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The DUNDRUM-1 structured professional judgment for triage to appropriate levels of therapeutic security: retrospective-cohort validation study.

ABSTRACT: BACKGROUND:The assessment of those presenting to prison in-reach and court diversion services and those referred for admission to mental health services is a triage decision, allocating the patient to the appropriate level of therapeutic security. This is a critical clinical decision. We set out to improve on unstructured clinical judgement. We collated qualitative information and devised an 11 item structured professional judgment instrument for this purpose then tested for validity. METHODS:All those assessed following screening over a three month period at a busy remand committals prison (n = 246) were rated in a retrospective cohort design blind to outcome. Similarly, all those admitted to a mental health service from the same prison in-reach service over an overlapping two year period were rated blind to outcome (n = 100). RESULTS:The 11 item scale had good internal consistency (Cronbach's alpha = 0.95) and inter-rater reliability. The scale score did not correlate with the HCR-20 'historical' score. For the three month sample, the receiver operating characteristic area under the curve (AUC) for those admitted to hospital was 0.893 (95% confidence interval 0.843 to 0.943). For the two year sample, AUC distinguished at each level between those admitted to open wards, low secure units or a medium/high secure service. Open wards v low secure units AUC = 0.805 (95% CI 0.680 to 0.930); low secure v medium/high secure AUC = 0.866, (95% CI 0.784 to 0.949). Item to outcome correlations were significant for all 11 items. CONCLUSIONS:The DUNDRUM-1 triage security scale and its items performed to criterion levels when tested against the real world outcome. This instrument can be used to ensure consistency in decision making when deciding who to admit to secure forensic hospitals. It can also be used to benchmark admission thresholds between services and jurisdictions. In this study we found some divergence between assessed need and actual placement. This provides fertile ground for future research as well as practical assistance in assessing unmet need, auditing case mix and planning care pathways.

SUBMITTER: Flynn G

PROVIDER: S-EPMC3066108 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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The DUNDRUM-1 structured professional judgment for triage to appropriate levels of therapeutic security: retrospective-cohort validation study.

Flynn Grainne G O'Neill Conor C McInerney Clare C Kennedy Harry G HG

BMC psychiatry 20110316

<h4>Background</h4>The assessment of those presenting to prison in-reach and court diversion services and those referred for admission to mental health services is a triage decision, allocating the patient to the appropriate level of therapeutic security. This is a critical clinical decision. We set out to improve on unstructured clinical judgement. We collated qualitative information and devised an 11 item structured professional judgment instrument for this purpose then tested for validity.<h4 ...[more]

PMID: 21410967

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Project description:BackgroundPrompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.MethodsWe trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19.FindingsIn the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940).InterpretationA deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19.FundingSpecial Project for Emergency of the Science and Technology Department of Hubei Province, China.

Project description:The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization's priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.

Dataset Information

The DUNDRUM-1 structured professional judgment for triage to appropriate levels of therapeutic security: retrospective-cohort validation study.

Publications

The DUNDRUM-1 structured professional judgment for triage to appropriate levels of therapeutic security: retrospective-cohort validation study.

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