Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.Design: Systematic review and meta-analysis.Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration number: CRD42020159839.
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Our current smart society, where problems and frictions are smoothed out with smart, often invisible technology like AI and smart sensors, calls for designers who unravel and open the smart fabric. Societies are not malleable, and moreover, a smooth society without rough edges is neither desirable nor livable. In this paper we argue for designing friction to enhance a more nuanced debate of smart cities in which conflicting values are better expressed. Based on our experiences with the Moral Design Game, an adversarial design activity, we came to understand the value of creating tangible vessels to highlight conflict and dipartite feelings surrounding smart cities.
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This is a critique of how designers deal with contending histories and multiple presents in design to speculate about socio-technical futures. The paper unpacks how embedded definitions and assumptions of temporality in current design tools contribute to coloniality in designed futures. As design practice becomes implicated in how oppression extends from physical systems to global digital platforms, our critique rejects the notion that it is only AI that needs fixing and it dissects the Futures Cone used in speculative design to make these issues visible. As an alternative, we offer a hauntological vocabulary to aid designers in reorienting their speculative tools and accommodating pluriversality in anticipatory futures. To illustrate the benefits of the proposed metaphors, we highlight examples of coloniality in digital spaces and emphasize the failure of speculative design to decolonize future imaginaries. Using points of reference from hauntology, those that engage with uncertain states of lingering or spectrality, and notions of nostalgia, absence, and anticipation, this paper contributes to rethinking the role that design tools play in colonizing future imaginaries, especially those pertaining to potentially disruptive technologies.
