Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

Wallace, Emma; Stuart, Ellen; Vaughan, Niall; Bennett, Kathleen; Fahey, Tom; Smith, Susan M

doi:10.1097/mlr.0000000000000171

Cited by 167 publications

(208 citation statements)

References 35 publications

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“…5,14 Provisional indications from a UK-wide survey led by one of the co-applicants are that > 70% of UK practices now have access to an EARP tool. The development and validity of the tools has been widely researched, 26 but little research has been undertaken into their effectiveness. 15,26 The 2015 NHS England paper Next Steps for Risk Stratification in the NHS notes a 'pressing need for further research' about the effectiveness of hospital-avoidance interventions.…”

Section: Implications For Policy and Practicementioning

confidence: 99%

“…The development and validity of the tools has been widely researched, 26 but little research has been undertaken into their effectiveness. 15,26 The 2015 NHS England paper Next Steps for Risk Stratification in the NHS notes a 'pressing need for further research' about the effectiveness of hospital-avoidance interventions. 15 There is some debate about how best to use EARP tools -and whether or not focus should be on those at the highest level of risk.…”

Section: Implications For Policy and Practicementioning

confidence: 99%

“…In calculating individualised risk for a given population, the models use data from up to three sources: self-reported data from patients; routinely collected administrative data; and data from the clinical record or other primary data source. 26 A 2014 systematic review of the technical performance of EARP models identified 27 tools, 18 using routine or primary data, with nine using self-reported data. 26 The majority of models (18 of 27) were developed to predict emergency hospital admission at 12-month follow-up.…”

Section: Identifying Those At Riskmentioning

confidence: 99%

“…26 A 2014 systematic review of the technical performance of EARP models identified 27 tools, 18 using routine or primary data, with nine using self-reported data. 26 The majority of models (18 of 27) were developed to predict emergency hospital admission at 12-month follow-up. Those models that performed best (in terms of predictive accuracy, as measured by c-statistics of > 0.8), featured 'prior health care utilization variables, multimorbidity or polypharmacy measures, and named medical diagnoses or named prescribed medications variables'.…”

Section: Identifying Those At Riskmentioning

confidence: 99%

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Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

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Background With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim To evaluate the introduction of predictive risk stratification in primary care. Objectives To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design Randomised stepped-wedge trial with economic and qualitative components. Setting Abertawe Bro Morgannwg University Health Board, south Wales. Participants Patients registered with 32 participating general practices. Intervention PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding The National Institute for Health Research Health Services Delivery and Research programme.

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Section: Implications For Policy and Practicementioning

confidence: 99%

Section: Implications For Policy and Practicementioning

confidence: 99%

Section: Identifying Those At Riskmentioning

confidence: 99%

Section: Identifying Those At Riskmentioning

confidence: 99%

See 2 more Smart Citations

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Snooks

Bailey-Jones

Burge-Jones

et al. 2018

Health Serv Deliv Res

View full text Add to dashboard Cite

show abstract

“…12 In the case of CCI and HCC, both have also been shown to identify patients responsive to care management interventions, and CCI has additionally been reported to predict hospital readmissions. [13][14][15][16] While there is growing evidence to support new applications of some indices, many have not been rigorously evaluated and most evaluation have only been conducted in majority insured cohorts, thus limiting the generalizability to a vulnerable, uninsured population. In a local context, the indices examined in this study have enjoyed widespread use within the sponsoring healthcare system; however, their performance has neither been tested, nor compared in an uninsured population.…”

Section: Introductionmentioning

confidence: 99%

Traditional Risk Indices as Predictors of Future Utilization and Charges in the Context of Population Health for an Uninsured Cohort

Lubanski¹,

Rozario²,

Moore³

et al. 2017

eGEMs

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Prediction Tool for Individual Outcome Trajectories Across the Next Year in First-Episode Psychosis in Coordinated Specialty Care

et al. 2023

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ImportanceIn coordinated specialty care (CSC) settings for people with a first episode of psychosis, the development of reliable, validated individual-level prediction tools for key outcomes may be informative for shared clinician and client decision-making.ObjectiveTo develop an individual-level prediction tool using machine-learning methods that predicts a trajectory of education/work status or psychiatric hospitalization outcomes over a client’s next year of quarterly follow-up assessments. Additionally, to visualize these predictions in a way that is informative to clinicians and clients.Design, Setting, and ParticipantsIndividual-level data were collected for all patients enrolled in the OnTrackNY program at enrollment and at quarterly follow-ups using standardized forms. The OnTrackNY program, a network of CSC sites in New York State, provides person-centered, recovery-oriented, and evidence-based psychosocial and pharmaceutical interventions to individuals aged 16 to 30 years with recent-onset (&lt;2 years) nonaffective psychosis. Although data collection is ongoing, data for this study were collected from October 2013 to December 2018, and the time frame for analysis was July 2020 to May 2021. Data were separated into a training/cross-validation set to perform internally validated model development and a separate holdout test set (~20% of the sample) for external validation. Random probability forest models were developed to predict individual-level trajectories of outcomes.ExposuresForty-three individual-level demographic and clinical features collected at enrollment in OnTrackNY, 25 of which were time-varying and updated at quarterly follow-up assessments, and 13 site-level demographic and economic census variables.Main Outcomes and MeasuresIndividual-level education and/or employment status and psychiatric hospitalization trajectories at quarterly follow-up periods across the first 2 years of CSC.ResultsThe total study sample consists of 1298 individuals aged 16 to 30 years and included 341 women (26.3%), 949 men (73.1%), and 8 (&lt;1%) with another gender. Prediction models performed well for 1-year trajectories of education/work across all validation sets, with areas under the receiver operating characteristic curve (AUCs) ranging from 0.68 (95% CI, 0.63-0.74) to 0.88 (95% CI, 0.81-0.96). Predictive accuracy for psychiatric hospitalization 3 months ahead reached AUC above 0.70; moreover, predictions of future psychiatric hospitalizations at 6 months and beyond were consistently poor, with AUCs below 0.60. Given the good externally validated performance for predicting education/work, a prototype interactive visualization tool displaying individual-level education/work trajectories and related features was developed.Conclusions and RelevanceThis study suggests that accurate prediction tools can be developed for outcomes in people with first-episode psychosis, which may help inform shared clinician/client decision-making. Future work should study the effectiveness of its deployment, including proper communication to inform shared clinician/client decision-making in the context of a learning health care system. At present, more work is needed to develop better performing prediction models for future psychiatric hospitalizations before any tool is recommended for this outcome.

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Risk Prediction Models to Predict Emergency Hospital Admission in Community-dwelling Adults

Abstract: Supplemental Digital Content is available in the text.

Cited by 167 publications

References 35 publications

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Traditional Risk Indices as Predictors of Future Utilization and Charges in the Context of Population Health for an Uninsured Cohort

Prediction Tool for Individual Outcome Trajectories Across the Next Year in First-Episode Psychosis in Coordinated Specialty Care

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