Real world validation of an AI-based CT hemorrhage detection tool.

Introduction: Intracranial hemorrhage (ICH) is a potentially life-threatening medical event that requires expedited diagnosis with computed tomography (CT). Automated medical imaging triaging tools can rapidly bring scans containing critical abnormalities, such as ICH, to the attention of radiologists and clinicians. Here, we retrospectively investigated the real-world performance of VeriScout™, an artificial intelligence-based CT hemorrhage detection and triage tool. Methods: Ground truth for the presence or absence of ICH was iteratively determined by expert consensus in an unselected dataset of 527 consecutively acquired non-contrast head CT scans, which were sub-grouped according to the presence of artefact, post-operative features and referral source. The performance of VeriScout™ was compared with the ground truths for all groups. Results: VeriScout™ detected hemorrhage with a sensitivity of 0.92 (CI 0.84-0.96) and a specificity of 0.96 (CI 0.94-0.98) in the global dataset, exceeding the sensitivity of general radiologists (0.88) with only a minor relative decrement in specificity (0.98). Crucially, the AI tool detected 13/14 cases of subarachnoid hemorrhage, a potentially fatal condition that is often missed in emergency department settings. There was no decrement in the performance of VeriScout™ in scans containing artefact or postoperative change. Using an integrated informatics platform, VeriScout™ was deployed into the existing radiology workflow. Detected hemorrhage cases were flagged in the hospital radiology information system (RIS) and relevant, annotated, preview images made available in the picture archiving and communications system (PACS) within 10 min. Conclusion: AI-based radiology worklist prioritization for critical abnormalities, such as ICH, may enhance patient care without adding to radiologist or clinician burden.

Prospective Validation of a Checklist to Predict Short-term Death in Older Patients After Emergency Department Admission in Australia and Ireland.

BACKGROUND: Emergency departments (EDs) are pressured environment where patients with supportive and palliative care needs may not be identified. We aimed to test the predictive ability of the CriSTAL (Criteria for Screening and Triaging to Appropriate aLternative care) checklist to flag patients at risk of death within 3 months who may benefit from timely end-of-life discussions. METHODS: Prospective cohorts of >65-year-old patients admitted for at least one night via EDs in five Australian hospitals and one Irish hospital. Purpose-trained nurses and medical students screened for frailty using two instruments concurrently and completed the other risk factors on the CriSTAL tool at admission. Postdischarge telephone follow-up was used to determine survival status. Logistic regression and bootstrapping techniques were used to test the predictive accuracy of CriSTAL for death within 90 days of admission as primary outcome. Predictability of in-hospital death was the secondary outcome. RESULTS: A total of 1,182 patients, with median age 76 to 80 years (IRE-AUS), were included. The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% confidence interval [CI] = 7.7-8.6) versus 5.7 (95% CI = 5.1-6.2) and Irish mean of 7.7 (95% CI = 6.9-8.5) versus 5.7 (95% CI = 5.1-6.2). The model with Fried frailty score was optimal for the derivation (Australian) cohort but prediction with the Clinical Frailty Scale (CFS) was also good (areas under the receiver-operating characteristic [AUROC] = 0.825 and 0.81, respectively). Values for the validation (Irish) cohort were AUROC = 0.70 with Fried and 0.77 using CFS. A minimum of five of 29 variables were sufficient for accurate prediction, and a cut point of 7+ or 6+ depending on the cohort was strongly indicative of risk of death. The most significant independent predictor of short-term death in both cohorts was frailty, carrying a twofold risk of death. CriSTAL's accuracy for in-hospital death prediction was also good (AUROC = 0.795 and 0.81 in Australia and Ireland, respectively), with high specificity and negative predictive values. CONCLUSIONS: The modified CriSTAL tool (with CFS instead of Fried's frailty instrument) had good discriminant power to improve certainty of short-term mortality prediction in both health systems. The predictive ability of models is anticipated to help clinicians gain confidence in initiating earlier end-of-life discussions. The practicalities of embedding screening for risk of death in routine practice warrant further investigation.

Which frailty scale for patients admitted via Emergency Department? A cohort study.

OBJECTIVES: To determine the prevalence of frailty in Emergency Departments (EDs); examine the ability of frailty to predict poor outcomes post-discharge; and identify the most appropriate instrument for routine ED use. METHODS: In this prospective study we simultaneously assessed adults 65+yrs admitted and/or spent one night in the ED using Fried, the Clinical Frailty Scale (CFS), and SUHB (Stable, Unstable, Help to walk, Bedbound) scales in four Australian EDs for rapid recognition of frailty between June 2015 and March 2016. RESULTS: 899 adults with complete follow-up data (mean (SD) age 80.0 (8.3) years; female 51.4%) were screened for frailty. Although different scales yielded vastly different frailty prevalence (SUHB 9.7%, Fried 30.4%, CFS 43.7%), predictive discrimination of poor discharge outcomes (death, poor self-reported health/quality of life, need for community services post-discharge, or reattendance to ED after the index hospitalization) for all identical final models was equivalent across all scales (AUROC 0.735 for Fried, 0.730 for CFS and 0.720 for SUHB). CONCLUSION: This study confirms that screening for frailty in older ED patients can inform prognosis and target discharge planning including community services required. The CFS was as accurate as the Fried and SUHB in predicting poor outcomes, but more practical for use in busy clinical environments with lower level of disruption. Given the limitations of objectively measuring frailty parameters, self-report and clinical judgment can reliably substitute the assessment in EDs. We propose that in a busy ED environment, frailty scores could be used as a red flag for poor follow-up outcome.

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

ABSTRACT: To 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. METHODS: Prospective 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. RESULTS: 2493 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. CONCLUSIONS: The 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.

Efficacy of a tool to predict short-term mortality in older people presenting at emergency departments: Protocol for a multi-centre cohort study.

BACKGROUND: Prognostic uncertainty inhibits clinicians from initiating timely end-of-life discussions and advance care planning. This study evaluates the efficacy of the CriSTAL (Criteria for Screening and Triaging to Appropriate aLternative care) checklist in emergency departments. METHODS: Prospective cohort study of patients aged ≥65 years with any diagnosis admitted via emergency departments in ten hospitals in Australia, Denmark and Ireland. Electronic and paper clinical records will be used to extract risk factors such as nursing home residency, physiological deterioration warranting a rapid response call, personal history of active chronic disease, history of hospitalisations or intensive care unit admission in the past year, evidence of proteinuria or ECG abnormalities, and evidence of frailty to be concurrently measured with Fried Score and Clinical Frailty Scale. Patients or their informal caregivers will be contacted by telephone around three months after initial assessment to ascertain survival, self-reported health, post-discharge frailty and health service utilisation since discharge. Logistic regression and bootstrapping techniques and AUROC curves will be used to test the predictive accuracy of CriSTAL for death within 90 days of admission and in-hospital death. DISCUSSION: The CriSTAL checklist is an objective and practical tool for use in emergency departments among older patients to determine individual probability of death in the short-term. Its validation in this cohort is expected to reduce clinicians' prognostic uncertainty on the time to patients' death and encourage timely end-of-life conversations to support clinical decisions with older frail patients and their families about their imminent or future care choices.