Performance of patient acuity rating by rapid response team nurses for predicting short-term prognosis
Autoři:
Hyung-Jun Kim aff001; Hyun-Ju Min aff002; Dong-Seon Lee aff003; Yun-Young Choi aff003; Miae Yoon aff003; Da-Yun Lee aff003; In-ae Song aff004; Jun Yeun Cho aff002; Jong Sun Park aff001; Young-Jae Cho aff001; You-Hwan Jo aff005; Ho Il Yoon aff001; Jae Ho Lee aff001; Choon-Taek Lee aff001; Yeon Joo Lee aff001
Působiště autorů:
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
aff001; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
aff002; Department of Nursing, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea
aff003; Department of Anesthesiology, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea
aff004; Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea
aff005
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225229
Souhrn
Background
Although scoring and machine learning methods have been developed to predict patient deterioration, bedside assessment by nurses should not be overlooked. This study aimed to evaluate the performance of subjective bedside assessment of the patient by the rapid response team (RRT) nurses in predicting short-term patient deterioration.
Methods
Patients noticed by RRT nurses based on the vital sign instability, abnormal laboratory results, and direct contact via phone between November 1, 2016, and December 12, 2017, were included. Five RRT nurses visited the patients according to their shifts and assessed the possibility of patient deterioration. Patient acuity rating (PAR), a scale of 1–7, was used as the tool of bedside assessment. Other scores, including the modified early warning score, VitalPAC early warning score, standardised early warning score, and cardiac arrest risk triage, were calculated afterwards. The performance of these scores in predicting mortality and/or intensive care unit admission within 1 day was compared by calculating the area under the receiver operating curve.
Results
A total of 1,426 patients were included in the study, of which 258 (18.1%) died or were admitted to the intensive care unit within 1 day. The area under the receiver operating curve of PAR was 0.87 (95% confidence interval [CI] 0.84–0.89), which was higher than those of modified early warning score (0.66, 95% CI 0.62–0.70), VitalPAC early warning score (0.69, 95% CI 0.66–0.73), standardised early warning score (0.67, 95% CI 0.63–0.70) and cardiac arrest risk triage (0.63, 95% CI 0.59–0.66) (P<0.001).
Conclusions
PAR assessed by RRT nurses can be a useful tool for assessing short-term patient prognosis in the RRT setting.
Klíčová slova:
Cardiac arrest – Heart rate – Intensive care units – Nurses – Oxygen – Physicians – Prognosis
Zdroje
1. Chan PS, Khalid A, Longmore LS, Berg RA, Kosiborod M, Spertus JA. Hospital-wide code rates and mortality before and after implementation of a rapid response team. JAMA. 2008;300(21):2506–2513. doi: 10.1001/jama.2008.715 19050194
2. Ludikhuize J, Brunsveld-Reinders AH, Dijkgraaf MG, Smorenburg SM, de Rooij SE, Adams R, et al. Outcomes Associated With the Nationwide Introduction of Rapid Response Systems in The Netherlands. Crit Care Med. 2015;43(12):2544–2551. doi: 10.1097/CCM.0000000000001272 26317569
3. Churpek MM, Yuen TC, Edelson DP. Risk stratification of hospitalized patients on the wards. Chest. 2013;143(6):1758–1765. doi: 10.1378/chest.12-1605 23732586
4. Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. Qjm. 2001;94(10):521–526. doi: 10.1093/qjmed/94.10.521 11588210
5. Niegsch M, Fabritius ML, Anhoj J. Imperfect implementation of an early warning scoring system in a Danish teaching hospital: a cross-sectional study. PLoS One. 2013;8(7):e70068. doi: 10.1371/journal.pone.0070068 23922906
6. Kim WY, Shin YJ, Lee JM, Huh JW, Koh Y, Lim CM, et al. Modified Early Warning Score Changes Prior to Cardiac Arrest in General Wards. PLoS One. 2015;10(6):e0130523. doi: 10.1371/journal.pone.0130523 26098429
7. Prytherch DR, Smith GB, Schmidt PE, Featherstone PI. ViEWS—Towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81(8):932–937. doi: 10.1016/j.resuscitation.2010.04.014 20637974
8. Paterson R, MacLeod DC, Thetford D, Beattie A, Graham C, Lam S, et al. Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit. Clin Med (Lond). 2006;6(3):281–284. doi: 10.7861/clinmedicine.6-3-281 16826863
9. Churpek MM, Yuen TC, Park SY, Gibbons R, Edelson DP. Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*. Crit Care Med. 2014;42(4):841–848. doi: 10.1097/CCM.0000000000000038 24247472
10. Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Crit Care Med. 2016;44(2):368–374. doi: 10.1097/CCM.0000000000001571 26771782
11. Edelson DP, Retzer E, Weidman EK, Woodruff J, Davis AM, Minsky BD, et al. Patient acuity rating: quantifying clinical judgment regarding inpatient stability. J Hosp Med. 2011;6(8):475–479. doi: 10.1002/jhm.886 21853529
12. O'Donnell C, Thomas S, Johnson C, Verma L, Bae J, Gallagher D. Incorporating Patient Acuity Rating Score Into Patient Handoffs and the Correlation With Rapid Responses and Unexpected ICU Transfers. Am J Med Qual. 2017;32(2):122–128. doi: 10.1177/1062860616630809 27037267
13. Hung SK, Ng CJ, Kuo CF, Goh ZNL, Huang LH, Li CH, et al. Comparison of the Mortality in Emergency Department Sepsis Score, Modified Early Warning Score, Rapid Emergency Medicine Score and Rapid Acute Physiology Score for predicting the outcomes of adult splenic abscess patients in the emergency department. PLoS One. 2017;12(11):e0187495. doi: 10.1371/journal.pone.0187495 29091954
14. Churpek MM, Yuen TC, Huber MT, Park SY, Hall JB, Edelson DP. Predicting cardiac arrest on the wards: a nested case-control study. Chest. 2012;141(5):1170–1176. doi: 10.1378/chest.11-1301 22052772
15. Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs*. Crit Care Med. 2012;40(7):2102–8. doi: 10.1097/CCM.0b013e318250aa5a 22584764
16. Yeh RW, Mauri L, Wolf RE, Romm IK, Lovett A, Shahian D, et al. Population trends in rates of coronary revascularization. JAMA Intern Med. 2015;175(3):454–456. doi: 10.1001/jamainternmed.2014.7129 25559059
17. Sinuff T, Adhikari NK, Cook DJ, Schunemann HJ, Griffith LE, Rocker G, et al. Mortality predictions in the intensive care unit: comparing physicians with scoring systems. Crit Care Med. 2006;34(3):878–885. doi: 10.1097/01.CCM.0000201881.58644.41 16505667
18. Semler MW, Stover DG, Copland AP, Hong G, Johnson MJ, Kriss MS, et al. Flash mob research: a single-day, multicenter, resident-directed study of respiratory rate. Chest. 2013;143(6):1740–1744. doi: 10.1378/chest.12-1837 23197319
19. Churpek MM, Yuen TC, Winslow C, Hall J, Edelson DP. Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest. Crit Care Med. 2015;43(4):816–822. doi: 10.1097/CCM.0000000000000818 25559439
20. West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet. 2016;388(10057):2272–2281. doi: 10.1016/S0140-6736(16)31279-X 27692469
21. Douw G, Huisman-de Waal G, van Zanten ARH, van der Hoeven JG, Schoonhoven L. Nurses’ ‘worry’ as predictor of deteriorating surgical ward patients: A prospective cohort study of the Dutch-Early-Nurse-Worry-Indicator-Score. Int J Nurs Stud. 2016;59:134–140. doi: 10.1016/j.ijnurstu.2016.04.006 27222458
22. Jones DA, DeVita MA, Bellomo R. Rapid-response teams. N Engl J Med. 2011;365(2):139–146. doi: 10.1056/NEJMra0910926 21751906
23. Trinkle RM, Flabouris A. Documenting Rapid Response System afferent limb failure and associated patient outcomes. Resuscitation. 2011;82(7):810–814. doi: 10.1016/j.resuscitation.2011.03.019 21497982
Článek vyšel v časopise
PLOS One
2019 Číslo 11
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy