Development and validation of multivariable mortality risk-prediction models in older people undergoing an interRAI home-care assessment (RiskOP)

Abstract Objective The study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow. Materials and Methods This was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses’ perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis. Results Two themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings. Discussion The findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action. Conclusions It is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.

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The angiostatic peptide endostatin enhances mortality risk prediction in pulmonary arterial hypertension

ERJ Open Research ◽

10.1183/23120541.00378-2021 ◽

2021 ◽

pp. 00378-2021

Author(s):

Catherine E. Simpson ◽

Megan Griffiths ◽

Jun Yang ◽

Melanie K. Nies ◽

R. Dhananjay Vaidya ◽

...

Keyword(s):

Risk Assessment ◽

Pulmonary Arterial Hypertension ◽

Arterial Hypertension ◽

Risk Prediction ◽

Mortality Risk ◽

Prediction Models ◽

Independent Predictor ◽

Right Atrial ◽

Risk Prediction Models ◽

Pulmonary Arterial

Currently available noninvasive markers for assessing disease severity and mortality risk in pulmonary arterial hypertension (PAH) are unrelated to fundamental disease biology. Endostatin, an angiostatic peptide known to inhibit pulmonary artery endothelial cell migration, proliferation, and survival in vitro, has been linked to adverse hemodynamics and shortened survival in small PAH cohorts. This observational cohort study sought to assess 1) the prognostic performance of circulating endostatin levels in a large, multicenter PAH cohort, and 2) the added value gained by incorporating endostatin into existing PAH risk prediction models.Endostatin ELISAs were performed on enrollment samples collected from 2017 PAH subjects with detailed clinical data, including survival times. Endostatin associations with clinical variables, including survival, were examined using multivariable regression and Cox proportional hazards models. Extended survival models including endostatin were compared to null models based on the REVEAL risk prediction tool and ESC/ERS low risk criteria using likelihood ratio tests, Akaike and Bayesian information criteria, and C-statistics.Higher endostatin was associated with higher right atrial pressure, mean pulmonary arterial pressure, and pulmonary vascular resistance and with shorter six-minute walk distance (p<0.01). Mortality risk doubled for each log-higher endostatin (hazard ratio 2.3, 95% confidence interval 1.6 to 3.4, p<0.001). Endostatin remained an independent predictor of survival when incorporated into existing risk prediction models. Adding endostatin to REVEAL-based and ESC/ERS criteria-based risk assessment strategies improved mortality risk prediction.Endostatin is a robust, independent predictor of mortality in PAH. Adding endostatin to existing PAH risk prediction strategies improves PAH risk assessment.

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