg 103 (2016), 1385–1393.
22. Prytherch, D.R., Smith, G.B., Schmidt, P.E., Featherstone, P.I., ViEWS—towards a national early warning score for detecting adult inpatient deterioration. Resuscitation 81 (2010), 932–937.
23. R Core Team, R: a language and environment for statistical computing. 2017.
24. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 2011, 77.
25. Rothman, M.J., Rothman, S.I., Beals, J., Development and validation of a continuous measure of patient condition using the electronic medical record. J Biomed Inform 46 (2013), 837–848.
26. Churpek, M.M., Yuen, T.C., Winslow, C., Robicsek, A.A., Meltzer, D.O., Gibbons, R.D., et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med 190 (2014), 649–655.
27. Futoma, J., Hariharan, S., Heller, K., Learning to detect sepsis with a multitask Gaussian process RNN classifier. Proceedings of the 34th international conference on machine learning, 2017.