Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

Uneno, Yu; Taneishi, Kei; Kanai, Masashi; Tamon, Akiko; Okamoto, Kazuya; Nishikawa, Yoshitaka; Brown, J.B.; Matsumoto, Shigemi; Okuno, Yasushi; Muto, Manabu

doi:10.1093/annonc/mdv471.84

Cited by 2 publications

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“…Attempts to identify older patients at risk of death in emergency departments [9] to improve prognostic accuracy are reported in the literature for 30-day mortality [24] or 1-year prediction [25]. For short-term mortality (3–4 months), our model results were better than those of risk stratification tools in France [26], Holland [8], and Japan [27]. Others have also found old age, multimorbidity, increasing age, frailty or cancer as common predictors of 3-month mortality [26, 28–30], but no validation has been reported for some (Table 3).…”

Section: Discussionmentioning

confidence: 98%

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

et al. 2018

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PurposeAbstractTo determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients.MethodsProspective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy.Results2493 patients, with median age 78–80 years (DK–Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7–8.6 vs. 5.8 95% CI 5.6–5.9) and Danish mean 7.1 (95% CI 6.6–7.5 vs. 5.5 95% CI 5.4–5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL’s accuracy was only modest for in-hospital death prediction in either setting.ConclusionsThe modified CriSTAL tool (with CFS instead of Fried’s frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician’s confidence in initiating earlier end-of-life discussions.Electronic supplementary materialThe online version of this article (10.1007/s41999-018-0123-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 98%

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

et al. 2018

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“…Because patients' condition during treatment course can change from the baseline, development of an adaptable prognosis prediction model, which could be applied at any time point after the initiation of chemotherapy, is warranted in practice. Thus, we are developing adaptable prognostic models for patients with cancer receiving chemotherapy [87]. In this case-crossover study, we recruited 2693 patients, and 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items that were monitored for 1 year before the death event, were applied in developing prognostic models.…”

Section: Future Planmentioning

confidence: 99%

Prognosis Prediction Models and their Clinical Utility in Palliative Care

Uneno¹,

Kanai²

2017

Highlights on Several Underestimated Topics in Palliative Care

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Prognosis prediction is a clinically relevant issue to facilitate optimal decision-making for both physicians and patients with cancer. Many previous studies revealed that prognosis prediction based on the physician's intuition and/or clinical experience is inaccurate and often optimistic, which means that there is a tendency to overestimate patient survival in daily clinical practice. In recent decades, many eforts have been made to develop prognosis prediction models which aid physicians to make more accurate prognosis prediction. In this chapter, we review the representative prognosis prediction models in palliative care and related studies. In addition, we refer to several prognosis prediction models developed by unique methods (for instance, case-crossover design or machine learning). Finally, we focus on the possible clinical utility of prognosis prediction models. In fact, no previous studies have clearly demonstrated whether the application of such prognosis prediction models truly beneits patient care in daily clinical practice.Therefore, we will discuss how the application of prognosis prediction models could beneit patients under palliative care.

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Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

Cited by 2 publications

References 0 publications

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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