Predictive risk modelling in health: options for New Zealand and Australia

Panattoni, Laura; Vaithianathan, Rhema; Ashton, Toni; Lewis, Geraint

doi:10.1071/ah09845

Cited by 22 publications

(11 citation statements)

References 21 publications

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“…In our study, inclusion of the deprivation index did not produce a significant difference in the models’ ability, which is in line with another study carried out in Canada using administrative and survey data to predict the number of doctor visits [35]. In any case, maintaining the social variables is justified as a means of ensuring greater equity in patient selection [36]. …”

Section: Discussionsupporting

confidence: 88%

Predictive risk modelling in the Spanish population: a cross-sectional study

Orueta

Nuño-Solinís

Mateos

et al. 2013

BMC Health Serv Res

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BackgroundAn increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.MethodsThis cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.ResultsThe coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.ConclusionThe case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.

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Section: Discussionsupporting

confidence: 88%

Predictive risk modelling in the Spanish population: a cross-sectional study

Orueta

Nuño-Solinís

Mateos

et al. 2013

BMC Health Serv Res

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“…Given that up to 80% of ADEs are thought to be predictable [25] and 50% are estimated to be preventable [26,27], a validated model incorporating significant ADE-related risk factors should help identify at-risk inpatients [7,[28][29][30][31][32][33]. Risk scores, which combine influential patient and medical variables to help clinicians rank patients, are becoming increasingly popular to assist in healthcare decisions [34][35][36]. Risk prediction for ADEs can alert the multidisciplinary team to high risk inpatients and facilitate pharmacovigilance and targeted interventions.…”

Section: Introductionmentioning

confidence: 99%

Systematic review of predictive risk models for adverse drug events in hospitalized patients

Falconer

Barras

Cottrell

2018

Brit J Clinical Pharma

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Keywords adverse drug events, adverse drug reactions, clinical pharmacology, clinical pharmacy, drug related problems, medication errors, predictive risk model, risk score AIMAn emerging approach to reducing hospital adverse drug events is the use of predictive risk scores. The aim of this systematic review was to critically appraise models developed for predicting adverse drug event risk in inpatients. METHODSEmbase, PubMed, CINAHL and Scopus databases were used to identify studies of predictive risk models for hospitalized adult inpatients. Studies had to have used multivariable logistic regression for model development, resulting in a score or rule with two or more variables, to predict the likelihood of inpatient adverse drug events. The Checklist for the critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to critically appraise eligible studies. RESULTSEleven studies met the inclusion criteria and were included in the review. Ten described the development of a new model, whilst one study revalidated and updated an existing score. Studies used different definitions for outcome but were synonymous with or closely related to adverse drug events. Four studies undertook external validation, five internally validated and two studies did not validate their model. No studies evaluated impact of risk scores on patient outcomes. CONCLUSIONAdverse drug event risk prediction is a complex endeavour but could help to improve patient safety and hospital resource management. Studies in this review had some limitations in their methods for model development, reporting and validation. Two studies, the BADRI and Trivalle's risk scores, used better model development and validation methods and reported reasonable performance, and so could be considered for further research.British Journal of Clinical Pharmacology Br J Clin Pharmacol (2018) 84 846-864 846

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“…Una exigencia para la puesta en marcha de intervenciones sanitarias es que benefi-cien a toda la población en riesgo. Un modelo predictivo que no tenga en cuenta variables socioeconómicas puede provocar que las necesidades de los grupos más desfavorecidos se vean peor reflejadas y generar una sutil discriminación contra ellos en cuanto a su inclusión en programas de salud 25 . Por ese motivo, en nuestro modelo predictivo se emplea el índice de privación.…”

Section: Discussionunclassified