The Norton scale is an important predictor of in-hospital mortality in internal medicine patients.
Elderly
In-hospital mortality
Internal medicine
Norton scale
Nursing assessment
Journal
Irish journal of medical science
ISSN: 1863-4362
Titre abrégé: Ir J Med Sci
Pays: Ireland
ID NLM: 7806864
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
received:
19
09
2022
accepted:
07
12
2022
medline:
1
8
2023
pubmed:
16
12
2022
entrez:
15
12
2022
Statut:
ppublish
Résumé
The Norton scale, a marker of patient frailty used to predict the risk of pressure ulcers, but the predictive value of the Norton scale for in-hospital mortality after adjustment for a wide range of demographic, and abnormal admission laboratory test results shown in themselves to have a high predictive value for in-hospital mortality is unclear. The study aims to determine the value of the Norton scale and the presence of a urinary catheter in predicting in hospital mortality. The study population included all acutely admitted adult patients in 2020 through October 2021 to one of three internal medicine departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. The main objective was to (a) identify the variables associated with the Norton Scale and (b) determine whether it predicts in-hospital mortality after adjustment for these variables. The Norton scale was associated with an older age, female gender, presence of a urinary catheter, and abnormal laboratory tests. The odds of in-hospital mortality in those with intermediate, high, and very high Norton scale risk groups were 3.10 (2.23-3.56), 6.48 (4.02-10.46), and 12.27 (7.37-20.44), respectively, after adjustment for the remaining predictors. Adding the Norton scale and the presence of a urinary catheter to the prediction logistic regression model that included age, gender, and abnormal laboratory test results increased the c-statistic from 0.870 (0.864-0.876) to 0.908 (0.902-0.913). The Norton scale and presence of a urinary catheter are important predictors of in-hospital mortality in acutely hospitalized adults in internal medicine departments.
Sections du résumé
BACKGROUND
BACKGROUND
The Norton scale, a marker of patient frailty used to predict the risk of pressure ulcers, but the predictive value of the Norton scale for in-hospital mortality after adjustment for a wide range of demographic, and abnormal admission laboratory test results shown in themselves to have a high predictive value for in-hospital mortality is unclear.
AIM
OBJECTIVE
The study aims to determine the value of the Norton scale and the presence of a urinary catheter in predicting in hospital mortality.
METHODS
METHODS
The study population included all acutely admitted adult patients in 2020 through October 2021 to one of three internal medicine departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. The main objective was to (a) identify the variables associated with the Norton Scale and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.
RESULTS
RESULTS
The Norton scale was associated with an older age, female gender, presence of a urinary catheter, and abnormal laboratory tests. The odds of in-hospital mortality in those with intermediate, high, and very high Norton scale risk groups were 3.10 (2.23-3.56), 6.48 (4.02-10.46), and 12.27 (7.37-20.44), respectively, after adjustment for the remaining predictors. Adding the Norton scale and the presence of a urinary catheter to the prediction logistic regression model that included age, gender, and abnormal laboratory test results increased the c-statistic from 0.870 (0.864-0.876) to 0.908 (0.902-0.913).
CONCLUSIONS
CONCLUSIONS
The Norton scale and presence of a urinary catheter are important predictors of in-hospital mortality in acutely hospitalized adults in internal medicine departments.
Identifiants
pubmed: 36520351
doi: 10.1007/s11845-022-03250-0
pii: 10.1007/s11845-022-03250-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1947-1952Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.
Références
Cecil E, Bottle A, Esmail A et al (2020) What is the relationship between mortality alerts and other indicators of quality of care? A national cross-sectional study. J Health Serv Res Policy 25(1):13–21. https://doi.org/10.1177/1355819619847689
doi: 10.1177/1355819619847689
pubmed: 31533490
Pine M, Norusis M, Jones B, Rosenthal GE (1997) Predictions of hospital mortality rates: a comparison of data sources. Ann Intern Med 126:347–354. https://doi.org/10.7326/0003-4819-126-5-199703010-00002
doi: 10.7326/0003-4819-126-5-199703010-00002
pubmed: 9054278
D’Hoore W, Sicotte C, Tilquin C (1993) Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med 32:382–387. PMID: 8295545
doi: 10.1055/s-0038-1634956
pubmed: 8295545
Marshall G, Shroyer AL, Grover FL, Hammermeister KE (1998) Time series monitors of outcomes. A new dimension for measuring quality of care. Med Care 36:348–356. https://doi.org/10.1097/00005650-199803000-00011
doi: 10.1097/00005650-199803000-00011
pubmed: 9520959
Moreno R, Miranda DR, Fidler V, Van Schilfgaarde R (1998) Evaluation of two outcome prediction models on an independent database. Crit Care Med 26:50–61. https://doi.org/10.1097/00003246-199801000-00016
doi: 10.1097/00003246-199801000-00016
pubmed: 9428543
Prytherch DR, Sirl JS, Schmidt P, Featherstone PI et al (2005) The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation 66:203–207. https://doi.org/10.1016/j.resuscitation.2005.02.011
doi: 10.1016/j.resuscitation.2005.02.011
pubmed: 15955609
Hucker TR, Mitchell GP, Blake LD et al (2005) Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department. Br J Anaesth 94:735–741. https://doi.org/10.1093/bja/aei122
doi: 10.1093/bja/aei122
pubmed: 15805142
Jollis J, Ancukiewicz M, DeLong E et al (1993) Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research. Ann Intern Med 119:844–850. https://doi.org/10.7326/0003-4819-119-8-199310150-00011
doi: 10.7326/0003-4819-119-8-199310150-00011
pubmed: 8018127
Dans P (1993) Looking for answers in the wrong places [Editorial]. Ann Intern Med 119:855–857. https://doi.org/10.7326/0003-4819-119-8-199310150-00014
doi: 10.7326/0003-4819-119-8-199310150-00014
pubmed: 8379608
Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13:818–829. PMID: 3928249
doi: 10.1097/00003246-198510000-00009
pubmed: 3928249
Norton D (1989) Calculating the risk: reflections on the Norton Scale. Decubitus 2:24–31
pubmed: 2775471
Norton D (1962) Geriatric nursing problems. Int Nurs Rev 9:39–41
pubmed: 14480428
Qaseem A, Mir TP, Starkey M, Denberg TD (2015) Clinical guidelines committee of the American college of physicians. Risk assessment and prevention of pressure ulcers: a clinical practice guideline from the American College of Physicians. Ann Intern Med 162:359–369. https://doi.org/10.7326/M14-1567
doi: 10.7326/M14-1567
pubmed: 25732278
Leshem-Rubinow E, Vaknin A, Sherman S, Justo D (2013) Norton scale, hospitalization length, complications, and mortality in elderly patients admitted to internal medicine departments. Gerontology 59:507–513. https://doi.org/10.1159/000353710
doi: 10.1159/000353710
pubmed: 23921132
Smolin B, Levy Y, Sabbach-Cohen E et al (2015) Predicting mortality of elderly patients acutely admitted to the Department of Internal Medicine. Int J Clin Pract 69:501–508. https://doi.org/10.1111/ijcp.12564
doi: 10.1111/ijcp.12564
pubmed: 25311361
Sanson G, Sadiraj M, Barbin I et al (2020) Prediction of early- and long-term mortality in adult patients acutely admitted to internal medicine: NRS-2002 and beyond. Clin Nutr 39:1092–1100. https://doi.org/10.1016/j.clnu.2019.04.011
doi: 10.1016/j.clnu.2019.04.011
pubmed: 31047715
Díez-Manglano J, Arnal-Longares MJ, Al-Cheikh-Felices P et al (2018) Norton scale score on admission and mortality of patients hospitalized in Internal Medicine departments. Rev Clin Esp (Barc) 218:177–184. https://doi.org/10.1016/j.rce.2018.02.009
doi: 10.1016/j.rce.2018.02.009
pubmed: 29555250
Natanzon SS, Maor E, Klempfner R et al (2020) Norton score and clinical outcomes following acute decompensated heart failure hospitalization. J Cardiol 76:335–341. https://doi.org/10.1016/j.jjcc.2020.05.016
doi: 10.1016/j.jjcc.2020.05.016
pubmed: 32534818
Gasperini B, Pelusi G, Frascati A et al (2021) Predictors of adverse outcomes using a multidimensional nursing assessment in an Italian community hospital. PLoS ONE 16:e0249630. https://doi.org/10.1371/journal.pone.0249630
doi: 10.1371/journal.pone.0249630
pubmed: 33857183
pmcid: 8049226
Silber H, Shiyovich A, Gilutz H (2017) Decreased Norton’s functional score is an independent long-term prognostic marker in hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) project. Int J Cardiol 228:694–699. https://doi.org/10.1016/j.ijcard.2016.11.112
doi: 10.1016/j.ijcard.2016.11.112
pubmed: 27886612
Rabinovitz E, Finkelstein A, Ben Assa E et al (2016) Norton scale for predicting prognosis in elderly patients undergoing trans-catheter aortic valve implantation: a historical prospective study. J Cardiol 67:519–525. https://doi.org/10.1016/j.jjcc.2016.01.017
doi: 10.1016/j.jjcc.2016.01.017
pubmed: 26936468
Froom P, Shimoni Z, Benbassat J, Silke B (2021) A simple index predicting mortality in acutely hospitalized patients. QJM 114:99–104. https://doi.org/10.1093/qjmed/hcaa293
doi: 10.1093/qjmed/hcaa293
pubmed: 33079191
Shimoni Z, Froom P, Silke B, Benbassat J (2022) The presence of a urinary catheteter is an important predictor of in hospital mortality in internal medicine patients. J Eval Clin Pract. https://doi.org/10.1111/jep.13694
doi: 10.1111/jep.13694
pubmed: 35510815
Conway R, Byrne D, O’Riordan D, Silke B (2020) Comparative influence of acute illness severity and comorbidity on mortality. Eur J Intern Med 72:42–46. https://doi.org/10.1016/j.ejim.2019.11.014
doi: 10.1016/j.ejim.2019.11.014
pubmed: 31767191
Faisal M, Scally AJ, Jackson N et al (2018) Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study. BMJ Open 8:e022939. https://doi.org/10.1136/bmjopen-2018-022939
doi: 10.1136/bmjopen-2018-022939
pubmed: 30530474
pmcid: 6286481
Schwartz N, Sakhnini A, Bisharat N (2018) Predictive modeling of inpatient mortality in departments of internal medicine. Intern Emerg Med 13:205–211. https://doi.org/10.1007/s11739-017-1784-8
doi: 10.1007/s11739-017-1784-8
pubmed: 29290047
O’Sullivan E, Callely E, O’Riordan D et al (2012) Predicting outcomes in emergency medical admissions — role of laboratory data and co-morbidity. Acute Med 2:59–65. PMID: 22685695
doi: 10.52964/AMJA.0547