Tecnologías utilizadas por la enfermería para predecir el deterioro clínico en adultos hospitalizados: revisión del alcance

Gondim, Érica Sobral; Gomes, Emiliana Bezerra; Matos, José Hiago Feitosa de; Pinto, Sarah de Lima; Oliveira, Célida Juliana de; Alencar, Ana Maria Parente Garcia

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Revista Brasileira de Enfermagem

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Sumário

REVIEW • Rev. Bras. Enferm. 75 (5) • 2022 • https://doi.org/10.1590/0034-7167-2021-0570 copiar

Tecnologías utilizadas por la enfermería para predecir el deterioro clínico en adultos hospitalizados: revisión del alcance

Autoría SCIMAGO INSTITUTIONS RANKINGS

RESUMEN

Objetivo:

mapear las tecnologías de deterioro clínico precoz utilizadas en la práctica profesional de enfermeros en el cuidado de pacientes adultos hospitalizados.

Métodos:

se trata de una revisión de alcance, según el Manual del Revisor del Instituto Joanna Briggs, que busca mapear las principales tecnologías para la detección temprana del deterioro clínico de los pacientes hospitalizados disponibles para uso de enfermería, resumiéndolas e indicando lagunas de conocimiento para ser investigadas.

Resultados:

se encontraron 27 estudios. Las variables más presentes en las tecnologías fueron signos vitales, gasto urinario, escalas de conciencia y riesgo, examen clínico y juicio de enfermería. Los principales desenlaces fueron activación de equipos de respuesta rápida, muerte, paro cardíaco e ingreso a unidades de cuidados críticos.

Consideraciones finales:

el estudio enfatiza las variables más precisas en la evaluación clínica del paciente, de modo que los signos indicativos de gravedad potencial puedan ser priorizados para orientar conductas de salud con el objetivo de intervenir tempranamente ante el deterioro clínico en curso.

Descriptores:
Tecnología; Enfermería; Deterioro Clínico; Pacientes Internos; Seguridad del Paciente

Keyword search	Scopus	WOS	MEDLINE	CINAHL
(injury severity score) AND (clinical deterioration) AND (inpatients)	4	0	5	0
(early warning score) AND (risk assessment) AND (inpatients)	209	1	62	4
(early warning score) AND (clinical deterioration) AND (inpatients)	292	9	66	7
TOTAL = 659	505	10	133	11

ID	Reference	Country	Design	LoE	Objective
A1	Fogerty et al. (2018)(²⁰20 Fogerty RL, Sussman LS, Kenyon K, Li F, Sukumar N, Kliger AS, et al. Using system inflammatory response syndrome as an easy-to-implement, sustainable, and automated tool for all-cause deterioration among medical inpatients. J Patient Saf. 2019;15(4):e74-7. https://doi.org/10.1097/PTS.0000000000000463 https://doi.org/10.1097/PTS.000000000000... )	United States	Observational prospective	3b	Develop and examine the use of a low-cost deterioration detection tool based on the Systemic Inflammatory Response Syndrome (SIRS) criterion.
A2	Albutt et al. (2020)(²¹21 Albutt A, O’Hara J, Conner M, Lawton R. Involving patients in recognising clinical deterioration in hospital using the patient wellness questionnaire: a mixed-methods study. J Res Nurs. 2020;25(1):68-86. https://doi.org/10.1177/1744987119867744 https://doi.org/10.1177/1744987119867744... )	United Kingdom	Mixed methods	4	Develop and assess a method of patient involvement in clinical deterioration recognition and explore its feasibility and acceptability from patients’ perspective.
A3	Luis e Nunes (2018)(⁵5 Luís L, Nunes C. Short national early warning score: developing a modified early warning score. Aust Crit Care 2018;31(6):376-81. https://doi.org/10.1016/j.aucc.2017.11.004 https://doi.org/10.1016/j.aucc.2017.11.0... )	Portugal	Cohort	2b	Assess whether a simplified National Early Warning Score (NEWS) model will improve data use and collection.
A4	Kia et al. (2020)(⁸8 Kia A, Timsina P, Joshi HN, Klang E, Gupta RR, Freeman RM, Reich DL, et al. MEWS++: enhancing the prediction of clinical deterioration in admitted patients through a machine learning model. J. Clin. Med. 2020;9(2):343. https://doi.org/10.3390/jcm9020343 https://doi.org/10.3390/jcm9020343... )	United States	Cohort	2b	Describe an artificial intelligence model that enables the identification of patients at risk of escalation of care or death six hours before the event.
A5	Kirkland et al.(2013)(⁴4 Kirkland LL, Malinchoc M, O’Byrne M, Benson JT, Kashiwagi DT, Burton MC, et al. A clinical deterioration prediction tool for internal medicine patients. Am J Med Qual. 2013;28(2):135-42. https://doi.org/10.1177/1062860612450459 https://doi.org/10.1177/1062860612450459... )	United States	Case-control	2b	Create and validate a clinical deterioration prediction tool using routinely collected nursing and clinical measures.
A6	Prytherch et al. (2010)(²²22 Prytherch DR, Smith GB, Schmidt PE, Featherstone PI. ViEWS: towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81(8):932-7. https://doi.org/10.1016/j.resuscitation.2010.04.014 https://doi.org/10.1016/j.resuscitation.... )	United Kingdom	Cohort	2b	Develop a screening and triggering system for early warning of patient deterioration detection.
A7	Romero-Brufau et al. (2019)(¹⁴14 Romero-Brufau S, Gaines K, Nicolas CT, Johnson MG, Hickman J, Huddleston JM. The fifth vital sign? nurse worry predicts inpatient deterioration within 24 hours. JAMIA Open. 2019;2(4):465-70. https://doi.org/https://doi.org/10.1093/jamiaopen/ooz033 https://doi.org/https://doi.org/10.1093/... )	United States	Cohort	2b	Assess the accuracy of nurses’ judgment in detecting imminent physiological deterioration.
A8	O’Connell et al. (2016)(⁶6 O’Connell A, Flabouris A, Kim SW, Horwood C, Hakendorf P, Thompson CH. A newly-designed observation and response chart’s effect upon adverse inpatient outcomes and rapid response team activity. Intern Med J. 2016;46(8):909-16. https://doi.org/10.1111/imj.13137 https://doi.org/10.1111/imj.13137... )	Australia	Retrospective observational	4	Assess the impact of a new standardized observation and response chart.
A9	Paterson et al. (2006)(²³23 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 (London). 2006;6(3):281-4. https://doi.org/10.7861/clinmedicine.6-3-281 https://doi.org/10.7861/clinmedicine.6-3... )	Scotland	Cohort	2b	Assess the impact of introducing a standardized early warning scoring system on patient assessments and outcomes in acute admissions.
A10	Kyriacos et al. (2014)(²⁴24 Kyriacos U, Jelsma J, James M, Jordan S. Monitoring vital signs: development of a modified early warning scoring (MEWS) system for general wards in a developing country. PLoS One. 2014;9(1):e87073. https://doi.org/10.1371/journal.pone.0087073 https://doi.org/10.1371/journal.pone.008... )	South Africa	Methodological study	5	Develop and validate an observation chart for nurses incorporating an early warning scoring system with physiological parameters for bedside monitoring in general wards in a public hospital in South Africa.
A11	Pirret e Kazula (2021)(²⁵25 Pirret AM, Kazula LM. The impact of a modified New Zealand Early Warning Score (M-NZEWS) and NZEWS on ward patients triggering a medical emergency team activation: a mixed methods sequential design. Intensive Crit Care Nurs. 2021;62:102963. https://doi.org/10.1016/j.iccn.2020.102963 https://doi.org/10.1016/j.iccn.2020.1029... )	New Zealand	Mixed methods	2b	Determine the impact of modified NZEWS (M-NZEWS) and NZEWS as emergency team activation triggers on medical ward patients.
A12	Gillies et al. (2020)(²⁶26 Gillies CE, Taylor DF, Cummings BC, Ansari S, Islim F, Kronick SL, et al. Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: a novel simulation and solution. J Biomed Inform. 2020;110:103528. https://doi.org/https://doi.org/10.1016/j.jbi.2020.103528 https://doi.org/https://doi.org/10.1016/... )	United States	Cohort	2b	Develop a new early warning system (PICTURE) to predict deterioration of hospitalized patients.
A13	Capan et al. (2018)(²⁷27 Capan M, Hoover S, Miller KE, Pal C, Glasgow JM, Jackson EV, et al. Data-driven approach to early warning score-based alert management. BMJ Open Qual. 2018;7(3):e000088. https://doi.org/10.1136/bmjoq-2017-000088 https://doi.org/10.1136/bmjoq-2017-00008... )	United States	Retrospective observational	4	Develop and assess a systematic approach to managing an early warning system.
A14	Churpek et al. (2014)(⁹9 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 on the wards. Crit Care Med. 2014;42(4):841-8. https://doi.org/10.1097/CCM.0000000000000038 https://doi.org/10.1097/CCM.000000000000... )	United States	Cohort	2b	Develop and validate a prediction model to detect cardiac arrest using data from electronic medical records.
A15	Rothman et al. (2013)(¹⁵15 Rothman MJ, Rothman SI, Beals-IV J. Development and validation of a continuous measure of patient condition using the Electronic Medical Record. J Biomed Inform. 2013;46(5):837-48. https://doi.org/10.1016/j.jbi.2013.06.011 https://doi.org/10.1016/j.jbi.2013.06.01... )	United States	Survey	1c	Report the development and validation of a continuous measure of general clinical condition that can be used for medical-surgical and critical care patients.
A16	Royal College of Physicians (2012)(¹²12 Royal College of Physicians. National Early Warning Score (NEWS). Standardising the assessment of acute-illness severity in the NHS: updated report of a working party [Internet]. London: RCP, 2017 [cited 2021 Jun 12];77p. Available from: https://www.rcplondon.ac.uk/file/8636/download https://www.rcplondon.ac.uk/file/8636/do... )	United Kingdom	Expert opinion - Manual	5	Provide training support for in-service use of NEWS.
A17	Jarvis et al. (2015)(⁷7 Jarvis S, Kovacs C, Briggs J, Meredith P, Schmidt PE, Featherstone PI, et al. Can binary early warning scores perform as well as standard early warning scores for discriminating a patient’s risk of cardiac arrest, death or unanticipated intensive care unit admission? Resuscitation. 2015;93:46-52. http://doi.org/10.1016/j.resuscitation.2015.05.025 http://doi.org/10.1016/j.resuscitation.2... )	United Kingdom	Cohort	2b	Develop the binary NEWS, investigating the effectiveness of two possible scores for each vital sign.
A18	Subbe et al. (2001)(²⁸28 Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified early warning score in medical admissions. QJM. 2001;94(10):521-6. https://doi.org/10.1093/qjmed/94.10.521 https://doi.org/10.1093/qjmed/94.10.521... )	United Kingdom	Cohort	2b	Assess the ability of a modified EWS to identify at-risk clinical patients and as an early assessment screening tool for admission to a high-dependency unit.
A19	Kho et al. (2007)(¹⁰10 Kho A, Rotz D, Alrahi K, Cárdenas W, Ramsey K, Liebovitz D, et al. Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration. AMIA Annu Symp Proc [Internet]. 2007 [cited 2021 Jul 9];3:404-8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655808/ https://www.ncbi.nlm.nih.gov/pmc/article... )	United States	Cohort	2b	Assess whether an automatically generated scale based on data from an electronic medical record can accurately detect patients at risk for cardiovascular collapse, death, or ICU transfer.
A20	Smith et al. (2006)(²⁹29 Smith GB, Prytherch DR, Schmidt P, Featherstone PI, Knight D, Clements G, et al. Hospital-wide physiological surveillance: a new approach to the early identification and management of the sick patient. Resuscitation. 2006;71(1):19-28. https://doi.org/10.1016/j.resuscitation.2006.03.008 https://doi.org/10.1016/j.resuscitation.... )	United Kingdom	Case series	4	Describe a bedside vital sign collection system using standardized personal digital assistant (PDA) integrated with physiological and laboratory data to assess disease severity and support clinical decision.
A21	Hodgetts et al. (2002)(³⁰30 Hodgetts TJ, Kenward G, Vlachonikolis IG, Payne S, Castle N. The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. Resuscitation. 2002;54(2):125-31. https://doi.org/10.1016/s0300-9572(02)00100-4 https://doi.org/10.1016/s0300-9572(02)00... )	United Kingdom	Quasi-experimental	2b	Identify risk factors for in-hospital cardiac arrest (CA), formulate medical emergency team (MET) activation criteria, and assess sensitivity and specificity for the scoring system.
A22	Nishijima et al. (2016)(³¹31 Nishijima I, Oyadomari S, Maedomari S, Toma R, Igei C, Kobata S, et al. Use of a modified early warning score system to reduce the rate of in-hospital cardiac arrest. J Intensive Care. 2016;4:12. https://doi.org/10.1186/s40560-016-0134-7 https://doi.org/10.1186/s40560-016-0134-... )	Japan	Non-randomized clinical trial	2b	Detect abnormalities early by vital sign classification.
A23	Preece et al. (2010)(³²32 Preece MHW, Horswill MS, Hill A, Watson MO. The development of the Adult Deterioration Detection System (ADDS) Chart Report prepared for the Australian Commission on Safety and Quality in Health Care’s program for Recognising and Responding to Clinical Deterioration [Internet]. 2010 [cited 2021 Jun 18]. 26p. Available from: https://www.safetyandquality.gov.au/sites/default/files/migrated/35981-ChartDevelopment.pdf https://www.safetyandquality.gov.au/site... )	Australia	Case series	4	Investigate the effectiveness of observation charts in recognizing and managing deterioration.
A24	Chatterjee et al. (2005)(³³33 Chatterjee MT, Moon JC, Murphy R, McCrea D. The “OBS” chart: an evidence based approach to re-design of the patient observation chart in a district general hospital setting. Postgrad Med J. 2005;81(960):663-6. https://doi.org/10.1136/pgmj.2004.031872 https://doi.org/10.1136/pgmj.2004.031872... )	United Kingdom	Cohort	2b	Assess whether the approach to redrawing observation chart improves detection of physiological decline.
A25	Bailey et al. (2013)(¹¹11 Bailey TC, Chen Y, Mao Y, Lu C, Hackmann G, Micek ST, et al. A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013;8(5):236-42. https://doi.org/10.1002/jhm.2009 https://doi.org/10.1002/jhm.2009... )	United States	Crossover	1b	Prospectively validate a predictive algorithm of clinical deterioration in general medical ward patients and conduct an assay based on this algorithm.
A26	Jacques et al. (2006)(³⁴34 Jacques T, Harrison GA, McLaws ML, Kilborn G. Signs of critical conditions and emergency responses (SOCCER): a model for predicting adverse events in the inpatient setting. Resuscitation. 2006;69(2):175-83. https://doi.org/10.1016/j.resuscitation.2005.08.015 https://doi.org/10.1016/j.resuscitation.... )	Australia	Cross-sectional	2b	Establish an association between records of altered physiological variables and adverse events.
A27	Kollef et al. (2016)(³⁵35 Kollef MH, Heard K, Chen Y, Lu C, Martin N, Bailey T. Mortality and length of stay trends following implementation of a rapid response system and real-time automated clinical deterioration alerts. Am J Med Qual. 2017;32(1):12-8. https://doi.org/10.1177/1062860615613841 https://doi.org/10.1177/1062860615613841... )	United States	Retrospective observational	3b	Determine the potential influence of a rapid response system employing real-time clinical deterioration alerts (RTCDAs) on patients at eight general medical facilities.

ID	Technology	Outcomes	Variables measured for determination of clinical deterioration
ID	Technology	Outcomes	BP	HR	RR	T	SPO₂	Other
A1	SIRS-based automated alerting	ICU transfer and death	X	X	X	X		Laboratory tests
A2	Patient Wellness Questionnaire (PWQ)	Self-perceived wellness						Ability of patients to recognize deterioration
A3	Short NEWS	ICU transfer and death	X	X	X		X	Oxygen therapy, level of consciousness
A4	MEWS++	ICU transfer and death	X	X	X		X	Demographic variables, level of consciousness, admission and stay characteristics, laboratory tests, clinical examinations and nursing notes ^* * Studies that bring specific nurses’ assessment variables.
A5	Clinical deterioration prediction tool	Clinical deterioration	X		X	X	X	Braden scale, shock index, fall risk scale
A6	VitalPAC- EWS (ViEWS)	Discharge status and rapid response team (RRT) triggers	X	X	X	X	X	Level of consciousness
A7	Nurses’ Worry Factor (WF) score	CA, RRT triggers, ICU transfer						Nurses’ clinical judgment^* * Studies that bring specific nurses’ assessment variables.
A8	Standardized observation and response chart	CA, RRT triggers, ICU transfer, death		X	X	X	X	Oxygen therapy, level of consciousness and sedation, pain scale
A9	Early warning scoring system chart (SEWS chart)	Parameter registration and death	X	X	X	X	X	Level of consciousness
A10	MEWS chart	Clinical deterioration	X	X	X	X	X	Urinary output, level of consciousness
A11	Modified NZEWS (M-NZEWS) and NZEWS	CA, RRT triggers	X	X	X	X	X	Level of consciousness, oxygen therapy
A12	PICTURE	ICU transfer, vasoactive medication administration, death	X	X	X		X	Oxygen therapy, Glasgow Coma Scale, urine output, laboratory tests, calculated values (shock index, age x shock index, pulse pressure)
A13	Christiana Care Early Warning Score (CEWS)	RRT and composite event triggers (RRT trigger, code blue activation, ICU transfer or death)	X	X	X	X	X	Level of consciousness, oxygen therapy and 8 clinical assessments derived from nursing assessments ^* * Studies that bring specific nurses’ assessment variables.
A14	Prediction model for adverse outcomes in wards	CA and ICU transfer	X	X	X	X	X	Laboratory tests, patient characteristics (age, level of consciousness, ambient air, previous ICU stay)
A15	Rothman Index	Discharge readiness, 30-day readmission, clinical deterioration, CA, and death	X	X	X	X	X	43 candidate variables: 13 nursing assessments^* * Studies that bring specific nurses’ assessment variables. , 6 vital signs, 23 laboratory tests and heart rate monitoring
A16	NEWS	Death	X	X	X	X	X	Level of consciousness and oxygen therapy
A17	Binary NEWS	CA, ICU transfer and death	X	X	X	X	X	Oxygen therapy, level of consciousness
A18	Modified Early Warning Scale (MEWS)	RRT triggers, ICU transfer, and death	X	X	X	X		Demographic data, level of consciousness
A19	Automatic scoring system with electronic medical record data	RRT triggers by CA, ICU transfer and death	X	X	X	X		Age and Body Mass Index (BMI)
A20	Hospital patient surveillance system	Disease severity, potential risk of developing adverse outcome	X	X	X	X		Level of consciousness, urinary output, laboratory tests
A21	Risk scoring system for hospital CA with criteria for alerting the MET	CA	X	X	X	X	X	Nurses’ clinical judgment^* * Studies that bring specific nurses’ assessment variables. , urinary output, level of consciousness, pain
A22	Modified MEWS	CA	X	X	X	X		Level of consciousness, nurse’s concern about patients’ condition
A23	Adult Deterioration Detection System (ADDS chart)	Clinical deterioration	X	X	X	X	X	Oxygen therapy, urinary output, level of consciousness, pain
A24	Bedside observation chart (OBS chart)	Clinical deterioration	X	X	X	X	X	-
A25	Deterioration prediction tool with real-time alert algorithm	Length of hospital stay, ICU transfer and death	X	X	X		X	Shock index, anticoagulant use, age, laboratory tests
A26	Signs of Critical Conditions and Emergency Response (SOCCER)	Severe respiratory problems, CA, ICU transfer and death	X	X	X		X	Variables (e.g., level of consciousness, urinary output, laboratory tests) listed in 02 tables: 1 with 26 early signs and another with 21 late signs of critical conditions
A27	Rapid response system and automated real-time clinical deterioration alerts	CA, length of hospital stay and death	X	X	X		X	Shock index, change in clotting

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[1] Corresponding author: Érica Sobral Gondim E-mail: erica.sobral@urca.br