What intensive care registries can teach us about outcomes

Abi Beane, Jorge I F Salluh, Rashan Haniffa, Abi Beane, Jorge I F Salluh, Rashan Haniffa

Abstract

Purpose of review: Critical care registries are synonymous with measurement of outcomes following critical illness. Their ability to provide longitudinal data to enable benchmarking of outcomes for comparison within units over time, and between units, both regionally and nationally is a key part of the evaluation of quality of care and ICU performance as well as a better understanding of case-mix. This review aims to summarize literature on outcome measures currently being reported in registries internationally, describe the current strengths and challenges with interpreting existing outcomes and highlight areas where registries may help improve implementation and interpretation of both existing and new outcome measures.

Recent findings: Outcomes being widely reported through ICU registries include measures of survival, events of interest, patient-reported outcomes and measures of resource utilization (including cost). Despite its increasing adoption, challenges with quality of reporting of outcomes measures remain. Measures of short-term survival are feasible but those requiring longer follow-ups are increasingly difficult to interpret given the evolving nature of critical care in the context of acute and chronic disease management. Furthermore, heterogeneity in patient populations and in healthcare organisations in different settings makes use of outcome measures for international benchmarking at best complex, requiring substantial advances in their definitions and implementation to support those seeking to improve patient care.

Summary: Digital registries could help overcome some of the current challenges with implementing and interpreting ICU outcome data through standardization of reporting and harmonization of data. In addition, ICU registries could be instrumental in enabling data for feedback as part of improvement in both patient-centred outcomes and in service outcomes; notably resource utilization and efficiency.

Conflict of interest statement

Conflicts of Interest: JIFS is a co-founder of Epimed Monitor®, an electronic healthcare system used to track ICU quality metrics. AB, RH and JIFS are members of the steering committee of LOGIC (Linking of Global Intensive Care – www.icubenchmarking.com) a non-profit, independent academic initiative to promote research and quality improvement in intensive care.

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.

Figures

Figure 1. Funnel plot graphic for the…
Figure 1. Funnel plot graphic for the benchmarking of ICU resource use.
The metric used is the standardized resource use (SRU) based on SAPS 3. Each yellow dot represents an ICU. Lowest rates represent better resource use and efficiency (lower observed/expected resource use rates).

References

    1. Reper P, Dicker D, Damas P, Huyghens L, Haelterman M. Improving the quality of the intensive care follow-up of ventilated patients during a national registration program. Public Health. 2017;148:159–166. doi: 10.1016/j.puhe.2017.03.014.
    1. Kruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, Adeyi O, Barker P, Daelmans B, Doubova SV, English M. High-quality health systems in the Sustainable Development Goals era: time for a revolution. The Lancet global health. 2018 Nov 1;6(11):e1196-252.
    1. CRIT Care Asia. Hashmi M, Beane A, Murthy S, Dondorp AM, Haniffa R. Leveraging a Cloud-Based Critical Care Registry for COVID-19 Pandemic Surveillance and Research in Low- and Middle-Income Countries. JMIR Public Health Surveill. 2020;6(4):e21939. doi: 10.2196/21939.
    1. Verburg IWM, de Jonge E, Peek N, de Keizer NF. The association between outcome-based quality indicators for intensive care units. PLoS One. 2018 Jun 13;13(6):e0198522. doi: 10.1371/journal.pone.0198522.
    1. cjlm@uw.edu GBD 2015 HA and QCE address:, Collaborators GBD 2015 HA and Q. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990-2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet (London, England) 2017;390:231–266. doi: 10.1016/S0140-6736(17)30818-8.
    1. Gliklich R, Dreyer N, Leavy M. Registries for Evaluating Patient Outcomes: A User’s Guide. 4th ed. Agency for Healthcare Research and Quality; US: 2020.
    1. Jawad Issrah, Rashan Sumayyah, Sigera Chathurani, et al. A scoping review of registry captured indicators for evaluating quality of critical care in ICU. 2021 Mar 29; doi: 10.21203/-365999/v1. PREPRINT (Version 1) available at Research Square. [(**) The study provides a systematically review the literature on quality indicators for evaluating critical care and describe their current evidence as well as the variances in measurement, and the reported challenges of implementation]
    1. Christiansen CF, Møller MH, Nielsen H, Christensen S. The Danish intensive care database. Clinical epidemiology. 2016;8:525.
    1. Chrusch CA, Martin CM. Quality improvement in critical care: selection and development of quality indicators. Canadian respiratory journal. 2016 Jan 1; 2016.
    1. Haniffa R, Pubudu De Silva A, Weerathunga P, et al. Applicability of the APACHE II model to a lower middle income country. J Crit Care. 2017;42:178–183.
    1. Paul E, Bailey M, Kasza J, Pilcher D. The ANZROD model: better benchmarking of ICU outcomes and detection of outliers. Crit Care Resusc. 2016 Mar;18(1):25–36.
    1. Harrison DA, Lone NI, Haddow C, MacGillivray M, Khan A, Cook B, Rowan KM. External validation of the Intensive Care National Audit & Research Centre (ICNARC) risk prediction model in critical care units in Scotland. BMC Anesthesiol. 2014 Dec 15;14:116. doi: 10.1186/1471-2253-14-116.
    1. Tirupakuzhi Vijayaraghavan BK, Priyadarshini D, Rashan A, Beane A, Venkataraman R, Ramakrishnan N, Haniffa R, Indian Registry of IntenSive care (IRIS) collaborators Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country. PLoS One. 2020 Dec 31;15(12):e0244989.
    1. Lalani HS, Waweru-Siika W, Mwogi T, Kituyi P, Egger JR, Park LP, Kussin PS. Intensive Care Outcomes and Mortality Prediction at a National Referral Hospital in Western Kenya. Ann Am Thorac Soc. 2018 Nov;15(11):1336–1343. doi: 10.1513/AnnalsATS.201801-051OC.
    1. Riviello ED, Kiviri W, Fowler RA, Mueller A, Novack V, Banner-Goodspeed VM, Weinkauf JL, Talmor DS, Twagirumugabe T. Predicting mortality in low-income country ICUs: the Rwanda Mortality Probability Model (R-MPM) PloS one. 2016 May 19;11(5):e0155858.
    1. Haniffa R, Mukaka M, Munasinghe SB, et al. Simplified prognostic model for critically ill patients in resource limited settings in South Asia. Crit Care. 2017;21:250. doi: 10.1186/s13054-017-1843-6.
    1. Adhikari NKJ, Arali R, Attanayake U, et al. Implementing an intensive care registry in India: preliminary results of the case-mix program and an opportunity for quality improvement and research. Wellcome Open Res. 2020;5:182. doi: 10.12688/wellcomeopenres.16152.2. version 2; peer review: 2 approved.
    1. Cuthbertson BH, Roughton S, Jenkinson D, Maclennan G, Vale L. Quality of life in the five years after intensive care: a cohort study. Crit Care. 2010;14:R6.
    1. Reper P, Dicker D, Damas P, Huyghens L, Haelterman M. Improving the quality of the intensive care follow-up of ventilated patients during a national registration program. Public Health. 2017;148:159–166. doi: 10.1016/j.puhe.2017.03.014.
    1. Litton E, Guidet B, de Lange D. National registries: Lessons learnt from quality improvement initiatives in intensive care. J Crit Care. 2020 Dec;60:311–318. doi: 10.1016/j.jcrc.2020.08.012. Epub 2020 Aug 18.
    1. Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315:788–800.
    1. Samanamalee S, Sigera PC, De Silva AP, et al. Traumatic brain injury (TBI)outcomes in an LMIC tertiary care centre and performance of trauma scores. BMC Anesthesiol. 2018;18(4)
    1. Worth LJ, Spelman T, Bull AL, Brett JA, Richards MJ. Central line-associated bloodstream infections in Australian intensive care units: Time-trends in infection rates, etiology, and antimicrobial resistance using a comprehensive Victorian surveillance program, 2009-2013. American journal of infection control. 2015 Aug 1;43(8):848–52.
    1. El-Kholy A, Saied T, Gaber M, Younan MA, Haleim MM, El-Sayed H, Bazara’a H, Talaat M. Device-associated nosocomial infection rates in intensive care units at Cairo University hospitals: first step toward initiating surveillance programs in a resource-limited country. American journal of infection control. 2012 Aug 1;40(6):e216-20.
    1. Lone NI, Gillies MA, Haddow C, Dobbie R, Rowan KM, Wild SH, Murray GD, Walsh TS. Five-year mortality and hospital costs associated with surviving intensive care. American journal of respiratory and critical care medicine. 2016 Jul 15;194(2):198–208.
    1. Christiansen CF, Møller MH, Nielsen H, Christensen S. The Danish intensive care database. Clinical epidemiology. 2016;8:525.
    1. Malacarne P, Langer M, Nascimben E, Moro ML, Giudici D, Lampati L, Bertolini G. Italian Group for the Evaluation of Interventions in Intensive Care Medicine. Building a continuous multicenter infection surveillance system in the intensive care unit: findings from the initial data set of 9,493 patients from 71 Italian intensive care units. Critical care medicine. 2008 Apr 1;36(4):1105–13.
    1. Li Y, Cao X, Ge H, Jiang Y, Zhou H, Zheng W. Targeted surveillance of nosocomial infection in intensive care units of 176 hospitals in Jiangsu province, China. Journal of Hospital Infection. 2018 May 1;99(1):36–41.
    1. Hicks P, Huckson S, Fenney E, Leggett I, Pilcher D, Litton E. The financial cost of intensive care in Australia: a multicentre registry study. Med J Aust. 2019;211:324–325. doi: 10.5694/mja2.50309. [(*) A registry-based study that describes real-world costs of intensive care]
    1. Rousseau AF, Prescott HC, Brett SJ, et al. Long-term outcomes after critical illness: recent insights. Crit Care. 2021;25:108. doi: 10.1186/s13054-021-03535-3.
    1. Dinglas VD, Cherukuri SP, Needham DM. Core outcomes sets for studies evaluating critical illness and patient recovery. Current opinion in critical care. 2020 Oct 1;26(5):489–99.
    1. Dinglas VD, Faraone LN, Needham DM. Understanding patient-important outcomes after critical illness: a synthesis of recent qualitative, empirical, and consensus-related studies. Curr Opin Crit Care. 2018;24:401–409.
    1. Baker T, Khalid K, Acicbe O, et al. Council of the World Federation ofSocieties of Intensive and Critical Care Medicine. Critical care of tropical disease in low income countries: report from the Task Force on TropicalDiseases by the World Federation of Societies of Intensive and Critical CareMedicine. J Crit Care. 2017;42:351–354.
    1. Lai PS, Bebell LM, Meney C, et al. Epidemiology of antibiotic-resistant wound infections from six countries in Africa. BMJ Glob Health. 2017;2:e000475
    1. Beane Abi, PhD1, Dongelmans Dave A, PhD2, Fernandez Ariel L, MD3, Guidet Bertrand, MD4, Haniffa Rashan, PhD1, Arias Lopez Mariadel Pilar, MD5, Pilcher David, FCICM6, Salluh Jorge, PhD7, Vijayaraghavan Bharath Kumar Tirupakuzhi, MD8, on behalf of the Linking of Global Intensive Care Collaboration (LOGIC) Time to Revisit Treatment Limitations in Critical Care Benchmarking. Critical Care Medicine. 2021 Apr;49(4):e472–e473. doi: 10.1097/CCM.0000000000004834.
    1. Riviello ED. Improving outcomes for severe sepsis in Africa: one step closer. Crit Care Med. 2014;42:2439–2440.
    1. Murthy S, Leligdowicz A, Adhikari NK. Intensive care unit capacity in low income countries: a systematic review. PLoS One. 2015;10:e0116949.
    1. Wunsch H, Angus DC, Harrison DA, et al. Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36:2787–2793.:e1-9.
    1. Dinglas VD, Faraone LN, Needham DM. Understanding patient-important outcomes after critical illness: a synthesis of recent qualitative, empirical, and consensus-related studies. Curr Opin Crit Care. 2018;24:401–409. [(**) The present study describes the current status and challenges for implementing core datasets for ICU patient during and after hospitalization, including patient-centered outcomes]
    1. Soares M, Ulysses V, Silva A, Homena WS, Jr, Fernandes GC, Paula A, De Moraes P, Brauer L, Lima MF, De Marco FV, Bozza FA. Family care, visiting policies, ICU performance, and efficiency in resource use: insights from the ORCHESTRA study. Intensive care medicine. 2017 Apr 1;43(4):590.
    1. Raffa Jesse, 1, Johnson Alistair, 1, Celi Leo Anthony, 2,,1, Pollard Tom, 1, Pilcher David, 3, Badawi Omar., 4 33: THE GLOBAL OPEN SOURCE SEVERITY OF ILLNESS SCORE (GOSSIS) Critical Care Medicine. 2019 Jan;47(1):17. doi: 10.1097/01.ccm.0000550825.30295.dd.
    1. Registry Data Standards. Duke Clinical Research Institute; [Accessed May 08, 2021].
    1. [accessed May 9th 2021]; .
    1. van de Klundert N, Holman R, Dongelmans DA, de Keizer NF. Data Resource Profile: the Dutch National Intensive Care Evaluation (NICE) Registry of Admissions to Adult Intensive Care Units. Int J Epidemiol. 2015;44(6) doi: 10.1093/ije/dyv2911850-1850h.
    1. McClean K, Mullany D, Huckson S, van Lint A, Chavan S, Hicks P, et al. Identification and assessment of potentially high-mortality intensive care units using the ANZICS centre for outcome and resource evaluation clinical registry. Crit Care Resusc. 2017;19:230–238.
    1. Zampieri FG, Soares M, Borges LP, Salluh JIF, Ranzani OT. The Epimed monitor ICU database®: a cloud-based national registry for adult intensive care unit patients in Brazil TT - Epimed monitor ICU database®: um Registro nacional baseado na nuvem, Para pacientes adultos internados em unidades de terapia intensiva. Rev Bras Ter intensiva. 2017;29:418–426. doi: 10.5935/0103-507X.20170062.
    1. Argentina Editorial RevistaDeTerapia Intensiva. Programa SATI-Q: una experiencia local en Quality Benchmarking. 2016;33(4)
    1. CRIT CARE ASIA. Beane A, Dondorp AM, et al. Establishing a critical care network in Asia to improve care for critically ill patients in low- and middle-income countries. Crit Care. 2020;24:608. doi: 10.1186/s13054-020-03321-7. [(*) The study describes how the present The ICU network, supported by the electronic registry, will facilitate epidemiological and clinical research in LMICs in Asia]
    1. Salluh JIF, Chiche JD, Reis CE, et al. New perspectives to improve critical care benchmarking. Ann Intensive Care. 2018;8:17. doi: 10.1186/s13613-018-0363-0.
    1. Shaw BE, Brazauskas R, Millard HR, Fonstad R, Flynn KE, Abernethy A, Vogel J, Petroske C, Mattila D, Drexler R, Lee SJ, et al. Centralized patient-reported outcome data collection in transplantation is feasible and clinically meaningful. Cancer. 2017 doi: 10.1002/cncr.30936.
    1. Zampieri FG, Soares M, Borges LP, Salluh JIF, Ranzani OT. The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil. Rev Bras Ter Intensiva. 2017;29(4):418–426.
    1. De Lange DW, Dongelmans DA, De Keizer NF. Small steps beyond benchmarking. Rev Bras Ter Intensiva. 2017;29(2):128–30.
    1. Roos-Blom M-J, Gude WT, de Jonge E, Spijkstra JJ, van der Veer SN, Peek N, et al. Impact of audit and feedback with action implementation toolbox on improving ICU pain management: cluster-randomised controlled trial. BMJ Qual Saf. 2019;28:1007–1015. doi: 10.1136/bmjqs-2019-009588. [(**) The present registry-based multicenter study describes an intervention based on audit-feedback to improve the adherence to evidence-based pain management in intensive care units in the Netherlands]
    1. Writing Group for the CHECKLIST-ICU Investigators and the Brazilian Research in Intensive Care Network (BRICNet) Cavalcanti AB, Bozza FA, Machado FR, Salluh JI, Campagnucci VP, Vendramim P, Guimaraes HP, Normilio-Silva K, Damiani LP, Romano E, Carrara F, et al. Effect of a Quality Improvement Intervention With Daily Round Checklists, Goal Setting, and Clinician Prompting on Mortality of Critically Ill Patients: A Randomized Clinical Trial. JAMA. 2016 Apr 12;315(14):1480–90. doi: 10.1001/jama.2016.3463.
    1. Indian Registry of IntenSive care (IRIS) Adhikari NKJ, Beane A, et al. Impact of COVID-19 on non-COVID intensive care unit service utilization, case mix and outcomes: A registry-based analysis from India [version 1; peer review: 1 approved with reservations] Wellcome Open Res. 2021;6:159. doi: 10.12688/wellcomeopenres.16953.1.

Source: PubMed

3
订阅