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. |