Prevention of AcuTe admIssioN algorithm (PATINA): study protocol of a stepped wedge randomized controlled trial

Anders Fournaise, Jørgen T Lauridsen, Mickael Bech, Uffe K Wiil, Jesper B Rasmussen, Kristian Kidholm, Kurt Espersen, Karen Andersen-Ranberg, Anders Fournaise, Jørgen T Lauridsen, Mickael Bech, Uffe K Wiil, Jesper B Rasmussen, Kristian Kidholm, Kurt Espersen, Karen Andersen-Ranberg

Abstract

Background: The challenges imposed by ageing populations will confront health care systems in the years to come. Hospital owners are concerned about the increasing number of acute admissions of older citizens and preventive measures such as integrated care models have been introduced in primary care. Yet, acute admission can be appropriate and lifesaving, but may also in itself lead to adverse health outcome, such as patient anxiety, functional loss and hospital-acquired infections. Timely identification of older citizens at increased risk of acute admission is therefore needed. We present the protocol for the PATINA study, which aims at assessing the effect of the 'PATINA algorithm and decision support tool', designed to alert community nurses of older citizens showing subtle signs of declining health and at increased risk of acute admission. This paper describes the methods, design and intervention of the study.

Methods: We use a stepped-wedge cluster randomized controlled trial (SW-RCT). The PATINA algorithm and decision support tool will be implemented in 20 individual area home care teams across three Danish municipalities (Kerteminde, Odense and Svendborg). The study population includes all home care receiving community-dwelling citizens aged 65 years and above (around 6500 citizens). An algorithm based on home care use triggers an alert based on relative increase in home care use. Community nurses will use the decision support tool to systematically assess health related changes for citizens with increased risk of acute hospital admission. The primary outcome is acute admission. Secondary outcomes are readmissions, preventable admissions, death, and costs of health care utilization. Barriers and facilitators for community nurse's acceptance and use of the algorithm will be explored too.

Discussion: This 'PATINA algorithm and decision support tool' is expected to positively influence the care for older community-dwelling citizens, by improving nurses' awareness of citizens at increased risk, and by supporting their clinical decision-making. This may increase preventive measures in primary care and reduce use of secondary health care. Further, the study will increase our knowledge of barriers and facilitators to implementing algorithms and decision support in a community care setup.

Trial registration: ClinicalTrials.gov , identifier: NCT04398797 . Registered 13 May 2020.

Keywords: Acute admission; Algorithm; Community-dwelling; Decision support; Geriatrics; Home care; Older people; Stepped-wedge cluster randomized controlled trial.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The PATINA study stepped-wedge design in three municipalities with three (Kerteminde), seven (Svendborg) and 10 (Odense) area home care teams. With a scheduled randomized inclusion of an area home care team every twelfth week in Kerteminde, every sixth week in Svendborg and every fourth week in Odense Municipality. With a one- and three-month follow-up
Fig. 2
Fig. 2
Graph from the PATINA algorithm visualizing a citizen’s utilization of home care (practical help and personnel care), nursing, training and total help
Fig. 3
Fig. 3
The IT-ecosystem and dataflow in the PATINA algorithm and decision support tool. The figure was created using draw.io (open-source freeware)

References

    1. European Commission. The 2018 Ageing report - Economic & Budgetary Projections for the 28 EU member states (2016-2070). 2018 9 Dec 2020.
    1. Turner G, Clegg A, British Geriatrics S. Age UK. Royal College of General P Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing. 2014;43:744–747. doi: 10.1093/ageing/afu138.
    1. Fournaise A, Espensen N, Jakobsen S, Andersen-Ranberg K. Increasing primary health-care services are associated with acute short-term hospitalization of Danes aged 70years and older. Eur Geriatr Med. 2017;8:435–439. doi: 10.1016/j.eurger.2017.07.018.
    1. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet. 2012;380:37–43. doi: 10.1016/S0140-6736(12)60240-2.
    1. Guthrie B, Payne K, Alderson P, McMurdo ME, Mercer SW. Adapting clinical guidelines to take account of multimorbidity. BMJ. 2012;345:e6341. doi: 10.1136/bmj.e6341.
    1. Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, Sutaria S, Hingorani M, Nitsch D, Parisinos CA, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Health. 2019;1:e63–e77. doi: 10.1016/S2589-7500(19)30012-3.
    1. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381:752–762. doi: 10.1016/S0140-6736(12)62167-9.
    1. Creditor MC. Hazards of hospitalization of the elderly. Ann Intern Med. 1993;118:219–223. doi: 10.7326/0003-4819-118-3-199302010-00011.
    1. Hubbard RE, Peel NM, Samanta M, Gray LC, Mitnitski A, Rockwood K. Frailty status at admission to hospital predicts multiple adverse outcomes. Age Ageing. 2017;46:801–806. doi: 10.1093/ageing/afx081.
    1. Walsh B, Addington-Hall J, Roberts HC, Nicholls PG, Corner J. Outcomes after unplanned admission to hospital in older people: ill-defined conditions as potential indicators of the frailty trajectory. J Am Geriatr Soc. 2012;60:2104–2109. doi: 10.1111/j.1532-5415.2012.04198.x.
    1. Theou O, Squires E, Mallery K, Lee JS, Fay S, Goldstein J, Armstrong JJ, Rockwood K. What do we know about frailty in the acute care setting? A scoping review. BMC Geriatr. 2018;18:139. doi: 10.1186/s12877-018-0823-2.
    1. Jarrett PG, Rockwood K, Carver D, Stolee P, Cosway S. Illness presentation in elderly patients. Arch Intern Med. 1995;155:1060–1064. doi: 10.1001/archinte.1995.00430100086010.
    1. Wallace E, Stuart E, Vaughan N, Bennett K, Fahey T, Smith SM. Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review. Med Care. 2014;52:751–765. doi: 10.1097/MLR.0000000000000171.
    1. Kurichi JE, Bogner HR, Streim JE, Xie D, Kwong PL, Saliba D, Hennessy S. Predicting 3-year mortality and admission to acute-care hospitals, skilled nursing facilities, and long-term care facilities in Medicare beneficiaries. Arch Gerontol Geriatr. 2017;73:248–256. doi: 10.1016/j.archger.2017.08.005.
    1. Morgan DJ, Bame B, Zimand P, Dooley P, Thom KA, Harris AD, Bentzen S, Ettinger W, Garrett-Ray SD, Tracy JK, et al. Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions. JAMA Netw Open. 2019;2:e190348. doi: 10.1001/jamanetworkopen.2019.0348.
    1. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306:1688–1698. doi: 10.1001/jama.2011.1515.
    1. Artetxe A, Beristain A, Grana M. Predictive models for hospital readmission risk: A systematic review of methods. Comput Methods Programs Biomed. 2018;164:49–64. doi: 10.1016/j.cmpb.2018.06.006.
    1. Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010;10:338. doi: 10.1186/1472-6963-10-338.
    1. Shelton P, Sager MA, Schraeder C. The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit. Am J Manag Care. 2000;6:925–933.
    1. Veyron JH, Friocourt P, Jeanjean O, Luquel L, Bonifas N, Denis F, Belmin J. Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study. PLoS One. 2019;14:e0220002. doi: 10.1371/journal.pone.0220002.
    1. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378:981–983. doi: 10.1056/NEJMp1714229.
    1. Verghese A, Shah NH, Harrington RA. What This Computer Needs Is a Physician: Humanism and Artificial Intelligence. JAMA. 2018;319:19–20. doi: 10.1001/jama.2017.19198.
    1. Agniel D, Kohane IS, Weber GM. Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ. 2018;361:k1479. doi: 10.1136/bmj.k1479.
    1. Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24:198–208. doi: 10.1093/jamia/ocw042.
    1. Olejaz M, Juul Nielsen A, Rudkjøbing A, Okkels Birk H, Krasnik A, Hernández-Quevedo C. Denmark: health system review. Health Syst Trans. 2012;14(2):1–192.
    1. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gotzsche PC, Krleza-Jeric K, Hrobjartsson A, Mann H, Dickersin K, Berlin JA, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158:200–207. doi: 10.7326/0003-4819-158-3-201302050-00583.
    1. Statistics Denmark [Danmarks Statistik]: StatBank Denmark [Statistikbanken]. [] (2020). Accessed 4 Dec 2020.
    1. Kvist J, Greve B. Has the Nordic Welfare Model Been Transformed? Soc Policy Administ. 2011;45:146–160. doi: 10.1111/j.1467-9515.2010.00761.x.
    1. Lyttkens CH, Christiansen T, Häkkinen U, Kaarboe O, Sutton M, Welander A. The core of the Nordic health care system is not empty. Nordic J Health Econ. 2016;4:7–27. doi: 10.5617/njhe.2848.
    1. Denmark VK. In: International Health Care System Profiles: The Commonwealth Fund. Tikkanen R, Osborn R, Mossialos E, Djordjevic A, Wharton G, editors. 2020.
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019;95:103208. doi: 10.1016/j.jbi.2019.103208.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Collin C, Wade DT, Davies S, Horne V. The Barthel ADL index: a reliability study. Int Disabil Stud. 1988;10:61–63. doi: 10.3109/09638288809164103.
    1. Nissen SK, Fournaise A, Lauridsen JT, Ryg J, Nickel CH, Gudex C, Brabrand M, Andersen-Ranberg K. Cross-sectoral inter-rater reliability of the clinical frailty scale - a Danish translation and validation study. BMC Geriatr. 2020;20:443. doi: 10.1186/s12877-020-01850-y.
    1. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173:489–495. doi: 10.1503/cmaj.050051.
    1. Beauchet O, Launay CP, Chabot J, Dejager S, Bineau S, Galery K, Berrut G. Prediction of unplanned hospital admissions in older community dwellers using the 6-item Brief Geriatric Assessment: Results from REPERAGE, an observational prospective population-based cohort study. Maturitas. 2019;122:1–7. doi: 10.1016/j.maturitas.2019.01.002.
    1. Local Government Denmark [Kommunernes Landsforening]: Common Language III [Fællessprog III]. [] (2020). Accessed November 14.
    1. Local Government Denmark . Ministry of Health, Danish regions, Ministry of Finance: prevention of admissions - visible results [Forebyggelse af indlæggelser - synlige resultater] Copenhagen: Ministry of Health; 2014.
    1. Hemming K, Taljaard M. Sample size calculations for stepped wedge and cluster randomised trials: a unified approach. J Clin Epidemiol. 2016;69:137–146. doi: 10.1016/j.jclinepi.2015.08.015.
    1. Schulz KF, Altman DG, Moher D, Group C CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMC Med. 2010;8:18. doi: 10.1186/1741-7015-8-18.
    1. Hemming K, Taljaard M, McKenzie JE, Hooper R, Copas A, Thompson JA, Dixon-Woods M, Aldcroft A, Doussau A, Grayling M, et al. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ. 2018;363:k1614. doi: 10.1136/bmj.k1614.
    1. Barbazza E, Langins M, Kluge H, Tello J. Health workforce governance: Processes, tools and actors towards a competent workforce for integrated health services delivery. Health Policy. 2015;119:1645–1654. doi: 10.1016/j.healthpol.2015.09.009.
    1. Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ. 2015;350:h391. doi: 10.1136/bmj.h391.
    1. Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007;28:182–191. doi: 10.1016/j.cct.2006.05.007.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir